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A Sourcebook of Methods and Procedures for Monitoring Essential Biodiversity Variables in Tropical Forests with Remote Sensing
Abstract
The Essential Biodiversity Variables (EBV) concept proposed by GEO BON, Space Agencies, and the Earth Observation research community at large aims to support efforts for biodiversity monitoring. GOFC-GOLD and GEO BON propose a new sourcebook to promote the best operational monitoring practices for the relevant EBVs based on scientific literature, and consensus.
The first version of the biodiversity sourcebook was released during the IPBES Plenary 2017 and follows the 13th UNCBD Conference of Parties (COP) held in December 2016. Updates will be made on a yearly basis following policy, scientific, and technical developments.
The biodiversity sourcebook is accessible in pdf format for free from the GOFC-GOLD Land Cover Office website (http://www.gofcgold.wur.nl/sites/gofcgold-geobon_biodiversitysourcebook.php) and the GEO BON website (http://geobon.org/).
... Research has used net primary productivity (NPP) and related indices successfully (Markon and Peterson, 2010;Pachavo, 2013;Wang et al., 2014;. The leaf area index (LAI) (Chen and Black, 1992;Carlson and Ripley, 1997;Wang et al., 2004;Fernandez et al., 2017) and fraction of photosynthetically active radiation (fAPAR) (Frouin, 1993;Senna et al., 2006) are common methods used. These are alleged to be laborious, time consuming and plant specific and are viewed as approximations that rely on mathematical models requiring assumptions and specific inputs, locations and scales . ...
... These are alleged to be laborious, time consuming and plant specific and are viewed as approximations that rely on mathematical models requiring assumptions and specific inputs, locations and scales . Vegetation productivity metrics such as base value, peak value, seasonal amplitude, small seasonal integral and large seasonal integral values (Ekhlundh and Jonsson, 2017) are combined with date metrics to estimate ecosystem health and status (Wessels et al., 2008(Wessels et al., , 2011Wang et al., 2014;Olsen et al., 2015;Fernandez et al., 2017). Both fineand coarse-resolution remote-sensingderived metrics have been extensively used to depict spatial vegetation productivity patterns at differing spatial and temporal scales (Prasad et al., 2007;Jeganathan et al., 2010;Guan et al., 2014;Braget, 2017;Tong et al., 2017;Uyeda et al., 2017). ...
... The maximum NDVI indicates that the tree population of Central GNP is able to withstand the stress of dry and drought years. The assured presence of vegetation benefits mammalian herbivores, as well as atmosphere and energy cycling (Jeganathan et al., 2010;Fernandez et al., 2017). This shows the ability of the GNP ecosystem to thrive in some areas due to varying types of vegetation (Wessels et al., 2008;Park et al., 2016). ...
Spatial and temporal patterns of vegetation productivity in semi-arid savanna national parks are influenced by differences in land cover and changes in time series trends. The main purpose of this paper is to analyse patterns of vegetation productivity metrics of base value, peak value, amplitude, and small and large integrals in Gonarezhou National Park (GNP) in south-eastern Zimbabwe from 1981 to 2015. Three sample sites comprising shrublands, deciduous broadleaved forested woodlands and mixed cover (shrublands, broadleaved deciduous forested woodlands and grasslands) were selected to show existing patterns of vegetation productivity for GNP. We used remotely sensed Normalised Difference Vegetation Index (NDVI) data which was further processed in the TIMESAT 3.3 program to derive productivity metrics. We then tested differences in land cover using analysis of variance and changes in time-series trends using Mann–Kendall and Theil–Sen’s tests. We note significant differences in land cover (P < 0.01) in selected samples. There are significant downward trends in the base value in shrublands (P < 0.01) and broadleaved deciduous forested woodlands (P = 0.04). Significant upward trends in the amplitude in the shrublands (P < 0.01) and mixed cover areas (P = 0.01) were noted. However, there are no changes in vegetation productivity, as indicated by the peak value and large and small integral indices. Shrublands are becoming vulnerable in terms of energy and vegetation productivity and need constant monitoring. Long-span coarse-resolution images are important stepping stones in providing a baseline for further studies from moderate and fine-resolution imagery. Research on vegetation productivity using fine-resolution imagery is more suitable for GNP.
... Earth observations through remote sensing enables the depiction of forest cover change across space and time. Assessment and periodical monitoring of the change areas is required to conserve and restore natural forest ecosystems (Gill et al., 2017). Remote sensing plays a pivotal role in sustainable forest management due to the synoptic coverage of the earth at various spatial, spectral, and temporal scales (Roughgarden et al., 1991;Lambin and Giest 2006;Kurnar 2011). ...
... Tropical forests are vanishing at unprecedented rates, affecting decision-making and the efficacy of sustainable management practices (Anaya et al., 2020). Conservation of natural ecosystems necessitates a timely identification of priority forest areas for preserving resources to provide ecosystems services and goods (Gill et al., 2017). Krishna et al. (2014) has quantified the long-term changes in forest cover of Andhra Pradesh using visual interpretation techniques and multi-source data. ...
Tropical deforestation is one of the widely recognised human-induced factors responsible for climate change. A clear understanding of the trends in forest cover loss hot spots provides baseline information for sustainable forest management. This study has utilised Landsat data and a novel spatial pattern mining tool to identify trends in forest cover loss from 2009 to 2020 in the Araku range of Eastern Ghats, India. This study focuses on identifying the annual rate of deforestation and significant trends in forest cover loss through statistical techniques. Analysis indicates no significant trend in forest loss (P = 0.7 and the trend statistic was Z = 0.27; S = 5) during the study period. Disturbances in the forest cover are mainly attributed to shifting cultivation in the study area. The Emerging Hot spot model has found new and sporadic hot spots in the Araku range. However, there are no intensifying hotspots, historical hotspots and persistent hotspots indicate effective management in the majority of the study area. This study helps identify and prioritise regions of deforestation influence for monitoring and preparing appropriate conservation plans and management strategies.
... Some of these variables are either calculated from direct satellite retrievals such as vegetation indices (Huete et al., 1997(Huete et al., , 2002Tucker & Sellers, 1986) or data products obtained from model-data fusion methods or data-driven models (Jiang & Ryu, 2016;Jung et al., 2020;Running et al., 2015). Nevertheless, tropical regions such as NSA usually face challenges related to data availability and quality due to cloud cover and lack of ground-truthing (Estupinan-Suarez et al., 2017;Hilker et al., 2012). ...
Interannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the heterogeneous impacts of ENSO at regional scales. Particularly, ENSO has contrasting impacts on climate in northern South America (NSA) depending on the ENSO phase and geographical location, which in turn affect the activity of vegetation. Furthermore, changes of vegetation activity during multiple ENSO events are not well understood yet. In this study, we investigated the spatial and temporal differences in vegetation activity associated with ENSO variability and its three phases (El Niño, La Niña, Neutral) to identify hotspots of ENSO impacts in NSA, a region dominated by rainforest and savannas. To achieve this, we investigated time series of vegetation variables from 2001 to 2014 at moderate spatial resolution (0.0083°). Data were aggregated through dimensionality reduction analysis (i.e., Global Principal Component Analysis). The leading principal component served as a proxy of vegetation activity (VAC). We calculated the cross‐correlation between VAC and the multivariate ENSO index separately for each ENSO phase. Our results show that El Niño phase has a stronger impact on vegetation activity both in intensity and duration than La Niña phase. Moreover, seasonally dry ecoregions were more susceptible to El Niño impacts on vegetation activity. Understanding these differences is key for regional adaptation and differentiated management of ecosystems.
... VP dynamics can enhance our understanding of changes within ecosystems (Gray & Ewers, 2021;Ryan et al., 2017). The VP patterns provide essential services and functions in semi-arid savannah ecosystems as they influence biogeochemical process like carbon and other nutrient cycling processes (Militino, Ugarte, & Perez-Goya, 2017;Q. Zhu et al., 2017;W. Zhu, Chen, Hayes, Fan, & Jiang, 2017). The patterns of VP are also important as they influence population dynamics of species connected to different trophic levels which in turn trigger temporal and spatial shifts for mammalian herbivores (Pettorelli et al., 2005;Menzel, 2003). Overall, VP patterns are important in controlling primary productivity, carbon sequestration, nut ...
Vegetation phenology (VP) patterns of semi-arid savannah woodlands ecosystems are essential for sustainable management and conservation since they are indicators of the health status of protected national parks. However, monitoring VP changes are intermittently carried out in semi-arid savannah woodlands ecosystems despite the links to ecosystem function, status, health and service. The paper analyses VP metric patterns, during the start of the season (SOS), maximum vegetation (PEAK), end of the season (EOS) and length of the growing season (LGS) trends from 1981 to 2015. This study was carried out in Gonarezhou National Park (GNP) in Southeastern Zimbabwe. Shrublands, broadleaved deciduous forested woodlands and mixed cover samples were used to depict existing VP patterns. Derivation of VP using remotely sensed Normalised Difference Vegetation Index (NDVI) data was done using the TIMESAT 3.3 programme. Analysis of variance (ANOVA) was used to test the land cover differences while Mann-Kendall (M-K) and Sen's slope tests were used to analyse time-series trends. Due to differences among the different land cover types in GNP, there are spatial variations in phenology, with the SOS, PEAK and EOS indicating later dates significantly. VP spatial and temporal patterns vary markedly as a result of differences in land cover. Further research in patterns of VP using fine and coarse satellite imagery in GNP is required. VP studies need to be linked to distribution and abundance of large mammalian herbivore species.
... LiDAR es una herramienta poderosa, ya que es altamente sensible a la estructura vertical y horizontal de los objetos que escanea, por lo tanto, en áreas boscosas puede discriminar con facilidad entre las bosques conservados y degradados. A pesar de ello, LiDAR es una tecnología aerotransportada o terrestre con altos costos y cobertura limitada (Gill et al. 2017;Mitchell et al., 2017). De otro lado, SAR es un sensor activo que genera sus propias señales electromagnéticas, y diferente a sensores ópticos no depende de la iluminación solar. ...
El libro se presenta como capítulos, siendo cada uno el resultado de un tema de investigación sobre el ecosistema y la biodiversidad del bosque muy húmedo tropical (bmh-T) del Bajo Calima. Se presentan 11 capítulos iniciando por la caracterización de la cuenca baja del río Calima, de sus bosques y sus suelos, la biodiversidad de flora y fauna y las características de los árboles y sus maderas, se continúa con la investigación sobre la degradación de los bosques como problema principal que amenaza el ecosistema y se analiza la pertinencia de la restauración como una medida de mitigación. Se finaliza con una disertación sobre el futuro de las investigaciones y las potencialidades de esta biodiversa región.
Varias Universidades han estado presentes en estos casi 50 años de existencia del Centro Forestal Tropical en el Bajo Calima y atendieron al llamado de publicar sus resultados que han sido realizados con algún tipo de apoyo desde el mismo.
La Universidad Nacional de Colombia, Sede Medellín con dos apuestas, desde el Laboratorio de Dendrocronología Tropical, el conocer la historia que guardan los anillos de crecimiento de los árboles, y desde el Grupo AGROXUE el estudio de los suelos y la vegetación. La Universidad de Caldas con su semillero de investigación abordó el tema de la diversidad de insectos y la de Leicester, mediante un trabajo de investigación doctoral estudió la degradación. La Universidad del Tolima especialmente ha hecho presencia con sus estudiantes de Ingeniería Forestal ydesde múltiples disciplinas ha caracterizado las cuencas, investigado la flora, la fauna y las comunidades rurales que habitan el ecosistema y ha hecho propuestas para su manejo sostenible
... SDMs have opened a new viewpoint on landscape and habitat level conservation and management practices by providing large-scale distribution maps of species under current and future scenarios. SDMs are categorized under the species populations class of the six essential biodiversity variables (EBVs) introduced by the Group on Earth Observations Biodiversity Observation Network (GEO BON) (Gill et al., 2017;Reddy et al., 2017a). They are therefore often used for a variety of biological resource conservation and management activities (Collier et al., 2010;Farrell et al., 2013). ...
Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors’ biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions.
... 471 The observed relationship between spectral and floristic diversity, in a complex and 472 anthropogenic landscape, supports SVH as a method to quickly estimate α and β diversity 473 and heterogeneity. Moreover, it is suggested to explore their variation across regions to 474 effectively implement monitoring and conservation plans allowing the production of 475 maps for modeling and monitoring diversity from local to global scales [6][7][8]. 476 ...
As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at fine scale in an ecological network (EN) embedded in a complex landscape, has been tested using two new and promising methodological approaches, based on “biodivMapR” and “rasterdiv” R packages. The first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao's Quadratic heterogeneity measure (Rao’s Q). Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and/or monitor over time floristic diversity maintained in an EN selected as an example and located in North-East Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. While the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.
... Going back to biological concepts, we know that natural ecosystems are heterogeneous in scale and relative size (Strayer et al., 2003;Bailey, 1985). Plus, they can be observed in multiple scales, from large territories (like the Arctic tundra) to small entities (such as a single tree in the Amazon rainforest) (Sayre & Hansen, 2017). Similarly, we need parameters to better capture the morphology of IEs, since they seem to follow a similar pattern of geographical reach as natural ecosystems (Ascani, Bettarelli, Resmini, & Balland, 2020). ...
In this opening editorial, we dedicate efforts to take stock on some of the pivotal elements of IEs discussed in the prior literature, present the five contributions to the issue and identify key themes that require further developments in future research. We hope these contributions can shed light on aspects of interest in the IE field and inspire researchers on their forthcoming undertakings.
... Rapidly growing populations and consumerism are driving continued loss and degradation of tropical forests to supply wood products, putting tremendous pressure on forests globally [5]. In recognizing these challenges, the United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme, which seeks to mitigate climate impacts and biodiversity losses through improved forest management practices [6]. To be eligible for REDD+ funding, developing countries must show progress toward reducing rates of degradation and deforestation. ...
Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data.
... According to the IPCC's guidelines, forest carbon stock assessment can be categorized into three Tiers ranging from the simple global/ecological zone level emission factor (Tier 1) to country-specific forest inventories (Tier 2) and high precision plot level carbon dynamics models [178,179]. The Global Observation of Forest Cover and Land Dynamics (GOFC-GOLD) which is an ad-hoc REDD working group of international experts, facilitates scientific communities, national space agencies and remote sensing analysts to maintain standards while using Earth Observation (EO) datasets [180]. REDD+ has had significant progress during the last decade. ...
Tropical forests are acknowledged for providing important ecosystem services and are renowned as "the lungs of the planet Earth" due to their role in the exchange of gasses-particularly inhaling CO 2 and breathing out O 2-within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450-650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for 'forest' classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.
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