Urbanisation increases land surface temperature (LST) in subtropical coastal cities, yet long-term, interpretable analyses of LST drivers are limited for the Fuzhou metropolitan area and its suburbs. We address this gap by combining a 25-year Landsat time series with n Explainable Gradient-Boosting (XGBoost) model to evaluate the land cover impacts. Multi-epoch Landsat data, XGBoost and SHAP were integrarted to assess the relative, nonlinear, and moderating effects of impervious surfaces, vegetation, and elevation on LST. Dense vegetation remained dominant (45-51%), while built-up area increased from 5% to 12%; by 1999-2023, built-up surfaces became the most prevalent class, replacing bare land. Enhanced scatter-regression attribution reveals imperviousness (NDBI, BSI) raises LST, while vegetation and moisture (NDVI, NDWI, NDMI) and higher elevation, reduce it, elevation strongly moderates LST in this humid coastal region. Multivariate regression and variance partitioning indicate that elevation is the primary and most often uniquely attributable LST driver. The XGBoost model accurately predicts LST with an RMSE of approximately 1.3. Policymakers should promote green infill, protect moisture-rich corridors, and limit impervious surfaces in heat-prone areas. This approach provides a transferable framework for climate-smart urban planning in Fuzhou and similar coastal cities.

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha(-1)yr(-1), totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha(-1)yr(-1) and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha(-1)yr(-1)) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha(-1)yr(-1)), followed by agricultural land (>30 t ha(-1)yr(-1)). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers' indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss.

Study region: Upper Blue Nile Basin, the largest in Ethiopia in terms of annual water runoff and soil loss. Study focus: A new framework for identifying soil conservation priority areas has been proposed. Existing methods often focus solely on soil loss, neglecting soil conservation indicators. Few studies consider multiple scenarios. The Sediment Delivery Ratio model was used to estimate soil conservation services (Avoided Erosion, AE; Sediment Trapping, ST) and soil erosion (Soil Loss, SL; Sediment Export, SE). These indicators were integrated using the Ordered Weighted Averaging method. New hydrological insights for the region: The results revealed an initial decline in all indicators (2000–2010), followed by an increase (2010–2020). The total values of AE, ST, SL, and SE ranged from 11.98 to 12.49 billion, 2.07–2.15 billion, 660.41–684.06 million, and 102.35–105.75 million tons/year, respectively. Factors such as rainfall, slope, and soil types caused non-linear effects of land cover change. The study proposes conservation priority areas under 11 scenarios, offering a range of decision-making options. Among these, the 7th is recommended as the most optimal, balancing conservation efficiency, moderate trade-offs, and planners' optimism. Implementing conservation measures in high and very high priority classes under this scenario can retain 2097.03 t/ha/yr of soil and reduce sediment export by 20.03 t/ha/yr. This advanced conservation prioritization framework can be replicated in regions beyond the UBNB.

The COVID-19 pandemic presented an unprecedented opportunity to assess the environmental effects of reduced anthropogenic activity on urban climates. This study investigates the impact of COVID-19-induced lockdowns on land surface temperature (LST) and the intensity of the surface urban heat island (SUHI) in Nowshera District, Khyber Pakhtunkhwa Province, Pakistan, which is experiencing rapid urbanization. Using Landsat 8/9 imagery, we assessed thermal changes across three periods: pre-lockdown (April 2019), during lockdown (April 2020), and post-lockdown (April 2021). Remote sensing indices, including NDVI and NDBI, were applied to evaluate the relationship between land cover and LST. Our results show a significant reduction in average LST during lockdown, from 31.38 °C in 2019 to 25.34 °C in 2020, a 6 °C decrease. Urban–rural LST differences narrowed from 9 °C to 6 °C. A one-way ANOVA confirmed significant differences in LST across the three periods (F (2, 3) = 3691.46, p < 0.001), with Tukey HSD tests indicating that the lockdown period differed significantly from both the pre- and post-lockdown periods (p < 0.001). SUHI intensity fell from 35.10 °C to 28.89 °C during lockdown, then rebounded to 35.37 °C post-lockdown. The indices analysis shows that built-up and rangeland areas consistently recorded the highest LST (e.g., 35.36 °C and 37.09 °C in 2021, respectively), while vegetation and water bodies maintained lower temperatures (34.68 °C and 32.69 °C in 2021). NDVI confirmed the cooling effect of green areas, while high NDBI values correlated with increased LST in urban areas. These findings underscore the impact of human activity on urban heat dynamics and highlight the role of sustainable urban planning and green infrastructure in enhancing climate resilience. By exploring the relationships among land cover, anthropogenic activity, and urban climate resilience, this research offers policymakers and urban planners’ valuable insights for developing adaptive, low-emission cities amid rapid urbanization and climate change.

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R2 = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R2 improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R2 up to 0.51). This represents major improvements compared with using topographic covariates alone (R2 increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R2 increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring.

Regional ecosystem service value (ESV) is significantly influenced by factors such as land use/cover change (LUCC). In this study, from the perspective of spatio-temporal heterogeneity, we constructed a dynamic and zonal equivalence table of ecosystem service values using the equivalence factor method and analyzed the spatio-temporal changes in ecosystem service values of different agricultural plantation regions of the karst mountainous areas of southwestern China (Yunnan Province, YP) in the years from 1990 to 2020. Also, the ESV of YP in 2030 was simulated using the Patch-generating Land Use Simulation (PLUS) model. The results showed the following: (1) land use/land cover (LULC) in YP from 1990 to 2020 was dominated by needle-leaved forestland, broadleaved forestland, grassland, and rainfed cropland. (2) The total ESV in YP fluctuated between CNY 876.74 and 1323.68 B from 1990 to 2020, expanding at a rate of 50.98%. The largest portion of the total ESV comes from climate regulation. The ESV increased from east to west, and the positive spatial correlation of the ESV gradually weakened. (3) The ESV in YP was projected to reach CNY 1320.70 B by 2030, representing a decrease of ~CNY 2.98 B since 2020. The results showed a decline in the ecological environment’s quality in YP.

Rapid industrialization and urbanization have significantly changed urban spatial patterns, resulting in the urban ecosystem degradation and urban spatial conflicts. The challenge requires the urban spatial planning more sophisticated for developing eco-city models in the perspective of urban land multifunctionality. The Production-Living-Ecological(PLE) spatial pattern is proposed for effective eco-city planning in Chinese urban cases. Given the differing climatic and cultural contexts, are the PLE spatial patterns comparable between cities from different continents? This study aims to compare the characteristics of PLE spatial patterns and the trade-offs & synergies of PLE spaces between Fuzhou city, China and Saskatoon, Canada for developing the eco-city models. First, the paper identified the PLE spaces by integrating multi-source data, then analyzed the PLE spatial agglomeration characteristics by using the average nearest neighbor and kernel density analysis, finally detected the trade-offs and synergies between functional spaces by Spearman correlation and bivariate spatial autocorrelation. The results showed the distinctly different PLE spatial patterns and the trade-offs & synergies of PLE spaces between the two eco-cities in Fuzhou, China and Saskatoon, Canada in 2022. (1) For the PLE space composition, the percentages of ecological space in Fuzhou and Saskatoon were 64.6% and 36.4%, respectively, while the proportions of the most suitable residential space in two cities from POI data were 2.4% and 4.1%, respectively. (2) For PLE spatial agglomeration, ecological space in Fuzhou was characterized with a random distribution with the average nearest neighbor index of 1.19, and scattered as small patches in urban hilly area covered with ever-green broadleaf trees, while in Saskatoon the index was less than 1.00 with a clustered distribution in numerous city parks covered with grass and shrubs; Fuzhou’s multifunctional spaces were clustered in the central urban area surrounded by ring roads and in Changle District, while Saskatoon’s were dispersed with large patches. (3) For the trade-offs & synergies of PLE space, the ecological spaces in two cities were suppressed. In Fuzhou, the trade-off area ratio of the ecological space to other fuctional spaces was ranged 50% to 58%, while in Saskatoon, it was 40% to 47%. (4) The PLE spatial pattern can clearly sketch the different eco-city frameworks in different continents. Fuzhou’s eco-city model was characterized by “high ecological space/compacted living space/strong trade-off between ES and other spaces” and Saskatoon’s was featured with “low ecological space/spacious residential space with high livability/ weak trade-off between ES and other spaces”. Therefore, Fuzhou faced more challenges of intense spatial competition in the context of dense population. Our findings reveals the practical requirements for optimizing urban space and functions in terms of economic, ecological, and livability considerations. Additionally, they would provide valuable insights for long-term urban spatial planning and development strategies.

Urban Ecosystem Health (UEH) refers to the integrated capacity of urban ecosystems to both maintain and regenerate themselves and to provide ecological services for urban populations. Rapid global urbanization over the last two decades has generally led to a deterioration of regional UEH. Coastal zones are hot spots for economic and urban development, fragile in the ecosystem, and sensitive to climate change and sea levels. How does the UEH change in coastal cities from a global perspective? This paper proposes the Pressure-Vigor-Organization-Resilience-Service (P-VOR-S) modeling framework to study the UEH dynamics in Asian and African coastal cities. The results show that the UEH indexes (UEHI) of coastal cities in Asia and Africa vary significantly. Changle, a coastal city located in the subtropical ocean monsoon climate zone, was classified as the healthy UEH class with a decreasing UEHI from 2006 to 2022. While Suez, located in the tropical desert climate zone, was in the UEH sick class with an increasing UEHI during the same period. In Changle, from 2006 to 2022, the healthy area of UEH decreased by 61 km2 with a 9.04 % reduction in area proportion, while the sick area of UEH increased by 70.65 km2 with a 10.47 % rise in area proportion, the reason was directly related to the sprawl of build-up land on the forest and arable land. In contrast, during the same period, the area of healthy space in Suez increased by 129.13 km2 with a rise of 10.16 % of the whole area, while the area of sick space decreased by 160.95 km2 with a lack of 12.66 % in area percentage, the reason was the enlarged planting of the desert sparse forest. Moreover, the spatial dynamics of UEH in Asian and African coastal cities differed apparently. From 2006 to 2022, in Changle, UEH gradually deteriorated towards the eastern coastal region due to rapid economic development and local government planning policies, which would potentially stress the neighboring coastal ecosystem, while in Suez during the same period, UEH gradually improved along both sides of the Suez Canal due to afforestation, which will positively affect the ecosystem health of the Gulf of Suez and the Suez Canal. The results would provide examples for global comparative studies on coastal ecosystem health assessment and climate change.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes.

Understanding the ecological security situation of Fuzhou City holds significant theoretical and practical value for the government departments in implementing development strategies and achieving Sustainable Development Goal 11 (Sustainable Cities and Communities). Using the Data, Information, Knowledge, and Wisdom (DIKW) framework, this study combined various remote sensing and GIS methods to comprehensively analyze Fuzhou's past, present, and future ecological security levels. The results showed a strong isotropic cluster in the city's ecological security. Among the influencing factors, the degree of regional development was found to have the greatest impact, while water body coverage had the least. The influencing factors are mutually reinforcing. Under the natural development scenario, the area of secure level in 2020 decreased by 1243.70 km(2), while under the ecological protection scenario, it declined by 1263.34 km(2). In the future, Fuzhou's ecological security level is expected to face increasing fragmentation. Based on these findings, the study proposes strategies to balance economic development and ecological protection in Fuzhou City. These recommendations aim to provide the government departments with relevant data support for land resource management and contribute to the high-level development of the City.

Understanding the relationships among ecosystem services (ESs) and their interactions with influencing factors is essential for spatially targeted ecosystem governance. However, classifying the spatial distribution of these diverse interactions still needs improvement. Furthermore, existing studies have insufficiently addressed the specific impacts of bidirectional land cover transitions on ESs. Taking the upper Blue Nile basin as a study area, we estimated the spatiotemporal distribution of annual water yield (AWY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, using InVEST models and associated formulas. Changes in ESs per inward -outward land cover transition were quantified based on the Cross -Tabulation Matrix. An improved pairwise method was employed to assess the spatially diverse interactions between ESs pairs and their relationship with influencing factors. The statistical significance of influencing factors was evaluated using partial least square regression. The findings indicated that high HQ values were prevalent in the west, while they were in the east for SR. The central and southern areas experienced higher CS and AWY values. During the study period, variations were observed in the mean values of SR (ranging from 22.89 to 23.88 x 10 2 t/ha/y), AWY (32.13 - 42.2 x 10 2 mm/ha/y), CS (90.5 - 102.9 x 10 3 gC/ha/y) and HQ (0.62 - 0.64). Synergies were predominant in AWY-CS, AWY-SR, and CS -SR pairs. HQ revealed more of a no -effect and tradeoff relationship with other ESs. The interactions between ESs and influencing factors were dominated by synergies, followed by tradeoffs and noeffect. The influence of landscape structure (gyrate and landscape shape index) and land surface temperature on all ESs and precipitation on AWY and SR was significant (1.049 <= Variable Importance in the Projection <= 1.371). Overall, the spatiotemporal dynamics of key ESs and the modeling of their drivers are essential policy information for taking spatially explicit conservation measures. This study will also serve as a valuable methodological reference for future research.

Electric Transmission Lines (ETL) are essential infrastructure for regional development, their construction in subtropical coastal mountainous areas with high rainfall (exceeding 1500 mm) presents a significant challenge due to soil erosion. Currently, The environmental impact assessments (EIAs) for ETL projects pay attention to the overall erosion evaluation and ignore the erosion during construction and its associated parameters. This study aims to monitor soil erosion and identify critical site parameters, such as shape, geometric features, and spatial erosion influence, using high-resolution satellite images for ETL construction supervision and erosion mitigation. This study selected Min-Yue ETL in Fujian, China as a case study and used Skysat images with a spatial resolution of 0.5m. The NDMVI (Normalized Difference Mountain Vegetation Index) indicator was used to identify the disturbed areas and to calculate the soil erosion by the Revised Universal Soil Loss Equation (RUSLE) model. The study also calculated the engineering parameters including the shapes, the geometric features such as PAR (Perimeter Area Ratio), AR (Area Ratio), and CR (Constant shape Ratio) of the disturbed area, as well as the erosion spatial diffusion. The results showed that: (1) The disturbed area and the soil erosion by ETL construction were estimated with 0.5m and 2.5m accuracy respectively. (2) The engineering parameters exhibited different responses to erosion: Blocks were the predominant source of soil erosion, accounting for 64% to 71% of the SumA (the cumulative soil erosion amount); A strong positive correlation between Lg(AR) and Lg(SA)(Soil erosion Amount of one tower base), with a significantly negative correlation between Lg(CR) and Lg (SA) were demonstrated; The spatial diffusion of soil erosion was confined to 5.0-15.0m. (3) The shapes, AR, and CR of the disturbed area could be recommended as the primary parameters for ETL construction supervision and erosion mitigation measures in ETL design and construction phases. The results provide insights for evaluating the environmental impact of ETL and developing practical strategies for erosion control, mitigation, and restoration to promote sustainable ETL construction.

Regional environmental management aims to maintain or improve regional ecosystem health (REH) and prevent its degradation over time. In the context of rapid urbanization and global sustainability over the last two decades, has land use change resulted in a deterioration of REH? By using the improved REH framework model as Pressure-Vigor-Organization-Resilience-Service (P-VOR-S), this paper proposed Regional Ecosystem Health Maintenance (REHM) as a dynamic quantitative indicator of REH, and detected REHM as well as its response to land use change in the coastal city of Fuzhou, China during 2003-2018. The results showed that: (1) During 2003-2018, the average REH gradually decreased in spite of the 62.50-67.55% area coverage of the REH "well" level. The REHM "maintenance" level covered 9764.69 km2 (83.74%), while the REHM "degradation" and "improvement" covered only 1485.17 km2 (12.74%) and 410.99 km2 (3.52%), respectively. (2) The REHM "degradation" in Fuzhou was predominantly caused by the conversion of forest to cultivated land and cultivated land to construction land, as well as the conversion of forest and water to construction land. (3) The REHM map highlighted the degraded areas as hotspots for environmental management concerns, and the area of REHM "degradation" or "improvement" could be served as key indicator for regional environmental management and spatial land planning.

The southeastern hilly region of China is ecologically significant but highly vulnerable to climate change and human activities. This study developed a Modified Remote Sensing Ecological Index (MRSEI) using satellite imagery and Human Footprint data to assess ecological quality across 14 cities surrounding the Wuyi Mountains. We applied Sen’s slope analysis, the Mann–Kendall test, and spatial autocorrelation to evaluate spatiotemporal ecological changes from 2000 to 2020, and used partial correlation analysis to explore the drivers of these changes. The main findings are as follows: (1) Ecological quality generally improved over the study period, with significant year-to-year fluctuations. The eastern region, characterized by higher altitudes, consistently exhibited better ecological quality than the western region. The area of low-quality ecological zones significantly decreased, while Ji’an, Ganzhou, Heyuan, and Meizhou saw the most notable improvements. In contrast, urban areas experienced a marked decline in ecological quality. (2) The region is undergoing warming and wetting trends. Increased precipitation, especially in the western and northern regions, improved ecological quality, except in urban areas, where it heightened flood risks. Rising temperatures had mixed effects: they enhanced ecological quality in high-altitude areas (~516 m) but negatively impacted low-altitude regions (~262 m) due to intensified heat stress. (3) Although industrial restructuring reduced environmental pressure, rapid population growth and urban expansion created new ecological challenges. This study provides an innovative method for the ecological monitoring of hilly regions, effectively integrating human activity and climatic factors into ecological assessments. The findings offer valuable insights for sustainable development and ecological management in similar sensitive regions.

The construction of the ecological security pattern (ESP) and ensuring its integrity are the focal points of ecological actions. However, existing evaluation frameworks lack comprehensiveness, and studies addressing temporal dimensions are limited. This study constructed a comprehensive framework to evaluate ESP and introduced a method to portray the relationship between ecological security status and landscape structure. Initially, multiple indicators were optimized to construct an ecological security index (ESI). The relationship between ESI and landscape structure was quantified. Then, fifteen spatiotemporal factors were combined using the Analytical Hierarchy Process to construct an ecological resistance layer. Finally, Circuit theory was applied to visualize trends in ESP. The findings indicated that ecological security was generally better but with a downward trend. The relationship between ESI and landscape fragmentation was mainly a tradeoff. The area of stable ecological sources was 1251.06 km(2) (1996-2021). The number of minimal-cost corridors decreased continuously. Ecological sources with the highest conservation priority were the largest (1336.2 km(2)) in 2021. The number of pinch points was 25, 31, and 30, while ecological barriers covered 51.5, 148.74, and 139.51 km(2) in 1996, 2008, and 2021, respectively. This study will serve as a valuable methodological reference and support ecological civilization projects in Fuzhou.

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen’s slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed.

Оперативный дистанционный мониторинг и моделирование потенциальных изменений наземного покрова -важные мероприятия при принятии решений по устойчивому управлению территориями.В работе проведён прогнозный анализ пространственно-временной динамики семи классов наземного (растительного) покрова Среднего Поволжья до 2041 г. по данным спутниковых изображений Landsat за 2001 и 2021 гг.и тематическим картам местности.Моделирование динамики классов проводилось методом клеточных автоматов и искусственной нейронной сети CA-ANN (англ.Cellular Automata -Artificial Neural Network) в плагине MOLUSСE (англ.Modules for Land-Use Change Simulation -модули для моделирования изменений в землепользовании) программы QGIS (англ.Quantum GIS) при условии существующих за прошедшие 20 лет тенденций в земле-и лесопользовании, а также природных нарушений в исследуемом регионе.Проведён анализ интенсивности и вероятности пространственно-временных переходов между исследуемыми классами наземного покрова за моделируемый период времени.В результате создан набор картографических материалов в программном обеспечении ArcGIS Pro и матриц вероятности перехода и интенсивности изменений в наземном покрове Среднего Поволжья.Проведённый прогнозный пространственно-временной анализ позволил определить будущие тренды динамики наземного покрова до 2041 г.Результаты показывают, что большинство исследуемых классов наземного покрова за период 2021-2041 гг.будут подвержены изменениям по площади.В первую очередь это затронет субъекты Среднего Поволжья, имеющие высокую лесистость на своей территории, такие как Кировская и Нижегородская области, а также Республика Марий Эл.Прогнозный анализ свидетельствует о том, что площадь лесного покрова на исследуемой территории в 2041 г. может увеличиться по сравнению с 2001 г. на 23,8 %.Максимум по интенсивности изменений до 2041 г. демонстрирует класс «молодняки», динамика ежегодного прироста которого по площади может составить 1,6 % в год.Часть хвойных насаждений площадью 0,31 млн га может перейти в смешанные, а 0,357 млн га -в лиственные насаждения.Полученные результаты могут быть использованы для дальнейшего прогнозного мониторинга наземного покрова по спутниковым изображениям с учётом дополнительных факторов по меняющемуся климату и социально-экономической деятельности на региональном и локальном уровнях.