Dune morphology and migration in the Nitzanim coastal dunes: Integrating ground penetrating radar and satellite-based analysis.

ABSTRACTLong‐term, individual‐level studies can provide valuable insights into the effects of climate and landscape change on the ecology and population dynamics of wild animals. However, many such studies lack environmental data collected at the spatial and temporal resolutions needed to determine how populations respond to changing conditions. In these cases, the retrospective use of satellite‐derived data can provide a way to recover past environmental information. Using a 27‐year dataset of an Australian insectivorous passerine, the superb fairy‐wren Malurus cyaneus, we assessed how climate variation influences vegetation productivity and, indirectly, superb fairy‐wren life history traits through potential changes in trophic interactions. Specifically, we combined long‐term, individual‐level monitoring of superb fairy‐wrens and local weather records with Landsat satellite imagery, from which we derived measures of vegetation productivity using the Normalised Difference Vegetation Index (NDVI) as a proxy for food availability through arthropod abundance. We found a complex set of associations between NDVI and different components of weather, when considering both concurrent and lagged effects. Our analyses of the causes of seasonal variation in superb fairy‐wren life history traits demonstrated that NDVI was associated with: (i) temporal variation in breeding success, with years with high spring and summer NDVI values having relatively high average breeding success; and (ii) spatial variation in adult mortality in autumn and winter, with superb fairy‐wren territories with low autumn–winter NDVI values having higher average mortality rates. Notably, autumn–winter NDVI values were found to have remained relatively consistent over time, indicating that vegetation productivity cannot explain recently observed increases in adult autumn–winter mortality. Our study illustrates the potential of using long‐term Landsat satellite imagery to investigate whether associations between animal life history traits and climate are mediated by vegetation productivity and to what extent temporal trends are influenced by climate change.

The Taihang Mountains, situated in the transitional zone between the Loess Plateau and the North China Plain, serve as a crucial ecological barrier in northern China. Analysing the spatiotemporal dynamics of vegetation cover and identifying the underlying drivers are fundamental for effective regional resource management and ecological conservation. In this study, the spatiotemporal patterns of vegetation change in the Taihang Mountains from 2000 to 2024 were analysed on the basis of MODIS normalized difference vegetation index (NDVI) data, climate records, and vegetation maps. Trend analysis, Mann‒Kendall significance tests, and the Hurst index were applied to characterize changes, while residual analysis was used to decompose and quantify the impacts of climate change versus human activities. Our findings reveal a significant greening trend, with the NDVI increasing by 0.0036 per year. High NDVI values were primarily found in southern regions, whereas low values were concentrated in the northwest. Increasing NDVI trends dominated 92.90% of the total area, while decreasing trends were limited (7.10%) and concentrated mainly in the eastern low-elevation foothills and populated urban areas, such as Jincheng. Hurst index analysis indicated that future vegetation changes are predominantly anti-persistent, with 56.97% of the area projected to experience degradation. Human activities dominated the variation in the NDVI (86.16%), compared to 13.84% from climatic factors, and contributed over 70.09% of the changes across all vegetation types. Among vegetation types, coniferous forests showed the most robust improvement under human interventions, whereas the "others" category and cultivated vegetation exhibited higher degradation levels. These findings offer a scientific basis for guiding ecological management strategies in the Taihang Mountains.

Water scarcity is emerging as a critical constraint for tea plantations in the Dooars region of West Bengal, India, where irrigation largely relies on groundwater resources and conventional overhead sprinklers often lack precision and uniformity. This study employed remote sensing techniques in sprinkler-irrigated and drip-irrigated plots in the Dooars during the pre-monsoon and post-monsoon seasons over a three-year period (2018-2020) to determine how the two different irrigation systems (sprinkler and drip) influence tea canopy growth. Correlations between canopy cover and its influencing factors, i.e., normalized difference vegetation index (NDVI), leaf area index (LAI), soil moisture content, and land surface temperature (LST) were assessed. Results showed that the mean NDVI increased by 11.14% in the pre-monsoon season and 6.17% in the post-monsoon season. These changes in the mean NDVI in the drip-irrigated plot indicated higher tea canopy growth due to adequate water availability in the root zone. The values of NDVI (0.47 to 0.55), LAI (2.79 to 3.14), and soil moisture (0.09 to 0.17 m3 m-3) were higher under drip irrigation system. Also, strong correlations among NDVI, LAI, LST and soil moisture parameters demonstrated that water availability in the root zone of the tea plantation under drip irrigation improved canopy growth driven by favorable soil-plant-water interactions. Therefore, drip irrigation outperformed sprinkler system in improving soil moisture retention and promoting tea canopy growth, making it a sustainable long-term solution for tea estates in the region.

Background: The proposed research presents a precise and efficient forecasting system for normalized difference vegetation index (NDVI) of districts in Karnataka State in India, utilizing machine learning and remote sensing data. NDVI values are employed to assess vegetation and signify varying levels of vegetative health from poor to high. This study aims to create a forecasting model for predicting NDVI values through time series trend analysis. The findings endorse governmental and agricultural initiatives aimed at enhancing resource management, crop surveillance, yield forecasting and strategic planning by providing an effective model for vegetation and climate change monitoring. Methods: The raster NDVI data from NASA’s earth observing system (EOS) is derived from the MODIS (Moderate resolution imaging spectroradiometer) dataset, covering the years 2000 to 2022. Raster NDVI values are mapped to all 31 districts in Karnataka using QGIS and corresponding numeric NDVI values are derived. Preprocessing techniques, including noise reduction and missing value imputation, are employed to enhance data quality. The simple moving average (SMA) and weighted moving average (WMA) machine learning methods are employed to compare the results of the study on temporal vegetation changes. To determine forecasting model yielding more accurate forecasts, the forecasting errors and performance are assessed using metrics: mean absolute deviation (MAD), mean absolute error (MAE), mean squared error (MSE) and root mean square error (RMSE). Result: The WMA model demonstrates superior accuracy with a lower MAE of 0.03752 compared to the SMA model MAE of 0.03797, followed by lower MSE and RMSE. Consequently, NDVI values are forecasted utilizing the WMA model, which demonstrated prediction exceeding accuracy 95.5% for all 31 districts (i.e. 100%) and 99% to 100% accuracy for 15 districts out of 31 (i.e. 48.39%). The mean absolute percentage error (MAPE) of WMA shows below 10%, signifies that the annual NDVI patterns prediction is highly precise.

Land use transformation contributes to land surface temperature (LST) change, which has been considered as one of the mostcritical environmental challenges. Thisstudy aim to analyze the impacts of land use dynamics on normalized difference vegetation index (NDVI) and LST between 1984 and 2024 in selected districts of Jimma Zone, southwestern Ethiopia. Landsat 5 TM (1984), Landsat 5 TM + (1997), Landsat 7 ETM + (2010), and Landsat 8 OLI-TIRS (2024) with 30m spatial resolution were acquired from the USGSwebsite. All spatial data were georeferenced to the UTM projection (Zone 37), and the WGS 84 datum. In this study, geospatial data were layer stacked and mosaicked using the geospatial analyst tool. The classified land use class was validated using GPS field survey points and Google Earth imagery. To realize the significant effects between NDVI and LST, correlation analysis was conducted for each year. One-way ANOVA was performed to test differences between land use classes. Mean separation analysis was performed using LSD at p= 0.05, using R statistical software to determine land use practices that are most effective. Results showed that the dense forest declined by 12.92%, indicating large-scale deforestation likely driven by agricultural expansion. In contrast, agricultural land increased by 7.09%, while open forest decreased by 4.66%. More expansion was observed in settlements, increasing by + 11.27%, underscoring intense unscientific infrastructure development that consumes surrounding landscapes. Evenwater bodies were reduced by 0.80%, a critical indicator of stress from drought and irrigation demands. The consistent and substantial decline in the maximum NDVI value, which has beendeclined from 0.51 in 1984 to a much lower 0.41 in 2024.Dense forest experienced a significant warming of 8.2°C, diminishing its natural cooling capacity. Agricultural land had its minimum temperature rise from (18.6°C-25.3°C) by 6.7°C. Open forest showed a consistent warming trend. The relationship among NDVI and LST is negative; this means that for each single unit of increase in NDVI, the LST drops. The most extreme LSTs were consistently found in settlements. The statistical test of surface temperature among LULC classes shows that settlements and agricultural land are a high level of significant warmer at the range of p = from 1.09 × 10^ (-5) to 2.24 × 10^ (-2).

Integrating dendrochronology and satellite NDVI to assess climate sensitivity and canopy resilience along a disturbance gradient in tropical moist forests.

Black pepper (Piper nigrum L.) is a crop of significant economic importance in the Amazon, especially in the state of Pará, where intensive production systems predominate. Understanding the spatial variability of soil attributes and their relationship with plant vigor is essential to optimize agricultural practices and input use. Geotechnology-based approaches enable the generation of more precise management zones, contributing to efficient resource use and increased profitability. This study aimed to delimit potential management zones in black pepper crops based on the spatial analysis of soil bulk density (BD) integrated with the NDVI (Normalized Difference Vegetation Index), evaluated using the Bivariate Moran’s Index. The research was conducted in a production area in the municipality of Baião, Pará, Brazil, using soil samples to determine bulk density and UAV images for NDVI calculation. Data were interpolated by kriging and analyzed to identify spatial associations between soil compaction and NDVI. Soil bulk density ranged from 1.14 to 1.80 Mg m−3, while NDVI values ranged from 0.07 to 0.91, revealing a clear inverse spatial relationship between soil compaction and vegetative vigor. The integration of BD and NDVI allowed the delineation of site-specific management zones, supporting more efficient decision-making in precision agriculture.

The Metropolitan Area of São Paulo (MASP), located within Brazil's Atlantic Forest, a global biodiversity hotspot, preserves forest remnants that are essential for biodiversity and ecosystem services. However, intense urbanization and air pollution increase pressures on these remnants, demanding continuous monitoring to guide conservation strategies. UAV-based multispectral remote sensing, particularly the Normalized Difference Vegetation Index (NDVI), enables fine-scale vegetation evaluation, supporting urban forest management. This study applied UAV-based multispectral remote sensing to monitor three urban forests within the MASP-Fontes do Ipiranga State Park (PEFI), Instituto de Biociências Forest Reserve (RFIB), and Morro Grande Forest Reserve (RFMG)-over 1 year. Monthly UAV flights acquired multispectral imagery, and NDVI was used to evaluate canopy greenness. Linear regression models assessed the influence of seasonality, air temperature, and precipitation, while non-parametric tests evaluated seasonal and inter-fragment differences. NDVI showed pronounced seasonal variation across all sites, with higher values during the rainy season. Site-specific models revealed contrasting climatic sensitivities: seasonality explained a large proportion of NDVI variability at PEFI (R2 = 0.84) and RFMG (R2 = 0.72), whereas RFIB showed weaker climatic control (R2 = 0.45). At PEFI, NDVI was most strongly associated with temperature (R2 = 0.66), potentially reflecting urban heat island effects. RFMG exhibited consistently lower NDVI, likely related to sandier soils and reduced water retention, while RFIB showed high NDVI values but weaker climate coupling, suggesting stronger anthropogenic and structural influences. These results demonstrate that NDVI effectively captures spatio-temporal vegetation dynamics in tropical urban forests.

The conservation of biodiversity presents a global challenge that requires coordinated action across spatial and governance scales, including local, national, and international levels. Wildlife parks, though often less formally protected than national parks, play a vital role in preserving biodiversity and sustaining ecosystem services. However, many are increasingly threatened by human encroachment, including urbanization, agriculture, and mining, which degrade habitats and undermine ecological integrity. This study assessed conservation practices and threats to biodiversity management in Jos Wildlife Park, Nigeria, through an integrated approach combining two decades of Landsat-derived Normalized Difference Vegetation Index (NDVI) analysis, Google Earth image processing, field surveys, and stakeholder interviews. Results showed that NDVI values were consistently higher in the park’s core than in its edge and buffer zones, indicating greater vegetation health at the center. However, declines in vegetation cover toward the park’s periphery reflected significant pressures from surrounding human activities. Field and interview data further revealed limited conservation practices, with challenges in stream and pond management, habitat restoration, and enforcement against illegal activities. Major threats included agricultural encroachment, poaching, and illegal logging, compounded by resource shortages and governance constraints. These findings highlight the urgent need for targeted habitat restoration, strengthened law enforcement, and enhanced management capacity to safeguard Nigeria’s urban conservation areas and support the country’s commitment to the global “30 × 30” biodiversity framework.

The conservation of biodiversity presents a global challenge that requires coordinated action across spatial and governance scales, including local, national, and international levels. Wildlife parks, though often less formally protected than national parks, play a vital role in preserving biodiversity and sustaining ecosystem services. However, many are increasingly threatened by human encroachment, including urbanization, agriculture, and mining, which degrade habitats and undermine ecological integrity. This study assessed conservation practices and threats to biodiversity management in Jos Wildlife Park, Nigeria, through an integrated approach combining two decades of Landsat-derived Normalized Difference Vegetation Index (NDVI) analysis, Google Earth image processing, field surveys, and stakeholder interviews. Results showed that NDVI values were consistently higher in the park’s core than in its edge and buffer zones, indicating greater vegetation health at the center. However, declines in vegetation cover toward the park’s periphery reflected significant pressures from surrounding human activities. Field and interview data further revealed limited conservation practices, with challenges in stream and pond management, habitat restoration, and enforcement against illegal activities. Major threats included agricultural encroachment, poaching, and illegal logging, compounded by resource shortages and governance constraints. These findings highlight the urgent need for targeted habitat restoration, strengthened law enforcement, and enhanced management capacity to safeguard Nigeria’s urban conservation areas and support the country’s commitment to the global “30 × 30” biodiversity framework.

Vegetation is one of the most important renewable resources of the Earth’s ecosystem. However, vegetation has been in a high state of decline both globally and locally. With the increase in urbanization across the globe, city microclimates are constantly changing, affecting the living environment and necessitating the need for sustainable urbanism. This research investigated the rate of vegetation degradation, assessed the influence of climate and human activities on vegetation loss, and aimed to inform mitigation strategies and promote sustainable urban development. The research applied a Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery using supervised classification, and maximum likelihood classification was employed for the reclassification of different land uses. A bivariate linear regression of NDVI values and rainfall data was conducted to evaluate the relationship between vegetation cover and climatic variables. The NDVI results showed a significant decrease in vegetation over the study period, while built-up areas and agricultural land expanded. The relationship between climate and vegetation was found to be weak, while human activities contributed massively to the degradation of vegetation. Agricultural practices, brick burning, deforestation, urbanization, and fuelwood collection emerged as the major drivers of vegetation loss. The study recommends public awareness campaigns on the importance of vegetation, large-scale afforestation policies, investment in agricultural innovation to reduce land pressure, the expansion of automated meteorological stations, and urgent climate change mitigation measures to promote environmental sustainability and resilient urban development.

Vegetation is one of the most important renewable resources of the Earth’s ecosystem. However, vegetation has been in a high state of decline both globally and locally. With the increase in urbanization across the globe, city microclimates are constantly changing, affecting the living environment and necessitating the need for sustainable urbanism. This research investigated the rate of vegetation degradation, assessed the influence of climate and human activities on vegetation loss, and aimed to inform mitigation strategies and promote sustainable urban development. The research applied a Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery using supervised classification, and maximum likelihood classification was employed for the reclassification of different land uses. A bivariate linear regression of NDVI values and rainfall data was conducted to evaluate the relationship between vegetation cover and climatic variables. The NDVI results showed a significant decrease in vegetation over the study period, while built-up areas and agricultural land expanded. The relationship between climate and vegetation was found to be weak, while human activities contributed massively to the degradation of vegetation. Agricultural practices, brick burning, deforestation, urbanization, and fuelwood collection emerged as the major drivers of vegetation loss. The study recommends public awareness campaigns on the importance of vegetation, large-scale afforestation policies, investment in agricultural innovation to reduce land pressure, the expansion of automated meteorological stations, and urgent climate change mitigation measures to promote environmental sustainability and resilient urban development.

The conversion of agricultural land to non-agricultural is an increasingly intensive phenomenon in urban and peri-urban areas, including in Napar Village, Payakumbuh City. This study aims to analyze changes in agricultural land cover and built-up land in the period 2015–2025 using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) methods as well as the additional extraction method Object-Based Image Analysis (OBIA). The data used were Landsat 8 and 9 images with a spatial resolution of 30 meters assisted by the results of Sentinel-2 image analysis with a resolution of 10 meters using the OBIA data extraction method to ensure changes in land objects. NDVI values with a range of 0.3–0.6 are categorized as agricultural land, while NDBI values >0.1 are categorized as built-up areas. The results of the analysis with images showed a significant decrease in the area of agricultural land followed by an increase in built-up areas, especially in zones with high accessibility near provincial roads and city centers. The NDVI and NDBI overlays revealed that the largest conversions occurred in the eastern and southern regions of Napar and the OBIA analysis clarified that there was a decrease in agricultural land area from 61 ha (2017) to 56 ha (2025) as a correction in NDVI results and followed by an increase in built-up land area from 27 ha (2017) to 36 ha (2025) as a correction in NDBI results using higher resolution image data. The main drivers of conversion include population growth, infrastructure development, and economic pressures. This study emphasizes the need for proper control of land use in maintaining a balance of land use and sustainable food sustainability.

Soil salinity and nutrient availability are major constraints affecting crop productivity, soil quality, and agroecosystem sustainability, particularly in coastal regions vulnerable to seawater intrusion. This study provides a comprehensive spatial and temporal assessment of soil properties and quality dynamics on Hainan Island by integrating field observations and multi-temporal remote sensing (RS) datasets. In 2024, a total of 152 sampling sites were surveyed, with three topsoil soil samples collected at each location. Multi-year RS data (2024–2021), including soil salinity reflectance indices (SRSI and SI), the Normalized Difference Vegetation Index (NDVI), and land use and land cover (LULC), were analyzed to evaluate temporal and spatial variability. The soil fertility index was calculated using alkali-hydrolyzed nitrogen (AN), available phosphorus (AP), available potassium (AK), soil pH, and soil organic matter (SOM). The soil quality index was calculated using the same parameters with the addition of chromium (Cr) to account for potential heavy metal contamination. Furthermore, in this study the Inverse Distance Weighting (IDW) method was used for spatial distribution maps of soil properties and other indices. The results indicated that soils were predominantly acidic (pH < 6.0) with generally low electrical conductivity (0.01–0.53 mS cm−1) across inland areas, whereas higher salinity levels (2.28–5.31 mS cm−1) were observed in southern and eastern coastal zones, suggesting potential seawater intrusion. Nutrient concentrations ranged from 60.1 to 150 mg kg−1 (AN), 4 to 332 mg kg−1 (AP), and 50.1 to 100 mg kg−1 (AK). NDVI values (0.70–0.94) indicated high vegetation density over most agricultural landscapes. Significant positive correlations were observed between soil EC and the SRSI (r = 0.781) and SI (r = 0.663; p < 0.01), demonstrating the reliability of RS-derived indices for salinity assessment. The integrated indicator-based framework developed in this study provides a scientific basis for precision agriculture, soil health monitoring, and sustainable land management in coastal agroecosystems.

Urban expansion and population pressure have accelerated and Land Use and Land Cover (LULC) changes, leading to degradation of vegetation and aquatic ecosystems. This research aims to analyze the spatiotemporal dynamics of Normalized Difference Vegetation Index (NDVI) and LULC pattern changes and how they affect urban area transformation and offensive land use for various endeavors in the contexts of Habiganj Sadar and Madhabpur in Sylhet, Bangladesh, from 1989 to 2024. Using multitemporal Landsat satellite imagery, LULC was classified into groups such as vegetation, urbanized regions, aquatic environments, and barren terrain. NDVI values, which ranged from −0.23 to 0.81, were used as indices to assess vegetative health. Overall classification accuracy varied between 86% and 92.5%, with Kappa coefficients ranging from 0.80 to 0.93. Destruction of the environment is the result of a substantial reduction in vegetation in the Madhabpur area (31.91% to 7.43%), as well as Habigonj Sadar (18.99% to 8.71%). The fast urbanisation is prompted by the overcrowding in the Habigonj Sadar (1.82% to 21.08%) and Madhabpur (1.80% to 16.33%) settlement regions. Madhabpur's water body experienced terrible environmental circumstances between 1989 and 2024, falling from 30.41 sq. km to 5.60 sq. km. The data show a significant loss in vegetation health, as demonstrated by a drop in NDVI values from 0.78 in 1989 to 0.43 in 2024, coinciding with an increase in settlement areas from 1.82% to 21.08% in Habiganj Sadar and 1.80% to 16.33% in Madhabpur. This paper demonstrated the pattern of urban expansion coincides with a dramatic decline in both vegetation and aquatic bodies.

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions.

Ecosystem services (ES) play a vital role in socioeconomic development. Existing research has primarily focused on single-service assessments and linear relationships, overlooking often neglecting non-linear interactions, threshold effects, and spatial heterogeneity among ES. The Yangtze River Economic Belt (YREB) was analyzed to evaluate water yield (WY), soil conservation (SC), carbon storage (CS), habitat quality (HQ), and total ecosystem services (TES). Methods including constraint lines, geographically weighted regression, and machine learning were applied to systematically identify spatiotemporal evolution patterns, interrelationships, and driving factors of ES. The results indicate that (1) from 2005 to 2020, ES in the YREB exhibited marked spatiotemporal variability. WY and SC increased over time, whereas CS, HQ, and TES declined. Spatially, SC, CS, and HQ were consistently higher in western and southern regions, while WY followed a southeast-to-northwest decreasing gradient. (2) Relationships between ES revealed distinct dynamics: CS-HQ, WY-CS, and SC-CS were dominated by trade-offs, whereas WY-SC, WY-HQ, and SC-HQ displayed synergies with constraint effects, demonstrating pronounced spatial heterogeneity. (3) Annual precipitation above 0.25 and Normalized Difference Vegetation Index values exceeding 0.46 significantly enhanced positive drivers of TES. Built-up area proportions greater than 0.49 reversed from negative to positive effects. The Nighttime Light index and the proportion of built-up areas emerged as the primary negative drivers. These findings provide a scientific basis for ecological conservation strategies and regional sustainable development, advancing comprehensive assessment and management of ecosystem services.

We investigated whether living in greener neighborhoods in midlife is associated with slower cognitive decline in later life. We used data on 2,881 participants from the population-based Multi-Ethnic Study of Atherosclerosis. Geocoded residential addresses (1980–2009) were used to derive midlife neighborhood greenness exposure defined as a 10-year mean of annual normalized difference vegetation index values (based on satellite imagery) during the midlife period (ages 45–54). Cognitive testing over ~ 10 years, when the participants were ≥ 55-year-olds, captured global cognition and processing speed. Multivariable linear mixed effects regression estimated associations between the 10-year midlife greenness measure and global cognition and processing speed z-scores in later life and whether greenness-cognition associations varied by age at first cognitive visit. Greater midlife greenness was associated with slower annual decline in processing speed in the overall sample. We found no differences in associations by age at first cognitive visit. In an ethnoracially and geographically diverse US cohort, living in greener neighborhoods in midlife was associated with slower cognitive decline (i.e., processing speed) in later life.

The Bosten Lake, as an ecological key hub in the arid region of Northwest China, has a relatively unique ecological environment, making it challenging to maintain ecological balance. Studying the dynamic changes of the normalized difference vegetation index (NDVI) in the Bosten Lake Basin and its driving factors is of great significance for maintaining the stability and sustainable development of the basin's ecosystem. Based on Landsat data from 2001 to 2023, the NDVI values of the Bosten Lake Basin were calculated. The Mann-Kendall trend significance test, Sen's slope estimation method, and Hurst index were used to analyze the spatiotemporal dynamic changes of NDVI in the Bosten Lake Basin, and the relationship between climatic factors and NDVI was explored. The results showed that: ① The annual maximum NDVI in the Bosten Lake Basin generally showed an increasing trend, with a growth rate of 0.003 3 a-1. The spatial distribution characteristics of NDVI were relatively obvious, mainly dominated by high vegetation coverage, with 52.18% of the area showing an increasing trend. ② Seasonally, the NDVI during the growing season showed an increasing trend, with the highest NDVI in summer and the lowest in spring, and the trend of summer NDVI changes was consistent with the annual maximum NDVI changes. ③ The Hurst index predicted that 34.72% of the area in the Bosten Lake Basin would show a degradation trend in NDVI, while 65.28% would show an improvement trend. ④ The annual maximum NDVI in the Bosten Lake Basin from 2001 to 2020 was positively correlated with rainfall, temperature, sunshine hours, and evaporation and significantly correlated with sunshine hours and total evaporation, with correlation coefficients of 0.374 and 0.494, respectively. Therefore, the NDVI in the Bosten Lake Basin has shown an improving trend over the past 23 years, positively correlated with climatic factors. This study provides a scientific basis for the ecological environment construction, ecosystem management, and ecological balance maintenance in the Bosten Lake Basin.