Search Results (1,080)

Groundwater is one of the key factors affecting the changes and evolution of surface processes in arid regions, determining the direction and scope of the evolution of surface eco-hydrological processes. To achieve sustainable water resource management in arid areas, this study aims to systematically explore the dynamic changes in groundwater level and their ecological effects on the basis of multi-source remote sensing data by multivariate statistical methods. The results show that groundwater levels in the Bayin River Basin increased from 2895.35 m in 2005 to 2906.75 m in 2022 at a rate of 6.7 m/decade, driven by increased runoff and irrigation. Conversely, groundwater levels in urbanized areas near Delingha City slightly decreased by approximately 0.3 m/decade, with a general west-to-east declining spatial gradient. These changes have generated cascading ecological effects. Overall, rising groundwater has coincided with increased vegetation index, wetland extent, and soil moisture. Annual average NDVI rose from 0.18 in 2000 to 0.23 in 2022, an increase of 27.7%, and wetland area expanded from 349.25 km² in 2005 to 355.25 km² in 2022. Soil moisture content showed an insignificant upward trend form 0.14% in 2003 to 0.15% in 2022, with the slope of 0.01%/yr. However, soil salinization has exhibited an aggravating trend, with salinization index (SI) values of 0.25, 0.26, and 0.31 in 2000, 2010, and 2020, respectively. Affected by human activities and geological constraints, the ecological effects associated with groundwater level changes display pronounced regional heterogeneity. This study provides a solid basis for regional water resource regulation and further quantification of water conveyance benefits. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

The normalized difference vegetation index (NDVI) is widely used to track vegetation cover and ecological change. However, in arid watersheds where irrigated farmland and natural vegetation coexist, it remains unclear how irrigation changes the relative effects of climate, terrain, and soil on vegetation growth. Using the Hetao irrigation district watershed in Inner Mongolia, this study analyzed NDVI dynamics and their environmental controls from 2001 to 2024 through trend analysis, spatial autocorrelation, XGBoost-SHAP, GeoDetector, and geographically weighted regression. NDVI increased significantly across the watershed at 0.0035 yr⁻¹, but the increase was much stronger inside the irrigation district (mean NDVI = 0.58; slope = 0.0061 yr⁻¹) than outside it (mean NDVI = 0.26; slope = 0.0015 yr⁻¹). Global Moran’s I values remained above 0.86, showing persistent spatial clustering. The main drivers also differed by zone. DEM, SOC, and precipitation were most important for the whole watershed; SOC, TP, pH, and TN were more important inside the irrigation district; and precipitation and DEM were more important outside it. GeoDetector confirmed that paired drivers strengthened each other, including SOC ∩ DEM at the watershed scale and DEM ∩ TP outside the irrigation district. GWR further showed that rainfall effects were stronger outside the irrigation boundary, while soil-related effects were stronger in the irrigated agricultural belt. These results show that irrigation not only increases NDVI but also changes how vegetation responds to environmental conditions by weakening direct rainfall limitation and strengthening soil-related controls in managed landscapes. The findings provide evidence for zone-specific vegetation restoration and land-water management in dryland irrigation watersheds. Full article

Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article

The surface disturbance caused by coal mining and the ecological restoration have changed the vegetation coverage and ecosystem functions of the Shengli mining area. This disturbance has affected the carbon and water cycles, resulting in complex response characteristics of water use effectiveness (WUE). To reveal these response characteristics, this paper uses multi-source remote sensing data from 2001 to 2024 and applies random forests to scale down MODIS 500 m net primary productivity (NPP) and MODIS 1 km evapotranspiration (ET) to 30 m resolution. Then, it calculates the WUE of the Shengli mining area to reveal the temporal and spatial variation patterns and characteristics of WUE in the mining area and the spoil dump. It also uses the Pearson correlation coefficient to analyze the driving factors of WUE. The results show that the determination coefficients R² of the NPP and ET scaling models are 0.961 and 0.7142 respectively. The WUE in the study area and four spoil dumps from 2001 to 2024 all follow the pattern of “decrease due to disturbance—recovery and rise—gradual stabilization”, with the peak WUE in the mining area reaching 1.123 g·C·m⁻²mm⁻¹ in 2002, a fluctuation decline from 2002 to 2011 with a valley value of 0.398 g·C·m⁻²mm⁻¹ in 2010, an annual increase trend from 2011 to 2013, and a basic stabilization from 2013 to 2024, with an average value of 1.001 g·C·m⁻²mm⁻¹ during this period. Compared to the average value of 1.061 g·C·m⁻²mm⁻¹ from 2001 to 2022, WUE has not yet returned to the initial level. The Pearson correlation coefficients ranked from high to low are: NDVI (0.59, +) > | deformation (−0.39, −) | > temperature (0.27, +) > rainfall (0.26, +) > mining area (0.072, +), indicating that NDVI and deformation are important factors affecting WUE. Further analysis of the relationship between NDVI disturbance and WUE reveals that the mean NDVI disturbance and recovery in the study area from 2001 to 2024 are 0.438 and 0.392 respectively. WUE shows a “first decline—then rise—then stabilization” phased evolution pattern during the “disturbance—recovery—stability” process of vegetation, and the disturbance intensity and recovery intensity are positively correlated with the rate of WUE decrease and increase. The combination analysis of deformation and WUE indicates that the deformation areas in the mining area and the inner spoil dump show a trend of WUE reduction due to the increase in deformation volume. The study shows that the continuous mining of open-pit coal mines continues to affect the water usage function of vegetation in the mining area. Subsequent restoration should prioritize strengthening surface stability, soil water retention, and vegetation reconstruction in the mining area, inner spoil dump, and areas with large deformation to improve the stability and water usage efficiency of ecological restoration. Full article

Large trees are keystone ecological structures that sustain biodiversity and ecosystem services, particularly in human-altered landscapes. However, their persistence is increasingly threatened by land-use change, urban expansion, and inadequate monitoring. This study develops and validates a scalable, automated framework for monitoring the loss of large individual trees using satellite image time series and breakpoint detection. We compared four spectral indices (SIs): Enhanced Vegetation Index 2–EVI2; Normalized Burn Ratio–NBR; Normalized Difference Red Edge–NDRE, and the Normalized Difference Vegetation Index–NDVI derived from Sentinel-2 imagery (2015–2025) for 691 georeferenced trees in Lousada, northern Portugal. Data were accessed and processed in Google Earth Engine and analyzed using a custom R-based workflow, including cloud masking, gap-filling, temporal interpolation, upper-envelope smoothing, deseasonalization, and break detection. Five breakpoint detection algorithms were compared: BFAST, energy-divisive, linear regression of structural changes, wild-binary segmentation, and change point models. Detected breakpoints were subsequently post-validated to determine whether they were associated with declines in SIs, using three pre-/post-breakpoint methods: comparisons of short- and long-term medians and a randomized trend analysis. As a baseline, these algorithms/post-validation logic were compared against the Continuous Change Detection and Classification (CCDC) approach. The results indicate moderate but consistent break detection performance, with a maximum balanced accuracy of 73% (for EVI2 or NDVI and using the energy-divisive algorithm coupled with the long-term median post-validator) under conservative validation criteria and high specificity for surviving trees. CCDC ranked comparatively lower at 62%. Algorithm performance varied substantially, with the energy-divisive providing the most conservative detection and the wild-binary segmentation yielding higher sensitivity. Performance was further influenced by tree structural attributes and species identity, with larger, taller and isolated trees, as well as particular genera, showing higher detection accuracy, with genus Eucalyptus, Tilia and Celtis yielding top performance results (79–65%) and Quercus, Castanea and Platanus the lowest (62–60%). By integrating satellite observations with large-tree inventory data from the Green Giants citizen science project, this study demonstrates the potential of decentralized, Earth observation-based monitoring to support tree-level loss assessments in fragmented landscapes. The proposed framework provides a transferable foundation for wide-scale monitoring of large trees in peri-urban and mixed-use environments. Full article

Detecting changes in vegetation condition is crucial for monitoring heterogeneous systems like natural grasslands. However, a background of high spatial and temporal variability in environmental variables and plant responses challenges field surveys and remote sensing. Monitoring fine-scale heterogeneity and transitions influenced by invasive species remains challenging. To address this gap, this study developed an approach to map vegetation condition across multiple years using condensed seasonal NDVI patterns derived from Sentinel-2 time series. The approach was evaluated in the temperate grasslands of South Australia (Mediterranean-type climate), dominated by iron-grass (Lomandra effusa) and impacted by invasive annuals. A beta regression model was trained using an NDVI time series and field-based iron-grass cover from a single year (2022), achieving a pseudo-R² of 0.63 (RMSE = 9.48 ± 3.43%). Extrapolating the model across 2019–2025 yielded similar spatial patterns in cover, revealing good agreement between field-based data and predictions (pseudo-R² = 0.53 to 0.69) and between predictions for each year (pseudo-R² = 0.84 to 0.9). Despite rainfall and NDVI variability, the approach enabled the detection of subtle changes and the identification of trends. This approach holds great potential for mapping continuous attributes of vegetation condition over time, contributing to the conservation and monitoring of grasslands. Full article

Vegetation cover plays a fundamental role in maintaining ecosystem structure and function. Understanding its spatial and temporal variability, along with its driving factors, is critical for advancing environmental studies. This research targets the subtropical Zhejiang region in southeastern China, utilizing MODIS-derived NDVI data covering 2001 to 2020. By integrating Sen’s slope estimator, Mann–Kendall trend analysis, spatial autocorrelation (Moran’s I), and the Geodetector framework, we assessed trends, patterns, and primary influencing factors of vegetation change. Our findings include: (1) a statistically significant upward trend in NDVI across 59.4% of the study area (Sen’s slope = 0.0025, p < 0.01), corresponding to an approximate annual increase of 0.44%; (2) notable spatial clustering of NDVI values, with high NDVI zones located in southwestern forested areas and low NDVI zones in expanding urban regions, indicating a clear spatial differentiation between natural and human-dominated landscapes; (3) elevation (Q = 0.64), nighttime lights (Q = 0.63), and slope (Q = 0.57) showed relatively higher explanatory power, and the interaction between nighttime lights and land use (NTL × LULC) exhibited the strongest explanatory power (Q = 0.72); (4) high-risk zones, associated with dense populations and intense urban development, coincided with lower NDVI values. These results deepen our understanding of vegetation dynamics in subtropical zones and provide insights for sustainable ecosystem and land management. Full article

Using GIMMS NDVI3g+ data (1982–2022) together with ERA5-Land temperature and precipitation, this study examined long-term vegetation dynamics in the Indus River Basin (IRB) and used a residual trend framework for cautious first-order attribution. Basin-averaged NDVI increased significantly at 0.0061 per decade (p < 0.05), and 65.5% of the basin showed greening, mainly in irrigated croplands and river-adjacent agricultural zones, whereas 12.6% showed degradation concentrated in rapidly urbanizing areas, cryosphere margins, and desert fringes. Partial correlation and residual analyses indicate that climate-related enhancement was most evident in upper-elevation cryosphere transition zones and some lower-basin barren lands, whereas non-climatic residual effects were especially important in intensively managed agricultural landscapes. Because the attribution model includes only temperature and precipitation, the residual component is interpreted here as a non-climatic residual rather than a direct measure of human activity. The study, therefore, provides a spatially explicit basin-wide assessment of vegetation change while highlighting the uncertainty and interpretation limits of residual-based attribution. Full article

Driven by global warming, increasing extreme precipitation events (EPEs) threaten low-lying coastal ecosystems. This study focused on the contemporary Yellow River Delta and established a continuous framework linking extreme precipitation, groundwater, and vegetation, based on long-term extreme precipitation changes during 1960–2022 and vegetation dynamics during 2001–2022. Using regional precipitation records, groundwater observations from 16 monitoring wells, and five-day kernel normalized difference vegetation index (kNDVI) data, we compared two EPEs that exceeded the 99th-percentile wet-day precipitation threshold and had complete precipitation–groundwater–vegetation observations. Our findings reveal that: (1) extreme precipitation was intensified in the study area, with an R99p trend of 19.1 mm/10 a; (2) vegetation disturbance was stronger and more persistent after the 2019 Lekima event, with a mean post-event kNDVI anomaly of −12.8%, whereas the 2022 Chaba event produced a weaker, later, and more spatially limited negative response; (3) groundwater response was also stronger in 2019, as the proportion of wells with above-surface water levels reached 43.8%, compared with 12.5% in 2022, indicating more extensive and longer-lasting inundation; (4) the shallowest post-event groundwater depth was significantly negatively correlated with kNDVI anomalies (r = 0.579, p < 0.001), and during the 2019 event, the kNDVI fell below about −17% when surface inundation lasted for 6 days. These results indicate that groundwater is a key hydrological link connecting extreme precipitation and vegetation response. This study provides new evidence for the identification and adaptive management of ecological risks in low-lying coastal deltas. Full article

NDVI-based approaches offer an efficient method for estimating the C-factor, providing continuous spatial coverage and enabling monitoring of short-term changes in vegetation and management practices. This study aims to evaluate the performance of nine well-established NDVI-based C-factor models compared to RUSLE2 model estimates across a specific crop type, different tillage methods, and multiple time scales (monthly, seasonal, and yearly). While some NDVI models showed promising agreement with RUSLE2 estimates, this alignment was not sufficient to ensure accurate C-factor representation in the Gully Creek watershed. The results show that NDVI-based model performance varies systematically with crop type, tillage practice, and temporal scale. Monthly estimates generally reflect broader seasonal patterns, indicating that finer temporal resolution captures intra-seasonal variability without altering overall trends. These findings highlight the importance of accounting for spatial and temporal heterogeneity in C-factor estimation, as model effectiveness depends on local crop composition, management intensity, and temporal resolution rather than a single universally applicable approach. Full article

The Yellow River Basin in Inner Mongolia (YRBIM) is a typical arid—semiarid ecological transition zone highly sensitive to climate change. Using long-term Normalized Difference Vegetation Index (NDVI) data together with meteorological and land cover data, this study applied the Sen+Mann–Kendall method and path coefficient analysis to quantify the direct and indirect effects of climatic factors on vegetation coverage. The YRBIM experienced a non-significant warm–wet trend from 1998 to 2019, characterized by slight increases in precipitation and temperature with asynchronous spatial patterns. Vegetation coverage generally improved, with high coverage areas expanding by 12.66% and low coverage areas decreasing by 10.04%. Improvement occurred mainly in eastern croplands and grasslands, while degradation in the northwest coincided with urban expansion and mining. Precipitation showed a highly significant positive correlation with the NDVI at 0.7510. The direct effect of precipitation was dominant at 0.7515, while the indirect effect was negligible at 0.0005. Temperature showed a weak inhibitory effect with a comprehensive effect of 0.0302, where the indirect inhibitory effect at 0.0400 slightly exceeded the direct promotional effect at 0.0098. These response patterns were consistent across most land cover types, except in rural settlements and unused land where temperature showed a weak positive influence. This study provides a scientific basis for ecological conservation and sustainable management in arid—semiarid transition zones. Full article

Quantifying vegetation dynamics has become a critical scientific imperative in the context of global ecosystem restoration initiatives targeting degraded forests. Previous studies have explored vegetation-cover change trends at different spatial scales worldwide using the Theil–Sen (TS) estimator and Mann–Kendall (MK) test, yet few have accounted for the uncertainty in resulting trends across time-series datasets of varying lengths. Taking the coastal zone of Fujian Province in Southeast China as a case study, we investigated the uncertainty of vegetation-cover change trends using normalized difference vegetation index (NDVI) datasets of different lengths (e.g., 20-year, 15-year, and 10-year) via the TS estimator and MK test. Additionally, piece-wise regression was employed to detect turning points and shifts in vegetation trends between 2001 and 2020. The results indicate significant discrepancies in trend estimation across datasets of different lengths, with consistency ratios ranging from 46.1% to 64.7% among the 20-year, 15-year, and 10-year series. The MK test is more sensitive to time-series length than the TS estimator, with areas of significant change decreasing by over 50% when transitioning from a 20-year to a 10-year dataset. The spatial distribution of trend shifts exhibits a distinct “coastal–inland” polarization pattern, with 2010 as the turning point. Eight modes of vegetation trend shifts were identified based on pre- and post-turning point dynamics. Furthermore, piece-wise regression improved trend accuracy by approximately 15%. This research advances the mechanistic understanding of spatiotemporal vegetation dynamics and supports adaptive ecosystem management strategies. Full article