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Supplementary Materials for "Airborne Laser-guided Imaging Spectroscopy to Map Forest Trait Diversity and Guide Conservation
Abstract
Airborne spectroscopy for forest traits
The development of conservation priorities in the tropics is often hampered by the sparseness of ground data on biological diversity and the relative crudeness of larger-scale remote sensing data. Asner et al. developed airborne instrumentation to make large-scale maps of forest functional diversity across 72 million hectares of the Peruvian Andes and Amazon basin (see the Perspective by Kapos). They generated a suite of forest canopy functional trait maps from laser-guided imaging spectroscopy and used them to define distinct forest functional classes. These were then compared with government deforestation and land allocation data, which enabled an analysis of conservation threats and opportunities across the region.
Science , this issue p. 385 ; see also p. 347
... Yet, the collection of detailed in situ data that captures intra-specific and intra-community variation is potentially labor-intensive, expensive, and logistically infeasible for many regions. The quality, representativeness, and comparability of field data also largely depend on sampling protocols-different sampling approaches can strongly affect the mean and variance of community-level traits (Asner et al., 2017a(Asner et al., , 2017bEnquist et al., 2017). ...
... Remote sensing techniques such as imaging spectroscopy provide an alternative approach to measure some foliar functional traits at regional scales, thus offering the opportunity to study the composition and environmental drivers of traits at multiple scales. Existing trait mapping work covers most biomes of Earth, including tropical forests (Asner et al., 2017a(Asner et al., , 2017bMartin et al., 2015), temperate forests (Singh et al., 2015;Z. Wang et al., 2020) and grasslands (Biewer et al., 2009;Mutanga et al., 2004;Ramoelo et al., 2013;Z. ...
... We used permutational partial least-squares regression (PLSR, Wold et al., 1984) to predict fieldmeasured traits as a function of image spectra. PLSR is widely used where data necessary for radiative transfer models (RTMs) are unavailable, impractical to implement at scale, or suitable RTMs do not exist for particular traits (Asner et al., 2017a(Asner et al., , 2017bSingh et al., 2015;Z. Wang et al., 2019). ...
Imaging spectroscopy offers great potential to characterize plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least‐squares regression to map seven key foliar chemical and morphological traits using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) for six sites spanning a climatological gradient in the Western Ghats of India. We studied the variation of trait space at spatial resolutions from the plot level (4 m), community level (30 and 100 m) to the ecosystem level (1,000 m). We observed a consistent pattern of trait space across different resolutions, with one axis defined by foliar nitrogen and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment‐trait correlations across resolutions with generally higher predictive capacity of our environment‐traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin were strongly influenced by environmental factors (model R² > 0.5 at 1,000 m). Calcium, sugars, and nitrogen were significantly affected by site conditions, incorporating site as a fixed effect largely improved model performance. LMA showed little dependence on environmental factors or site conditions, suggesting a stronger influence of species composition and site history on LMA variation. Our results show that reliable trait‐trait relationships can be identified in coarse resolution imagery, but that local scale trait‐trait relationships (resolutions finer than 30 m) are not sensitive to broad‐scale abiotic/biotic factors.
... However, the species sampled in the TRY database, which include plant trait data, account for only 5% of the currently identified vascular plant species on Earth for leaf mass per area (LMA) and 3.4% for leaf nitrogen content (Kattge et al., 2020). Given the logistical constraints of characterizing spatial-wise trait variation through field sampling, there has been growing interest in exploring trait upscaling at landscape (Wessman et al., 1988;Martin et al., 2008;Singh et al., 2015;Asner et al., 2015;Wang et al., 2020), regional (Asner et al., 2017;Aguirre-Gutiérrez et al., 2021;Loozen et al., 2020;Wallis et al., 2019) and global scales Butler et al., 2017;Madani et al., 2018;Moreno-Martínez et al., 2018;Schiller et al., 2021;Vallicrosa et al., 2022;van Bodegom et al., 2014). Although the motivations for these scaling studies were numerous, the approaches they used can generally be split into three categories: the PFT-based approach, the statistical modeling approach relying on environmental variables, and the remote sensingbased approach. ...
... However, airborne imaging spectroscopy is expensive and because the resulting limited spatial coverage can only upscale in-situ observations to the landscape scales at which it is feasible to conduct airplane imaging. To date, the most extensive collection effort for airborne hyperspectral imagery for trait upscaling has gathered over 2 million ha of imagery (Asner et al., 2017) using the Carnegie Airborne Observatory (Asner et al., 2012). Nevertheless, this only covered 2.5% of the study area in the Peruvian Andes-Amazon region and required use of a random forest model with environmental data to cover the larger region of interest (Asner et al., 2017). ...
... To date, the most extensive collection effort for airborne hyperspectral imagery for trait upscaling has gathered over 2 million ha of imagery (Asner et al., 2017) using the Carnegie Airborne Observatory (Asner et al., 2012). Nevertheless, this only covered 2.5% of the study area in the Peruvian Andes-Amazon region and required use of a random forest model with environmental data to cover the larger region of interest (Asner et al., 2017). Utilizing satellite remote sensing data can address the issue of large-scale scalability. ...
Foliar functional traits are essential for understanding plant adaptation strategies and ecosystem function. Due to limited in-situ observational data, there is a growing interest in upscaling these traits from field sites to regional and global levels. However, limitations persist: (1) global/national scale upscaling that relies on plant functional type (PFT) maps, environmental variables or coarse resolution multispectral images, which fail to capture local-scale trait variability; (2) airborne imaging spectroscopy that enables high-resolution and accurate mapping but is restricted to site scale and is costly; and (3) multispectral satellites like Sentinel-2 that offer global coverage but have limited spectral bands and resolution. While previous research has demonstrated the connection between traits and vegetation phenology, our study seeks to build upon this foundation by further exploring the integration of phenological information for large-scale trait prediction. We examined the integration of Sentinel-2 data with its time series (for phenology information) to map 12 foliar functional traits across 14 National Ecological Observatory Network (NEON) sites in the eastern United States. Our results show that time-series Sentinel-2 models effectively capture the variance in these 12 traits (R2 = 0.60–0.80) when compared with benchmark trait data generated by state-of-the-art airborne imaging spectroscopy. The models adequately capture considerable trait variations observed within sites and PFTs. Our approach outperforms existing methods that rely on environmental variables, or a single Sentinel-2 image as predictors across examined NEON sites in eastern United States. Interestingly, including environmental variables in our models does not significantly improve predictive power. Further analysis reveals that a ‘fast-slow’ principal axis predominantly explains the covariation in Enhanced Vegetation Index amplitude (a proxy for leaf longevity), leaf mass per area, and leaf nitrogen content across PFTs. This finding highlights the importance of incorporating phenological information for trait mapping and suggests a potential mechanism underlying these spectra-based models. Our proposed method, which simultaneously achieves high accuracy, large-scale scalability, and high spatial resolution, represents a promising avenue for future global trait mapping. Validation on a larger scale to fully realize its potential in addressing fundamental ecological questions will be a key future focus.
... A growing corpus o research has investigated the relationship between spectral diversity and plant unctional diversity (Beccari et al., 2024;Schneider et al., 2017;Schweiger et al., 2018). Most studies to date have ocused on the local scale (α-diversity) component o unctional diversity, with very ew assessing plant unctional β-diversity components (Asner et al., 2017). In contrast there are numerous examples o tracking taxonomic β-diversity (changes in species composition) across space with hyperspectral data (Baldeck and Asner, 2013;Féret and Asner, 2014;Féret and De Boissieu, 2020;Laliberté et al., 2020;Rocchini et al., 2018). ...
... Our results highlight the potential utility o using hyperspectral imagery in the detection o ne-scale unctional (and taxonomic) β-diversity across space within a single alpine tundra habitat. This supports the much-anticipated promise o using spectral refectance in unctional change detection (Jetz et al., 2016) and the results o other studies that highlighted signicant links between spectral refectance and elements o taxonomic (Baldeck and Asner, 2013;Laliberté et al., 2020;Marzialetti et al., 2021) and unctional β-diversity (Asner et al., 2017). Spectral dissimilarity was signicantly related to unctional and taxonomic dissimilarity across space, although the relationships were only o moderate strength and exhibited considerable scatter ( Fig. 3a; 4a). ...
Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change over space and time through remotely sensed methods. Here, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time in an alpine vascular plant community in the Front Range, Colorado, USA. Functional and taxonomic dissimilarity were signicantly related to spectral dissimilarity across space, but lacked robust relationships with spectral dissimilarity over time. Biomass showed stronger relationships with spectral dissimilarity than either taxonomic or functional dissimilarity over space, but exhibited no significant associations with spectral dissimilarity over time. Comparative analyses using NDVI revealed that NDVI alone explains much of the variation explained by the full-range spectra. Our results support the use of hyperspectral data to detect fine-scale changes in vascular plant β-diversity over space, but suggest that methodological limitations still preclude the use of this technology for long-term monitoring and change detection.
... Most studies to date have focused on the local scale (α-diversity) component of functional diversity, with very few assessing plant functional β-diversity components (Asner et al., 2017). In contrast there are numerous examples of tracking taxonomic β-diversity (changes in species composition) across space with hyperspectral data (Baldeck and Asner, 2013;Féret and Asner, 2014;Féret and De Boissieu, 2020;Laliberté et al., 2020;. ...
... between spectral reflectance and elements of taxonomic (Baldeck and Asner, 2013;Laliberté et al., 2020;Marzialetti et al., 2021) and functional β-diversity (Asner et al., 2017). Spectral dissimilarity was significantly related to functional and taxonomic dissimilarity across space, although the relationships were only of moderate strength and exhibited considerable scatter (Figure 4.3a; 4.4a). ...
Widespread vegetation change is underway throughout the northern high latitudes in response to pervasive and accelerating Arctic warming. Such changes are becoming increasingly well documented with key aspects such as phenology, species composition and trait make-up known to be shifting across the tundra biome. However, one area that remains underrepresented in tundra vegetation studies is functional diversity, known to be a principal determinant of ecosystem function and consequently, services. Communities comprising higher functional diversity are considered more stable and resistant to global change impacts and as such, any loss of functional diversity under the influence of warming could have cascading impacts on ecosystem services and resultant feedbacks. Tundra functional diversity is hence an overlooked subject area with the potential to strongly influence ecosystems and communities throughout the far north in the coming decades. This thesis tackles this knowledge gap by: characterising its biome-scale biogeographic patterns and potential drivers (Chapter 3); identifying limitations in currently accepted gap-filling methodologies (Chapter 2); and developing new remotely-sensed approaches to better understand patterns in tundra functional diversity (Chapter 4).
In Chapter 2, I used in situ trait data collected on individuals of multiple species under near-identical environmental and temporal conditions to determine the influence of explicitly incorporating spatial hierarchies on gap-filling performance in tundra trait matrices. I found that gap-filling across progressively higher spatial and taxonomic hierarchies reduced the accuracy of trait estimates, although such patterns were seen to be both scale and trait-specific. In Chapter 3, I undertook a biome-wide, in situ, cross-site synthesis of over 2,000 plots spanning ~40 years to determine, for what I believe is the first time, biome-scale biogeographic patterns in tundra vascular plant functional diversity across space and time and identify potential drivers of such patterns. I found that spatial patterns in functional diversity conform to those seen in species and functional diversity across latitudes globally and that whilst functional diversity exhibited no net directional change over time, plot-scale changes were strongly related to changes in functional group cover. Finally, in Chapter 4, I used airborne hyperspectral imagery from the Front Range, Colorado, USA to determine whether optical remote sensing can accurately track fine-scale differences in functional diversity throughout alpine tundra ecosystems across both space and time. I found that the method showed promise across space, tracking patterns in functional beta-diversity within years across our sample region, but exhibited limited potential over time, highlighting continued issues with remotely sensed time series in assessments of biodiversity. Overall, I believe this thesis has helped tackle large unknowns surrounding tundra functional diversity and has highlighted key research areas to target in the near future as rapid Arctic warming continues.
... Many remote-sensing methods have been developed to monitor plant diversity from variation in spectral reflectance or vegetation indices (spectral diversity) across space (spectral variability hypothesis [SVH]) and time (phenology), and variation in functional traits (functional trait diversity) (Asner et al., 2017;Laliberté et al., 2020;Rocchini et al., 2021;R. Wang & Gamon, 2019;Zhao et al., 2018). ...
... Many spectral vegetation indices have been proposed as proxies for functional traits, given that specific physiological traits drive the variations in spectral absorption and scattering signatures of plants at different spectral regions (Asner et al., 2017;Z. Wang et al., 2023;Zhao, Sun, Lu, et al., 2021). ...
Global spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate‐dominated environment–diversity relationships and, where possible, at finer grain using remote sensing. However, for grasslands with their small plant sizes, the limited availability of vegetation plot data has caused large uncertainties in fine‐grained mapping of species diversity. Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m²), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m² across the Mongolian Plateau at 500 m resolution. Combining all variables yielded a predictive accuracy of 69% compared with 64% using remotely sensed variables or 65% using abiotic variables alone. Among remotely sensed variables, functional traits showed the highest predictive power (55%) in species richness estimation, followed by productivity and phenology (48%), spectral diversity (48%) and habitat heterogeneity (48%). When considering spatial autocorrelation, remotely sensed variables explained 52% and abiotic variables explained 41%. Moreover, Remotely sensed variables provided better prediction at smaller grain size (<∼1,000 km), while water‐ and energy‐dominated macro‐environment variables were the most important drivers and dominated the effects of remotely sensed variables on diversity patterns at macro‐scale (>∼1,000 km). These findings indicate that while remotely sensed vegetation characteristics and climate‐dominated macro‐environment provide similar predictions for mapping grassland plant species richness, they offer complementary explanations across broad spatial scales.
... Đa dạng chức năng, từ biểu hiện gen đến các quá trình cảnh quan, là một thành phần đa dạng sinh học quan trọng được đánh giá bởi các chương trình bảo tồn, vì nó liên kết đa dạng sinh học với chức năng (Asner et al., 2017;Cadotte et al., 2011), dịch vụ (Balvanera et al., 2006;Duncan et al., 2015), và khả năng phục hồi (Mouchet et al., 2010) của hệ sinh thái. Ước tính đa dạng chức năng được thực hiện bằng cách gom các loài thành các nhóm chức năng dựa trên cấu trúc (ví dụ: cây bụi, cây gỗ, v.v.), phát sinh loài (ví dụ, Coniferae, Poaceae, v.v.) hoặc cơ chế trao đổi chất (ví dụ: C3, C4, v.v.) liên quan đến các quá trình sinh học có ý nghĩa (Lavorel et al., 2007;Lavorel & Garnier, 2002) hoặc sử dụng các đặc điểm hình thái chức năng của loài (Malaterre et al., 2019). ...
... Quan trắc từ xa bằng vệ tinh có thể hướng dẫn các hoạt động bảo tồn bằng cách mô tả sự đa dạng chức năng ở cấp độ đặc điểm loài (Jetz et al., 2016) và hệ sinh thái (Asner et al., 2017;Gamon et al., 2019). Trước tiên, các mô tả về chức năng hệ sinh thái trích xuất từ vệ tinh có thể liên quan đến các biến đa dạng sinh học quan trọng (Alcaraz-Segura et al., 2017;Pettorelli et al., 2018). ...
Sinh học bảo tồn phải thiết lập các ưu tiên bảo tồn địa lý không chỉ dựa trên thành phần hoặc cấu trúc mà còn trên các khía cạnh chức năng của đa dạng sinh học. Tuy nhiên, đánh giá đa dạng chức năng là một thách thức ở quy mô khu vực. Nghiên cứu này đề xuất sử dụng các Nhóm Chức năng Sinh thái (Ecosystem Functional Types - EFT) trích xuất từ vệ tinh để mô tả tính không đồng nhất theo vùng của các động lực sản xuất sơ cấp trong Khu Dự trữ Sinh quyển Rừng ngập mặn Cần Giờ, Việt Nam. Các EFT được xác định dựa trên ba đặc điểm chức năng sinh thái rút ra từ động lực học theo mùa của Chỉ số thực vật nâng cao (Enhanced Vegetation Index): giá trị trung bình năm (đại diện cho sản lượng chính), hệ số biến thiên theo mùa (mô tả tính thời vụ) và ngày đạt cực đại (chỉ báo hiện tượng học). Đánh giá dựa trên EFT của nghiên cứu đã chứng minh mức độ không đồng nhất của khu vực được cảm nhận từ xa trong các chức năng sinh thái có thể củng cố và bổ sung cho các thiết lập ưu tiên bảo tồn truyền thống.
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Geographic conservation priorities must be determined by conservation biology based on both the functional and compositional aspects of biodiversity. However, at the regional level, evaluating functional diversity is difficult. To quantify the regional heterogeneity of primary production dynamics in the Can Gio Mangrove Biosphere Reserve, Vietnam, we propose using Ecosystem Functional Types (EFTs), which are land surface patches that share comparable primary production dynamics and are generated from satellite data. Three ecosystem functional attributes—the annual mean (a proxy for primary production), the seasonal coefficient of variation (a descriptor of seasonality), and the date of maximum (an indicator of phenology)—were used to identify EFTs. These attributes were generated from the seasonal dynamics of the Enhanced Vegetation Index. Our EFT-based analysis shows how remotely sensed regional variation in ecosystem functions may support and enhance established conservation priority choices.
... Moreover, with increasing amounts of new SRS products (Kuenzer et al., 2014) and new satellite missions emerging (Briottet et al., 2022;Cawse-Nicholson et al., 2021), the range of observations that can directly contribute to biodiversity indicators will further broaden. Examples include imaging spectrometers for estimating phylogenetic and trait diversity of plants (Asner et al., 2017;Helfenstein et al., 2022;Schneider et al., 2017), radar for mapping forest biodiversity (Bae et al., 2019), LiDAR sensors for measuring the 3D structure of ecosystems (Valbuena et al., 2020), or a combination of multispectral images from different satellite sensors to count large terrestrial mammals (Wu et al., 2023). However, an ecological perspective of how SRS can be improved in the context of the final Kunming-Montreal GBF is currently lacking, apart from a preliminary perspective on the 1st draft version (Cavender-Bares et al., 2022). ...
... SRS is rarely used for directly mapping species distributions because spatial resolutions of openly accessible products are most often not high enough for individual species detection (Sladonja and Damijanić, 2021). While classifications of native and invasive plant species in tropical forests (Asner et al., 2017;Somers and Asner, 2013) have been performed with imaging spectroscopy data, these applications usually use airborne (rather than spaceborne) observations. Using SRS for directly mapping species distributions is thus unlikely to become operational in the near future unless spaceborne instruments with very high resolutions will become publicly and freely available. ...
Satellite remote sensing (SRS) provides huge potential for tracking progress towards conservation targets and goals, but SRS products need to be tailored towards the requirements of ecological users and policymakers. In this viewpoint article, we propose to advance SRS products with a terrestrial biodiversity focus for tracking the goals and targets of the Kunming-Montreal global biodiversity framework (GBF). Of 371 GBF biodiversity indicators , we identified 58 unique indicators for tracking the state of terrestrial biodiversity, spanning 2 goals and 8 targets. Thirty-six shared enough information to analyse their underlying workflows and spatial information products. We used the concept of Essential Biodiversity Variables (EBV) to connect spatial information products to different dimensions of biodiversity (e.g. species populations, species traits, and ecosystem structure), and then counted EBV usage across GBF goals and targets. Combined with published scores on feasibility, accuracy, and immaturity of SRS products, we identified a priority list of terrestrial SRS products representing opportunities for scientific development in the next decade. From this list, we suggest two key directions for advancing SRS products and workflows in the GBF context using current instruments and technologies. First, existing terrestrial ecosystem distributions and live cover fraction SRS products (of above-ground biomass, ecosystem fragmentation, ecosystem structural variance, fraction of vegetation cover, plant area index profile, and land cover) need to be refined using a co-design approach to achieve harmonized ecosystem taxonomies, reference states and improved thematic detail. Second, new SRS products related to plant physiology and primary productivity (e.
... Axis 2: Assessing the ecological influence of landslides through the lens of plant functional diversity Understanding how populations of landslides influence carbon uptake and storage, primary productivity, and related ecosystem processes at the landscape scale will help elucidate the resilience of montane landscapes to global change (Restrepo et al., 2009). Quantifying the ecological influence of landslides can be achieved by using air-and/or satellite-borne hyperspectral imaging, or imaging spectroscopy, to measure plant functional diversity (Jetz et al., 2016;Asner et al., 2017) of landslide-affected and undisturbed areas. This approach views plant biodiversity through the lens of species' structural and biochemical functional traits, which correspond to their roles in ecosystem processes and services (Díaz et al., 2007). ...
... While many studies have applied imaging spectroscopy to the detection, monitoring, and characterization of landslides (e.g., Vellico et al., 2010;Ye et al., 2019), to our knowledge this method has not yet been applied to studies of landslide regeneration in highly diverse tropical forests. However, given its power for measuring tropical plant functional diversity and linking plant communities to ecosystem-level processes (see review in Asner et al., 2017), it is a powerful option for understanding the role of landslides in Andean TMF. ...
Landslides are a central component of tropical montane forest disturbance regimes, including in the tropical Andes biodiversity hotspot, one of the most biodiverse ecosystems in the world. Technological developments in remote sensing have made landscape-scale landslide studies possible, unlocking new avenues for understanding montane biodiversity, ecosystem functioning, and the future effects of climate change. Here, we outline three axes of inquiry for future landslide ecology research in Andean tropical montane forest. We focus exclusively on the Andes due to the vast floral diversity and high endemicity of the tropical Andes biodiversity hotspot, and its importance for global biodiversity and regional ecosystem service provisioning; the broad elevational, latitudinal, and topographic gradients across which landslide dynamics play out; and the existence of long-term plot networks that provide the necessary baseline data on mature forest structure, composition, and functioning to contextualize disturbance impacts. The three lines of study we outline, which draw heavily on remote sensing data and techniques, will deepen scientific understanding of tropical montane forest biodiversity and ecosystem functioning, and the potential impacts of climate change on both. They are: (1) tracking landslide biodiversity dynamics across time and space with high spatial and temporal resolution satellite and unoccupied aerial vehicle imagery; (2) assessing the ecological influence of landslides through the lens of plant functional diversity with imaging spectroscopy; and (3) understanding current and predicting future landslide regimes at scale by building a living landslide inventory spanning the tropical Andes. The research findings from these three axes of inquiry will shed light on the role of landslides and the process of forest recovery from them in both the Andes and worldwide.
... Combining the "spectral variation hypothesis" with the "height variation hypothesis" to assess the forest diversity often yields satisfactory results. For example, Asner et al. [40] combined LiDAR and hyperspectral techniques to evaluate the biochemical characteristics of canopy leaves in 79 sample plots in the Andes, and extended the study to the entire tropical rainforest of Peru. They retrieved the leaf nitrogen and phosphorus content of different forest canopy tree species and obtained the forest functional diversity index of the region. ...
... However, it is possible to obtain sensitive indices for other traits that contribute to tree growth, reproduction, survival, vegetation dynamics, and ecosystem function. Incorporating such traits, including functional traits such as chlorophyll, nitrogen, carbon content, and other functional traits of leaves of different mangrove species [40,84], into the biodiversity assessment index system may result in more accurate mangrove biodiversity assessment results. ...
Mangrove forests are a valuable resource for biological and species diversity, and play a critical role in maintaining biodiversity. However, traditional plant biodiversity survey methods, which rely on labor-intensive field surveys, are not suitable for large-scale continuous spatial observations. To overcome this challenge, we propose an innovative framework for mangrove biodiversity assessment and zoning management based on drone low-altitude remote sensing, integrating data such as vertical structure features and spectral diversity features extracted from on-site measurements, airborne LiDAR, and hyperspectral data. This study focuses on the Maowei Sea mangrove community, located in the estuary of China’s first Pinglu Canal since the founding of the People’s Republic of China. Using the proposed framework, we construct an evaluation index for mangrove biodiversity at the levels of species diversity, ecosystem diversity, and landscape diversity, achieving a quantitative calculation of mangrove biodiversity and an evaluation of spatial distribution patterns. The results show that the biodiversity index of mangroves ranges from 0 to 0.63, with an average value of 0.29, and high-biodiversity areas are primarily concentrated in the southwest of the study area, while low-value areas are mainly located in the north. We also select the elevation and offshore distance of mangrove growth for the spatial zoning of biodiversity. The core area of biodiversity occupies the smallest area, at 2.32%, and is mainly distributed in areas with an elevation of 1.43–1.59 m and an offshore distance of 150.08–204.28 m. Buffer zones and experimental zones account for a significant proportion, with values of 35.99% and 61.69%, respectively. Compared to traditional methods for monitoring mangrove biodiversity, such as community field-sample surveys, the proposed method using unmanned-aerial-vehicle LiDAR and hyperspectral coupling technology to assess mangrove biodiversity and establish a zoning management framework is more conducive to formulating mangrove biodiversity conservation strategies. The study provides a feasible solution for the large-scale biodiversity mapping of mangroves in the Maowei Sea at the estuary of the Pinglu Canal.
... These ecological applications all benefit from an accurate and spatially continuous representation of forest canopy structure in the three-dimensional (3D) space and over time to account for disturbances and growth dynamics (Goetz and Dubayah, 2011;Maltamo et al., 2014;Northrup et al., 2022). Remote sensing methods, and in particular applications based on airborne laser data (ALS) using Light Detection and Ranging (LiDAR), are vectors of biodiversity conservation (Asner et al., 2017;Beland et al., 2019;Marvin et al., 2016). ...
... Changes in tree phenology influence laser penetration rates and consequently, the retrieval of forest structural information such as forest canopy height (Davison et al., 2020), usually expressed as canopy height models (CHM) of high resolution . The trend in laser scanning over the last decade has been to increase the density of lidar 3D point clouds to advance the understanding of spatial forest ecology and support conservation needs in detail (Asner et al., 2017;Reis et al., 2022). The gain in accuracy in contemporary ALS surveys is potentially high for mountain forest where conditions can exacerbate inconsistencies in the quality and uniformity of ALS point clouds (Hollaus et al., 2006). ...
... Remote sensing could provide rapid measurements of genotypic variation related to genetic diversity and plant structure. Quaking aspen genotypes have been discriminated using airborne spectral data [19][20][21]. Imaging spectroscopy data have recently been used to map the within-species population genetic variation of trembling aspen Populus tremuloides in two ecoregions of the USA [21]. ...
... The spectral variability of genetic patterns remains strong in Bermuda grass along both the longitudinal and latitudinal gradients. The spectral variation is pronounced for genotypic variation because genetic variation can lead to phenotypic variation in the functional traits of plant tissues adapted to different geographic regions [20]. It is clear from our data that variations along the latitudinal gradients were captured well by certain spectral features, while spectral variation along the longitudinal gradients was low. ...
Genetic variation among populations within plant species can have huge impact on canopy biochemistry and structure across broad spatial scales. Since canopy spectral reflectance is determined largely by canopy biochemistry and structure, spectral reflectance can be used as a means to capture the variability of th genetic characteristics of plant species. In this study, we used spectral measurements of Bermuda grass [Cynodon dactylon (L.) Pers.] at both the leaf and canopy levels to characterize the variability of plant traits pertinent to phylogeographic variation along the longitudinal and latitudinal gradients. An integration of airborne multispectral and hyperspectral data allows for the exploitation of spectral variations to discriminate between the five different genotypic groups using ANOVA and RF models. We evaluated the spectral variability among high-latitude genotypic groups and other groups along the latitudinal gradients and assessed spectral variability along longitudinal gradients. Spectral difference was observed between genetic groups from the northern regions and those from other regions along the latitudinal gradient, which indicated the usefulness of spectral signatures for discriminating between genetic groups. The canopy spectral reflectance was better suited to discriminate between genotypes of Bermuda grass across multiple scales than leaf spectral data, as assessed using random forest models. The use of spectral reflectance, derived from remote sensing, for studying genetic variability across landscapes is becoming an emerging research topic, with the potential to monitor and forecast phenology, evolution and biodiversity.
... Manned airborne images have 1 m or smaller pixels (Asner et al. 2015(Asner et al. , 2017 with multispectral, hyperspectral, and lidar sensors. However, manned airborne images cost up to $500,000 per flight over each study area, which limits repeated images for individual projects, especially at annual or seasonal scales. ...
Context
Climate change is causing landscape shifts and locally-adapted plants are becoming increasingly maladapted. As a foundation species, Fremont cottonwood facilitates adaptation to changing climate for the whole community. Populations within this species, however, have varying adaptive responses and facilitative capacity due to genetic variation. It is important to identify these differences to inform landscape restoration and management.
Objectives
UAV hyperspectral, thermal, and lidar images might reveal genetic trait differences within a single tree species. This study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content as indicators of population-level productivity, fitness, adaptability, and biodiversity they can support, and (2) UAV hyperspectral-thermal-lidar fusion in detecting and classifying 16 populations sourced from different environments across Arizona, USA.
Methods
UAV hyperspectral, thermal, and lidar images were acquired from a common garden with 16 different Fremont cottonwood populations growing together. The UAV hyperspectral image was used to calculate spectral indices for canopy leaf area (LAI), canopy water content, nitrogen, carbon, and carbon-to-nitrogen ratio (C:N). The hyperspectral indices (EVI, LAI, PRI, MSI, NDWI, NDNI, NDLI, and C:N) were also examined with the UAV thermal image-derived canopy temperature data for potential correlations. Finally, all hyperspectral bands (n = 487 bands), thermal image-derived canopy temperature, and lidar-derived maximum canopy height estimates were stacked into a single image and then classified to detect 16 different populations of Fremont cottonwood using a random forest classification.
Results
The UAV hyperspectral indices and canopy temperature were significantly different among populations suggesting that the productivity, fitness, and adaptability of varying populations are significantly different. Many of the UAV hyperspectral indices were strongly correlated with canopy temperature. Populations with greater canopy cover, lower canopy temperature, and greater canopy height were well detected in the UAV hyperspectral-thermal-lidar fusion-based classification (producer’s accuracies of > 75%), whereas populations at low abundance were poorly classified (producer’s accuracies of < 41–65%).
Conclusions
This study demonstrates the first application of UAV hyperspectral-thermal-lidar data fusion in phenotyping. The machine learning-based classification detects various populations within a single tree species. Future studies can use similar UAV data sources, derived variables, and data fusion to detect populations that have better fitness and adaptability to changing environments. Such populations can be strategically managed to sustain healthy landscapes that support diverse communities and species.
... UAV remote-sensing technology has been increasingly applied to vegetation diversity monitoring. Research on plant diversity using UAV remote sensing has primarily focused on aspects such as species identification [7,8], vegetation structure [9], species diversity [10,11], and plant traits [12]. ...
Biodiversity conservation is a critical environmental challenge, with accurate assessment being essential for conservation efforts. This study addresses the limitations of current plant diversity assessment methods, particularly in recognizing mixed and stunted grass species, by developing an enhanced species recognition approach using unmanned aerial vehicle (UAV) hyperspectral data and deep learning models in the steppe region of Xilinhot, Inner Mongolia. We compared five models—support vector machine (SVM), two-dimensional convolutional neural network (2D-CNN), three-dimensional convolutional neural network (3D-CNN), hybrid spectral CNN (HybridSN), and the improved HybridSN+—for grass species identification. The results show that SVM and 2D-CNN models have relatively poor recognition effects on mixed distribution and stunted individuals, while HybridSN and HybridSN+ models can effectively identify important grass species in the region, and the recognition accuracy of the HybridSN+ model can reach 96.45 (p < 0.05). Notably, the 3D-CNN model’s recognition performance was inferior to the HybridSN model, especially for densely populated and smaller grass species. The HybridSN+ model, optimized from the HybridSN model, demonstrated improved recognition performance for smaller grass species individuals under equivalent conditions, leading to a discernible enhancement in overall accuracy (OA). Diversity indices (Shannon–Wiener diversity, Simpson diversity, and Pielou evenness) were calculated using the identification results from the HybridSN+ model, and spatial distribution maps were generated for each index. A comparative analysis with diversity indices derived from ground survey data revealed a strong correlation and consistency, with minimal differences between the two methods. This study provides a feasible technical approach for efficient and meticulous biodiversity assessment, offering crucial scientific references for regional biodiversity conservation, management, and restoration.
... Long historical series, such as those provided by Landsat, can help track changes in forest cover, such as the occurrence of disturbances and the classification of successional stages (Berveglieri et al., 2021;Decuyper et al., 2022;Silva Junior et al., 2021). Very-high-spectral resolution (airborne hyperspectral) data can measure biochemical and biodiversity attributes, allowing tree species identification (Ferreira et al., 2016) and functional attribute estimation (Asner et al., 2017). ...
Tropical forests are increasingly threatened by deforestation and degradation, impacting carbon storage, climate regulations and biodiversity. Restoring these ecosystems is crucial for environmental sustainability, yet monitoring these efforts poses significant challenges. Secondary forests are in a constant state of flux, with growth depending on multiple factors.
Remote sensing technologies offer cost‐effective, scalable and transferable solutions, advancing forest restoration monitoring towards more accurate, efficient and real‐time data analysis and interpretation. This review provides a comprehensive evaluation of the current state and advancements in remote sensing technologies applied to monitoring tropical forest restoration.
Synthesis and applications: This review brings together the state of the art of remote sensing technologies, such as very‐high‐resolution RGB imagery, multi‐ and hyperspectral imaging, lidar, radar and thermal‐infrared technologies and their applicability in monitoring forest restoration. In conclusion, this review emphasizes the potential of remote sensing technologies, coupled with advanced computational techniques, to enhance global efforts towards effective and sustainable forest restoration monitoring.
... Among those applications, imaging spectroscopy is considered the most promising remote sensing approach for mapping species distribution and functional diversity of plants [25]. Spectral diversity, which was calculated from remote sensing imagery, has been considered an indicator for vegetation biodiversity assessment [26][27][28]. ...
Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate change on forest biodiversity in Northeast China, time-series NDVI data, meteorological data and land cover data from 2010 to 2021 were acquired, and the forest biodiversity of Northeast China was evaluated. The effect of climate change on forest biodiversity was analyzed, and the results indicated that the forest biodiversity features increased from west to east in Northeast China. There was also an increasing trend from 2010 to 2021, but the rate at which forest biodiversity was changing varied with different forest types of Northeast China, as different climatic factors had a different impact on forest biodiversity in different forest types. Average annual temperature, annual accumulated precipitation, CO2 fertilization and solar radiation were the main factors affecting forest biodiversity changing trends. This research indicated the potential impact of climate change on forest ecosystems, as it emphasized with evidence that climate change has a catalytic effect on forest biodiversity in Northeast China.
... Ecologists and foresters have long identified major differences between forest types, leading to different ecosystem services (e.g. carbon stocks, water regulation, food supplies, etc.; Watson et al., 2018) and different floristic and faunistic compositions influencing conservation priorities (Asner et al., 2017;Cannon et al., 2007;Fonteyn et al., 2023). 1 However, the large-scale distribution of forest types and their underlying floristic and faunistic compositions are poorly known in tropical forests (Fonteyn et al., 2023;Ordway et al., 2022). The main reasons for this knowledge gap are the inherent difficulties of satellite-based approaches to detect subtle changes in the structure and composition of tropical dense forests, and the low signal/ noise ratio and artefacts due to imperfect pre-processing (Hoekman et al., 2020;Jha et al., 2021). ...
Tropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.
... The monitoring of forests using remote sensing (RS) is welladopted in conservation science and in operational forestry (Maltamo et al. 2014;Marvin et al. 2016;Asner et al. 2017). Qualitative and quantitative indicators are used to describe forest structure, species diversity and structural complexity to estimate carbon dynamics and biodiversity (McElhinny et al. 2005;Pettorelli et al. 2016;Pretzsch and Zenner 2017;Dubayah et al. 2022). ...
the deciduous season (February 2022) using several scanning paths. Ground reference data (418 trees, 15 snags) was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attributes (DBH, height and volume). The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology. Ninety-six percent of all pairs matched below 65 cm. For DBH, phenology barely altered estimates. We observed a strong agreement when comparing HLS-based tree height distributions. The values exceeded 2 m when comparing height measurements, confirming height data should be carefully used as reference in remote sensing-based inventories, especially for deciduous species. Tree volume was more precise for pines (r = 0.95, and relative RMSE = 21.3-23.8%) compared to deciduous species (r = 0.91-0.96, and relative RMSE = 27.3-30.5%). HLS data and the forest structural complexity tool performed remarkably , especially in tree positioning considering mixed forests and mixed phenology conditions.
... However, this is expensive and time-consuming in relation to the associated costs of the expert knowledge needed and also it is difficult to apply for large-size inventories (e.g., several hectares) of forest communities (G. P. Asner, 2017). ...
... Vertical forest structure is among the more important remotely sensed characteristics that can provide relevant information for studies of carbon sequestration, species habitat modeling, and biodiversity patterns at local scales, and airborne lidar is frequently the source of those vertical structure data Davies and Asner, 2014;. At local scales, the use of airborne lidar data, also referred to as airborne laser scanning (ALS), has improved our understanding of species distributions for organisms that range in size from beetles and spiders (Müller and Brandl, 2009;Vierling et al., 2011) to elephants (Davies et al., 2018), and ALS has been incorporated in studies of biodiversity that address patterns of alpha, beta, and functional diversity perspectives (Asner et al., 2017;Bae et al., 2018). ...
Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R ² values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.
... large spatial scales to understand drivers of biodiversity and inform protection priorities (Asner et al. 2017;Féret and Asner 2011). Imaging spectroscopy generates high-spectralresolution data spanning the visible to shortwave-infrared (SWIR; 400-2500 nm) electromagnetic spectrum. ...
Imaging spectroscopy has the potential to map closely related plant taxa at landscape scales. Although spectral investigations at the leaf and canopy levels have revealed relationships between phylogeny and reflectance, understanding how spectra differ across, and are inherited from, genotypes of a single species has received less attention. We used a common-garden population of four varieties of the keystone canopy tree, Metrosideros polymorpha, from Hawaii Island and four F1-hybrid genotypes derived from controlled crosses to determine if reflectance spectra discriminate sympatric, conspecific varieties of this species and their hybrids. With a single exception, pairwise comparisons of leaf reflectance patterns successfully distinguished varieties of M. polymorpha on Hawaii Island as well as populations of the same variety from different islands. Further, spectral variability within a single variety from Hawaii Island and the older island of Oahu was greater than that observed among the four varieties on Hawaii Island. F1 hybrids most frequently displayed leaf spectral patterns intermediate to those of their parent taxa. Spectral reflectance patterns distinguished each of two of the hybrid genotypes from one of their parent varieties, indicating that classifying hybrids may be possible, particularly if sample sizes are increased. This work quantifies a baseline in spectral variability for an endemic Hawaiian tree species and advances the use of imaging spectroscopy in biodiversity studies at the genetic level.
... Remote sensing provides a tool for large area monitoring of lichen cover. Hyperspectral remote sensing data have proved promising in assessing functional plant traits and diversity (Anderson et al. 2008;Singh et al. 2015;Asner et al. 2017;Schneider et al. 2017), and the new hyperspectral satellite missions (e.g., PRISMA, EnMAP) are therefore expected to provide a tool for monitoring species-level traits across time (Skidmore et al. 2021). Similarly as the spectral properties of plants have been related to, e.g., their morphology and chemical composition, the laboratory or in situ measurements of lichen spectra have shown the spectral reflectance of lichens to vary strongly not only between species (Petzold and Goward 1998;Rees et al. 2004;Kuusinen et al. 2020), but also within species due to, e.g., variation in lichen structure (Kuusinen et al. 2020) or height (Nordberg and Allard 2002), across viewing angles (Peltoniemi et al. 2005;Solheim et al. 2000;Kuusinen et al. 2020), and with varying water content (Nordberg and Allard 2002;Rees et al. 2004;Neta et al. 2010;Granlund et al. 2018;Kuusinen et al. 2020). ...
Lichens are sensitive to competition from vascular plants, intensive silviculture, pollution and reindeer and caribou grazing, and can therefore serve as indicators of environmental changes. Hyperspectral remote sensing data has been proved promising for estimation of plant diversity, but its potential for forest floor lichen cover estimation has not yet been studied. In this study, we investigated the use of hyperspectral data in estimating ground lichen cover in boreal forest stands in Finland. We acquired airborne and in situ hyperspectral data of lichen-covered forest plots, and applied multiple endmember spectral mixture analysis to estimate the fractional cover of ground lichens in these plots. Estimation of lichen cover based on in situ spectral data was very accurate (coefficient of determination (r2) 0.95, root mean square error (RMSE) 6.2). Estimation of lichen cover based on airborne data, on the other hand, was fairly good (r2 0.77, RMSE 11.7), but depended on the choice of spectral bands. When the hyperspectral data were resampled to the spectral resolution of Sentinel-2, slightly weaker results were obtained. Tree canopy cover near the flight plots was weakly related to the difference between estimated and measured lichen cover. The results also implied that the presence of dwarf shrubs could influence the lichen cover estimates.
... Furthermore, leaf-level spectra have become an invaluable tool to capture the diversity in leaf traits that have accumulated over the course of seed plant evolution (Reich et al. (2003), Cornwell et al. (2014)), enabling estimation of functional diversity (Kokaly et al. (2009), Schneider et al. (2017) and taxonomic diversity (Clark, Roberts and Clark (2005), Cavender- Bares et al. (2016)). These spectra provide drivers for ecosystem processes (Schweiger et al. (2018)) and guide conservation (Asner et al. (2017)). ...
... Traditionally, forest diversity is calculated by counting the number and types of trees, which is an expensive, time-consuming process. Additionally, due to accuracy problems and difficulty in recognizing intertwined tree species, such a strategy is difficult to implement in large (e.g., hundreds of hectares) forest communities [5]. The challenges are more significant in natural forests with dense canopies. ...
Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R2 = 0.76) models outperformed the other models (R2 of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.
... We used the scaled He values to investigate potential patterns in genetic structure using simple correlations with latitude and longitude, which provide little information about the historical and environmental drivers of observed relationships. Remote sensing and earth observation technologies constitute spatially resolved and contiguous approaches increasingly promising in linking genetic data to environmental information relevant to conservation of plant genetic resources, as is now established for the analysis of many plant traits (e.g., Asner et al. 2017;Wang et al. 2020). Additionally, models and simulation studies projecting possible future distributions and challenges for F. sylvatica under probable climatic scenarios are in continuous development (e.g., Capblancq et al. 2020a, b;Falk and Hempelmann 2013;Kramer et al. 2010). ...
Unlabelled:
Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic diversity in European beech and created a meta-data set of expected heterozygosity (He) from all past studies providing nuclear microsatellite data. We propose a novel approach, based on population genetic theory and a min-max scaling to make past studies comparable. Using a new microsatellite data set with unprecedented geographic coverage and various re-sampling schemes to mimic common sampling biases, we show the potential and limitations of the scaling approach. The scaled meta-dataset reveals the expected trend of decreasing genetic diversity from glacial refugia across the species range and also supports the hypothesis that different lineages met and admixed north of the European mountain ranges. As a result, we present a map of genetic diversity across the range of European beech which could help to identify seed source populations harboring greater diversity and guide sampling strategies for future genome-wide and functional investigations of genetic variation. Our approach illustrates how to combine information from several nuclear microsatellite data sets to describe patterns of genetic diversity extending beyond the geographic scale or mean number of loci used in each individual study, and thus is a proof-of-concept for synthesizing knowledge from existing studies also in other species.
Supplementary information:
The online version contains supplementary material available at 10.1007/s11295-022-01577-4.
... Cluster 5 (purple), "Remote sensing and monitoring", which contains 468 authors, deals with issues related to estimating carbon cycle products and changes in coverage visualized through satellite images. Gregory Asner (1580) studies estimates of biomass and greenhouse gas emissions [171,316], as well as land cover and its different uses, using satellite images [317,318]. For his part, Matthew Hansen (1352) focuses on cover change detection to determine an area's vulnerability [319,320]. ...
Tropical ecosystems play an important role in the environment. They provide multiple ecosystem services, such as carbon capture and sequestration, food supply, and climate regulation. Studying land use and land cover change makes it possible to understand the land’s alterations associated with deforestation, degradation, erosion, soil desertification, and biodiversity loss. The objective of this study is to evaluate the different approaches to land use and land cover research in tropical forests based on the evolutionary and qualitative analysis of the last 44 years of scientific production. The data were collected using the Scopus database and was based on the PRISMA methodology’s four phases: (i) identification, (ii) screening, (iii) eligibility, and (iv) included. The results showed a significant increase in the study of land use and land cover consolidated in 4557 articles, with contributions from 74 countries, revealing 14 themes and seven lines of research. Core research areas such as biodiversity, land use, and conservation exist due to the ongoing interest in the value of tropical forests and their response to climate change. The present research allowed us to consider future study topics such as the relationship between sustainable development goals and land use and cover in tropical forests, as well as the evaluation of the environmental impact of economic activities in forests.
The mapping of plant biodiversity represents a fundamental stage in establishing conservation priorities, particularly in identifying groups of species that share ecological requirements or evolutionary histories. This is often achieved by assessing different spatial diversity patterns in plant population distributions. In this paper, we present two primary data sources crucial for biodiversity monitoring: in situ measurements from botanical observations and remote sensing (RS). In situ methods involve directly collecting data from specific sites, providing detailed insights into ecological patterns but often constrained by resource limitations. Integrating in situ and RS data highlights their complementary strengths, which depend on factors such as study scale, resolution, and logistical feasibility. While in situ approaches are characterized by precision, RS offers efficiency and extensive, repeated coverage. This research integrates in situ and RS data to analyze plant and spectral diversity across France at a spatial resolution of 5 km, encompassing over 23 000 grid cells. We employ four established diversity metrics leveraging the spatial distribution of 6650 plant species and 250 spectral clusters (derived from MODIS data at a 500‐m resolution). Through bioregionalization network analysis combining these data sources, we identified five distinct bioregions that capture the biogeographical structure of plant biodiversity in France. Additionally, we explore the relationship between plant species diversity and spectral cluster diversity within and between these bioregions, offering novel insights into the spatial dynamics of plant biodiversity.
There are repeated calls for remote sensing observations to produce accessible data products that improve our understanding and conservation of biodiversity. The Biodiversity Survey of the Cape (BioSCape) addresses this need by integrating field, airborne, satellite, and modeling datasets to advance the limits of global remote sensing of biodiversity. Over six weeks, an international team of ~150 scientists collected data across terrestrial, marine, and freshwater ecosystems in South Africa. In situ biodiversity observations of plant and animal communities, estuaries, kelp, and plankton were made using traditional field methods as well as novel approaches like environmental DNA and acoustic surveys. Biodiversity observations were accompanied by an unprecedented combination of airborne imaging spectroscopy and lidar measurements acquired across 45,000 km². Here, we review how the approaches applied in BioSCape will help us measure and monitor biodiversity at scale and the role of remote sensing in accomplishing this.
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.
Context
Forest traits and characteristics are challenging to measure across ecosystems with traditional field methods. There is a ripe opportunity for unoccupied aerial systems (UAS) to contribute to landscape ecology through mapping forest traits and characteristics and linking scales between ground surveys and airborne/spaceborne remote sensing.
Objectives
We consider the unique perspective of UAS in forests and the considerations that come with working with an emerging technology.
Methods
We performed a literature review of which forest traits and characteristics have been derived from UAS and dive into a case example of how researchers derive a particular trait, aboveground carbon stock, from UAS-based data.
Results
UAS are most useful and cost-effective in contexts where high resolution data are required across a limited spatial extent. Due to the high spatial resolution and ability to fly close to top-of-canopy, UAS excel at measuring morphological and physiological characteristics, like canopy structure and foliar chemical traits. Combining spectral and structural information can be done particularly easily with UAS data and enhances aboveground carbon estimation from UAS. UAS-based lidar is best for measuring forest structural attributes, but RGB imagery with post-processing is an acceptable alternative for a tight budget.
Conclusions
UAS contribute to landscape ecology through measuring forest traits and characteristics in novel ways. We need better metadata and validation reporting and method standardization to improve reproducibility and comparison across UAS forest studies. This review is written for ecologists interested in measuring forests at a landscape scale, and particularly for researchers interested in adding UAS to their toolkit.
Detecting unexpected objects (anomalies) in real time has great potential for monitoring, managing, and protecting the environment. Hyperspectral line-scan cameras are a low-cost solution that enhance confidence in anomaly detection over RGB and multispectral imagery. However, existing line-scan algorithms are too slow when using small computers (e.g. those onboard a drone or small satellite), do not adapt to changing scenery, or lack robustness against geometric distortions. This paper introduces the Exponentially moving RX algorithm (ERX) to address these issues, and compares it with four existing RX-based anomaly detection methods for hyperspectral line scanning. Three large and more complex datasets are also introduced to better assess the practical challenges when using line-scan cameras (two hyperspectral and one multispectral). ERX is evaluated using a Jetson Xavier NX edge computing module (6-core CPU, 8GB RAM, 20W power draw), achieving the best combination of speed and detection performance. ERX was 9 times faster than the next-best algorithm on the dataset with the highest number of bands (108 band), with an average speed of 561 lines per second on the Jetson. It achieved a 29.3% AUC improvement over the next-best algorithm on the most challenging dataset, while showing greater adaptability through consistently high AUC scores regardless of the camera’s starting location. ERX performed robustly across all datasets, achieving an AUC of 0.941 on a drone-collected hyperspectral line scan dataset without geometric corrections (a 16.9% improvement over existing algorithms). This work enables future research on the detection of anomalous objects in real time, adaptive and automatic threshold selection, and real-time field tests. The datasets and the Python code are openly available at https://github.com/WiseGamgee/HyperAD, promoting accessibility and future work.
Optical remote sensing (RS) enables the study of the elemental composition of Earth’s surface over broad spatial extents by detecting reflected electromagnetic radiation. Covalent bonds of macromolecular structures often reflect electromagnetic radiation at specific wavelengths, and in some cases relate to bonds of specific elemental identity. In other cases, interfering optical properties greatly impact the ability of RS to measure elements directly, but advances in statistical methods and the theoretical understanding of optical properties expand the capacity to quantify diverse elements in many systems. When applied under the framework of ecological stoichiometry, spatially and temporally explicit measurements of elemental composition permit understanding of the drivers of ecological processes and variation over space and through time. However, the multitude of available technologies and techniques present a large barrier of entry into RS. In this paper we summarize the capabilities and limitations of RS to quantify elements in terrestrial and aquatic systems. We provide a practical guide for researchers interested in using RS to quantify elemental ratios and discuss RS as an emerging tool in ecological stoichiometry. Finally, we pose a set of emerging questions which integrating RS and ecological stoichiometry is uniquely poised to address.
Environmental risk assessment (ERA) is a systematic process of evaluating the potential adverse effects of human activities and natural phenomena on the environment, ecosystems, and human health.
Deforestation and degradation of the global forests have led to the degradation of the environment, the economy, and the esthetics of the forestlands. Deforestation and degradation have been compensated to some degree by the natural regeneration of the forests and the setting up of plantations, but much-regenerated forest is composed of a small number of species designed to produce one or two types of products rather than to produce a wider variety of forest products and services that contribute to the prosperity of the local community. Conventional models of plantation forestry rarely provide the multiple values of forests and do not adequately address the needs of the forest-dependent communities and the water users downstream. In reality, such systems can lead to a decrease in the variety, quality, and volume of forest products and services, as well as social and economic displacement and an increase in vulnerability to climate and other natural shocks. There is a pressing need to both enhance the quality of the restoration and rehabilitation of the forest at site level, as well as to identify effective ways to carry out these activities within the context of wider environmental, social, or economic interests. While forest land use has traditionally been seen as a local environmental challenge, it is now becoming a global challenge. Changes to forests, farms, waterways, and air are driving global changes to the food supply, fiber supply, water supply, shelter supply, and air supply for more than 6 billion people. In recent decades, global cropland, pasture, plantation, and city areas have grown in size and increased energy, water, and fertilizer use, with significant biodiversity loss. These land-use changes have allowed humans to appropriate more and more of the planet's resources. But they also threaten the ability of ecosystems to support food production, freshwater and forest supply, climate and air regulation, and disease control. We are confronted with the challenge of balancing immediate human needs with maintaining the biosphere's capacity to deliver goods and services over the long term. As our population continues to grow and our demand for land and resources increases, so too does the pressure on forest ecosystems. Many forests that remain are decimated by logging, cutting firewood, pollution, and pests. Even trees that are left are disappearing to make room for houses, roads, dams, and intensive agriculture. Climate change-driven wildfires can also wreak havoc on forest ecosystems. Forest restoration is the process of returning trees to previously forested land and improving the state of degraded forests. It involves planting native tree species to restore the tree cover in existing forests. It also includes the conservation of wild plants and animals, as well as preserving the soils and Sustainable Forest Management-Surpassing Climate Change and Land Degradation 2 water resources that are part of a forest ecosystem. Land that has been cleared for farming but is now being used for other purposes is a great place to restore forests. In some instances, forest trees will naturally re-grow. Restoration can also include the nurturing of forest and woodland patches in landscapes that include busy farms and settlements.
The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts.
Owl monkeys are small, elusive, and nocturnal, making them some of the hardest animals to survey and study in the wild. We summarize the use of traditional field methods as well as more recent technology that has improved opportunities for investigating the behavior, ecology, and population biology of these neotropical primates. Over the years, research on owl monkeys has embraced the use of night vision equipment; radiocollars; identification collars; animal-mounted activity recorders; handheld GPSs; motion-detecting, infrared camera traps; passive acoustic recorders; and automatic weather data loggers. Many of these technologies have been tested and used across countries, habitats, and species. Additional technologies, such as drones and thermal cameras, are also likely to play a valuable role in research on owl monkeys and other nocturnal mammals in the near future, particularly as these become cheaper and more readily accessible to the wildlife research community.
The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.
Early naturalists such as Humboldt observed that changes in topography and anthropogenic disturbances influenced vegetation structure and the composition of animal communities. This holistic view of community assembly continues to shape conservation and restoration strategies in an era of landscape degradation and biodiversity loss. Today, remote sensing affords ecologists the tools for obtaining rapid and precise measures of topography, disturbance history and vegetation structure. Nonetheless, the capacity of such measures to predict the structure of diverse and functionally important insect communities has not been fully explored.
We sampled ground‐dwelling ant assemblages with pitfall traps along a successional gradient (15 grasslands, 21 shrublands and 44 forests) in subtropical Asia, and measured the taxonomic (TD) and functional diversity (FD). We used airborne Light Detection and Ranging (LiDAR) and aerial photography—to measure topography, anthropogenic‐fire history and vegetation structure at each site. Using structural equation models, we tested the hypothesis that vegetation structure mediated the effects of topography and anthropogenic‐fire history on ant assemblage TD and FD, with stronger effects on the latter.
We found that low elevation and anthropogenic‐fire history promoted ant TD, and by mediating vegetation structure, these factors further controlled ant FD. Specifically, assemblages of ant species occupying more similar niches—as indicated by their lower FD—were found in secondary forests that had more structurally homogeneous vegetation. These sites also had low insolation and high water moisture content, and were not recently burned as revealed by LiDAR‐derived metrics and aerial images. Furthermore, remotely sensed vegetation structures were closely associated with individual ant traits, such as body size and eye length, which reflect species' preferences for habitat structure.
Synthesis. Our study uncovers the interactive effects of topography, disturbance history and vegetation structure in determining the TD and FD of ant assemblages in subtropical landscapes. Moreover, it demonstrates that remote sensed data can be leveraged to efficiently elucidate the complex effects of environmental change and disturbances on vegetation structure and consequently insect biodiversity, representing ecological proxies to refine ground investigation plans and support appropriate conservation and restoration measures for degraded landscapes.
The effectiveness of Protected Areas in conserving forest ecosystems has been examined at the continental scale using area-based habitat parameters, but knowledge of the three-dimensional structure of forest habitats is still lacking. Here, we assess the effectiveness of European Protected Areas in conserving the vertical structure of forests by analysing more than 30 million records from the Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR (Light Detection And Ranging) mission. We compare a suite of indicators of the vertical structure of forests inside and outside nearly 10,000 protected areas. We find that European forests are on average 2 m taller and vertically more complex in protected areas than in nearby unprotected areas, albeit with some regional differences. At the same time, forests outside protected areas show greater variations in canopy height than inside, probably as a result of past and current forest management operations. Our findings highlight the positive imprint of environmental policies on forest structure across Europe and underscore how spaceborne LiDAR enables the large-scale monitoring of forest vertical structural attributes that are key to conservation and restoration policies.
Functional diversity is a critical component driving ecosystem functioning. Spatially explicit data of plant functional traits and diversity are essential for understanding biodiversity effects on ecosystem functioning. Here we retrieved three morphological traits (95th quantile height, leaf area index, foliage height diversity) and three physiological traits (chlorophyll a + b content, specific leaf area, equivalent water thickness) from airborne laser scanning and multispectral Sentinel-2 data, respectively. We found airborne LiDAR-derived parameters correlated well with in-situ plot-level morphological data (R2 ≥ 0.67). For satellite-derived physiological traits, partial least squares regression (PLSR) obtained higher prediction accuracy (R2 = 0.26–0.43, cross-validation with in-situ community-weighted mean (CWM) leaf physiological trait data) than a vegetation index (VI) approach. The remotely-sensed traits were used as input to estimate multi-trait functional diversity (FD) indices in a species-rich subtropical mountainous forest. Finally, we investigated the influence of single-trait CWMs, multi-trait FD indices and environmental variables on remotely-derived aboveground ecosystem carbon stocks (aboveground biomass, AGB) and primary productivity (kernel normalized difference vegetation index, kNDVI). CWMs of all functional traits were significant predictors of AGB and kNDVI, as suggested by the mass-ratio hypothesis. Morphological FD indices were also important predictors of AGB and kNDVI, indicating effects of complementarity in crown architectures. In best-fit multivariate models, the first principal component CWM of morphological traits and that of physiological traits were the most important predictors of AGB and kNDVI, respectively. The FD index of morphological richness was additionally selected in the best-fit models for AGB and kNDVI at ecosystem and landscape scales. Our work highlights the potential of using remotely-sensed functional traits to assess the relationship between trait diversity and ecosystem functioning across large, contiguous areas.
Structural features of the overstorey in managed and unmanaged forests can significantly influence plant community composition. Native Acacia species are common in temperate eucalypt forests in southeastern Australia. In these forests, intense disturbances, such as logging and wildfire, lead to high densities of regenerating trees, shrubs, and herbs. The tree layer is dominated by Acacia and Eucalyptus, that compete intensely for resources in the first decades after stand establishment. The relative abundance and size of Acacia and Eucalyptus varies widely due to stochastic factors such as dispersal, microsite variability, and weather and climatic conditions. This variability may influence the structure and composition of the herbaceous and shrub species. In the temperate forests of southeastern Australia, understorey plant diversity is assumed to be influenced by Acacia species density, rather than Eucalyptus density. To quantify the influence of Acacia and Eucalyptus density on plant community composition, we used remote sensing and machine learning methods to map canopy composition and then compare it to understorey composition. We combined unoccupied aerial vehicle (UAV or drone) imagery, supervised image classifications, and ground survey data of plant composition from post-logging regrowth forests in the Central Highlands of southeastern Australia. We found that aggregation and patch metrics of Eucalyptus and Acacia were strongly associated with understorey plant beta diversity. Increasing aggregation of Acacia and the number of Acacia patches had a significant negative effect on plant beta diversity, while the number of Eucalyptus patches had a positive influence. Our research demonstrates how accessible UAV remote sensing can be used to quantify variability in plant biodiversity in regrowth forests. This can help forest managers map patterns of plant diversity at the stand-scale and beyond to guide management activities across forested landscapes.
The development of tree-ring chronologies of tropical trees allows to reconstruct the environmental history of the Neotropics on extensive temporal and spatial scales. This article presents a historic, state-of -the-art overview/ review of dendrochronological studies in Perú, a megadiverse country in its flora, types of climate and ecosystems. We reviewed all available information on dendrochronological studies by assessing scientific articles in indexed, and non-indexed journals as well university thesis repositories. Dendrochronological studies began in the late 1980s and have botanically involved 20 families, 34 genera and 52 tree species. The most studied families are Fabaceae (16 studies), Meliaceae (12), Rosaceae (06), and Bignoniaceae (04), and the most studied genera were Cedrela (13), Polylepis (08) and Prosopis (06). The development of chronologies was mainly applied in climatic reconstructions, forest conservation and management. We identify underrepresentation or sampling gaps regarding climatic and geographic complexity. The high tree diversity of Perú constitutes a natural laboratory to develop tree-ring studies to better understand the growth and functioning of tropical tree species, their interaction with climate, and to derive climate reconstructions during the last centuries. This review aims to contribute to the direction of future dendrochronological studies in Perú.
Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) as-sess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and ana-lyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture meas-urements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R2) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, re-spectively. For both response variables, the most important explanatory variables were Senti-nel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings high-light the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, car-bon storage, and biodiversity conservation.
In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the "spectral spe-cies"-sets of pixels with a similar spectral signal-and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept.
Although tropical forests differ substantially in form and function, they are often represented as a single biome in global change models, hindering understanding of how different tropical forests will respond to environmental change. The response of the tropical forest biome to environmental change is strongly influenced by forest type. Forest types differ based on functional traits and forest structure, which are readily derived from high resolution airborne remotely sensed data. Whether the spatial resolution of emerging satellite-derived hyperspectral data is sufficient to identify different tropical forest types is unclear. Here, we resample airborne remotely sensed forest data at spatial resolutions relevant to satellite remote sensing (30 m) across two sites in Malaysian Borneo. Using principal component and cluster analysis, we derive and map seven forest types. We find ecologically relevant variations in forest type that correspond to substantial differences in carbon stock, growth, and mortality rate. We find leaf mass per area and canopy phosphorus are critical traits for distinguishing forest type. Our findings highlight the importance of these parameters for accurately mapping tropical forest types using space borne observations.
Ecological restoration is critical for climate and biodiversity resilience over the coming century. Today, there is strong evidence that wildlife can significantly influence the distribution and stoichiometry of elements across landscapes, with subsequent impacts on the composition and functioning of ecosystems. Consequently, any anthropogenic activity that modifies this important aspect of zoogeochemistry, such as changes to animal community composition, diet, or movement patterns, may support or hinder restoration goals. It is therefore imperative that the zoogeochemical effects of such anthropogenic modifications are quantified and mapped at high spatiotemporal resolutions to help inform restoration strategies. Here, we first discuss pathways through which human activities shape wildlife‐mediated elemental landscapes and outline why current frameworks are inadequate to characterize these processes. We then suggest improvements required to comprehensively model, validate, and monitor element recycling and redistribution by wildlife under differing wildlife management scenarios and discuss how this might be implemented in practice through a specific example in the southern Kalahari Desert. With robust ecological forecasting, zoogeochemical impacts of wildlife can thus be used to support ecological restoration and nature‐based solutions to climate change. If ignored in the restoration process, effects of wildlife on elemental landscapes may delay, or even prevent, restoration success. This article is protected by copyright. All rights reserved.
Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R2: 0.45-0.67, relative RMSE: 11%-14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems.
The world's greatest terrestrial stores of biodiversity and carbon are found in the forests of northern South America, where large-scale biogeographic patterns and processes have recently begun to be described(1-4). Seven of the nine countries with territory in the Amazon basin and the Guiana shield have carried out large-scale forest inventories, but such massive data sets have been little exploited by tropical plant ecologists(5-8). Although forest inventories often lack the species-level identifications favoured by tropical plant ecologists, their consistency of measurement and vast spatial coverage make them ideally suited for numerical analyses at large scales, and a valuable resource to describe the still poorly understood spatial variation of biomass, diversity, community composition and forest functioning across the South American tropics(9). Here we show, by using the seven forest inventories complemented with trait and inventory data collected elsewhere, two dominant gradients in tree composition and function across the Amazon, one paralleling a major gradient in soil fertility and the other paralleling a gradient in dry season length. The data set also indicates that the dominance of Fabaceae in the Guiana shield is not necessarily the result of root adaptations to poor soils ( nodulation or ectomycorrhizal associations) but perhaps also the result of their remarkably high seed mass there as a potential adaptation to low rates of disturbance.
Field studies in Amazonia have found a relationship at continental scales between soil fertility and broad trends in forest structure and function. Little is known at regional scales, however, about how discrete patterns in forest structure or functional attributes map onto underlying edaphic or geological patterns. We collected airborne LiDAR (Light Detection and Ranging) data and VSWIR (Visible to Shortwave Infrared) imaging spectroscopy measurements over 600 km2 of northwestern Amazonian lowland forests. We also established 83 inventories of plant species composition and soil properties, distributed between two widespread geological formations. Using these data, we mapped forest structure and canopy reflectance, and compared them to patterns in plant species composition, soils, and underlying geology. We found that variations in soils and species composition explained up to 70% of variation in canopy height, and corresponded to profound changes in forest vertical profiles. We further found that soils and plant species composition explained more than 90% of the variation in canopy reflectance as measured by imaging spectroscopy, indicating edaphic and compositional control of canopy chemical properties. We last found that soils explained between 30% and 70% of the variation in gap frequency in these forests, depending on the height threshold used to define gaps. Our findings indicate that a relatively small number of edaphic and compositional variables, corresponding to underlying geology, may be responsible for variations in canopy structure and chemistry over large expanses of Amazonian forest.
Significance
Land use is a principal driver of carbon emissions, either directly through land change processes such as deforestation or indirectly via transportation and industries supporting natural resource use. To minimize the effects of land use on the climate system, natural ecosystems are needed to offset gross emissions through carbon sequestration. Managing this critically important service must be achieved tactically if it is to be cost-effective. We have developed a high-resolution carbon mapping approach that can identify biogeographically explicit targets for carbon storage enhancement among all landholders within a country. Applying our approach to Perú reveals carbon threats and protections, as well as major opportunities for using ecosystems to sequester carbon. Our approach is scalable to any tropical forest country.
Download at : http://www.jstatsoft.org/v61/i06/paper
Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity.
The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them.
The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform kmeans and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the dataset of interest.
Understanding, modeling, and predicting the impact of global change on ecosystem functioning across biogeographical gradients can benefit from enhanced capacity to represent biota as a continuous distribution of traits. However, this is a challenge for the field of biogeography historically grounded on the species concept. Here we focus on the newly emergent field of functional biogeography: the study of the geographic distribution of trait diversity across organizational levels. We show how functional biogeography bridges species-based biogeography and earth science to provide ideas and tools to help explain gradients in multifaceted diversity (including species, functional, and phylogenetic diversities), predict ecosystem functioning and services worldwide, and infuse regional and global conservation programs with a functional basis. Although much recent progress has been made possible because of the rising of multiple data streams, new developments in ecoinformatics, and new methodological advances, future directions should provide a theoretical and comprehensive framework for the scaling of biotic interactions across trophic levels and its ecological implications.
Significance
Canopy trees are keystone organisms that create habitat for an enormous array of flora and fauna and dominate carbon storage in tropical forests. Determining the functional diversity of tree canopies is, therefore, critical to understanding how tropical forests are assembled and predicting ecosystem responses to environmental change. Across the megadiverse Andes-to-Amazon corridor of Peru, we discovered a large-scale nested pattern of canopy chemical assembly among thousands of trees. This nested geographic and phylogenetic pattern within and among forest communities provides a different perspective on current and future alterations to the functioning of western Amazonian forests resulting from land use and climate change.
Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including-in the latter case-x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
Question
Ecologists are increasingly interested in making accurate predictions of plant response to climate change. Many studies have attempted to document plant response to warming by grouping species into functional groups. Within functional groups, however, species often display divergent responses. Determining how foliar functional traits might be used to predict plant responses to warming could reduce analytical complexity while maintaining generalizations across systems.
Methods
We conducted a meta‐analysis on 18 studies (consisting of 38 species) of plant biomass response to experimental or natural warming. We determined whether plant trait estimates associated with the leaf economics spectrum [leaf life span ( LL ), leaf mass per area ( LMA ), leaf nitrogen ( N mass ), leaf phosphorus ( P mass ), photosynthetic capacity ( A max ) and stomatal conductance ( G s )] from a global plant database of experimentally unmanipulated plants, G lo PN et, could be used to predict biomass response to experimental warming.
Results
We found that three single leaf traits ( LL , N mass and A max ) were significant predictors for the response of plant biomass to warming treatments, perhaps due to their association with plant growth rates, adaptation rate and ability, each explaining between 21–46% of the variation in plant biomass responses. The magnitude of response to warming decreased with increasing LL , but increased with increasing N mass and A max . We found no linear combination of any of these traits that predicted warming response.
Conclusions
These results show that foliar traits can aid in understanding the mechanisms by which plants respond to temperature across species. Because each trait only explained a portion of variation in how plant growth responded to warming, however, future studies that examine how plant communities respond to warming should simultaneously measure multiple leaf traits, especially those most sensitive to warming, across plant species, to determine whether the predictive ability of functional traits changes between different ecosystems or plant taxonomic groups.
Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges-reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas-enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.
Significance
Commodity gold prices increased substantially following the 2008 global financial crisis. Gold demand has fueled a massive increase in mining activity, some of which is centered in the Amazon basin. Western Amazonian forests of Peru have become an epicenter for mostly illegal gold mining, but the clandestine nature of mining activities has made monitoring and reporting of forest losses extremely challenging. We combined high-resolution satellite and aircraft-based imaging with field surveys to address this issue in one of the highest biodiversity regions on Earth: Madre de Dios, Peru. We found the gold mining extent and rates are far higher than previously reported, with critically important implications for the ecology and environmental policy of this unique tropical rainforest region.
Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.
We map the extent, infer the life-cycle length and describe spatial and temporal patterns of flowering of sarmentose bamboos (Guadua spp) in upland forests of the southwest Amazon. We first examine the spectra and the spectral separation of forests with different bamboo life stages. False-color composites from orbital sensors going back to 1975 are capable of distinguishing life stages. These woody bamboos flower produce massive quantities of seeds and then die. Life stage is synchronized, forming a single cohort within each population. Bamboo dominates at least 161,500 km(2) of forest, coincident with an area of recent or ongoing tectonic uplift, rapid mechanical erosion and poorly drained soils rich in exchangeable cations. Each bamboo population is confined to a single spatially continuous patch or to a core patch with small outliers. Using spatial congruence between pairs of mature-stage maps from different years, we estimate an average life cycle of 27-28 y. It is now possible to predict exactly where and approximately when new bamboo mortality events will occur. We also map 74 bamboo populations that flowered between 2001 and 2008 over the entire domain of bamboo-dominated forest. Population size averaged 330 km(2). Flowering events of these populations are temporally and/or spatially separated, restricting or preventing gene exchange. Nonetheless, adjacent populations flower closer in time than expected by chance, forming flowering waves. This may be a consequence of allochronic divergence from fewer ancestral populations and suggests a long history of widespread bamboo in the southwest Amazon.
Remote sensing data provide essential input for today's climate and ecosystem models. It is generally agreed that many model
processes are not accurately depicted by current remotely sensed indices of vegetation and that new observational capabilities
are needed at different spatial and spectral scales to reduce uncertainty. Recent advances in materials and optics have allowed
the development of smaller, more stable, accurately calibrated imaging spectrometers that can quantify biophysical properties
on the basis of the spectral absorbing and scattering characteristics of the land surface. Airborne and spaceborne imaging
spectrometers, which measure large numbers (hundreds) of narrow spectral bands, are becoming more widely available from government
and commercial sources; thus, it is increasingly feasible to use data from imaging spectroscopy for environmental research.
In contrast to multispectral sensors, imaging spectroscopy produces quantitative estimates of biophysical absorptions, which
can be used to improve scientific understanding of ecosystem functioning and properties. We present the recent advances in
imaging spectroscopy and new capabilities for using it to quantify a range of ecological variables.
The net primary productivity, carbon (C) stocks and turnover rates (i.e. C dynamics) of tropical forests are an important aspect of the global C cycle. These variables have been investigated in lowland tropical forests, but they have rarely been studied in tropical montane forests (TMFs). This study examines spatial patterns of above- and belowground C dynamics along a transect ranging from lowland Amazonia to the high Andes in SE Peru. Fine root biomass values increased from 1.50 Mg C ha−1 at 194 m to 4.95 ± 0.62 Mg C ha−1 at 3020 m, reaching a maximum of 6.83 ± 1.13 Mg C ha−1 at the 2020 m elevation site. Aboveground biomass values decreased from 123.50 Mg C ha−1 at 194 m to 47.03 Mg C ha−1 at 3020 m. Mean annual belowground productivity was highest in the most fertile lowland plots (7.40 ± 1.00 Mg C ha−1 yr−1) and ranged between 3.43 ± 0.73 and 1.48 ± 0.40 Mg C ha−1 yr−1 in the premontane and montane plots. Mean annual aboveground productivity was estimated to vary between 9.50 ± 1.08 Mg C ha−1 yr−1 (210 m) and 2.59 ± 0.40 Mg C ha−1 yr−1 (2020 m), with consistently lower values observed in the cloud immersion zone of the montane forest. Fine root C residence time increased from 0.31 years in lowland Amazonia to 3.78 ± 0.81 years at 3020 m and stem C residence time remained constant along the elevational transect, with a mean of 54 ± 4 years. The ratio of fine root biomass to stem biomass increased significantly with increasing elevation, whereas the allocation of net primary productivity above- and belowground remained approximately constant at all elevations. Although net primary productivity declined in the TMF, the partitioning of productivity between the ecosystem subcomponents remained the same in lowland, premontane and montane forests.
Although inference is a critical component in ecological modeling, the balance between accurate predictions and inference
is the ultimate goal in ecological studies (Peters 1991; De’ath 2007). Practical applications of ecology in conservation planning,
ecosystem assessment, and bio-diversity are highly dependent on very accurate spatial predictions of ecological process and
spatial patterns (Millar et al. 2007). However, the complex nature of ecological systems hinders our ability to generate accurate
models using the traditional frequentist data model (Breiman 2001a; Austin 2007). Well-defined issues in ecological modeling,
such as complex non-linear interactions, spatial autocorrelation, high-dimensionality, non-stationary, historic signal, anisotropy,
and scale contribute to problems that the frequentist data model has difficulty addressing (Olden et al. 2008). When one critically
evaluates data used in ecological models, rarely do the data meet assumptions of independence, homoscedasticity, and multivariate
normality (Breiman 2001a). This has caused constant reevaluation of modeling approaches and the effects of reoccurring issues
such as spatial autocorrelation.
In this review, we discuss the ecological and evolutionary consequences of plant- herbivore interactions in tropical forests. We note first that herbivory rates are higher in tropical forests than in temperate ones and that, in contrast to leaves in temperate forests, most of the damage to tropical leaves occurs when they are young and expanding. Leaves in dry tropical forests also suffer higher rates of damage than in wet forests, and damage is greater in the understory than in the canopy. Insect herbivores, which typically have a narrow host range in the tropics, cause most of the damage to leaves and have selected for a wide variety of chemical, developmental, and phenological defenses in plants. Pathogens are less studied but cause considerable damage and, along with insect herbivores, may contribute to the maintenance of tree diversity. Folivorous mammals do less damage than insects or pathogens but have evolved to cope with the high levels of plant defenses. Leaves in tropical forests are defended by having low nutritional quality, greater toughness, and a wide variety of secondary metabolites, many of which are more common in tropical than temperate forests. Tannins, tough- ness, and low nutritional quality lengthen insect developmental times, making them more vulnerable to predators and parasitoids. The widespread occurrence of these defenses suggests that natural enemies are key participants in plant de- fenses and may have influenced the evolution of these traits. To escape damage, leaves may expand rapidly, be flushed synchronously, or be produced during the dry season when herbivores are rare. One strategy virtually limited to tropical forests is for plants to flush leaves but delay "greening" them until the leaves are mature. Many of these defensive traits are correlated within species, due to physiological constraints and tradeoffs. In general, shade-tolerant species invest more in defenses than do gap-requiring ones, and species with long-lived leaves are better defended than those with short-lived leaves.
Aim Conservation and land-use planning require accurate maps of patterns in species composition and an understanding of the factors that control them. Substantial doubt exists, however, about the existence and determinants of large-area floristic divisions in Amazonia. Here we ask whether Amazonian forests are partitioned into broad-scale floristic units on the basis of geological formations and their edaphic properties.
Location Western and central Amazonia.
Methods We used Landsat imagery and Shuttle Radar Topography Mission (SRTM) digital elevation data to identify a possible floristic and geological discontinuity of over 300 km in northern Peru. We then used plant inventories and soil sampling to document changes in species composition and soil properties across this boundary. Data were obtained from 138 sites distributed along more than 450 km of road and river. On the basis of our findings, we used broad-scale Landsat and SRTM mosaics to identify similar patterns across western and central Amazonia.
Results The discontinuity identified in Landsat and SRTM data corresponded to a 15-fold change in soil cation concentrations and an almost total change in plant species composition. This discontinuity appears to be caused by the widespread removal of cation-poor surface sediments by river incision to expose cation-rich sediments beneath. Examination of broad-scale Landsat and SRTM mosaics indicated that equivalent processes have generated a north–south discontinuity of over 1500 km in western Brazil. Due to similarities with our study area, we suggest that this discontinuity represents a chemical and ecological limit between western and central Amazonia.
Main conclusions Our findings suggest that Amazonian forests are partitioned into large-area units on the basis of geological formations and their edaphic properties. The evolution of these units through geological time may provide a general mechanism for biotic diversification in Amazonia. These compositional units, moreover, may correspond to broad-scale functional units. The existence of large-area compositional and functional units would suggest that protected-area, carbon sequestration, and other land-use strategies in Amazonia be implemented on a region-by-region basis. The methods described here can be used to map these patterns, and thus enable effective conservation and management of Amazonian forests.
Humans are altering the composition of biological communities through a variety of activities that increase rates of species invasions and species extinctions, at all scales, from local to global. These changes in components of the Earth's biodiversity cause concern for ethical and aesthetic reasons, but they also have a strong potential to alter ecosystem properties and the goods and services they provide to humanity. Ecological experiments, observations, and theoretical developments show that ecosystem properties depend greatly on biodiversity in terms of the functional characteristics of organisms present in the ecosystem and the distribution and abundance of those organisms over space and time. Species effects act in concert with the effects of climate, resource availability, and disturbance regimes in influencing ecosystem properties. Human activities can modify all of the above factors; here we focus on modification of these biotic controls. The scientific community has come to a broad consensus on many aspects of the relationship between biodiversity and ecosystem functioning, including many points relevant to management of ecosystems. Further progress will require integration of knowledge about biotic and abiotic controls on ecosystem properties, how ecological communities are structured, and the forces driving species extinctions and invasions. To strengthen links to policy and management, we also need to integrate our ecological knowledge with understanding of the social and economic constraints of potential management practices. Understanding this complexity, while taking strong steps to minimize current losses of species, is necessary for responsible management of Earth's ecosystems and the diverse biota they contain. Based on our review of the scientific literature, we are certain of the following conclusions: 1)Species' functional characteristics strongly influence ecosystem properties. Functional characteristics operate in a variety of contexts, including effects of dominant species, keystone species, ecological engineers, and interactions among species (e.g., competition, facilitation, mutualism, disease, and predation). Relative abundance alone is not always a good predictor of the ecosystem-level importance of a species, as even relatively rare species (e.g., a keystone predator) can strongly influence pathways of energy and material flows. 2)Alteration of biota in ecosystems via species invasions and extinctions caused by human activities has altered ecosystem goods and services in many well-documented cases. Many of these changes are difficult, expensive, or impossible to reverse or fix with technological solutions. 3)The effects of species loss or changes in composition, and the mechanisms by which the effects manifest themselves, can differ among ecosystem properties, ecosystem types, and pathways of potential community change. 4)Some ecosystem properties are initially insensitive to species loss because (a) ecosystems may have multiple species that carry out similar functional roles, (b) some species may contribute relatively little to ecosystem properties, or (c) properties may be primarily controlled by abiotic environmental conditions. 5)More species are needed to insure a stable supply of ecosystem goods and services as spatial and temporal variability increases, which typically occurs as longer time periods and larger areas are considered. We have high confidence in the following conclusions: 1)Certain combinations of species are complementary in their patterns of resource use and can increase average rates of productivity and nutrient retention. At the same time, environmental conditions can influence the importance of complementarity in structuring communities. Identification of which and how many species act in a complementary way in complex communities is just beginning. 2)Susceptibility to invasion by exotic species is strongly influenced by species composition and, under similar environmental conditions, generally decreases with increasing species richness. However, several other factors, such as propagule pressure, disturbance regime, and resource availability also strongly influence invasion success and often override effects of species richness in comparisons across different sites or ecosystems. 3)Having a range of species that respond differently to different environmental perturbations can stabilize ecosystem process rates in response to disturbances and variation in abiotic conditions. Using practices that maintain a diversity of organisms of different functional effect and functional response types will help preserve a range of management options. Uncertainties remain and further research is necessary in the following areas: 1)Further resolution of the relationships among taxonomic diversity, functional diversity, and community structure is important for identifying mechanisms of biodiversity effects. 2)Multiple trophic levels are common to ecosystems but have been understudied in biodiversity/ecosystem functioning research. The response of ecosystem properties to varying composition and diversity of consumer organisms is much more complex than responses seen in experiments that vary only the diversity of primary producers. 3)Theoretical work on stability has outpaced experimental work, especially field research. We need long-term experiments to be able to assess temporal stability, as well as experimental perturbations to assess response to and recovery from a variety of disturbances. Design and analysis of such experiments must account for several factors that covary with species diversity. 4)Because biodiversity both responds to and influences ecosystem properties, understanding the feedbacks involved is necessary to integrate results from experimental communities with patterns seen at broader scales. Likely patterns of extinction and invasion need to be linked to different drivers of global change, the forces that structure communities, and controls on ecosystem properties for the development of effective management and conservation strategies. 5)This paper focuses primarily on terrestrial systems, with some coverage of freshwater systems, because that is where most empirical and theoretical study has focused. While the fundamental principles described here should apply to marine systems, further study of that realm is necessary. Despite some uncertainties about the mechanisms and circumstances under which diversity influences ecosystem properties, incorporating diversity effects into policy and management is essential, especially in making decisions involving large temporal and spatial scales. Sacrificing those aspects of ecosystems that are difficult or impossible to reconstruct, such as diversity, simply because we are not yet certain about the extent and mechanisms by which they affect ecosystem properties, will restrict future management options even further. It is incumbent upon ecologists to communicate this need, and the values that can derive from such a perspective, to those charged with economic and policy decision-making.
We review the current knowledge on biodiversity in soils, its role in ecosystem processes, its importance for human purposes, and its resilience against stress and disturbance. The number of existing species is vastly higher than the number described, even in the macroscopically visible taxa, and biogeographical syntheses are largely lacking. A major effort in taxonomy and the training of a new generation of systematists is imperative. This effort has to be focussed on the groups of soil organisms that, to the best of our knowledge, play key roles in ecosystem functioning. To identify such groups, spheres of influence (SOI) of soil biota - such as the root biota, the shredders of organic matter and the soil bioturbators - are recognized that presumably control ecosystem processes, for example, through interactions with plants. Within those SOI, functional groups of soil organisms are recognized. Research questions of the highest urgency are the assignment of species to functional groups and determining the redundancy of species within functional groups. These priorities follow from the need to address the extent of any loss of functioning in soils, associated with intensive agriculture, forest disturbance, pollution of the environment, and global environmental change. The soil biota considered at present to be most at risk are species-poor functional groups among macrofaunal shredders of organic matter, bioturbators of soil, specialized bacteria like nitrifiers and nitrogen fixers, and fungiforming mycorrhizas. An experimental approach in addressing these research priorities is needed, using longterm and large-scale field experiments and modern methods of geostatistics and geographic information systems.
Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ± 6% to ± 53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ± 5% and ca. ± 1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.
The Amazonian rainforest is arguably the most species-rich terrestrial ecosystem in the world, yet the timing of the origin
and evolutionary causes of this diversity are a matter of debate. We review the geologic and phylogenetic evidence from Amazonia
and compare it with uplift records from the Andes. This uplift and its effect on regional climate fundamentally changed the
Amazonian landscape by reconfiguring drainage patterns and creating a vast influx of sediments into the basin. On this “Andean”
substrate, a region-wide edaphic mosaic developed that became extremely rich in species, particularly in Western Amazonia.
We show that Andean uplift was crucial for the evolution of Amazonian landscapes and ecosystems, and that current biodiversity
patterns are rooted deep in the pre-Quaternary.
In 2002, world leaders committed, through the Convention on Biological Diversity, to achieve a significant reduction in the
rate of biodiversity loss by 2010. We compiled 31 indicators to report on progress toward this target. Most indicators of
the state of biodiversity (covering species’ population trends, extinction risk, habitat extent and condition, and community
composition) showed declines, with no significant recent reductions in rate, whereas indicators of pressures on biodiversity
(including resource consumption, invasive alien species, nitrogen pollution, overexploitation, and climate change impacts)
showed increases. Despite some local successes and increasing responses (including extent and biodiversity coverage of protected
areas, sustainable forest management, policy responses to invasive alien species, and biodiversity-related aid), the rate
of biodiversity loss does not appear to be slowing.
Climate change threatens ecosystems worldwide, yet their potential future resilience remains largely unquantified. In recent years many studies have shown that biodiversity, and in particular functional diversity, can enhance ecosystem resilience by providing a higher response diversity. So far these insights have been mostly neglected in large-scale projections of ecosystem responses to climate change. Here we show that plant trait diversity, as a key component of functional diversity, can have a strikingly positive effect on the Amazon forests' biomass under future climate change. Using a terrestrial biogeochemical model that simulates diverse forest communities on the basis of individual tree growth, we show that plant trait diversity may enable the Amazon forests to adjust to new climate conditions via a process of ecological sorting, protecting the Amazon's carbon sink function. Therefore, plant trait diversity, and biodiversity in general, should be considered in large-scale ecosystem projections and be included as an integral part of climate change research and policy.
The functional biogeography of tropical forests is expressed in foliar chemicals that are key physiologically-based predictors of plant adaptation to changing environmental conditions including climate. However, understanding the degree to which environmental filters sort the canopy chemical characteristics of forest canopies remains a challenge. Here we report on the elevation and soil-type dependence of forest canopy chemistry among 75 compositionally and environmentally distinct forests in nine regions, with a total of 7819 individual trees representing 3246 species collected, identified and assayed for foliar traits. We assessed whether there are consistent relationships between canopy chemical traits and both elevation and soil type, and evaluated the general role of phylogeny in mediating of patterns of canopy traits within and across communities. Chemical trait variation and partitioning suggested a general model based on four inter-connected findings. First, geographic variation at the soil Order level, expressing broad changes in fertility, underpins major shifts in foliar phosphorus (P) and calcium (Ca). Second, elevation-dependent shifts in average community leaf dry mass per area (LMA), chlorophyll, and carbon allocation (including non-structural carbohydrates) are most strongly correlated with changes in foliar Ca. Third, chemical diversity within communities is driven by differences between species rather than by plasticity within species. Finally, elevation- and soil-dependent changes in N, LMA and leaf carbon allocation are mediated by canopy compositional turnover, whereas foliar P and Ca are driven more by changes in site conditions than by phylogeny. Our findings have broad implications for understanding the global ecology of humid tropical forests, and their functional responses to changing climate. This article is protected by copyright. All rights reserved.
Partial least-squares (PLS) methods for quantitative spectral analyses are compared with classical least-squares (CLS) and principal component regression (PCR) methods by using simulated data and infrared spectra from bulk seven-component, silicate-based glasses. Analyses of the simulated data sets show the effect of data pretreatment, base-line variations, calibration design, and constrained mixtures on PLS and PCR prediction errors and model complexity. Analyses of the simulated data sets also illustrate some qualitative differences between PLS and PCR.
Species diversity is a major determinant of ecosystem productivity stability invasibility, and nutrient dynamics. Hundreds of studies spanning terrestrial, aquatic, and marine ecosystems show that high-diversity mixtures are approximately twice as productive as monocultures of the same species and that this difference increases through time. These impacts of higher diversity have multiple causes, including interspecific complementarity, greater use of limiting resources, decreased herbivory and disease, and nutrient-cycling feedbacks that increase nutrient stores and supply rates over the long term. These experimentally observed effects of diversity are consistent with predictions based on a variety of theories that share a common feature: All have trade-off-based mechanisms that allow long-term coexistence of many different competing species. Diversity loss has an effect as great as, or greater than, the effects of herbivory, fire, drought, nitrogen addition, elevated CO2, and other drivers of environmental change. The preservation, conservation, and restoration of biodiversity should be a high global priority.
Tropical forest functional diversity, which is a measure of the diversity of organismal interactions with the environment, is poorly understood despite its importance for linking evolutionary biology to ecosystem biogeochemistry. Functional diversity is reflected in functional traits such as the concentrations of different compounds in leaves or the density of leaf mass, which are related to plant activities such as plant defence, nutrient cycling, or growth. In the Amazonian lowlands, river movement and microtopography control nutrient mobility, which may influence functional trait distributions. Here we use airborne laser-guided imaging spectroscopy to develop maps of 16 forest canopy traits, throughout four large landscapes that harbour three common forest community types on the Madre de Dios and Tambopata rivers in southwestern Amazonia. Our maps, which are based on quantitative chemometric analysis of forest canopies with visible-to-near infrared (400-2,500 nm) spectroscopy, reveal substantial variation in canopy traits and their distributions within and among forested landscapes. Forest canopy trait distributions are arranged in a nested pattern, with location along rivers controlling trait variation between different landscapes, and microtopography controlling trait variation within landscapes. We suggest that processes of nutrient deposition and depletion drive increasing phosphorus limitation, and a corresponding increase in plant defence, in an eastward direction from the base of the Andes into the Amazon Basin. Chapin, F. S.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Oil palm expansion is a major threat to forest conservation in the tropics. Oil palm can also be a sustainable economic alternative if incentives for expansion outside forests are set in place. Consistent methods to monitor the time and location of oil palm expansion and the area converted from different land covers are essential for the success of such incentives. We developed methods to detect and quantify annual land cover changes associated with oil palm expansion in the Peruvian Amazon between 2001 and 2010 at two spatial scales and for two production modes. At the coarse scale, comprising the whole Peruvian Amazon, we used MODIS data to detect forest conversion to large-scale, industrial oil palm plantations based on metrics characterizing temporal changes in vegetation greenness associated with the conversion. At the fine scale, we used data from the satellite sensors Landsat TM/ETM + and ALOS-PALSAR to map and quantify the area from different land covers converted into large and small-scale oil palm plantations annually, in a focus area near the city of Pucallpa. Estimates were obtained from the elaboration and further combination of maps representing oil palm plantations by ages in 2010 and non-oil palm land covers in each year between 2001 and 2010. Validation data were obtained in the field and from geospatial information from previous studies. At the coarse scale, MODIS detected deforestation in 73% of training events larger than 50 ha. Detected events added up to 95% of the training areas. Total area converted to oil palm annually was quantified visually by using data from Landsat TM/ETM + with 96.3% accuracy. At the fine scale, the combination of data from Landsat TM/ETM + and ALOS-PALSAR identified oil palm expansion in areas larger than 5 ha with 94% accuracy and the year of expansion with an uncertainty of ± 1.3 years. This work underscores the need for data from multiple satellite sensors for a comprehensive monitoring of oil palm expansion, considering needs for information not only on the area expanded but also the time of conversion and land cover transitions associated with large- and small-scale plantations.
The Carnegie Airborne Observatory (CAO) was developed to address a need for macroscale measurements that reveal the structural, functional and organismic composition of Earth's ecosystems. In 2011, we completed and launched the CAO-2 next generation Airborne Taxonomic Mapping Systems (AToMS), which includes a high-fidelity visible-to-shortwave infrared (VSWIR) imaging spectrometer (380–2510 nm), dual-laser waveform light detection and ranging (LiDAR) scanner, and high spatial resolution visible-to-near infrared (VNIR) imaging spectrometer (365–1052 nm). Here, we describe how multiple data streams from these sensors can be fused using hardware and software co-alignment and processing techniques. With these data streams, we quantitatively demonstrate that precision data fusion greatly increases the dimensionality of the ecological information derived from remote sensing. We compare the data dimensionality of two contrasting scenes — a built environment at Stanford University and a lowland tropical forest in Amazonia. Principal components analysis revealed 336 dimensions (degrees of freedom) in the Stanford case, and 218 dimensions in the Amazon. The Amazon case presents what could be the highest level of remotely sensed data dimensionality ever reported for a forested ecosystem. Simulated misalignment of data streams reduced the effective information content by up to 48%, highlighting the critical role of achieving high precision when undertaking multi-sensor fusion. The instrumentation and methods described here are a pathfinder for future airborne applications undertaken by the National Ecological Observatory Network (NEON) and other organizations.
Remote sensing of canopy chemistry could greatly advance the study and monitoring of functional processes and biological diversity in humid tropical forests. Imaging spectroscopy has contributed to canopy chemical remote sensing, but efforts to develop general, globally-applicable approaches have been limited by sparse and inconsistent field and laboratory data, and lacking analytical methods. We analyzed leaf hemispherical reflectance and transmittance spectra, along with a 21-chemical portfolio, taken from 6136 fully sunlit humid tropical forest canopies, and developed an up-scaling method using a combination of canopy radiative transfer, chemometric and high-frequency noise modeling. By integrating these steps, we found that the accuracy and precision of multi-chemical remote sensing of tropical forest canopies varies by leaf constituent and wavelength range. Under conditions of varying canopy structure and spectral noise, photosynthetic pigments, water, nitrogen, cellulose, lignin, phenols and leaf mass per area (LMA) are accurately estimated using visible-to-shortwave infrared spectroscopy (VSWIR; 400–2500nm). Phosphorus and base cations are retrieved with lower yet significant accuracy. We also find that leaf chemical properties are estimated far more consistently, and with much higher precision and accuracy, using the VSWIR range rather than the more common and limited visible to near-infrared range (400–1050nm; VNIR). While VNIR spectroscopy proved accurate for predicting foliar LMA, photosynthetic pigments and water, VSWIR spectra provided accurate estimates for three times the number of canopy traits. These global results proved to be independent of site conditions, taxonomic composition and phylogenetic history, and thus they should be broadly applicable to multi-chemical mapping of humid tropical forest canopies. The approach developed and tested here paves the way for studies of canopy chemical properties in humid tropical forests using the next generation of airborne and space-based high-fidelity imaging spectrometers.
Monitoring deforestation and forest degradation is central to assessing changes in carbon storage, biodiversity, and many other ecological processes in tropical regions. Satellite remote sensing is the most accurate and cost-effective way to monitor changes in forest cover and degradation over large geographic areas, but the tools and methods have been highly manual and time consuming, often requiring expert knowledge. We present a new user-friendly, fully automated system called CLASlite, which provides desktop mapping of forest cover, deforestation and forest disturbance using advanced atmospheric correction and spectral signal processing approaches with Landsat, SPOT, and many other satellite sensors. CLASlite runs on a standard Windows-based computer, and can map more than 10,000 km2, at 30 m spatial resolution, of forest area per hour of processing time. Outputs from CLASlite include maps of the percentage of live and dead vegetation cover, bare soils and other substrates, along with quantitative measures of uncertainty in each image pixel. These maps are then interpreted in terms of forest cover, deforestation and forest disturbance using automated decision trees. CLASlite output images can be directly input to other remote sensing programs, geographic information systems (GIS), Google EarthTM serif}, or other visualization systems. Here we provide a detailed description of the CLASlite approach with example results for deforestation and forest degradation scenarios in Brazil, Peru, and other tropical forest sites worldwide.
Airborne remote sensing could play a more integrative role in regional ecosystem studies if the information derived from airborne observations could be readily converted to physical and chemical quantities representative of ecosystem processes and properties. We have undertaken an effort to specify, deploy, and apply a new system - the Carnegie Airborne Observatory (CAO) - to remotely measure a suite of ecosystem structural and biochemical properties in a way that can rapidly advance regional ecological research for conservation, management and resource policy development. The CAO "Alpha System" provides in-flight fusion of high-fidelity visible/near-infrared imaging spectrometer data with scanning, waveform light detection and ranging (wLiDAR) data, along with an integrated navigation and data processing approach, that results in geo-orthorectified products for vegetation structure, biochemistry, and physiology as well as the underlying topography. Here we present the scientific rationale for developing the system, and provide sample data fusion results demonstrating the potential breakthroughs that hybrid hyperspectral-wLiDAR systems might bring to the scientific community.
Aim Climate change causes shifts in species distributions, or ‘migrations’. Despite the centrality of species distributions to biodiversity conservation, the demonstrated large migration of tropical plant species in response to climate change in the past, and the expected sensitivity of species distributions to modern climate change, no study has tested for modern species migrations in tropical plants. Here we conduct a first test of the hypothesis that increasing temperatures are causing tropical trees to migrate to cooler areas.
The maize (Zea mays) -cassava (Manihot esculenta)-Musarelay-cropped sequence as practiced among 28 Yanesha families in the Palcazu Valley in Central Amazonian Peru was surveyed to determine why farmers rotate fields into bush fallow. Typically, this sequence is planted on alluvial Inceptisols after the clearing of 2–5 year tree and shrub growth. During the 18–30 months of cropping, fields showed a buildup of weeds and an increase in banana stem borer and nutrient deficiency symptoms. Farmers attributed the decline in Musayields from the first to third harvests to stem borer, weeds, or soil infertility but were not unanimous. Any or all of these factors facilitate the decision to abandon a field. A 3-year record of fields cleared by each farm family indicated that field rotation stems from two independent decisions: selection of a fallowed field to clear for maize and vegetables, important for family nutrition, which are planted right after field clearing, and discontinuation of weeding in old fields as labor is used primarily on new fields, a decision leading to bush fallow recovery. Work with these families during 4 years suggests that the distribution of land and labor is based on a sense of relative labor productivity, on diverse dietary needs and preferences, and on the maintenance of a balance between areas in cropping and under fallow.
Land cover is a crucial, spatially and temporally varying component of global carbon and climate systems. Therefore accurate estimation and monitoring of land cover changes is important in global change research. Although, land cover has dramatically changed over the last few centuries, until now there has been no consistent way of quantifying the changes globally.In this study we used long-term climate, soils data along with coarse resolution satellite observations to quantify the magnitude and spatial extent of global land cover changes due to anthropogenic processes. Differences between potential leaf area index, derived from climate-soil-leaf area equilibrium and actual leaf area index obtained from satellite data were used to estimate changes in land cover.Forest clearing for agriculture and irrigated farming in arid and semi-arid lands are found to be two major sources of climatically important land cover changes. Satellite derived Spectral Vegetation indices (SV I) and surface temperatures (T s) show strong impact of land cover changes on climatic processes. Irrigated agriculture in dry areas increased energy absorption and evapotranspiration (ET) compared to natural vegetation. On the other hand, forest clearing for crops decreased energy absorption andET.
A land cover classification and monitoring system is proposed using satellite derivedSV I andT s that simultaneously characterize energy absorption and exchange processes. This completely remote sensing based approach is useful for monitoring land cover changes as well as their impacts on climate. Monitoring the spatio-temporal dynamics of land cover is possible with current operational satellites, and could be substantially improved with the Earth Observing System (EOS) era satellite sensors.
Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400–2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.
The 400 000 km2-wide Fitzcarrald Arch constitutes a wide topographic high of the Amazon Basin against the central Andes. In order to constrain its formation mechanisms and in particular to test its relationships to the Nazca ridge subduction, a quantitative geomorphology analysis of the Arch is performed using hypsometric integrals, elongation and azimuths of 7th- and 5th-order catchments. They all express a trend from high maturity to low maturity from NW towards SE. This maturity gradient coupled with the local drainage direction demonstrate that the Fitzcarrald Arch is not a ‘classical’ alluvial fan, since its apex is located 100 km east to the Subandean Thrust Front and the corresponding sedimentary pile is lacking. Nor is the Arch the superficial expression of an inherited transfer zone, because its geomorphic shape is radial and it does not diverge from a symmetry axis; moreover, such a reactivated structure is not found at depth on seismic profiles. In addition, our data show that underlying geomorphic control on catchment initiation and development has progressed from NW to SE, which in combination with the observation of crustal doming by Espurt et al. [Espurt, N., Baby, P., Brusset, S., Roddaz, M., Hermoza, W., Regard, V., Antoine, P.O., Salas-Gismondi, R., Bolaños, R., 2007. How does the Nazca Ridge subduction influence the modern Amazonian foreland basin? Geology 35, 515–518.] suggests that this relief is caused by the eastward sliding of the buoyant Nazca ridge beneath the South American lithosphere.
For two decades, remotely sensed data from imaging spectrometers have been used to estimate non-pigment biochemical constituents of vegetation, including water, nitrogen, cellulose, and lignin. This interest has been motivated by the important role that these substances play in physiological processes such as photosynthesis, their relationships with ecosystem processes such as litter decomposition and nutrient cycling, and their use in identifying key plant species and functional groups. This paper reviews three areas of research to improve the application of imaging spectrometers to quantify non-pigment biochemical constituents of plants. First, we examine recent empirical and modeling studies that have advanced our understanding of leaf and canopy reflectance spectra in relation to plant biochemistry. Next, we present recent examples of how spectroscopic remote sensing methods are applied to characterize vegetation canopies, communities and ecosystems. Third, we highlight the latest developments in using imaging spectrometer data to quantify net primary production (NPP) over large geographic areas. Finally, we discuss the major challenges in quantifying non-pigment biochemical constituents of plant canopies from remotely sensed spectra.
Why and how the Partial Least Squares Regression (PLSR) was developed, is here described from the author's perspective. The paper outlines my frustrating experiences in the 70'ies with two conflicting and equally over-ambitious and over-simplified modelling cultures - in traditional chemistry and in traditional statistics. It describes my mental progress of first learning to combine them into least squares “unmixing” of known chemical mixtures, and later extending this into the “unscrambling” of partially unknown structures as well. The bi-linear regression framework is summarised in terms of the development from Principal Component Regression into the PLSR. Finally, the versatility of the PLSR is discussed in light of the urgent need for better eduacation in scientific data analysis.
Partial least-squares (PLS) methods for spectral analyses are related to other multivariate calibration methods such as classical least-squares (CLS), inverse least-squares (ILS), and principal component regression (PCR) methods which have been used often in quantitative spectral analyses. The PLS method which analyzes one chemical component at a time is presented, and the basis of each step in the algorithm is explained. PLS calibration is shown to be composed of a series of simplified CLS and ILS steps. This detailed understanding of the PLS algorithm has helped to identify how chemically interpretable qualitative spectral information can be obtained from the intermediate steps of the PLS algorithm. These methods for extracting qualitative information are demonstrated by use of simulated spectral data. The qualitative information directly available from the PLS analysis is superior to that obtained from PCR but is not complete as that which can be generated during CLS analyses. Methods are presented for selecting optimal numbers of loading vectors for both the PLS and PCR models in order to optimize the model while simultaneously reducing the potential for overfitting the calibration data. Outlier detection and methods to evaluate the statistical significance of results obtained from the different calibration methods applied to the same spectral data are also discussed.