Advanced Spectral Fusion and Deep Learning Networks for Land Use Land Cover Change Classification

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.

Understanding the spatiotemporal changes in land use and land cover (LULC) in the watershed is crucial for maintaining the sustainability of land resources. This study intents to understand the historical (1972–2015) and future (2030–2060) spatiotemporal distribution of LULC changes in the Upper Awash Basin (UAB). The supervised Maximum Likelihood Classifier technique (MLC) was implemented for historical LULC classification. The Cellular Automata-Markov (CA–Markov) model was employed to project two scenarios of LULC, ‘business-as-usual’ (BAU) and ‘governance’ (Gov). Results from the historical LULC of the study area show that urban and cropland areas increased from 52.53 km2 (0.45%) to 354.14 km2 (3.01%) and 6040.75 km2 (51.25%) to 8472.45 km2 (71.97%), respectively. Whereas grassland, shrubland, and water bodies shrunk from 2052.08 km2 (17.41%) to 447.63 km2 (3.80%), 2462.99 km2 (20.89%) to 1399.49 km2 (11.89%) and 204.87 km2 (1.74%) to 152.44 km2 (1.29%), respectively, from 1972 to 2015. The historical LULC results indicated that the forest area was highly vulnerable and occupied by urban and cropland areas. The projected LULC under the BAU scenario shows substantial cropland and urban area expansion, increasing from 8472.45 km2 (71.97%) in 2015 to 9159.21 km2 (77.71%) in 2060 and 354.14 km2 (3.1%) in 2015, 1196.78 km2 (10.15%) in 2060, respectively, at the expense of vegetation cover. These results provide insight intothe LULC changes in the area, thus requiring urgent attention by watershed managers, policymakers, and stakeholders to provide sustainable practices for the UAB. Meanwhile, the Gov scenario indicates an increase in vegetable covers and a decrease in cropland, encouraging sustainable development compared to the BAU scenario.

Effective analysis of satellite imagery is crucial for understanding the spatiotemporal changes in land use and land cover (LULC) types within river basins. The current study aimed to compare the performance of three advanced classification algorithms for LULC changes and to predict using the output from the most effective method for the Ruvu River Basin. The algorithms evaluated were Random Forest, Support Vector Machine, and K-Nearest Neighbours. Landsat images from 1987, 2001, 2012, and 2023 were classified, and the classification precision of each algorithm was assessed using statistical indices. The prediction was made using a hybrid Artificial Neural Network-Cellular Automata (ANN-CA) model in the Modules for Land Use Change Evaluation (MOLUSCE) plugin for Q GIS, which incorporated LULC maps classified by the best algorithm and spatial variables. All three algorithms demonstrated strong classification performance with marginal variations in assessment metrics. Random Forest outperformed other algorithms by achieving the highest overall accuracy and kappa values of 98.58% and 97.70% or higher, respectively. Analysis of LULC changes showed consistent declines in forest and shrubland and increases in agriculture and settlements, possibly due to population growth. Prediction indicates that these trends will continue. These findings may be useful for basin management.

In this study, the spatiotemporal changes in land use and land cover (LULC) were evaluated from 1992 to 2015 for the Rajang River Basin (RRB) located in Sarawak State of Malaysia. The changes in water bodies, cropped lands, and forests were assessed based on the available remotely sensed satellite data. Supervised classification with the MaximumLikelihood-Algorithm technique was adopted for monitoring the LULC changes using Geographic Information System (GIS) and ERDAS Imagine tools. The results revealed increasing trend of agricultural areas, mosaic natural vegetation and water bodies, while other classified areas such as tree broadleaved evergreen closed to open, tree cover flooded fresh or brackish water followed decreasing trend. More importantly, tree broadleaved evergreen closed to open and tree cover flooded fresh or brackish water showed drastic changes over the last decade. These LULC changes could be attributed to rapid economic development, disproportionate urbanization, population growth, and climate change. If unchecked, these changes might lead to a wide range of environmental impacts including aquatic and terrestrial habitat disruption.

Monitoring spatio-temporal changes in land use and land cover (LULC) and the value of ecosystem services (ESV) contributes significantly to sustainable development and management. Over the last 30 years, LULC has changed enormously in the Mila region of eastern Algeria, covering approximately 69,052 hectares. The Mila municipality, located on a minor affluent of the Oued Rhumel and dominated by the Marchau mountain, is one of the most crucial functional ecological and environmental zones in the country. Utilizing remote sensing, four satellite images of the study area, dated between 1994 and 2024, were visually interpreted to obtain LULC data classification and global value coefficients, which were then used to evaluate local spatio-temporal changes in ESV and LUC over this period. Five LULC types were identified in the study area: Urban Area, Active Agriculture, Vegetation, Soil (bare land), and Water Body. These classifications were used in conjunction with ecosystem service value coefficients to analyze the changes. The results indicated that from 1994 to 2024, vegetation (shrubs and grasslands) decreased, while built-up land (urban areas), water bodies (due to the construction of the Beni Haroun dam), and cultivated land increased. This study underscores the vital role of the wilaya of Mila in the regional system of maintaining landscape change and provides a scientific reference and cartographic tool for the sustainable development of land resources and ecosystem services in semi-arid regions.

Spatio-temporal land use and land cover change assessment: Insights from the Ouémé River Basin

Multiscenario simulation of land use and land cover in the Zhundong mining area, Xinjiang, China

Climate change and land use dynamics are critical issues facing many regions worldwide, particularly in developing countries. This study examines the spatiotemporal changes in land use and land cover (LULC) and their impact on climate variability in the Bilate Watershed, Ethiopia, from 1994 to 2024. Utilizing multispectral satellite imagery from Landsat 5, 7, and 8, along with meteorological data from five weather stations, LULC classification was performed using the Random Forest algorithm on the Google Earth Engine platform. To analyze climatic variability and trends, the Mann–Kendall trend test, the Standardized Precipitation Index (SPI), and the Standardized Temperature Index (STI) were employed. The findings indicate a significant decline in forest cover, with an accelerated annual loss of approximately 4681.2 hectares between 2014 and 2024. Concurrently, agricultural land expanded by about 1141 hectares annually, and urban areas grew by 24.3 hectares per year in recent years. Seasonal mean rainfall variation showed significant declines in the upper catchment, with Bega (p = 0.004, Sen’s slope = − 3.819 mm), Belg (p = 0.006, Sen’s slope = − 7.972 mm), and Kiremt (p = 0.005, Sen’s slope = − 7.117 mm), while the lower catchment experienced a notable increase during the Belg season (p = 0.025, Sen’s slope = 6.424 mm), highlighting uneven water availability across the watershed. Furthermore, pronounced warming trends were observed in the upper catchment (Bega: p = 0.002, Sen’s slope = 0.029; Belg: p = 0.001, Sen’s slope = 0.030; Kiremt: p = 0.004, Sen’s slope = 0.018), with moderate warming noted in the middle catchment during the Kiremt season (p = 0.020, Sen’s slope = 0.016). These LULC changes have significantly impacted climate variability, emphasizing the critical influence of human activities on regional climate dynamics. This study underscores the urgent need for sustainable land management and conservation strategies to mitigate the challenges posed by deforestation, urbanization, and agricultural expansion.

This study investigates the spatio-temporal changes in land use and land cover (LU/LC) in Ranchi City from 1999 to 2024 and evaluates their impact on the climate. Satellite imagery from LISS III Resourcesat 1 (NRSC) and LANDSAT 7 (USGS) for the years 1999, 2004, 2009, 2014 and 2024 were utilized. A supervised image classification approach using maximum likelihood classification (MLC) was employed to generate LU/LC maps. To assess the accuracy of these classifications, 400 sample verification points were selected through purposive random sampling. The results indicate a consistent increase in built-up areas, alongside a continued decrease in forest cover, agricultural land, water bodies and open spaces. This transformation underscores significant shifts in land use patterns and provides insights into their potential impacts on local climate dynamics.

Effects of disturbances on the spatiotemporal patterns and dynamics of coastal wetland vegetation

Understanding spatiotemporal changes in land use, land cover (LULC), and vegetation dynamics is crucial for sustainable environmental management and planning. This study investigated LULC and vegetation changes in the Gambela region of Ethiopia using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data from 2004 to 2024. This study relied on MOD13A3 (NDVI, 1 km, monthly) to track vegetation changes from to 2004–2024, as well as Landsat image classification was used to LULC estimation. The IGBP was refined using a random forest with NDVI thresholding to identify shifts. The accuracy was 87% through Sentinel-2 and ground truth, and NDVI deviations were associated (0.80) with yields. Geospatial and statistical techniques were employed to detect and quantify transitions between land cover classes and fluctuations in greenness in the study area. Six LULC classes, namely forest, agricultural land, grassland, irrigated land, built‑up area, and water bodies, were mapped and analyzed. Between 2004 and 2024, forest cover declined by 2 693.9 km 2 (from 74.2% to 65.3%), agricultural land expanded by 4 618.4 km 2 (from 5.3% to 20.6%), and grasslands contracted by 2 397.8 km 2 (from 19.5% to 11.5%). Irrigated areas more than tripled (0.4% to 1.2%), and built‑up extent grew nearly five‑fold (0.2% to 0.9%), whereas water bodies remained largely stable during this period. NDVI analysis revealed a 12% reduction in high-greenness areas, typically corresponding to NDVI values ≥ 0.6 (often 0.6–0.8), and a mean NDVI drop from 0.62 to 0.59 in non-forest zones, indicating declining vegetation health in converted landscapes. The study found significant LULC changes driven by agricultural expansion, settlement growth, and climate variability, with declining natural vegetation and increasing cultivated and built-up areas in the western and central regions. MODIS data are valuable for environmental monitoring, offering insights into land management and climate adaptation.

Analyzing spatiotemporal changes in land use and land cover could provide basic information for appropriate decision-making and thereby plays an essential role in promoting the sustainable use of land resources, especially in ecologically fragile regions. In this paper, a case study was taken in Zhenlai County, which is a part of the farming-pastoral ecotone of Northern China. This study integrated methods of bitemporal change detection and temporal trajectory analysis to trace the paths of land cover change for every location in the study area from 1954 to 2005, using published land cover data based on topographic and environmental background maps and also remotely sensed images including Landsat MSS (Multispectral Scanner) and TM (Thematic Mapper). Meanwhile, the Lorenz curve and Gini coefficient derived from economic models were also used to study the land use structure changes to gain a better understanding of human impact on this fragile ecosystem. Results of bitemporal change detection showed that the most common land cover transition in the study area was an expansion of arable land at the expense of grassland and wetland. Plenty of grassland was converted to other unused land, indicating serious environmental degradation in Zhenlai County during the past decades. Trajectory analysis of land use and land cover change demonstrated that settlement, arable land, and water bodies were relatively stable in terms of coverage and spatial distribution, while grassland, wetland, and forest land had weak stability. Natural forces were still dominating the environmental processes of the study area, while human-induced changes also played an important role in environmental change. In addition, different types of land use displayed different concentration trends and had large changes during the study period. Arable land was the most decentralized, whereas forest land was the most concentrated. The above results not only revealed notable spatiotemporal features of land use and land cover change in the time series, but also confirmed the applicability and effectiveness of the methodology in our research, which combined bitemporal change detection, temporal trajectory analysis, and a Lorenz curve/Gini coefficient in analyzing spatiotemporal changes in land use and land cover.

اهداف: پایش تغییرات کاربری‌ها و درک پویایی آن در یک حوضۀ آبخیز، از جایگاه خاصی در مدیریت پایدار آن حوضه برخوردار است. هدف تحقیق حاضر، استفاده از سنجش از دور و GIS جهت تهیۀ نقشۀ تغییرات و شناسایی انتقالات کاربری اراضی و پوشش زمین با به‌کارگیری ماتریس انتقال و تصاویر ماهوارۀ لندست در حوضۀ آبخیز دریاچۀ ارومیه می‌باشد. روش: جهت انجام تحقیق، از تصاویر ماهوارۀ لندست در دورۀ زمانی 2015 ـ 1988 استفاده گردید. بدین‌منظور پس از انجام پیش‌پردازش‌های موردنظر، جهت انجام طبقه‌بندی از روش‌های ماشین‌بردار پشتیبان و روشیءگرا استفاده و سپس اعتبارسنجی گردیدند. همچنین جهت برآورد میزان انتقالات و دیگر ویژگی‌های حوضۀ آبخیز دریاچۀ ارومیه، ابتدا ماتریس انتقالی استخراج شده و سپس طبقه‌بندی شئ‌گرا بین دوره‌های زمانی 2015ـ1988 ارائه شد. سپس با استفاده از فرمول‌های موردنظر، میزان پایداری، افزایش، کاهش، تغییرات کل، تغییرات خالص و مبادلۀ همزمان کاربری‌های اراضی و پوشش زمین مشخص گردید. یافته‌ها/ نتایج: پس از ارزیابی صحت، صحت کلی برای نقشه‌های حاصل از ماشین بردار پشتیبان و روش شئ‌گرا به‌ترتیب برابر با 94 و 92 درصد و مقدار کاپای آنها به‌ترتیب 92 و 89 برآورد شد که نشان‌دهندۀ برتری روش شئ‌گرا در مقایسه با روش ماشین بردار پشتیبان است. در کل، هر دو روش طبقه‌بندی توانستند صحت قابل‌قبولی برای نقشه‌های کاربری اراضی و پوشش زمین ارائه دهند. نتایج حاصل از انتقالات نشان داد به‌طور میانگین، 59 درصد از چهرۀ زمین در حوضۀ آبخیز دریاچۀ ارومیه در فاصلۀ زمانی 2015ـ 1988 پایداری پوشش داشته است، که بیشترین میزان این تداوم براساس مقدار این کاربری در فاصلۀ زمانی 2015ـ1988 مربوطه به مناطق مسکونی می-باشد. حدود 14 درصد از سطح حوزۀ آبخیز دریاچۀ ارومیه به‌صورت تبادل همزمان بوده است. همچنین سطوح آبی حوضۀ آبخیز دریاچۀ ارومیه در دورۀ زمانی فوق، بیشترین ازدست‌دادگی و کمترین تبادل همزمان را تجربه کرده است. نتیجه‌گیری: حوضۀ آبخیز دریاچۀ ارومیه در این فاصلۀ زمانی (2015ـ1988) تغییرات و انتقالات شدیدی را تجربه کرده است، تاجایی‌که تنها 59 درصد از چهرۀ زمین، ثابت مانده و قسمت‌های دیگر، انواعی از انتقال‌ها را تجربه کرده‌اند. همچنین سطوح آبی و سپس مراتع، بیشترین آسیب-پذیری را تجربه کرده‌اند که نشان از افزایش اراضی فاقد پوشش و اراضی زراعی (کشاورزی) می-باشد. این تجزیه‌وتحلیل ما را به سنجش و تجسم میزان انتقالات عمدۀ LULC درجهت برنامه‌ریزی آیندۀ حوضۀ آبخیز دریاچۀ ارومیه توصیه می‌کند.

Urbanization, changes in land use and land cover (LULC), and an increase in population collectively have significant impacts on urban catchments. However, a vast majority of LULC studies have been conducted using readily available satellite imagery, which often presents limitations due to its coarse spatial resolution. Such imagery fails to accurately depict the surface characteristics and diverse spectrum of LULC classifications contained within a single pixel. This study focused on the highly urbanized Dry Creek catchment in Adelaide, South Australia and aimed to determine the impact of urbanization on spatiotemporal changes in LULC and its implications for the land surface condition of the catchment. Very high spatial resolution imagery was utilized to examine changes in LULC over the past four decades. Support Vector Machine-learning-based image classification was utilized to classify and identify the changes in LULC over the study area. The classification accuracy showed strong agreement, with a kappa value greater than 0.8. The findings of this analysis showed that extensive urban development, which expanded the built-up area by 34 km2, were responsible for the decline in grass cover by 43.1 km2 over the last 40 years (1979–2019). Moreover, built-up areas, plantation, and water features, in contrast to grass cover, have demonstrated an increasing trend during the study period. The overall urban expansion over the study period was 136.6%. Urbanization intensified impervious area coverage, increasing the runoff coefficient, equivalent impervious area, and curve number by 60.6%, 60.6%, and 7.9%, respectively, while decreasing the retention capacity by 38.6%. These modifications suggest a potential variability in catchment surface runoff, prompting the need for further research to understand the surface runoff changes brought by the changes in LULC resulting from urbanization. The findings of this study can be used for land use planning and flood management.

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China