Breaks For Additive Season And Trend Research Articles

Contributions of climatic and anthropogenic drivers to vegetation dynamics indicated by NDVI in a large dam-reservoir-river system

With the cascade hydropower stations (CHSs) development and climate change in a large dam-reservoir-river (DRR) system, distinguishing the impact of climatic and anthropogenic factors on vegetation dynamics is vital for a sustainable watershed ecosystem management. In this study, a Breaks for Additive Seasonal and Trend (BFAST) algorithm was adopted to detect the breakpoints (BP) and trend change of climatic factors (CFs) and vegetation dynamics at the pixel scale in the DRR system. The partial correlation and time lag analysis methods were chosen to examine the relationship between vegetation dynamics and CFs (i.e., temperature and precipitation). The multiple linear regression analysis method was adopted to separate the impacts of climatic and anthropogenic factors on vegetation dynamics in multiple periods (i.e., from 1999 to 2013, before BP (BBP), and after BP (ABP)). The most significant results indicated that the area fraction of the BP of vegetation dynamics was 19% of the basin, and the trend BP mainly occurred in 2008. The trend of BP mainly was positive BBP and negative ABP in the basin, and its area fraction was 53.01%. For the eight CHSs, the vegetation dynamics around Ludila and Guanyinyan were more affected by their constructions than that around the other hydropower stations. For the CFs, the vegetation dynamics was mainly affected by precipitation. The lag periods of temperature and precipitation on vegetation dynamics were four months and one month, respectively. Most of the vegetation dynamics appeared to have been impacted by anthropogenic factors in the DRR system. For the eight CHSs, the impact degree of anthropogenic factors on vegetation dynamics BBP was more severe than that ABP, especially around Ludila and Guanyinyan hydropower stations. These results are beneficial for sustainable watershed ecosystem management in the DRR system and ecological development planning around the CHSs.

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

The effects of water stress on croplands in the Aral Sea basin

Water scarcity constitutes an important challenge faced by croplands in the Aral Sea basin (ASB). The croplands in the ASB are highly vulnerable to water stress and land degradation and experience high anthropogenic disturbances. This study investigated the spatiotemporal patterns of water stress and land degradation and detected the years with abrupt changes in the water stress index (WSI) by applying linear regression analysis and the breaks for additive seasonal and trend (BFAST) method. Furthermore, these analyses were combined with statistical data on water intake to explore the relationship between water stress and land degradation in the sub-basins. The results revealed that water stress was more serious in the downstream areas than in the upstream area. The downstream regions experienced increasing water stress, resulting in severe land degradation and cropland abandonment. The BFAST results indicated that abrupt changes in the WSI mainly occurred in 2005, 2008 and 2010. The correlations between water intake, water stress and land degradation in the lower reach were stronger than those in the upper reach. Subsequently, the water intake had a great influence on the cropping intensity and cotton yield in the downstream regions. Water stress in different regions was caused by different drivers. In the upstream areas, precipitation was the major driver of water stress in crops. In contrast, the water allocation in the middle and lower reaches was affected by anthropogenic disturbances and resulted in serious water deficits in the Amudarya River delta in 2000 and 2008. Because each country had a unique irrigation scheme and cropping pattern, obvious spatial differences in water stress and land degradation changes were detected between Tashauz and Karakalpakstan. This study can provide a decision-making reference for the implementation of sustainable development goals in the ASB.

Temporal and Spatial Change Monitoring of Drought Grade Based on ERA5 Analysis Data and BFAST Method in the Belt and Road Area during 1989–2017

Drought is a worldwide natural disaster with a wide range of influences and a long duration, which has a huge impact on the agricultural production activities and social economy of local residents. The Belt and Road Initiative has always received much attention due to its special geographical location and great potential for economic development. At the same time, the Belt and Road region is also deeply affected by drought, especially in some countries and regions, where the agricultural infrastructure is weak and the ecological environment is fragile. How to effectively monitor and evaluate drought has become an urgent problem to be solved. In this study, the ERA5 atmospheric reanalysis data were used, and the self-calibrating Palmer Drought Severity Index was combined with Breaks for Additive Seasonal and Trend (BFAST) to study the temporal and spatial distribution of the 1989–2017 monthly scale of drought in different climate regions of the Belt and Road region. The results show that the overall change trend of arid area shows a change of “up-down-up-down.” The winter drought area is larger than the summer drought area, and the drought center gradually moves from the Southeast Asia region in winter to the West–Central Asia region in summer. In the past five years, the drought area decreased gradually at the rate of approximately 0.38 million km2 per year.

Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)

In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions.

Intra-seasonal variability of sea surface temperature influences phenological decoupling in anchovy (Engraulis ringens)

Changes in phenology are an ecological response to climate changes, in addition to other factors that may stress or dampen phenological responses. For this study, we investigated how climate variability can affect the phenology of the reproductive process of the anchovy Engraulis ringens. We used 29 years of sea surface temperature (SST) data and biological information from northern Chile stock, particularly, gonadosomatic index (GI), body condition (K), and mean weight (MTW) between 1988 and 2017. The Breaks for Additive Season and Trend (BFAST) method was applied to SST to estimate seasonal breaks and to correlate them with the seasonality of the GI. Seasonality and trend were extracted from the series to evaluate them as co-variables in GAM and GLM models in the explanation of the GI variability. The GAM model explained 56% of the variance, because it rescues the nonlinear relationships of the variables. The seasonality of the temperature explained 40%, followed by the body condition of the fish (10%). BFAST results indicate a seasonal change in SST, recording three periods: prior to 1993, 1994–1998 and after 2009. This seasonal change in the SST determines a delay in the start and the maximum in the GI. The K presented a negative relationship such as the MTW, associated with energy expenditure during the reproductive period. Faced with projections of climate change, the effects of the decoupling between larvae and the abundance during spring are not known; therefore, knowing the mechanisms that control phenology can improve our capacity to monitor and manage marine resources under climate change.

Detection of Vegetation Cover Change in Renewable Energy Development Zones of Southern California Using MODIS NDVI Time Series Analysis, 2000 to 2018

New solar energy facilities on public lands in the deserts of southern California are being monitored long-term to detect environmental impacts. For this purpose, we have developed a framework for detecting changes in vegetation cover region-wide using greenness index data sets from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. This study focused on three sites, Joshua Tree National Park (JOTR), Mojave National Preserve (MOJA), and a proximal group of solar energy Development Focus Areas (DFAs). Three MODIS vegetation indices (VIs), the normalized difference (NDVI), enhanced (EVI), and soil-adjusted (SAVI), all at 250-m spatial resolution, were evaluated using the Breaks for Additive Season and Trend (BFAST) methodology to estimate significant time series shifts (“breakpoints”) in green vegetation cover, from February 2000 to May 2018. The sample cross-correlation function with local precipitation records and comparison with timing of wildfires near the study sites for breakpoint density (proportion of area with a breakpoint) showed that NDVI had the strongest response and hence greatest sensitivity to these major disturbances compared to EVI and SAVI, supporting its use over the other VIs for subsequent analysis. Time series of NDVI breakpoint change densities for individual solar energy DFAs did not have a consistent vegetation response following construction. Bootstrap-derived 95% confidence intervals show that the DFAs have significantly larger kurtosis and standard deviation in positive NDVI breakpoint distribution than protected National Park System (NPS) sites, but no significant difference appeared in the negative distribution among all sites. The inconsistent postconstruction NDVI signal and the large number of detected breakpoints across all three sites suggested that the largest shifts in greenness are tied to seasonal and total annual precipitation amounts. Further results indicated that existing site-specific conditions are the main control on vegetation response, mostly driven by the history of human disturbances in DFAs. Although the results do not support persistent breakpoints in solar energy DFAs, future work should seek to establish links between statistical significance and physical significance through ground-based studies to provide a more robust interpretation.

Controls on Land Surface Temperature in Deserts of Southern California Derived from MODIS Satellite Time Series Analysis, 2000 to 2018

The land surface temperature (LST) in arid regions is a primary controller of many ecological processes. Consequently, we have developed a framework for detection of LST change on a regional scale using data sets covering all deserts of southern California from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite sensor. The Breaks for Additive Season and Trend (BFAST) methodology was applied to MODIS 1-km monthly LST data from the years 2000 to 2018 to estimate significant time series shifts (breakpoints) and gradual trends. Area-wide results showed five times more positive LST breakpoints (abrupt temperature warming events) than negative (surface cooling) breakpoints. Cross-correlations with high rainfall periods around Mojave dry lake playas, and comparison with timing of wildfire burns for breakpoint patterns, showed that abrupt shifts in LST had the strongest response to these controllers. We detected negative LST (abrupt cooling) breakpoints as consistently associated with the construction of new solar energy facilities. Over the majority of the study area, BFAST results showed warming LST trends between the years 2000 and 2018. The western-most margins of the study area showed consistent widespread warming trends, whereas the eastern portions of the Mojave and Lower Colorado Deserts showed a mix of positive and neutral LST trends. Long-term cooling LST trends were detected only in some of the largest dry lake formations in the Antelope Valley, Death Valley, and Bristol, Cadiz, and Danby playas.

Integrating Temporal Evolution with Cellular Automata for Simulating Land Cover Change

Simultaneously considering the spatial and temporal processes is essential for land cover simulation models. A cellular automaton (CA) usually simulates the spatial conversion of land cover through post-classification comparisons between the beginning and the end of the training period. However, such an approach does not consider the temporal evolution of land cover. As a result, a CA model fails to explain the realistic land cover change. This paper proposes a temporal-dimension-extension CA (TDE-CA) by integrating the temporal evolution of land cover with a CA. In the TDE-CA, the Breaks for Additive Season and Trend (BFAST) monitor algorithm was employed in the temporal evolution simulation module (TESM) to simulate the gradual evolution of land cover, and an optimized random forest CA (optimized RF-CA) was used to simulate the spatial conversion driven by many spatial variables. Subsequently, the Ensemble Kalman Filter (EnKF) was employed to integrate the TESM with the optimized RF-CA. The TDE-CA was then tested in the land cover simulation of Shendong mining area during the period 2005–2015. The TDE-CA was compared with a Null model, with its sub-models, and with the traditional CA models, including the Logistic-CA and the MLP-CA (Multilayer Perceptron CA) models. The results show that the TDE-CA is superior to the Null model. Furthermore, the overall accuracy and the Kappa coefficient of the TDE-CA were 79.84% and 71.61%, respectively; compared with the TESM and the optimized RF-CA, the values showed 17.14% and 4.48% improvements in the overall accuracies and 0.2167 and 0.0512 improvements in the Kappa coefficients, respectively. When compared with the Logistic-CA and the MLP-CA, we measured 8.41% and 8.25% improvements in the overall accuracies and 0.0985 and 0.0964 improvements in the Kappa coefficients. These experiments indicate that the TDE-CA not only provides an effective model for the spatiotemporal dynamical simulation of land cover, but also enhances the development of the existing simulation theory.

Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017

The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area.

Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor

Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user’s accuracy 87%, producer’s accuracy 71%) and closed forest (user’s accuracy 92%, producer’s accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user’s accuracy was achieved when methods were combined, and the best performance for producer’s accuracy was obtained when random forest was used.

Spatial distribution and temporal variation of drought in Inner Mongolia during 1901–2014 using Standardized Precipitation Evapotranspiration Index

With intensification of climate change and human activities, warming and drying trend has brought severe challenges to pastoral areas in arid and semi-arid regions. Consequently, it becomes imperative to explore non-stationarity features of drought in such regions. In this research, the SPEIbase v2.4 datasets with a 0.5 degree spatial resolution was employed to extract Standardized Precipitation Evapotranspiration Index (SPEI) in Inner Mongolia, China. We explored non-stationarity characteristics of drought using Breaks For Additive Seasonal and Trend (BFAST) method, investigated the variation characteristics of drought intensity in each time interval using intensity analysis method, and finally assessed the spatial and temporal gathering characteristics of drought with Empirical Orthogonal Function (EOF). The results showed that trend of regional drought had a tendency towards drought conditions, which is particularly significant from the year 1945 onwards in the overall Inner Mongolia. We have explored a long behavior of drought in semiarid and central regions of cold semihumid climate zone throughout the whole study period, and detected a drying trend in northeastern regions of Inner Mongolia at the latter decades. The overall drought intensity displayed an increasing trend first, which was followed by a decreasing trend, among which the extreme drought was dominant in period of 1960–1970. EOF mode1 showed that variation intensity of drought showed a not significantly increasing trend in the entire region, and the drought with high amplitude was likely to occur in the central region. EOF mode2 showed that variation intensity of drought displayed the opposite phases between the eastern and the western regions. The northeastern regions were prone to display a high amplitude of drought.

Remote Sensing Detection of Vegetation and Landform Damages by Coal Mining on the Tibetan Plateau

In order to satisfy the needs of constant economic growth, the pressure to exploit natural resources has been increasing rapidly in China. Particularly with the implementation of the National Western Development Strategies since 1999, more and more mining activities and related infrastructure constructions have been conducted on the Tibetan Plateau (TP). Mining activities are known to have substantial impacts on plant dynamics and hence the water and energy cycles. Identifying mining activities and quantifying their effects on vegetation cover are critical to the monitoring and protection of the pristine TP environment. Thus, this study aims to develop an automated approach that detects the timing of initial mining development and assess the spatial distribution of mining-ruined vegetation. The Breaks for Additive Seasonal and Trend (BFAST) algorithm was used to decompose the signal in the normalized difference vegetation index (NDVI) time series derived from high-frequency MODIS images, and to detect abrupt changes of surface vegetation. Results show that the BFAST algorithm is able to effectively identify abrupt changes in vegetation cover as a result of open-mining development on the studied alpine grassland. The testing study in Muli Town of Qinghai Province shows that the mining development began in 2003 and massive destructions of vegetation cover followed between 2008 and 2012. The integrated use of Landsat imagery and multi-temporal DEMs further reveals detailed areal and volumetric changes in the mining site. This study demonstrates the potential of applying multi-mission satellite datasets to assess large-scale environmental influences from mining development, and will be beneficial to environmental conservation and sustainable use of natural resources in remote regions.

Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

Regional differences in shifts of temperature trends across China between 1980 and 2017

As the climate system is known to be warming unevenly, the open question of whether there has been a significant warming “hiatus” at global or regional scales has attracted worldwide attention. With annual mean air temperature (Tmean) setting new records subsequent to 2012, it is reasonable to reanalyse the presence of an apparent decelerated warming trend in China. We therefore examined the shifts of temperature trends in China between 1980 and 2017 using homogenized records from 798 meteorological stations via the Breaks For Additive Season and Trend (BFAST) method and Mann–Kendall and Sen's slope estimator statistical tests. The results show that Tmean at the country level exhibited monotonic increase with a positive break occurring around 1997, and the warming rate during 1980–2017 (0.36 °C/decade) was slightly higher than that during 1980–1997, which suggests that this positive break did not overwhelm the overall continuous warming trend. Spatial records show that most areas west of 108°E or south of 30°N within the country experienced a continual warming trend over the study period and that negative break that occurred around 2010 was major contributor to temporary decelerated warming trend detected in other areas, especially in Northeast China. Hence, this study highlights annual air temperature has continued to rise overall in China since 1980, while considerable regional differences have been detected within the shifts of temperature trends.

Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data

South Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.

Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.

Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series

The Colombian Andes foothills have seen an expansion of forest disturbance since the 1950s. While understanding the drivers of disturbance is important for quantifying the implications of land use change on regional biodiversity, methods for attributing disturbance to specific drivers of change at a high temporal and spatial resolution are still lacking in the Andes region, in part due to persistent cloud cover. Using 20 years of Landsat images (1996–2015) covering Picachos National Park in the Colombian Andes, we detected sub-annual forest cover disturbances using the Breaks For Additive Season and Trend (BFAST) Monitor algorithm; characterized different types of disturbance using spectral, spatial, and topographic indicators; and attributed causes of forest disturbance such as conversion to pasture, conversion to agriculture, and non-stand replacing disturbance (i.e., thinning) using a Random Forest (RF) classifier. Conversion to pasture has been the main driver of forest disturbance in Picachos, responsible for 11,395 ± 72 ha (17%) of forest cover loss, followed by non-stand replacing disturbance and conversion to agriculture. Disturbance detection had 96% overall agreement with validation data, although we had a high omission error of 21% primarily associated with forest to agriculture conversion. Other change drivers had a much more reliable attribution with forest to pasture conversion or non-stand-replacing disturbance, showing only 1–5% commission and 2–14% omission errors. Our results provide spatially-explicit information on sub-annual disturbances and associated drivers of change that are necessary for evaluating and improving domestic conservation efforts and establishing systematic ecological observations, which is currently absent from Colombia. While effective at revealing forest change dynamics in a geographically remote and socio-politically complex region like Picachos, our approach is highly automated and it can be easily extended to the rest of Andes-Amazon transition belt where low availability of remote sensing data and high cloud cover impede efforts at consistent monitoring of forest cover change dynamics and drivers.

Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada

Monitoring vegetation dynamics at global scale is equally important in the context of terrestrial ecosystem carbon exchange and climate-biosphere interactions. The Breaks For Additive Seasonal and Trend (BFAST) method and Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Normalized Difference Vegetation Index (NDVI) at a spatial resolution of 250m were used to detect vegetation dynamics in Quebec during 2000–2011. The overall agreement between BFAST detected breaks and observed disturbances was about 64% with the highest agreement up to 80% for “Fire” disturbance. The results presented in this study indicated that 25.7% of the total study area experienced NDVI trend changes with one or more breaks during 2000–2011, most of which were detected in the Boreal Shield eco-zone along the coastline of the Gulf of St. Lawrence. Abrupt vegetation changes barely varied under different eco-zones while considerably varied with different land cover types. The abrupt changes areas in 2002 and 2009 were the two greatest, with area percentages of 17.4% and 29.1% of the whole area, respectively. The area percentages of years with abrupt trend changes indicated that abrupt vegetation greening occurred in 2008 and 2009, especially in 2009, with 58.3% of the overall abrupt greening. Abrupt vegetation browning occurred in 2002, 2003, 2005 and 2007, especially in 2002, with 28.2% of the overall abrupt browning. Moreover, our results indicated that the detected vegetation trends varied temporally and spatially. Disturbances from existing field observations or remotely sensed images could only interpret <40% of the vegetation changes. The impact of climate change on vegetation dynamic is particularly worth being investigated in the future work. To our knowledge, this study is one of the few attempting to explore large-scale detection of vegetation dynamics and their potential drivers in eastern Canada.

Comparative study of three satellite image time-series decomposition methods for vegetation change detection

ABSTRACTSatellite image time-series (SITS) methods have contributed notably to detection of global change over the last decades, for instance by tracking vegetation changes. Compared with multi-temporal change detection methods, temporally highly resolved SITS methods provide more information in a single analysis, for instance on the type and consistency of change. In particular, SITS decomposition methods show a great potential in extracting various components from non-stationary time series, which allows for an improved interpretation of the temporal variability. Even though many case studies have applied SITS decomposition methods, a systematic comparison of common algorithms is still missing.In this study, the seasonal trend loess (STL), breaks for additive season and trend (BFAST) and multi-resolution analysis-wavelet transform (MRA-WT) were explored in order to evaluate their performance in modelling, monitoring and detecting land-cover changes with pronounced seasonal variations from simulated normal difference vegetation index time series. The selected methods have all proven their ability to characterize the non-stationary vegetation dynamics along with different physical processes driving the vegetation dynamics. Our results indicated that BFAST is the most accurate method for the examined simulated dataset in terms of RMSE, whereas MRA-WT showed a great potential for the extraction of multi-level vegetation dynamics. Considering the computational efficiency, both STL and MRA-WT outperformed BFAST.