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Detecting trend and seasonal changes in satellite image time series
Abstract
A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01–0.07 σ) and seasonal amplitudes (0.1–0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.
... Originally, several assumptions had to be made when applying these methods, which are too restrictive in the case of time series from climate or ecological systems. Consequently, change-point detection methods have been extended to be able to cope with large autocorrelation (Mudelsee 2000;Rodionov 2004Rodionov , 2006Wang 2008;Ducré-Robitaille et al. 2003), trends and non-stationary/non-iid residuals (Matyasovszky 2011;Gallagher et al. 2013), seasonality (Verbesselt et al. 2010a), non-Gaussian distributions (Lazante 1996;Chu 2002), as well as measurement and dating uncertainties (Goswami et al., 2018). ...
... We note that these procedures to remove e.g., seasonality and trends are not necessary for estimating recovery times after large and rare perturbations as those discussed in Sect. 3, which focuses on the actual recovery after a perturbation (Verbesselt et al. 2010a). ...
... A wide variety of approaches exists to remove seasonal and long-term trends from data (Smith and Boers 2023b;Verbesselt et al. 2016); the most suitable method is driven by the data set in question. For example, highly seasonal data such as NDVI or other vegetation time series can be decomposed into a trend and seasonal component with harmonic models treating seasonality as a combination of sine waves (e.g., BFAST, Verbesselt et al. 2010a;Verbesselt et al. 2010b, Masiliunas, 2021. There is no one optimal deseasoning method; the efficacy of different procedures depends strongly on the underlying signal that is to be isolated. ...
As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.
... We explore both abrupt and gradual vegetation change over time using three different vegetation indices (VI) and the algorithm Breaks For Additive Seasonal Trend (BFAST). BFAST decomposes time series into trend, seasonal, and remainder components and detects abrupt changes (breakpoints) in the time series trend fitting the data iteratively to a piecewise linear model (Gao et al., 2021;Verbesselt et al., 2010). This algorithm also characterizes the magnitude and direction of change where larger breakpoints represent abrupt changes in the vegetation, such as forest clearings (Verbesselt et al., 2010). ...
... BFAST decomposes time series into trend, seasonal, and remainder components and detects abrupt changes (breakpoints) in the time series trend fitting the data iteratively to a piecewise linear model (Gao et al., 2021;Verbesselt et al., 2010). This algorithm also characterizes the magnitude and direction of change where larger breakpoints represent abrupt changes in the vegetation, such as forest clearings (Verbesselt et al., 2010). To compare the recent disturbance history of forests with their structural attributes, we use the Mexican Forest Inventory database (CONAFOR, 2017). ...
... 0.05 and a dummy seasonal trend (Verbesselt et al., 2010). We validated breakpoints using Planet high-resolution images available from 2016 to present in Planet Explorer (Planet Team, 2017). ...
Several efforts have been made to model forest structure and estimate global aboveground biomass (AGB) magnitude and distribution. However, AGB models still show large uncertainties, especially over tropical mountains that are difficult to survey and understudied. Although forest disturbance and land use greatly influence forest structure and AGB accumulation, they are rarely considered in forest structure and AGB models. Including the effect of forest disturbance and land use could improve model performance. In this study, we used Landsat time series analysis and forest inventory data to assess and model the effect of small-scale disturbance and land use on tropical montane forest structure and AGB. We explored two approaches: abrupt forest change detection, using the algorithm Breaks For Additive Seasonal Trend (BFAST), and gradual vegetation change by calculating the variation of remote sensing vegetation indices over time. We found that tropical montane landscapes are very dynamic and heterogenous. Out of the 284 forest plots analyzed, 27% had abrupt disturbances at least once between 1993 and 2014, and some of them showed abrupt disturbances several times. Forest plots with abrupt disturbances exhibit statistically significant lower basal area, tree height, and AGB than plots without abrupt disturbances. Using linear mixed-effects models, we found that the number of breakpoints, annual Normalized Difference Vegetation Index SD, and Normalized Difference Water Index minimum values over time were the most informative variables for predicting forest structure, particularly basal area (adjusted R 2 = 0.61). These variables are easy to calculate and could add significant power to forest structure and AGB models. In conclusion, incorporating small-scale disturbance to model forest structure and AGB at regional scales is feasible through the identification of recent abrupt and gradual forest change using remote sensing time series. Including recent forest disturbance variables as predictors in forest structure models can greatly improve biomass predictability and mapping in dynamic landscapes. K E Y W O R D S aboveground biomass, BFAST, forest disturbance, land use, Landsat, time series analysis, tropical montane cloud forests
... To address these research gaps, satellite time-series of Sentinel-1 and Sentinel-2 are examined to assess the predisturbance, implementation (disturbance) and post-disturbance conditions of BETA-FOR treatments at patch-level. Various time-series algorithms have been developed in recent years to assess seasonal and trend dynamics: The early study by Verbesselt et al. (2010) proposed the BFAST algorithm detecting seasonal and trend changes in earth observation time-series through the decomposition of an original time-series into seasonal, trend and residual components. The publication by Kennedy et al. (2010) presented the LandTrendr algorithm based on annual Landsat time-series to analyze trends in forests (disturbance, recovery) through temporal segmentation. ...
... Therefore, the susceptibility to false positives is reduced (Awty-Carroll et al., 2019;Ghaderpour & Vujadinovic, 2020) compared to other single-best-model algorithms (e.g. BFAST, Verbesselt et al. (2010)). Furthermore, trend and seasonal change points are assessed separately since there does not necessarily need to be a temporal overlap. ...
Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 ( n = 84) and Sentinel‐2 ( n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.
... Change detection of ecosystem functioning was subsequently performed by applying the BFAST algorithm (Verbesselt, Hyndman, Newnham, & Culvenor, 2010) to the RaUE time series. BFAST makes use of piecewise linear trend and seasonal models to decompose a time series into trend, seasonal and remainder components, further allowing the detection and characterisation of gradual and abrupt changes within the trend and seasonal components (Verbesselt, Hyndman, Newnham, & Culvenor, 2010). ...
... Change detection of ecosystem functioning was subsequently performed by applying the BFAST algorithm (Verbesselt, Hyndman, Newnham, & Culvenor, 2010) to the RaUE time series. BFAST makes use of piecewise linear trend and seasonal models to decompose a time series into trend, seasonal and remainder components, further allowing the detection and characterisation of gradual and abrupt changes within the trend and seasonal components (Verbesselt, Hyndman, Newnham, & Culvenor, 2010). We make use of the BFAST01 variant, which detects one breakpoint in the RaUE time series. ...
The accelerating pace of climate change has led to unprecedented shifts in surface temperature and precipitation patterns worldwide, with African savannas being among the most vulnerable regions. Understanding the impacts of these extreme changes on ecosystem health, functioning and stability is crucial. This paper focuses on the detection of breakpoints, indicative of shifts in ecosystem functioning, while also determining relevant ecosystem characteristics and climatic drivers that increase susceptibility to these shifts within the semi-arid to arid savanna biome. Utilising a remote sensing change detection approach and rain use efficiency (RaUE) as a proxy for ecosystem functioning, spatial and temporal patterns of breakpoints in the savanna biome were identified. We then employed a novel combination of survival analysis and remote sensing time series analysis to compare ecosystem characteristics and climatic drivers in areas experiencing breakpoints versus areas with stable ecosystem functioning. Key ecosystem factors increasing savanna breakpoint susceptibility were identified, namely higher soil sand content, flatter terrain and a cooler long-term mean temperature during the wet summer season. Moreover, the primary driver of changes in ecosystem functioning in arid savannas, as opposed to wetter tropical savannas, was found to be the increased frequency and severity of rainfall events, rather than drought pressures. This research highlights the importance of incorporating wetness severity metrics alongside drought metrics to comprehensively understand climate–ecosystem interactions leading to abrupt shifts in ecosystem functioning in arid biomes. The findings also emphasise the need to consider the underlying ecosystem characteristics, including soil, topography and vegetation composition, in assessing ecosystem responses to climate change. While this research primarily concentrated on the southern African savanna as a case study, the methodological robustness of this approach enables its application to diverse arid and semi-arid biomes for the assessment of climate–ecosystem interactions that contribute to abrupt shifts.
... One of the greatest challenges in large-area land-cover change detection is to select the optimal algorithm to capture the land-cover changes from time-series observations (Healey et al., 2018;. Over the past few decades, a series of change-detection algorithms have been proposed for monitoring forest disturbance (Huang et al., 2009;Jin et al., 2023;Kennedy et al., 2007Kennedy et al., , 2010Qin et al., 2021), urban expansion X. Zhang et al., 2021a), cropland dynamics (Dong et al., 2015;Potapov et al., 2021), and land-cover changes (Bullock et al., 2019;Jin et al., 2017;Verbesselt et al., 2010;Zhu et al., 2019). However, most of them are only suitable for regional land-cover change monitoring, and some of the algorithms need prior knowledge (such as that for urban expansion). ...
... Specific measures were taken to build a high-quality, continuous Landsat time-series collection. First, all Landsat images underwent atmospheric correction to convert them to surface reflectance using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Land Surface Reflectance Code (LaSRC) methods (Vermote, 2007;Vermote and Kotchenova, 2008). Then, although the Landsat 5, 7, 8, and 9 missions share similar spectral bands, the wavelength differences between the TM, ETM+, and OLI cannot be ignored. ...
Land-cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed; however, long-time-series global land-cover-change monitoring data are still sparse, especially those at 30 m resolution. In this study, we describe GLC_FCS30D, a novel global 30 m land-cover dynamics monitoring dataset containing 35 land-cover subcategories and covering the period 1985–2022 in 26 time steps (maps were updated every 5 years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. Specifically, we first take advantage of the continuous change-detection model and the full time series of Landsat observations to capture the time points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, local adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84 526 globally distributed validation samples from 2020. It achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.04 % (±0.30 %) for the LCCS (Land Cover Classification System) level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series datasets used for validation from the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that GLC_FCS30D offers significant stability in terms of variation across the accuracy time series and achieves mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Lastly, we draw conclusions about the global land-cover-change information from the GLC_FCS30D dataset; namely, that forest and cropland variations have dominated global land-cover change over past 37 years, the net loss of forests reached about 2.5 million km2, and the net gain in cropland area is approximately 1.3 million km2. Therefore, the novel dataset GLC_FCS30D is an accurate land-cover-dynamics time-series monitoring product that benefits from its diverse classification system, high spatial resolution, and long time span (1985–2022); thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D dataset is available via https://doi.org/10.5281/zenodo.8239305 (Liu et al., 2023).
... This method can be directly applied to raw time series data without the need for additional standardization and predefined phenology trajectories [26]. Additionally, BFAST incorporates phenology harmonics models, making it effective for handling limited sample data with high accuracy when monitoring vegetation breakpoints at the pixel level [25]. The decomposition model is generally represented as ...
... Gradual analysis can reveal long-term trends in vegetation, identifying relatively stable changes. This is crucial for monitoring the health of ecosystems and assessing the long-term impacts of human or climatic factors on vegetation [25,65]. However, the gradual analysis does not provide information about specific time points and is unable to capture the nonlinear and non-stationary characteristics of vegetation time series induced by climate change and human activities, which limits its utility in understanding the impact of abrupt events [26,79,80]. ...
Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution.
... Breaks for Additive Season and Trend (BFAST) was employed to identify significant abrupt changes in the streamflow and baseflow series (Brakhasi et al., 2021;Mendes et al., 2022;Wang et al., 2023). In this algorithm, the time series (Y t ) was decomposed into the trend component (T t ), seasonal component (S t ), and remainder component, e t (Verbesselt et al., 2010): ...
Study region: The Chaohe watershed is Beijing's primary potable water source. Study focus: The initiation of ecological restoration (ER), combined with the rapid acceleration of climate change (CC), has precipitated severe water shortages in North China. The seasonal responses of baseflow (BF), pivotal for sustaining rivers' fundamental flow and ecological equilibrium , to ER and CC are poorly understood. This study provides a precise depiction of the seasonal variations in BF by leveraging multiple separation methodologies. By applying the BFAST algorithm and a comprehensive sensitivity analysis, we unveil the nuanced seasonal patterns of BF adjustments in reaction to ER and CC. New hydrological insights for the region: Baseflow, primarily influenced by the wet season, constituted 64.21% of the annual aggregate. Considerable decreases in BF during the dry (− 32.61%) and wet (− 68.21%) seasons pose increasing threats to available water resources. The decrease in sub-surface runoff (− 1.91 mm per decade) dominated the reduction of dry season BF. Indeed, vegetation regulated seasonal water distribution, maintaining the essential flow throughout the dry season. In the wet season, the reduction in BF acts as a supplemental water source to fulfill the escalating evapotranspiration needs due to afforestation and a drying climate. This study highlights the persistent hydrological consequences of ER and CC on water resources, emphasizing the crucial function of vegetation in baseflow, a key component for ecological restoration and water resource management in water-limited areas.
... To evaluate the effectiveness of ecological restoration projects, some studies quantitatively analyzed the time-series trends of individual ecological factors (e.g., through RSEI) or vegetation indices [e.g., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] (Shen et al., 2018;Zhang et al., 2018;Xu et al., 2020). Some studies have used change detection algorithms and models to evaluate the ecological restoration effects in different regions and identify the abrupt points in the time series of comprehensive ecological indices, e.g., continuous change detection and classification, continuous monitoring of land disturbance, Landsat-based detection of the trends in disturbance and recovery, breaks for additive seasonal and trend (BFAST), and vegetation change tracker (Huang et al., 2010;Verbesselt et al., 2010;Zhu and Woodcock, 2014;Zhang et al., 2018;Zhu et al., 2020). For example, short-term abrupt changes (disturbance time and recovery rate) in the gross primary productivity have been analyzed to describe and quantify vegetation health . ...
Multi-time scale assessment of ecological restoration effects based on objective and scientific approaches can provide crucial information for implementing environmental protection policies and ensuring sustainable regional development. This study evaluated the effect of ecological restoration based on a natural evolution as a reference frame, using yearly Landsat time series. Southern Ningxia in China was selected as the study area. The remote sensing ecological index (RSEI) was calculated. The features of natural evolution were derived from the time series of the RSEI in the natural reserve areas (NRAs). LandTrendr was employed to characterize the disturbance–recovery processes. Furthermore, we adopted the dynamic time-warping method for the entire study period, along with the relative variation ratio (during the disturbance–recovery cycle) to capture the long-term and short-term ecological restoration effects, respectively. The following conclusions were drawn: First, a time-series RSEI based on LandTrendr was used to successfully monitor disturbance–recovery processes. Second, the majority of RSEI disturbances (i.e., >60%) occurred between 2000 and 2005. It is characterized by fewer disturbance times and obvious spatial heterogeneity in disturbance duration. Notably, from 2000 to 2022, the RSEI improved. Additionally, approximately 40% of the study area portrayed a strong similarity to the RSEI of the NRAs. We conclude that quantifying the ecological restoration effect at multi-time scales is a practical operational approach for policymakers and environmental protection. Our study presents novel insights for assessing regional ecological quality, by capturing the processes of natural evolution features in NRAs.
... Natural phenomena time-series data, such as NDVI, climate variables, and hydrological variables, are typically nonlinear and non-stationary (Verma and Dutta, 2013;Wen et al., 2017). Interannual variability is often affected by noise, fluctuations, or mutations, leading to an insufficient understanding of the interannual variability problem (Verbesselt et al., 2010). To analyze the complex interannual changes in climate factors, Primary analysis process and framework for this study. ...
Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9–16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake’s level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake’s water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.
... The results of seasonal group characteristics and distribution pattern were shown in Table S1. To detect and characterize tipping points (TPs) in dolphin distribution, we applied the Breaks for Additive Seasonal and Trend (BFAST) function to all the five metrics in Table 1 using the bfast package (Verbesselt et al. 2010) in R 4.2.3. BFAST is one of the publicly available and widely used change detection methods (Zhu 2017), which is also applicable to marine ecosystems (Zhao and Li 2023). ...
Global ocean warming and extreme climate events pose a severe threat to marine biodiversity by inducing species redistribution and ecosystem reorganization. It is important to quantify the impacts of marine heatwaves (MHWs) on marine cetacean habitats to avoid rapid ecosystem shifts. Here we utilized detected breakpoints and early warning indicators derived from sightings data spanning from 2009 to 2021 to assess the distribution change of Indo-Pacific humpback dolphin (Sousa chinensis). We found that (1) during the 2014 MHWs, the encounter rates (number of on-effort sightings per 100 km) of humpback dolphins in Hong Kong waters significantly decreased, with the breakpoint occurring during the autumn of 2014; (2) Since 2014, Hong Kong waters have experienced more prolonged and frequent MHWs, with a significant reduction of core habitat by 26%; according to Granger causality analysis, changes in sea surface temperature drove shifts in dolphin distribution; (3) Our analysis revealed a co-existence of rapid annual increases in MHWs and high habitat usage, with the marine park located in Southwest Lantau being particularly at risk. This study on dolphin distribution shifts and their relationships with marine heatwaves in Southern China made a contribution to our understanding of the action of marine cetaceans' response to climate change. Additionally, it highlights the importance of considering MHWs in dolphin conservation efforts.
... The BFAST method iteratively decomposes original time series data into three components: seasonal, trend, and residual, detecting and characterizing data trends and seasonal local change patterns [83,84] as shown in Equation (8). This technique has been widely employed for long-term trend detection and breakpoint identification in time series [85], visually representing the trend and cyclic model of data changes within a specified interval with confidence intervals and breakpoint locations. ...
Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.
... Over the years, various approaches have been developed to disentangle and quantify these overlapping components, each offering unique insights that may be of interest depending on the specific research focus [21]. ...
Xylella Fastidiosa has been recently detected for the first time in southern Italy, representing a very dangerous phytobacterium capable of inducing severe diseases in many plants. In particular, the disease induced in olive trees is called olive quick decline syndrome (OQDS), which provokes the rapid desiccation and, ultimately, death of the infected plants. In this paper, we analyse about two thousands pixels of MODIS satellite evapotranspiration time series, covering infected and uninfected olive groves in southern Italy. Our aim is the identification of Xylella Fastidiosa-linked patterns in the statistical features of evapotranspiration data. The adopted methodology is the well-known Fisher–Shannon analysis that allows one to characterize the time dynamics of complex time series by means of two informational quantities, the Fisher information measure (FIM) and the Shannon entropy power (SEP). On average, the evapotranspiration of Xylella Fastidiosa-infected sites is characterized by a larger SEP and lower FIM compared to uninfected sites. The analysis of the receiver operating characteristic curve suggests that SEP and FIM can be considered binary classifiers with good discrimination performance that, moreover, improves if the yearly cycle, very likely linked with the meteo-climatic variability of the investigated areas, is removed from the data. Furthermore, it indicated that FIM exhibits superior effectiveness compared to SEP in discerning healthy and infected pixels.
... The trend component represents the overall direction of the series, which can be positive, negative, or no trend. The seasonal component refers to regular, consistent fluctuations in the series, such as those that occur at specific times of the year (Verbesselt et al. 2010). The residual component of a time series represents the random fluctuations that are not accounted for by the trend, cycle, or seasonal components. ...
The current study aims to analyze the trend of monthly average temperature in Basrah province to understand the role of land use changes in this pattern. Data which was recorded at Hay Al-Hussain station in the city of Basrah over a span of 74 years (1948–2022) were utilized for this purpose. The non-parametric Mann–Kendall test and Sen’s slope estimator were used to estimate the trend and the magnitude of the trend, respectively. Findings revealed that the average temperature trend is increasing at a rate of 0.00475 °C/month (4.218 °C in total). This indicates that Basrah is experiencing a faster temperature increase than the global average (1 °C) and the Middle East region (2 °C). Five main factors contributed to the temperature rise in the study area: increasing oil fields, reduction of green cover, global warming, urban expansion, and wetland shrinkage. Furthermore, the study revealed that the impact of changes in land use outweighs the effect of global warming on the temperature rise in the study area. This finding could facilitate the implementation of measures to reduce the warming rate within Basrah and other regions inside and outside Iraq, for the purpose of adapting to the climate change effects that are already occurring. This can be achieved by controlling unplanned changes in land use and minimizing their negative effects on the increase of temperature.
... Time series segmentation has been applied in many research fields maturely, such as in earth science (Verbesselt et al. 2010), security (Albertetti et al. 2016) and machine fault detection (Liu et al. 2014) as well. The segmentation process is defined as decomposition of time series into homogeneous subsequences or groups based with similar characteristics. ...
Detection of unexpected events (e.g. anomalies and faults) from monitoring data is very challenging in machine health assessment. Hence, abrupt or incipient fault detection from the monitoring data is very crucial to increase asset safety, availability and reliability. This paper presents a generic methodology for abrupt and incipient fault detection and feature fusion for health assessment of complex systems. Proposed methodology consists of feature extraction, feature fusion, segmentation and fault detection steps. First of all, different features are extracted using descriptive statistics. Secondly, based on linearly weighted data fusion algorithm, extracted features are combined to get the generic and representative feature. Afterward, combined feature is divided into homogeneous segments by sliding window segmentation algorithm. Finally, each segment is further evaluated by coefficient of variability which is used in inferential statistics, to evaluate health state changes that indicate asset faults. To illustrate its effectiveness, the methodology is implemented on point machine and Li-ion battery monitoring data to detect abrupt and incipient faults. The results show that proposed methodology can be effectively used in fault detection for asset monitoring.
... The piecewise trend analysis of vegetation greenness is highly needed for Arctic tundra. Time series analysis techniques, such as Breaks For Additive Seasonal and Trend (BFAST) Verbesselt et al. 2010a;Verbesselt et al. 2010b), CCDC (Zhu and Woodcock 2014) or Land-Trendr (Kennedy, Yang, and Cohen 2010), which have been integrated into the platform of Google Figure 11. The discrepancies between Landsat vs. MODIS and Landsat vs. GIMMS due to resampling. ...
The circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.
... Remote sensing optical time series data, with their rich spectral content and temporal information on land surface features, play a crucial role in advancing our understanding of Earth's surface dynamics over time (Verbesselt et al. 2010). Consequently, these data are indispensable for various applications such as land cover mapping, management of natural resources, disaster response, and mitigation of climate change impacts (Friedl et al. 2002;Pekel et al. 2016). ...
Optical remotely sensed time series data have various key applications in Earth surface dynamics. However, cloud cover significantly hampers data analysis and interpretation. Despite synthetic aperture radar (SAR)-to-optical image translation techniques emerging as a promising solution, their effectiveness is diminished by their inability to adequately account for the intertwined nature of temporal and spatial dimensions. This study introduces U-SeqNet, an innovative model that integrates U-Net and Sequence-to-Sequence (Seq2Seq) architectures. Leveraging a pioneering spatiotemporal teacher forcing strategy, U-SeqNet excels in adapting and reconstructing data, capitalizing on available cloud-free observations to improve accuracy. Rigorous assessments through No Reference and Full Reference Image Quality Assessments (NR – IQA and FR – IQA) affirm U-SeqNet’s exceptional performance, marked by a Natural Image Quality Evaluator (NIQE) score of 5.85 and Mean Absolute Error (MAE) of 0.039. These results underline U-SeqNet’s exceptional capabilities in image reconstruction and its potential to improve remote sensing analysis by enabling more accurate and efficient multimodal and multitemporal cloud removal techniques.
... NDVI is sensitive to greenness and vegetation health (Alatorre et al., 2016;Li et al., 2019;Alibakhshi, 2020;Cabello et al., 2021) and is widely utilized and validated in various studies. NDVI proves effective in assessing vegetation dynamics by capturing the reflectance of nearinfrared (NIR) radiation and absorption of red light, indicative of healthy vegetation (Verbesselt et al., 2010a;Verbesselt et al., 2010b;Ruan et al., 2022;Tran et al., 2022). 10.3389/feart.2024.1317188 ...
Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.
... We selected this nonparametric method because it is robust to outliers and non-normal residuals. If the fractional cover time series in a pixel had more than 17 years with no data values, the slope was set as no data, otherwise, missing values were linearly interpolated between neighboring values before calculating the Theil-Sen slope (Gnauck, 2004;Verbesselt et al., 2010). The result was a set of maps, one per lifeform, at ~4 km resolution representing the median annual change in fractional cover for the analysis period, from now on, this change is indicated by units of "∆/year". ...
The effects of climate change on vegetation composition and distribution are evident in different ecosystems around the world. Although some climate‐derived alterations on vegetation are expected to result in changes in lifeform fractional cover, disentangling the direct effects of climate change from different non‐climate factors, such as land‐use change, is challenging. By applying “Liebig's law of the minimum” in a geospatial context, we determined the climate‐limited potential for tree, shrub, herbaceous, and non‐vegetation fractional cover change for the conterminous United States and compared these potential rates to observed change rates for the period 1986 to 2018. We found that 10% of the land area of the conterminous United States appears to have climate limitations on the change in fractional cover, with a high proportion of these sites located in arid and semiarid ecosystems in the Southwest part of the country. The rates of change in lifeform fractional cover for the remaining area of the country are likely limited by non‐climate factors such as the disturbance regime, land management, land‐use history, soil conditions, and species interactions and adaptations.
... In recent years, with the launch of various earth observation satellites and the rapid development of photogrammetry, ecological environment monitoring based on remotesensing technology has become indispensable (Pettorelli et al., 2014;Zhu et al., 2016;Gao et al., 2020). Out of the various vegetation indices derived from remote sensing data, the Normalized Difference Vegetation Index (NDVI) has been extensively employed to track fluctuations in vegetation dynamics attributed to its straightforward inversion algorithm and clear physical interpretation (Verbesselt et al., 2010;Fensholt and Proud;. Several researchers have made fruitful studies on global and regional vegetation change based on NDVI datasets (Fan and Liu, 2016;Liao et al., 2016). ...
... Compared to the discrete separability characteristics-based algorithms that focus on eucalyptus extraction, Remote Sens. 2024, 16, 744 3 of 21 change detection algorithms fulfil all of these requirements and are a potential means to reconstruct the historical dynamics of artificial forests. Currently, a growing number of change detection algorithms have been commonly used for mapping forest changes, including Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) [23][24][25], Continuous Change Detection and Classification (CCDC) [26,27], Breaks For Additive Seasonal and Trend (BFAST) [28,29], and Cumulative Sum (CUSUM) [30]. By comparing and analyzing these different algorithms, it can be concluded that the LandTrendr algorithm is more robust than other change detection methods, which sheds new light on mapping short-rotation eucalyptus plantations. ...
Eucalyptus plantations are expanding rapidly in southern China owing to their short rotation periods and high wood yields. Determining the plantation dynamics of eucalyptus plantations facilitates accurate operational planning, maximizes benefits, and allows the scientific management and sustainable development of eucalyptus plantations. This study proposes a sliding-time-window change detection (STWCD) approach for the holistic characterization and analysis of eucalyptus plantation dynamics between 1990 and 2019 through dense Landsat time-series data. To achieve this, pre-processing was first conducted to obtain high-quality reflectance data and the monthly composite maximum normalized-difference vegetation index (NDVI) time series was determined for each Landsat pixel. Second, a sliding time window was used to segment the time series and obtain the NDVI change characteristics of the subsequent segments, and a sliding time window-based LandTrendr change detection algorithm was applied to detect the crucial growth or harvesting phases of the eucalyptus plantations. Third, pattern-matching technology was adopted based on the change detection results to determine the characteristics of the eucalyptus planting dynamics. Finally, we identified the management history of the eucalyptus plantations, including planting times, generations, and rotation cycles. The overall accuracy of eucalyptus identification was 90.08%, and the planting years of the validation samples and the planting years estimated by our algorithm revealed an apparent correlation of R2 = 0.98. The results showed that successive generations were mainly first- and second-generations, accounting for 75.79% and 19.83% of the total eucalyptus area, respectively. The rotation cycles of the eucalyptus plantations were predominantly in the range of 4–8 years. This study provides an effective approach for identifying eucalyptus plantation dynamics that can be applied to other short-rotation plantations.
... In the second phase, we de-trended the NDVI and dNBR time series data using the Breaks For Additive Season and Trend (BFAST) algorithm [54,55]. We also utilized the BFAST algorithm to detect breaks in trend and wrote an R program to match negative breaks in trend with spikes in dNBR. ...
Ankarafantsika National Park (ANP), the last significant remnant of Northwestern Madagascar’s tropical dry forests, is facing rapid degradation due to increased incidences of fire. This poses severe threats to biodiversity, local livelihoods, and vital ecosystem services. Our study, conducted on 3,052-ha of ANP’s pristine forests, employed advanced remote-sensing techniques to assess fire impacts during the past 37 years. Our aims were to understand historical fire patterns and evaluate forest recovery and susceptibility to repeated fires following initial burns. Using data from multiple Landsat satellite sensors, we constructed a time series of fire events since 1985, which revealed no fire activity before 2014. The Global Ecosystem Dynamics Investigation (GEDI) lidar sensor data were used to observe forest structure in both post-fire areas and undisturbed zones for comparison. We recorded six fire incidents from 2014–2021, during which the fire-affected area exponentially grew. A significant fire incident in October 2021 impacted 1,052 hectares, 59% of which had experienced at least one fire in two-to-four years prior, with 60% experiencing two preceding incidents: one in 2017 and another in 2019. The initial fire drastically reduced plant cover and tree height, with subsequent fires causing minor additional loss. Post-fire recovery was negligible within the initial four years, even in patches without recurrent fires. The likelihood for an initial burn to trigger subsequent fires within a few years was high, leading to larger, more severe fires. We conclude that ANP’s dry forests exhibit high vulnerability and low resilience to anthropogenic fires. Prompt preventive measures are essential to halt further fire spread and conserve the park’s unique and invaluable biodiversity.
... To carry out unsupervised land cover change detection, a user-defined R function was created and passed into the (Verbesselt et al. 2010(Verbesselt et al. , 2011. A total of 457 image scenes (approximately 275 Gigabytes) were utilized for this task. ...
In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.
... However, due to the effects of non-stationary climate change, the trend of vegetation change over time is not always monotonous. The linear method can capture short-term variations and shifts but is limited in its ability to detect long-term trend changes and shifts (Jamali et al., 2015;Tian et al., 2015;Verbesselt et al., 2010). ...
... El estudio de la vegetación natural o biomasa natural, ha cobrado renovada relevancia en la actualidad por dos razones principales: por un lado, existe la inminente amenaza de un cambio climático global, cuyas tendencias pueden ser revertidas a partir de la mitigación de emisiones de gases efecto invernadero (GEI)-por diferentes mecanismos-como por ejemplo el secuestro de carbono de la atmósfera que realiza la vegetación (el 50% de la biomasa es carbono). Por otro lado, los combustibles fósiles tienen pronosticados horizontes de reservas limitados, por lo que nuevas fuentes combustibles y renovables están siendo profusamente estudiadas, entre ellas, la biomasa con fines energéticos (Foody et al., 2003;Hall et al., 2006;Kindermann et al., 2008;Verbesselt et al., 2010). ...
RESUMEN El objetivo de este trabajo fue la puesta a punto de una metodología de modelización y mapeo de biomasa aérea leñosa (BAL), a partir de datos de sensores remotos (reflectancia), radiometría de terreno (reflectancia) y mediciones estructurales de la vegetación (biomasa), registrados para idéntica fecha y lugar. Los ambientes estudiados fueron: Chaco, Selva y arbustales del Valle de Lerma (Salta). Las transformaciones realizadas a los datos obtenidos (índices de vegetación), permitieron encontrar el modelo con mejor ajuste, que incluye el NDVI (Normalized Difference Vegetation Index) de campo y de satélite. Este modelo arroja valores de biomasa para el Valle que oscilan entre 0 a 200 t/ha, con la mayor superficie ubicada en la categoría de 60 a 130 t/ha de biomasa. Nuevos muestreos son recomendados para mejorar el modelo logrado a partir de las parcelas experimentales, y estimar la biomasa del Valle de Lerma con mayor precisión.
... La eliminación de tales ecosistemas, puede por tanto, contribuir a la acumulación de GEI en la atmósfera y un mayor sobrecalentamiento global (Carvalho et al., 2008;Apezteguía et al., 2009). De allí la importancia de su estudio, cuantificación y monitoreo (DeFries et al., 2007;Verbesselt et al., 2010). ...
RESUMEN La biomasa, englobando un conjunto de materiales orgánicos de naturaleza diversa, puede ser un instrumento de desarrollo local en los sitios en los que se halla presente. Si bien a nivel mundial se discuten beneficios y perjuicios de su aprovechamiento, en general estas discusiones se circunscriben a la producción de biodiesel y bioetanol (como biocarburantes), y nada tienen que ver con los posibles beneficios de otras fuentes de biomasa y otras aplicaciones. A partir de su detección, estudio, cuantificación y análisis, se definen y especifican las oportunidades y limitaciones de cada uno de los recursos de biomasa disponibles en el Valle de Lerma (Salta, Argentina) en una perspectiva de sustentabilidad. Se resumen en este trabajo los principales aspectos en los cuales las fuentes de biomasa disponibles podrían redundar en impactos positivos, promoviendo sistemas energéticos más sustentables. Dichos aspectos podrían asimismo verificarse en otras regiones y/o países del tercer mundo. PALABRAS CLAVE: biomasa, bioenergía, mitigación de emisiones, desarrollo local, sustentabilidad.
... Time series analysis has the potential to reveal long-term surface dynamics based on the temporal profile of the data for a given pixel. Time series data with higher temporal resolution provide a more accurate representation of vegetation changes, including seasonal variations, gradual shifts, and sudden, abrupt changes and are often used to evaluate smaller/shorter-term changes [3]. Vegetation indices derived from satellite data are commonly used to monitor vegetation, as they provide a high correlation with vegetation growth. ...
Accurate vegetation behavior forecasting is essential for understanding the dynamics of plant life in the context of climate change and other natural or human-induced disturbances. RNN deep learning models represent a modern approach to predict vegetation behavior with a high level of precision. In this paper, we explore the potential of different deep learning and more traditional methods to forecast the normalized difference vegetation index (NDVI), which is directly related to the state of vegetation and its dynamics. A time-series dataset consisting of 70 NDVI images calculated from PlanetScope data from April 2017 to January 2023 were used. Initially, all selected methods were evaluated and compared. From the six tested methods, Simple RNN (SRNN) proved to be the most accurate method for predicting vegetation dynamics. The SRNN model results achieved a mean RMSE of 0.051 when compared to the actual 2022 NDVI values. The high accuracy was reflected in all five studied vegetation classes characterizing the selected Mediterranean test area. The SRNN method performs very well in most months, except in autumn where it underestimates NDVI values. To get thorough insight into the results, we also compared them to the Sentinel-2 NDVI data and climate data consisting of temperature and precipitation values. It was found that most of the prediction differences were due to the irregular variations in meteorological conditions during the year analyzed. The predictive capabilities of RNNs are an effective tool for forecasting vegetation dynamics, but can be further improved by incorporating climate data into the prediction process.
... Extreme events like droughts, floods, fires, and urbanization can lead to rapid changes in NDVI and EVI time series. However, NDVI and EVI time series are non-stationary due to the presence of seasonal, gradual, and abrupt changes [17,18] Various methods have been developed to analyse the nonstationarity of NDVI time series to detect vegetation changes. Wavelet transform (WT) is an important method for analysing nonstationary time series and has been widely used in remote sensing, finance, medicine, and other fields [19]. ...
... Scholars worldwide have long conducted extensive research on vegetation changes and ecosystems, underscoring the central role of ecosystem stability in ecology (Ives and Carpenter, 2007). The establishment of prolonged vegetation monitoring serves as a foundational underpinning for uncovering the determinants of change and delving into the dynamics of ecosystems (De Jong et al., 2013;Verbesselt et al., 2010;Virtanen et al., 2010). Previous research often adopted a reductionist framework, involving the direct correlation of regional and categorical vegetation indices with meteorological variables such as precipitation, radiation, and temperature (Lamchin et al., 2018;Liu and Lei, 2015;Xu et al., 2014;Xu et al., 2017). ...
... We quantified the co-occurrence of extreme precipitation (Preth) lower than the different percentiles of total growing season precipitation and growing season WUE anomalies lower than the 20th percentile in 2001-2018(Verbesselt et al., 2010Rammig et al., 2015;Li et al., 2023d). The statistics for each grid cell are based on the above PCA clusters (that is, n × 18 records involved in the calculation for an n similar grid-centered cluster). ...
... Remote-sensing time-series analysis commonly applies trajectorybased and classification-based change detection approaches (Banskota et al., 2014). Trajectory-based approaches include Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) (Kennedy et al., 2010), Breaks For Additive Season and Trend (BFAST) (Verbesselt et al., 2010a;Verbesselt et al., 2010b), Continuous Change Detection and Classification (CCDC) (Zhu and Woodcock, 2014), which have been widely used for forest change analysis (e.g., Hansen et al., 2013). However, they strongly rely on robust radiometric corrections and the availability of near-anniversary images to minimize false detection of changes caused by phenology (Banskota et al., 2014). ...
Understanding the spatial-temporal dynamics of both natural and planted forests is critical for sustainable forest management and assessing ecological benefits or impacts. In 2020, China's forest area encompassed approximately 220 million hectares, accounting for around 5 % of the global forest area. Furthermore, China boasts the largest planted forest area worldwide. However, knowledge regarding the spatial-temporal dynamics of China's natural and planted forests has remained elusive. To fill this gap, we first generated wall-to-wall natural and planted forest maps at 30-m resolution for China every-five years from 1990 to 2020 by using 447,730 images from Landsat-4/5/7/8/9 surface reflectance archive and 656,920 field samples, which were a combination of crowdsourced data and expert knowledge. Secondly, we analyzed the spatial-temporal dynamics of natural and planted forests at multiple-scales from 1990 to 2020. The resultant maps achieved an overall accuracy ranging from 77.33 %±0.67 % to 81.78 % ± 0.59 % and revealed opposite trends in the areas of natural and planted forests from 1990 to 2020. During these three decades, China's planted forest area increased by 447,500 km 2 while the natural forest area decreased by 219,100 km 2. The spatial-temporal dynamics of China's natural and planted forests result from China's vast forest management programs in addition to social and economic factors. Variations of these factors at different scales not only determined the primary goals of protecting natural forests and expand planted forests but also resulted in increasing forest areas for many consecutive years by planting forests that offset the loss of natural forests. Our maps provide timely and valuable insights into the benefits and impacts of natural and planted forests in China, which are both closely linked to sustaining human well-being.
... Notably, the Bayesian mcp method infers the location of breakpoints with segment specific regression models but the number of breakpoints needs to be known a priori (Lindeløv, 2020a). In case of timeseries exhibiting a clear seasonal component, the proposed classification could be preceded by an appropriate deseasoning step, but we advise to compare the result with other methods like bfast (Verbesselt et al., 2010) or bayesian version Rbeast (Zhao et al., 2019) that are specifically suited for such cases as they look for breakpoints in periodic systems after decomposing the signal into trend, seasonal, and noise components. In general for our classification, poor model fit would be evidenced by high normalized root mean square error (NRMSE) indicating that the model explains little variation, low AICc weight meaning that other models describe equally well or better the timeseries and low Fig. 5. Three empirical timeseries classified either as "abrupt", "quadratic", or "linear" following our classification approach, namely, (A) Atlantic bluefin tuna (Thunnus thynnus) catch in Eastern Atlantic (source: RAM Legacy Data Base), (B) Northern wheatear (Oenanthe oenanthe) abundance in Europe (source: EBCC/ BirdLife/RSPB/CSO), and (C) Red-eyed damselfly (Erythromma najas) occupancy in Britain (source: Termaat et al., 2019). ...
Conservation efforts and sustainable use of natural populations often seek to reach or maintain viable abundance levels for a target population. Yet, this goal can be undermined by a number of events resulting from out-of-equilibrium dynamics, including large and sudden changes in abundance. The dynamical properties of such temporal changes are valuable indications about population's capacity to cope with environmental changes. Correctly identifying past or anticipating impending occurrences of temporal abrupt shifts in ecological systems is thus of major importance to adjust conservation and management strategies. Despite many available abrupt shift detection methods, few offer the possibility to compare and agree on the best model among linear, nonlinear, or abrupt models. By combining several existing methods, we develop an approach that classifies any timeseries to a trajectory type – no change, linear, nonlinear (quadratic), abrupt – and confirms the occurrence of potential abrupt shifts. We assessed the classification performances using a set of simulated data for which we had deterministic predictions for each type of trajectories. We used various levels of noise and perturbation events to make the simulations more realistic. This classification can be of particular interest when comparing dynamics of many populations across space or time. We show this by applying this classification approach to three different temporal datasets commonly used in conservation: catch tonnage, bird index, and insect occupancy timeseries. With this tool, we hope to promote conservation and management practices that explicitly take into account the likelihood of out-of-equilibrium trajectories and especially abrupt shifts in ecological systems.
Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days, the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management.
Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner.
This study investigates how blade aerodynamic modifications, including Leading Edge Roughness (LER), influence offshore wind turbine performance over their operational lifespan. Developing a novel methodology, this research analyses data from twelve multi-megawatt turbines over a twelve-year period, focusing on the intricate relationship between blade erosion, blade enhancements, operations and maintenance events, control PLC parameter updates, and their cumulative impact on turbine efficiency. The analysis hinges on the integration of SCADA data, Operations and Maintenance (O\\&M) records, and air density corrections. A key contribution is the development of a Turbine Performance Integral (TPI) method, which leverages generator speed and power output data to track performance trajectories. Seasonal-Trend decomposition using Locally Estimated Scatterplot Smoothing (STL) further isolates long-term trends and seasonal variations in performance. Overcoming data availability and quality limitations, the study reveals significant findings concerning software updates impacts on turbine control strategies, the variable effects of blade repairs and enhancements and the complex interaction between O\\&M events and performance. This study's strength lies in its methodical approach and statistical rigour, offering a path forward in the quest for optimised wind turbine efficiency and advancing renewable energy.
Understanding how resilient forests are to ecological engineering projects (EEPs) is essential to forest management and ecosystem health. Despite growing evidence that EEPs achieve increasing carbon stocks, whether such benefits can be sustainable and what are the consequences of EEPs on forest health remain unclear. This study aimed to investigate the long‐term effects of EEPs using forest resilience from aspects of resistance and recovery, by applying a change detection algorithm (breaks for additive seasonal and trend; BFAST) spatially on net ecosystem production (NEP) (proxy for carbon stocks) time series (1981–2019) in red soil hilly region (RSHR) of subtropical China. The spatial parameters (e.g., the number, magnitude, and time of changes) used to construct resilience metrics were generated based on BFAST‐derived breakpoints. These metrics were then utilized to analyze the dynamics of forest resilience in relation to EEPs factors in terms of plantation area, forest type, and stand age. Our results observed 92.77% of breakpoints in NEP after 2000, which corresponds well with the periods that multiple EEPs were conducted. NEP resilience showed great variability during 2001–2019, with a positive increasing trend in resistance ( R ² = 0.72) and a continuous decline ( R ² = 0.37) in recovery, indicating an unhealthy ecosystem in RSHR. Our findings revealed that forest resistance was strongly associated with plantation area ( R = 0.71), and the presence of monoculture and young coniferous forest may be the potential factors for the decline in recovery. This suggested that forest resilience in RSHR is mainly modulated by large‐scale EEPs.
This study emphasizes the noticeable impact of climate trend changes on the
activation of geomorphological processes and landscape alterations in the lake catchment areas of the High Tatra Mts. Current morphodynamic transformations - rock avalanches and increased debris areas within the catchments of the studied lakes (Zelené pleso Kežmarské and Čierne pleso Kežmarské), underline the necessity for rigorous monitoring of these events and their interrelationships. The study contemplates how these changes will affect these pivotal natural ecosystems (lake siltation – water resources, water quality) and human activities (tourism, etc.) in this high-altitude environment.
A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at https://doi.org/10.5281/zenodo.8354262 (Cheng et al., 2023a).
Coarse spatial resolution satellites are capable of observing large swaths of the planetary surface in each overpass resulting in image time series with high temporal resolution. Many change-detection strategies commonly used in remote sensing studies were developed in an era of image scarcity and thus focus on comparing just a few scenes. However, change analysis methods applicable to images with sparse temporal sampling are not necessarily efficient and effective when applied to long image time series. We present a statistical framework that gathers together: (1) robust methods for multiple comparisons; (2) seasonally corrected Mann–Kendall trend tests; (3) a testing sequence for quadratic models of land surface phenology. This framework can be applied to long image time series to partition sources of variation and to assess the significance of detected changes. Using a standard image time series, the Pathfinder AVHRR Land (PAL) NDVI data, we apply the framework to address the question of whether the institutional changes accompanying the collapse of the Soviet Union resulted in significant changes in land surface phenologies across the ecoregions of Kazakhstan.
Vegetation phenology derived from satellite data has increasingly received attention for applications in environmental monitoring and modelling. The accuracy of phenological estimates, however, is unknown at the regional and global level because field validation data are insufficient. To assess the accuracy of satellite‐derived phenology, this study investigates the sensitivity of phenology detection to both the temporal resolution of sampling and the number of consecutive missing values (usually representing cloud cover) in the time series of satellite data. To do this, time series of daily vegetation index data for various ecosystems are modelled and simulated using data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) data. The annual temporal data are then fitted using piecewise logistic functions, which are employed to calculate curvature change rate for detecting phenological transition dates. The results show that vegetation phenology can be estimated with a high precision from time series with temporal resolutions of 6–16 days even if daily data contains some uncertainties. If the temporal resolution is no coarser than 16 days for time series sampled using an average composite, the absolute errors are less than 3 days. On the other hand, the phase shift of temporal sampling is shown to have limited impacts on phenology detection. However, the accuracy of phenology detection may be reduced greatly if missing values in the time series of 16‐day MODIS data occur around the onsets of phenological transition dates. Even so, the probability that the absolute error in phenological estimates is greater than 5 days is less than 4% when only one period is missing in the time series of 16‐day data during vegetation growing seasons; this probability increases to 20% if there are two consecutive missing values.
Techniques based on multi-temporal, multi-spectral, satellite-sensor- acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches,digital change,detection from,three angles. First, the different perspectives from which the variability in ecosystems and the change,events have been dealt with are summarized.,Change,detection between pairs of images,(bi-temporal) as well as between,time profiles of imagery,derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change,detection,methods,themselves,are categorized,in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological,aspects are being,fine-tuned as this review,is being written, and we summarize the new developments that can be expected in the near future. The review,highlights the high complementarity,between,different change,detection methods.
Commercial softwood growers in Australia are keen to improve the efficiency and precision of resource inventory underpinning their timber supply commitments.
The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R2 (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.
Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (TM) imagery. The central premise of the method is the recognition that many phenomena associated with changes in land cover have distinctive temporal progressions both before and after the change event, and that these lead to characteristic temporal signatures in spectral space. Rather than search for single change events between two dates of imagery, we instead search for these idealized signatures in the entire temporal trajectory of spectral values. This trajectory-based change detection is automated, requires no screening of non-forest area, and requires no metric-specific threshold development. Moreover, the method simultaneously provides estimates of discontinuous phenomena (disturbance date and intensity) as well as continuous phenomena (post-disturbance regeneration). We applied the method to a stack of 18 Landsat TM images for the 20-year period from 1984 to 2004. When compared with direct interpreter delineation of disturbance events, the automated method accurately labeled year of disturbance with 90% overall accuracy in clear-cuts and with 77% accuracy in partial-cuts (thinnings). The primary source of error in the method was misregistration of images in the stack, suggesting that higher accuracies are possible with better registration.
The distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Satellite data provide the opportunity to monitor continuously the dynamics of vegetation, its changes and its impact on the environment. Monthly normalized difference vegetation index (NDVI) values which had been extracted from NOAA-AVHRR GAC data for Southern Africa (south of the Equator) from 1981 till 1991 were analysed using the Fast Fourier Transform (FFT). The FFT gives a new representation of the time series of images, i.e. a set of images of amplitude and phase (pixelwise) of periodic functions with different frequencies, which allows the analysis of the vegetation phenology using only the amplitude and phase of the most important periodic components. This approach is a powerful way to monitor various dynamic parameters of the vegetation in Southern Africa. The relationship between amplitude values and aridity and vegetation type was studied by correlating the amplitude and phase images with maps of the Budyko Aridity Index (ratio of net radiation to rainfall) and of the vegetation (White map). Images of amplitude and phase values were then used as attributes to map land units homogeneous as regards leaf display phenology. The map of these land units in Southern Africa, which has a 7.6 km resolution, was based on long term (9 years) information of 19 different vegetation-soil-climate complexes. Ten out of the 19 vegetation-soil-climate complexes are described in detail.
Forest systems cover more than 4.1 x 109 hectares of the Earth's land area. Globally, forest vegetation and soils contain about 1146 petagrams of carbon, with approximately
37 percent of this carbon in low-latitude forests, 14 percent in mid-latitudes, and 49 percent at high latitudes. Over two-thirds
of the carbon in forest ecosystems is contained in soils and associated peat deposits. In 1990, deforestation in the low latitudes
emitted 1.6 ± 0.4 petagrams of carbon per year, whereas forest area expansion and growth in mid- and high-latitude forest
sequestered 0.7 ± 0.2 petagrams of carbon per year, for a net flux to the atmosphere of 0.9 ± 0.4 petagrams of carbon per
year. Slowing deforestation, combined with an increase in forestation and other management measures to improve forest ecosystem
productivity, could conserve or sequester significant quantities of carbon. Future forest carbon cycling trends attributable
to losses and regrowth associated with global climate and land-use change are uncertain. Model projections and some results
suggest that forests could be carbon sinks or sources in the future.
The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.
Empirical studies report several plausible correlations between
transforms of spectral reflectance, called vegetation indexes, and
parameters descriptive of vegetation leaf area, biomass and
physiological functioning. However, most indexes can be generalized to
show a derivative of surface reflectance with respect to wavelength.
This derivative is a function of the optical properties of leaves and
soil particles. In the case of optically dense vegetation, the spectral
derivative, and thus the indexes, can be rigorously shown to be
indicative of the abundance and activity of the absorbers in the leaves.
Therefore, the widely used broad-band red/near-infrared vegetation
indexes are a measure of chlorophyll abundance and energy absorption
This paper introduces ideas and methods for testing for structural change in linear regression models and presents how these have been realized in an R package called strucchange. It features tests from the generalized fluctuation test framework as well as from the F test (Chow test) framework. Extending standard significance tests it contains methods to fit, plot and test empirical fluctuation processes (like CUSUM, MOSUM and estimatesbased processes) on the one hand and to compute, plot and test sequences of F statistics with the supF , aveF and expF test on the other. Thus, it makes powerful tools available to display information about structural changes in regression relationships and to assess their significance. Furthermore it is described how incoming data can be monitored online. Keywords: structural change, CUSUM, MOSUM, recursive estimates, moving estimates, online monitoring, R, S. 1
The array of Normalized Difference Vegetation Index (NDVI) products now being derived from satellite imagery open up new opportunities for the study of short and long-term variability in climate. Using a time series analysis procedure based on the Principal Components transform, and a sequence of monthly Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI imagery from 1986 through 1990, we examine trends in variability of vegetation greenness for Africa for evidence of climatic trends. In addition to the anticipated seasonal trends, we identify signals of interannual variability. The most readily identified is one that periodically affects Southern Africa. It is shown that the temporal loadings for this component exhibit a very strong relationship with the El Nino/Southern Oscillation (ENSO) Index derived from atmospheric pressure patterns in the Pacific, Pacific sea surface temperature (SST) anomalies, and with anomalous Outgoing Longwave Radiation (OLR). However, we have also detected a second interannual variation, affecting most particularly East Africa and the Sahel, that does not exhibit a consistent ENSO relationship. The results show the teleconnection patterns between climatic conditions in the Pacific Ocean basin and vegetation conditions at specific regional locations over Africa. The comprehensive spatial character and high temporal resolution of these data offer exciting prospects for deriving a land surface index of ENSO and mapping the impacts of ENSO activity at continental scale. This study illustrates that vegetation reflectance data derived from polar orbiting satellites can serve as good proxy for the study of interannual climate variability.
Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.
STL is a filtering procedure for decomposing a time series into trend, seasonal, and remainder components. STL has a simple design that consists of a sequence of applications of the loess smoother; the simplicity allows analysis of the properties of the procedure and allows fast computation, even for very long time series and large amounts of trend and seasonal smoothing. Other features of STL are specification of amounts of seasonal and trend smoothing that range, in a nearly continuous way, from a very small amount of smoothing to a very large amount; robust estimates of the trend and seasonal components that are not distorted by aberrant behavior in the data; specification of the period of the seasonal component to any integer multiple of the time sampling interval greater than one; and the ability to decompose time series with missing values.
This paper provides a statistically unified method for modelling trends in groundwater levels for a national project that aims to predict areas at risk from salinity in 2020. It was necessary to characterize the trends in groundwater levels in thousands of boreholes that have been monitored by Agriculture Western Australia throughout the south-west of Western Australia over the last 10 years. The approach investigated in the present paper uses segmented regression with constraints when the number of change points is unknown. For each segment defined by change points, the trend can be described by a linear trend possibly superimposed on a periodic response. Four different types of change point are defined by constraints on the model parameters to cope with different patterns of change in groundwater levels. For a set of candidate change points provided by the user, a modified Akaike information criterion is used for model selection. Model parameters can be estimated by multiple linear regression. Some typical examples are presented to demonstrate the performance of the approach.
Three classes of structural change tests (or tests for parameter instability) that have been receiving much attention in both the statistics and the econometrics communities but have been developed in rather loosely connected lines of research are unified by embedding them into the framework of generalized M-fluctuation tests (Zeileis and Hornik, 200326.
Zeileis , A. ,
Hornik , K. ( 2003 ). Generalized M-fluctuation tests for parameter instability . Report 80, SFB “Adaptive Information Systems and Modelling in Economics and Management Science”. URL http://www.wu-wien.ac.at/am/reports.htm#80 . View all references).These classes are tests based on maximum likelihood scores (including the Nyblom–Hansen test), on F statistics (sup F, ave F, exp F tests), and on OLS residuals (OLS-based CUSUM and MOSUM tests). We show that (representatives from) these classes are special cases of the generalized M-fluctuation tests, based on the same functional central limit theorem but employing different functionals for capturing excessive fluctuations.After embedding these tests into the same framework and thus understanding the relationship between these procedures for testing in historical samples, it is shown how the tests can also be extended to a monitoring situation. This is achieved by establishing a general M-fluctuation monitoring procedure and then applying the different functionals corresponding to monitoring with ML scores, F statistics, and OLS residuals. In particular, an extension of the sup F test to a monitoring scenario is suggested and illustrated on a real-world data set.
This study uses a global terrestrial carbon cycle model (the Carnegie-Ames-Stanford Approach (CASA) model), a satellite-derived map of existing vegetation, and global maps of natural vegetation to estimate the effects of human-induced land cover change on carbon emissions to the atmosphere and net primary production. We derived two maps approximating global land cover that would exist for current climate in the absence of human disturbance of the landscape, using a procedure that minimizes disagreements between maps of existing and natural vegetation that represent artifacts in the data. Similarly, we simulated monthly fields of the Normalized Difference Vegetation Index, required as input to CASA, for the undisturbed land cover case. Model results estimate total carbon losses from human-induced land cover changes of 182, and 199 Pg for the two simulations, compared with an estimate of 124 Pg for total flux between 1850 and 1990 [Houghton, 1999], suggesting that land cover change prior to 1850 accounted for approximately one-third of total carbon emissions from land use change. Estimates of global carbon loss from the two independent methods, the modeling approach used in this paper and the accounting approach of Houghton [1999], are comparable taking into account carbon losses from agricultural expansion prior to 1850 estimated at 48-57 Pg. However, estimates of regional carbon losses vary considerably, notably in temperate midlatitudes where our estimates indicate higher cumulative carbon loss. Overall, land cover changes reduced global annual net primary productivity (NPP) by approximately 5 percent, with large regional variations. High-input agriculture in North America and Europe display higher annual NPP than the natural vegetation that would exist in the absence of cropland. However, NPP has been depleted in localized areas in South Asia and Africa by up to 90 percent. These results provide initial crude estimates, limited by the spatial resolution of the data sets used as input to the model and by the lack of information about transient changes in land cover. The results suggest that a modeling approach can be used to estimate spatially-explicit effects of land cover change on biosphere-atmosphere interactions.
Insect-induced tree mortality can cause substantial timber and carbon losses in many regions of the world. There is a critical need to forecast tree mortality to guide forest management decisions. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery provides inexpensive and frequent coverage over large areas, facilitating forest health monitoring. This study examined time series of MODIS satellite images to forecast tree mortality for a Pinus radiata plantation in southern New South Wales, Australia. Dead tree density derived from ADS40 aerial imagery was used to evaluate the performance of change metrics derived from time series of MODIS-based vegetation indices. Continuous subset selection by LASSO regression and model assessment using a variant of the bootstrap were used to select the best performing change metrics out of a large amount of predictor variables to account for over-fitting. The results suggest that 250 m 16-daily MODIS images are effective for forecasting tree mortality. Seasonal change metrics derived from the Normalized Difference Vegetation Index (NDVI) outperformed the Enhanced Vegetation Index (EVI) and the Normalized Difference Infrared Index (NDII). Temporal analysis illustrated that optimal forecasting power was obtained using change metrics based on three years of satellite data for this population. The forecast could be used to optimise the scheduling of detailed forest health surveys and silvicultural operations which currently are planned based on stratified, annual assessments. This coarse-scale, spatio-temporal analysis represents a potentially cost-effective early warning approach to forecasting tree mortality in pine plantations by identifying compartments that require more detailed investigation.
Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.
AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000–2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000–2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002–2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel–Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian–Guinean zones should be done with certain reservations.
Herbivore damage is generally detrimental to plant fitness, and the evolu- tionary response of plant populations to damage can involve either increased resistance or increased tolerance. While characters that contribute to resistance, such as secondary chem- icals and trichomes, are relatively well understood, characters that contribute to a plant's ability to tolerate damage have received much less attention. Using Helianthus annuus (wild sunflower) and simulated damage of Haplorhynchites aeneus (head-clipping weevil) as a model system, we examined morphological characters and developmental processes that contribute to compensatory ability. We performed a factorial experiment that included three levels of damage (none, the first two, or the first four inflorescences were clipped with scissors) and eight sires each mated to four dams. We found that plants compensated fully for simulated head-clipper damage and that there was no variation among plant families in compensatory ability: seed production and mean seed mass did not vary among treat- ments, and sire X treatment interactions were not significant. Plants used four mechanisms to compensate for damage: (1) Clipped plants produced significantly more inflorescences than unclipped plants. Plants produced these additional inflorescences on higher order branches at the end of the flowering season. (2) Clipped plants filled significantly more seeds in their remaining heads than did unclipped plants. (3) Clipped plants, because they effectively flowered later than unclipped plants, were less susceptible to damage by seed- feeding herbivores other than Haplorhynchites. (4) In later heads, seed size was greater on clipped plants, which allowed mean seed size to be maintained in clipped plants. Although there was genetic variation among the families used in this experiment for most of the characters associated with compensation for damage (seed number, mean seed size, mean flowering date, length of the flowering period, and branching morphology), in analyses of these characters, no sire X treatment interactions were significant indicating that all of the families relied on similar mechanisms to compensate for damage.
This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery.The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.
The correct interpretation of scientific information from global, long-term series of remote sensing products requires the ability to discriminate between product artifacts and changes in the Earth processes being monitored. A suite of global land surface products is made from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument data. Quality assessment (QA) is an integral part of this production chain and focuses on evaluating and documenting the scientific quality of the products with respect to their intended performance. This paper describes the QA approach adopted by the MODIS Land (MODLAND) Science Team and coordinated by the MODIS Land Data Operational Product Evaluation (LDOPE) facility. The described methodology represents a new approach for assessing and ensuring the performance of land remote sensing products that are generated on a systematic basis.
An approach to the analysis of data that contains (multiple) structural changes in a linear regression setup is presented. Various strategies which have been suggested in the literature for testing against structural changes as well as a dynamic programming algorithm for the dating of the breakpoints are implemented in the R statistical software package. Using historical data on Nile river discharges, road casualties in Great Britain and oil prices in Germany, it is shown that statistically detected changes in the mean of a time series as well as in the coefficients of a linear regression coincide with identifiable historical, political or economic events which might have caused these breaks.
Landsat satellite data has become ubiquitous in regional-scale forest disturbance detection. The Tasseled Cap (TC) transformation for Landsat data has been used in several disturbance-mapping projects because of its ability to highlight relevant vegetation changes. We used an automated composite analysis procedure to test four multi-date variants of the TC transformation (called “data structures” here) in their ability to facilitate identification of stand-replacing disturbance. Data structures tested included one with all three TC indices (brightness, greenness, wetness), one with just brightness and greenness, one with just wetness, and one called the Disturbance Index (DI) which is a novel combination of the three TC indices. Data structures were tested in the St. Petersburg region of Russia and in two ecologically distinct regions of Washington State in the US. In almost all cases, the TC variants produced more accurate change classifications than multi-date stacks of the original Landsat reflectance data. In general, there was little overall difference between the TC-derived data structures. However, DI performed better than the others at the Russian study area, where slower succession rates likely produce the most durable disturbance signal. Also, at the highly productive western Washington site, where the disturbance signal is likely the most ephemeral, DI and wetness performed worse than the larger data structures when a longer monitoring interval was used (eight years between image acquisitions instead of four). This suggests that both local forest recovery rates and the re-sampling interval should be considered in choosing a Landsat transformation for use in stand-replacing disturbance detection.
We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.
Analysis of change vectors in the multitemporal space, applied to multitemporal local area coverage imagery obtained by the Advanced Very-High Resolution Radiometer on NOAA-9 and NOAA-11 orbiting platforms, clearly reveals the nature and magnitude of land-cover change in a region of West Africa. The change vector compares the difference in the time-trajectory of a biophysical indicator, such as the normalized difference vegetation index, for two successive time periods, such as hydrological years. In establishing the time-trajectory, the indicator is composited for each pixel in a registered multidate image sequence. The change vector is simply the vector difference between successive time-trajectories, each represented as a vector in a multidimensional measurement space. The length of the change vector indicates the magnitude of the interannual change, while its direction indicates the nature of the change. A principal components analysis of change vectors for a Sudanian-Sahelian region in West Africa shows four major classes of change magnitude and four general contrasting types of change. Scene-specific changes, such as reservoir water level storage changes, are also identified. The technique is easily extended to other biophysical parameters, such as surface temperature, and can incorporate noneuclidean distance measures. Change vector analysis is being developed for application to the land-cover change product to be produced using NASA's Moderate-Resolution Imaging Spectroradiometer instrument, scheduled for flight in 1998 and 2000 on EOS-AM and -PM platforms.
Tracking land cover changes using remotely-sensed data contributes to evaluating to what extent human activities impact the environment. Recent studies have pointed out some limitations of single-date comparisons between years and have emphasized the usefulness of time series. However, less effort has hitherto been dedicated to properly account for the temporal dependences typifying the successive images of a time series. An automated change detection method based on a per-object approach and on a probabilistic procedure is proposed here to better cope with this issue. This innovative procedure is applied to a tropical forest environment using high temporal resolution SPOT-VEGETATION time series from 2001 and 2004 in the Brazilian state of Rondônia. The principle of the method is to identify the objects that most deviate from an unchanged reference defined by objective rules. A probabilistic changed-unchanged threshold provides a change map where each object is associated with a likelihood of having changed. This improvement on a binary diagnostic makes the method relevant to meet the requirements of different users, ranging from a comprehensive detection of changes to a detection of the most dramatic changes. According to the threshold value, overall accuracy indices of up to 91% were obtained, with errors involving change omissions for the most part. The isolation of changes within objects was made possible through a segmentation procedure implemented in a temporal context. In addition, the method was formulated so as to differentiate between inter- and intra-annual vegetation dynamics. These technical peculiarities will likely make this analytical framework suitable for detecting changes in environments subject to a strongly marked phenology.
The characteristics and extent of data which is obtainable by electromagnetic spectrum sensing and the application to earth resources survey are discussed. The wavelength and frequency ranges of operation for various remote sensors are tabulated. The spectral sensitivities of various sensing instruments are diagrammed. Examples of aerial photography to show the effects of lighting and seasonal variations on earth resources data are provided. Specific examples of multiband photography and multispectral imagery to crop analysis are included.
The spatial resolution of the next generation of sensors for the global monitoring of vegetation is assessed with particular reference to the proposed Moderate Resolution Imaging Spectrometer (MODIS). The main innovative use of such instruments will lie in their ability to monitor land transformations at global and continental scales. Reliable monitoring is shown to rely on the success with which the changes in the phenomena being analysed can be separated from other temporal changes. Depending on the type of spatial change being monitored, sensor properties such as accuracy of registration, resolution and radiometric sensitivity are shown to have greatest importance.
An empirical investigation of the required spatial resolution is based on eight Landsat multispectral scanner system images of the normalized difference vegetation index degraded to candidate resolutions between 250 m and 4000 m. Pairs of images from different dates were registered and different images were then generated. Spatial analysis by Fourier and scale variance analyses indicate that resolutions finer than I km are highly desirable for change detection. A resolution of 250 m will probably generate an impractically high quantity of data on a global basis if all the proposed spectral bands are included. A sensor with a resolution of 500 m is recommended as providing the best compromise between detail of changes detected and the size of the resultant data volume but several other options are also suggested, including one involving one or two finer resolution bands to assist multitemporal registration.
In a recent article, Bai and Perron (2003, Journal of Applied Econometrics) present a comprehensive discussion of computational aspects of multiple structural change models along with several empirical examples. Here, we report on the results of a replication study using the R statistical software package. We are able to verify most of their findings; however, some confidence intervals associated with breakpoints cannot be reproduced. These confidence intervals require computation of the quantiles of a nonstandard distribution, the distribution of the argmax functional of a certain stochastic process. Interestingly, the difficulties appear to be due to numerical problems in GAUSS, the software package used by Bai and Perron. Copyright © 2005 John Wiley & Sons, Ltd.
In a recent paper, Bai and Perron ( 1998 ) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least‐squares operations of order O ( T ² ) for any number of breaks. Our method can be applied to both pure and partial structural change models. Second, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program. Copyright © 2002 John Wiley & Sons, Ltd.
The relationships between various linear combinations of red and photographic infrared radiances and vegetation parameters are investigated. In situ spectrometers are used to measure the relationships between linear combinations of red and IR radiances, their ratios and square roots, and biomass, leaf water content and chlorophyll content of a grass canopy in June, September and October. Regression analysis shows red-IR combinations to be more significant than green-red combinations. The IR/red ratio, the square root of the IR/red ratio, the vegetation index (IR-red difference divided by their sum) and the transformed vegetation index (the square root of the vegetation index + 0.5) are found to be sensitive to the amount of photosynthetically active vegetation. The accumulation of dead vegetation over the year is found to have a linearizing effect on the various vegetation measures.
In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.
A new method for extracting seasonality information from time-series of satellite sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of satellite-derived time-series data.
submitted for publication. Comparison of time series similarity measures for monitoring ecosystem dynamics: a review of methods for time series clustering and change detection
- S Lhermitte
- J Verbesselt
- W W Verstraeten
- P Coppin
Lhermitte, S., Verbesselt, J., Verstraeten, W.W., Coppin, P., submitted for publication. Comparison of time series similarity measures for monitoring ecosystem dynamics: a review of methods for time series clustering and change detection. Remote Sensing of Environment.
strucchange: An R package for testing for structural change in linear regression models Sensitivity of vegetation phenology detection to the temporal resolution of satellite data
- A Zeileis
- F Leisch
- K Hornik
- C Kleiber
Zeileis, A., Leisch, F., Hornik, K., & Kleiber, C. (2002). strucchange: An R package for testing for structural change in linear regression models. Journal of Statistical Software, 7(2), 1−38. Zhang, X., Friedl, M., & Schaaf, C. (2009). Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30(8), 2061−2074.
The MODIS land product quality assessment approach Modelling trends in groundwater levels by segmented regression with constraints Integrating plantation health surveillance and wood resource inventory systems using remote sensing
- R Environment
- Foundation
- Computing
- Vienna
- Austria
- Url
- D P Org Roy
- J S Borak
- S Devadiga
- R E Wolfe
- M Zheng
- J Descloitres
- −
- Q Shao
- N A Campbell
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL www.R-project.org Roy, D. P., Borak, J. S., Devadiga, S., Wolfe, R. E., Zheng, M., & Descloitres, J. (2002). The MODIS land product quality assessment approach. Remote Sensing of Environment, 83(1–2), 62−76. Shao, Q., & Campbell, N. A. (2002). Modelling trends in groundwater levels by segmented regression with constraints. Australian & New Zealand Journal of Statistics, 44(2), 129−141. Stone, C., Turner, R., & Verbesselt, J. (2008). Integrating plantation health surveillance and wood resource inventory systems using remote sensing. Australian Forestry, 71(3), 245−253.
Digital change detection methods in ecosystem monitoring: A review A physically-based transformation of thematic mapper data—The TM tasseled cap A statistical framework for the analysis of long image time series
- P Jonckheere
- I Nackaerts
- K Muys
- B Lambin
- E 1565−
- E P Crist
- R C K M Cicone
- G M Henebry
, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565−1596. Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of thematic mapper data—The TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing, 22(3), 256−263. de Beurs, K. M., & Henebry, G. M. (2005). A statistical framework for the analysis of long image time series. International Journal of Remote Sensing, 26(8), 1551−1573.
Trending seasonal data with multiple structural breaks
- Haywood