Search Results (75)

Landsat time series data, which have become freely available in recent years, are commonly used to detect changes in land cover and monitor ecosystem disturbances. Thyme habitats are areas under protection due to their high ecological value. However, human activity leading to land use competition, mainly from overgrazing, poses an increased threat to these habitats. The impact of these disturbances is underreported, and their detection remains essential for thyme conservation. The island of Lemnos was chosen as the study area, because of the significant areas of thyme habitats, which are currently under pressure due to rural abandonment, desertification, overgrazing, and systematic fires in recent decades. A long-term Landsat time series was generated, and the Normalized Difference Vegetation Index (NDVI) was calculated. The change detection algorithm (BFAST) was used to detect and characterize significant changes (breakpoints) within the time series and compare them to local fire events. The analysis showed that Lemnos thyme habitats have been significantly reduced in size due to fires and their conversion to new grazing areas for livestock production. Measures should be taken to conserve thyme habitats with the participation of local stakeholders, including livestock farmers and beekeepers. Satellite monitoring techniques are important tools that could facilitate this conservation process. Full article

Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and integrating emerging spatiotemporal hotspot analysis, BFAST, and geographic detectors, we systematically analyzed spatiotemporal patterns and drivers of LUI, BDLUS, and their Coupling Coordination Degree (CCD) from 2000 to 2022. Key findings: (1) LUI strongly correlated with economic growth, with core areas reaching high-intensity development (average > 2.96) versus ecologically constrained marginal zones (<2.42), marked by abrupt changes during 2011–2014; (2) BDLUS improvements covered 82.22% of the study area, driven by the Yangtze River Economic Belt strategy (21.96% hotspot concentration), yet structural imbalance persisted in transitional zones (18.81% cold spots); (3) CCD exhibited center-edge dichotomy, contrasting high-value cores (CCD > 0.68) with ecologically sensitive edges (9.80% cold spots), peaking in regulatory shifts around 2010; (4) terrain constraints and intensified human activities (the interaction effect between nighttime lighting and population density increased by 219.49% after 2020) jointly governed coupling mechanisms, with urbanization and industrial transition becoming dominant drivers. This research advances an “intensity–structure–coordination” framework and elucidates “dual-core resonance” dynamics, offering theoretical foundations for spatial optimization and ecological civilization. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Changes in grassland vegetation coverage (GVC) and their causes in the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding the ecological environment and sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from 2000 to 2023 based on random forest (RF) regression inversion. The nonlinear characteristics such as the number of mutations, magnitude of mutations, and time of mutations were detected and analyzed using the BFAST model. Driving factors such as climatic factors were introduced to quantitatively explain the driving mechanism of GVC changes. The results showed that: (1) RF model is the optimal model for the inversion of GVC in this region. The R² of the RF training set reached 0.94, the RMSE of the test set was 12.86%, the correlation coefficient between the predicted and actual values was 0.76, and the CVRMSE was 18.07%. (2) During the period of 2000–2023, the number of mutations in GVC ranged from 0 to 5, and there were at least 1 mutation in 58.83% of the study area. The years with the largest proportion of mutations was 2010, followed by 2016, accounting for 14.57% and 11.60% of all mutations, respectively. The month with the highest percentage of mutations was October, and followed by June, accounting for 31.73% and 22.19% of all mutations, respectively. (3) The sustained and stable positive effect was shown by precipitation on GVC before and after the maximum mutation. Wind speed was a negative effect on GVC in areas with more severe desertification, such as Inner Mongolia, China and parts of Mongolia. On the other hand, GVC was reduced by the wind speed before and after the maximum mutations. Therefore, to guarantee the ecological security of the CMREC, governments should formulate new countermeasures to prevent desertification in the region according to the laws of nature and strengthen international cooperation. Full article

Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m³, PM2.5 > 30 μg/m³), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m³, PM2.5 > 20 μg/m³) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events. Full article

Accurate long-term and high-resolution forest disturbance monitoring are pivotal for forest carbon modeling and forest management. Many algorithms have been developed for this purpose based on the Landsat time series, but their nationwide performance across different regions and disturbance types remains unexplored. Here, we conducted a comprehensive comparison and validation of six widely used forest disturbance- monitoring algorithms using 12,328 reference samples in China. The algorithms included three annual-scale (VCT, LandTrendr, mLandTrendr) and three daily-scale (BFAST, CCDC, COLD) algorithms. Results indicated that COLD achieved the highest accuracy, with F1 and F2 scores of 81.81% and 81.25%, respectively. Among annual-scale algorithms, mLandTrendr exhibited the best performance, with F1 and F2 scores of 73.04% and 72.71%, and even outperformed the daily-scale BFAST algorithm. Across China’s six regions, COLD consistently achieved the highest F1 and F2 scores, showcasing its robustness and adaptability. However, regional variations in accuracy were observed, with the northern region exhibiting the highest accuracy and the southwestern region the lowest. When considering different forest disturbance types, COLD achieved the highest accuracies for Fire, Harvest, and Other disturbances, while CCDC was most accurate for Forestation. These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles. Full article

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved. Full article

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme. Full article

In the absence of forest ecosystem time series data, monitoring proxies such as the enhanced vegetation index (EVI) can inform the capacity of forests to provide ecosystem services. We used MODIS-derived EVI at 250 m and 16-day resolution and Breaks for Additive and Seasonal Trend (BFAST) algorithms to monitor forest EVI changes (breaks and trends) in and around the Algonquin Provincial Park (Ontario, Canada) from 2003 to 2022. We found that relatively little change occurred in forest EVI pixels and that most of the change occurred in non-protected forest areas. Only 5.3% (12,348) of forest pixels experienced one or more EVI breaks and 27.8% showed detectable EVI trends. Most breaks were negative (11,969, 75.3%; positive breaks: 3935, 24.7%) with a median magnitude of change of −755.5 (median positive magnitude: 722.6). A peak of negative breaks (2487, 21%) occurred in the year 2013 while no clear peak was seen among positive breaks. Most breaks (negative and positive) and trends occurred in the eastern region of the study area. Boosted regression trees revealed that the most important predictors of the magnitude of change were forest age, summer droughts, and warm winters. These were among the most important variables that explained the magnitude of negative (R² = 0.639) and positive breaks (R² = 0.352). Forest composition and protection status were only marginally important. Future work should focus on assessing spatial clusters of EVI breaks and trends to understand local drivers of forest vegetation health and their potential relation to forest ecosystem services. Full article

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions. Full article

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m²·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies. Full article

During the last 50 years, the increase in the efforts of the longline fleet in the Eastern Pacific Ocean (EPO) resulted in an increase in the capture of the swordfish Xiphias gladius. We analyzed a historical database of swordfish catches (1980–2020) reported by the industrial longline fleet to the Inter-American Tuna Tropical Commission (IATTC), which contains catch and effort data aggregated in monthly quadrants of 5° × 5° in the EPO. The swordfish catch reported by the international longline fleets was analyzed to evaluate the spatiotemporal variation of the catch and the different phases through which this important fishery has gone through. Different statistical models such as the Generalized Additive Mixed Model (GAMM) and the breaks for additive season and trend BFAST algorithm were used for the decomposition of the time series. Results indicated that the effort directed towards the swordfish increased in recent years and that the highest catches occurred by Peru. The adjusted GAMM explained 80% of the total temporal variation of the swordfish catch per unit effort CPUE and had a 90% prediction efficiency. The BFAST algorithm found three break points in the time series of the standardized CPUE, points associated with abrupt changes, thus defining four distinct periods, all of them statistically significant. According to the BFAST model, the current trend of swordfish CPUE is upward. It is recommended to take this finding with caution to obtain the sustainable exploitation of the swordfish fishery resource. Full article

Tugai wetlands, including the forests of Populus euphratica Oliv. and P. pruinosa Schrenk, are major biodiversity hotspots within semi-arid and arid ecozones. However, for over a century, Central Asian river systems have been severely affected by dam regulation, water withdrawals for large-scale irrigated agriculture, and deforestation. To support sustainable use and protection of this threatened forest type, we provide information on the distribution and degradation status of Tugai wetlands in the Syr Darya floodplain using Normalized Difference Vegetation Index (NDVI) time series from Landsat 7 and Moderate Resolution Imaging Spectroradiometer (MODIS). An accuracy assessment confirmed the validity of the MODIS-based wetland map, with an overall accuracy of 78.6%. This was considerably better than the Landsat product, mainly due to the greater temporal frequency of the MODIS time series. We further calculated trends and breakpoints between 2001 and 2016 using the BFAST algorithm. We found negative trends for nearly a third of the wetlands. Breakpoint detection showed major stress events in the years 2001, 2009, and 2016. Our study revealed the temporal and spatial distribution and vitality of an endangered forest ecosystem that has rarely been studied thus far. Climate change may accelerate the destabilization of the Tugai forests at the Syr Darya floodplain. Full article

Forest change monitoring is a fundamental and routine task for forest survey and planning departments, and the resulting forest change information acts as an underlying asset for sustainable forest management strategy development, ecological quality assessment, and carbon cycle research. The traditional ground-based manual monitoring of forest change has the disadvantages of high time and labor costs, low accessibility, and poor timeliness over wide regions. Remote sensing technology has become a popular approach for multi-scale forest change monitoring due to its multiple available spatial, spectral, temporal, and radiometric resolutions and wide coverage. Particularly, the free access policy of long time series archive data of Landsat (around 50 years) has triggered many automated analysis algorithms for landscape-scale forest change analysis, such as VCT, LandTrendr, BFAST, and CCDC. These automated algorithms differ in their principles, parameter settings, execution complexity, and disturbance types to be detected. Thus, selecting a suitable algorithm to satisfy the particular forest management demands is an urgent and challenging task for forest managers in a given forested area. In this study, Lishui City, Zhejiang Province, a typical plantation forest region in Southern China where forest disturbance widely and frequently exists, was selected as the study area. Based on the GEE platform, the algorithmic adaptability of VCT, LandTrendr, and CCDC in monitoring abrupt forest disturbance events was compared and assessed. The results showed that the kappa coefficients of the abrupt disturbance events detected by the three algorithms were at 0.704 (LandTrendr), 0.660 (VCT), and 0.727 (CCDC), and the corresponding overall accuracies were at 0.852, 0.830, and 0.862, respectively. The validated disturbance occurrence time consistency reached nearly 80% for the three algorithms. In light of the characteristics of forest disturbance occurrence in southeastern China as well as the algorithmic complexity, efficiency, and adaptability, LandTrendr was recommended as the most suitable one in this region or other similar regions. Overall, the forest change monitoring process based on GEE is becoming more simplified and easily implemented, and the comparisons and tradeoffs in this study provide a reference for the choice of long time series forest monitoring algorithms. Full article