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The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region's socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R2 = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10−3 y−1. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with "good" and "excellent" ecological grades, whereas unused land types represent more than 44% of the total area classified with "poor" ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China's first national standard "Cultivated Land Quality Grade" (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO   2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO   2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia). [ABSTRACT FROM AUTHOR]

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land ( R 2 = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field ( R 2 = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of R 2 of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data. [ABSTRACT FROM AUTHOR]

• Landsat bands alone may not give adequate quality of LULC mapping in a heterogeneous landscape. • Remote sensing-derived indices and auxiliary data improve the accuracy of LULC mapping. • LULC maps classified under different classification algorithms have shown a significant discrepancy. Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagation of errors in LULC maps to further applications such as water resources assessment, it is highly recommended to explicitly evaluate the available classifiers. Users should also harness the power of RS indices and auxiliary datasets for improved LULC mapping. [ABSTRACT FROM AUTHOR]

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks. [ABSTRACT FROM AUTHOR]

Reporting land use changes over time is important for evaluating land resource management. This study applied GIS technology to determine land use fluctuations for the entire mainland territory in Vietnam. In particular, the research focused on two main issues: (1) spatial fluctuations of some main land groups in Vietnam, and(2) the rate of land use change in socio-economic regions in the two periods 2007–2016 and 2016–2020. Research results showed that the Forests land group showed a growth of over 14% and spatial fluctuations took place in all regions, except for two regions with little change in this land group: Red River Delta (RRD) and Mekong River Delta (MRD). Meanwhile, crops decreased by over 16% from 2007–2020 and appeared heavily in the Northern Midlands and Mountains (NMR), North Central and Central Coast regions (NCR), and Central Highlands region (CHR). Urban land group increased by 3% during 2007–2020. The speed of land use conversion also showed different results between economic regions and inthe two research periods. The results showed that the recent research period witnessed a higher rate of land use conversion in the regions compared to the period 2007–2016. In particular, the NMR region showed that the conversion rate was the largest in both stages. [ABSTRACT FROM AUTHOR]

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. [ABSTRACT FROM AUTHOR]

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study's aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts. [ABSTRACT FROM AUTHOR]

The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region. [ABSTRACT FROM AUTHOR]

Strawberries in California are grown in specific coastal areas where land is scarce and climate change threatens future production. Strawberry growers are under pressure to adopt sustainable production strategies such as crop rotation, but this practice requires more land than back-to-back planting. The objectives of this research were to quantify the rate of crop rotation across the three main strawberry producing regions in California (Ventura, Santa Maria, and Monterey Bay), and evaluate geographic and edaphic influences on crop rotation. All strawberry fields in the main strawberry producing regions of California were identified by satellite imagery and manual inspection for the years 2017 through 2022. ArcGIS Pro was used to outline each strawberry field and compare among years to determine the period between successive strawberry plantings. Edaphic characteristics and shapefiles for surrounding fields were retrieved from public datasets. The three regions significantly differed in their rates of crop rotation. On average, 95, 52, and 25% of strawberry hectares were rotated each year in the Monterey Bay, Santa Maria, and Ventura regions, respectively. Shorter rotation lengths were associated with reduced distance from the ocean and soil with a higher percentage of sand. Based on 2 years of disease surveys, fields infested with Macrophomina phaseolina tended to be further inland than fields infested with Fusarium oxysporum f. sp. fragariae in the Monterey Bay region. This study determined that distance from the ocean and soil texture are associated with crop rotation lengths in California strawberry production. Enough land may be available in the Santa Maria and Monterey Bay regions for growers to lengthen crop rotations, but water quality, social networks, and financial considerations that were outside the scope of this study are likely to limit the ability for strawberry growers to maximize the duration of crop rotations. [ABSTRACT FROM AUTHOR]

The upper Tekeze River Basin is facing challenges of widespread deforestation and natural vegetation cover degradation that could exacerbate the water scarcity, food insecurity and extreme poverty in the region. Using remote sensing and GIS, this study quantified the land use land cover change trend in the last three decades and analyzed the current land use / cover statues in the basin. A hybrid classification technique is applied to obtain better classification accuracy. Moreover, for automated cloud and cloud shadow detection the newly developed Mountainous Fmask is used. Using post classification change detection technique, seven major land use/cover classes were identified. These classes remained the dominant classes during the study period, showing marked changes in the area coverage within them. Based on the error matrix statistical indices, the classification accuracies of each class are found to be strong. The overall accuracy and the kappa coefficient for the 2021 map are 91% and 89%, respectively. The techniques used have contributed to improving the accuracy of the classification process and helped the classified images to practically match the ground truths. The analysis revealed settlement expansion by 570.31% in parallel with the expansion of farmland by 52.32% during the period 1991–2021. In contrast, the forestland decreased significantly, by 75.55%. The environmental degradation and unplanned use of land resources could have contributed to why the upper Tekeze basin is experiencing worsening poverty, water scarcity and food insecurity. Thus, land use/cover time series modeling is essential for various purposes, including land use planning and, managing natural resources. In this regards this study provides basic information for implementing sustainable environmental conservation strategies in the area. Furthermore, the applied methodologies may have practical applications in other similar areas. [ABSTRACT FROM AUTHOR]

Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China's diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km2, 31.53 km2, and 21.98 km2, respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km2, respectively, with a gully density of 1.65, 5.25, and 0.50 km km−2, and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr−1, significantly surpassing HL (12.24 m yr−1) and ML (7.11 m yr−1). Areal erosion rates were highest in HL (277.79 m2 yr−1) and lowest in YKS (105.22 m2 yr−1), with ML intermediate at 243.36 m2 yr−1. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr−1) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. [ABSTRACT FROM AUTHOR]

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy. [ABSTRACT FROM AUTHOR]

Kolonnawa marsh (KM) is an important wetland ecosystem in Colombo district, Sri Lanka that provides essential ecosystem services, and has undergone significant changes over recent decades due to continuous exploitation and reclamation. The values of wetlands are disregarded by decision-makers, despite the fact that they are crucial for improving the quality of water and offer chances for relaxation and amusement in metropolitan areas. Underestimation of the value of wetlands contributes to their continuing deterioration and inevitable loss. Investigating the changes in wetlands can provide crucial information for decision-making. This study aimed to monitor the spatiotemporal land-cover dynamics of KM with the prospect prediction as reduced total extent of KM gradually with time and marsh area being transformed into terrestrial vegetation with time. The collective images from Google Earth (2000 to 2021) and drone data (2022) were analyzed with the GIS application. Subsequently, 50-m2 grid squares with unique cell IDs are designed to link among land cover maps for spatiotemporal land-cover change analysis. Then, we calculate land cover category: surface water, marsh, and terrestrial vegetation proportions for each map in 50-m2 grid cells. Statistical comparison of the land cover changes in grid square cells shows that each land cover category has significant change with the time. The results showed that the reduction of KM marsh resulting in land cover changes has a positive implication on wetland degradation. Thus, interventions should be made for the restoration and sustainable management of KM. [ABSTRACT FROM AUTHOR]

Food security is a major challenge for China at present and will be in the future. Revealing the spatiotemporal changes in cropland and identifying their driving forces would be helpful for decision-making to maintain grain supply and sustainable development. Hainan Island is endowed with rich agricultural resources due to its unique climatic conditions and is facing tremendous pressure in cropland protection due to the huge variation in natural conditions and human activities over the past few decades. The purpose of this study is to assess the spatiotemporal changes in and driving forces of cropland on Hainan Island in the past and predict future cropland changes under different scenarios. Key findings are as follows: (1) From 2000 to 2020, the cropland area on Hainan Island decreased by 956.22 km2, causing the center of cropland to shift southwestward by 8.20 km. This reduction mainly transformed into construction land and woodland, particularly evident in coastal areas. (2) Among anthropogenic factors, the increase in the human footprint is the primary reason for the decrease in cropland. Land use changes driven by population growth, especially in economically active and densely populated coastal areas, are key factors in this decrease. Natural factors such as topography and climate change also significantly impact cropland changes. (3) Future scenarios show significant differences in cropland area changes. In the natural development scenario, the cropland area is expected to continue decreasing to 597 km2, while in the ecological protection scenario, cropland conversion is restricted to 269.11 km2; however, in the cropland protection scenario, the trend of cropland reduction is reversed, increasing by 448.75 km2. Our findings provide a deep understanding of the driving forces behind cropland changes and, through future scenario analysis, demonstrate the potential changes in cropland area under different policy choices. These insights are crucial for formulating sound land management and agricultural policies to protect cropland resources, maintain food security, and promote ecological balance. [ABSTRACT FROM AUTHOR]

The functional structure of territorial space is an important factor for analyzing the interaction between humans and nature. However, the classification of remote sensing images struggles to distinguish between multiple functions provided by the same land use type. Therefore, we propose a framework to combine multi-source data for the recognition of dominant functions at the block level. Taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, its block-level 'production–living–ecology' functions were interpreted. The whole GBA was first divided into different blocks and its total, average, and proportional functional intensities were then calculated. Each block was labeled as a functional type considering the attributes of human activity and social information. The results show that the combination of land use/cover data, point of interest identification, and open street maps can efficiently separate the multiple and mixed functions of the same land use types. There is a great difference in the dominant functions of the cities in the GBA, and the spatial heterogeneity of their mixed functions is closely related to the development of their land resources and socio-economy. This provides a new perspective for recognizing the spatial structure of territorial space and can give important data for regulating and optimizing landscape patterns during sustainable development. [ABSTRACT FROM AUTHOR]

Ensuring the quality protection of geospatial elements entails environmental control, a task unattainable without precise measurement results. This article aims to conduct a spatio-temporal analysis of soil degradation and vegetation status within the influence zone of the Kolubara Mining Basin in Serbia. Remote sensing is employed to assess vulnerability to erosion using the erosion potential method. A geographic information system environment is utilized to generate an erosion map, illustrating erosive processes across different time periods, particularly comparing the present situation (2022) to 1983. Results indicate that observed areas are experiencing erosion due to changes in land use. Furthermore, this study investigates the use of the normalized difference vegetation index to monitor vegetation cover changes from 1992 to 2022. The objective is to demonstrate that these methods effectively depict degradation levels and vegetation status in the area. This comprehensive overview provides insights into the changes occurring across the analyzed years. Such insights are crucial for informing future efforts to restore the region to its natural state prior to lignite mining.

The subject of the research represents the application of remote sensing in monitoring land degradation in the municipality of Stanari in Bosnia and Herzegovina (B&H). The aim of the study is to identify spatial changes during the research period (2017–2023) by processing remote sensing “products.” Within the changes in land use, the focus is placed on alterations resulting from surface mineral resource mining activities. The total approved exploitation area amounts to 3841.08 ha, covering almost a quarter of the municipality (23.89%). Monitoring changes in land use was conducted through the processing of Sentinel-2 images using supervised classification (SC). The classification results were compared with the Dynamic World (DW) dataset. Results indicate significant losses in forested areas, reduced by −7.23% (DW) or −2.43% (SC) at the municipal level and −11.48% (DW) or −5.90% (SC) within the exploitation area. Spatial extents of deforestation were identified and analyzed based on processing the Hansen Global Forest Data Change dataset. The deforestation area, caused by mining activity in B&H over the research period (2001–2021), amounts to 977.94 ha, with 264.25 ha (27.02%) within the Stanari municipality. Over 90% of the cleared forest area is within the exploitation area. High erosion coefficient values (Z) were recorded within the approved exploitation area, coinciding with the spatial coverage of deforestation. Additionally, results obtained using an empirical method for studying mechanical water erosion (W y) indicate a high intensity within the exploitation area. For the approved exploitation area, possibilities for the restoration of degraded land were analyzed, including an overview of activities conducted so far and mapping some of the previously reclaimed areas. The research contributes to a better understanding of spatial changes resulting from mining activities, and the presented data are significant for numerous practical issues, such as sustainable land use planning, spatial planning, protection of forest ecosystems, and the environment.

Changes in rice fields are an important phenomenon that affects the sustainability of agriculture and the environment. This study aims to analyze the pattern of changes in rice fields in Makroman Village, using a Geographic Information System (GIS) approach. The method used is spatial analysis which involves such as spatial data creation, spatial data conversion, spatial data clustering, and mathematical calculation of spatial data. The spatial data used in this study include Ikonos Satellite Images of Makroman Village in 2011 and UAV Aerial Photos of Makroman Village in 2019. The results showed that the area of rice fields in Makroman Village increased from 96.196 ha to 129.343 ha in 2011-2019. Spatially, the increase occurred due to the presence of new rice fields and existing rice fields that did not decrease in number. Shrubs become land uses that have the potential to be converted in rice field extensification activities. GIS application is a method that can be used to answer the problems in this study to the level of detail, but some things that need to be considered in the application of this method are the quality of spatial data, especially remote sensing data used.

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely used to improve land cover classification. However, fully integrating optical and SAR image data is not straightforward due to the distinct distributions of their features. To this end, we propose a land cover classification network based on multimodal feature fusion, i.e., MFFnet. We adopt a dual-stream network to extract features from SAR and optical images, where a ResNet network is utilized to extract deep features from optical images and PidiNet is employed to extract edge features from SAR. Simultaneously, the iAFF feature fusion module is used to facilitate data interactions between multimodal data for both low- and high-level features. Additionally, to enhance global feature dependency, the ASPP module is employed to handle the interactions between high-level features. The processed high-level features extracted from the dual-stream encoder are fused with low-level features and inputted into the decoder to restore the dimensional feature maps, generating predicted images. Comprehensive evaluations demonstrate that MFFnet achieves excellent performance in both qualitative and quantitative assessments on the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the OA and Kappa metrics by 7.7% and 11.26% on the WHU-OPT-SAR dataset, respectively. [ABSTRACT FROM AUTHOR]

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects. [ABSTRACT FROM AUTHOR]

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%. [ABSTRACT FROM AUTHOR]

• LULC changes of three decades from 1991 to 2021 were examined and predicted. • Multi-spectral remote sensing data and RFT were utilized to generate LULC maps. • ACR were considered to analyze the categorical land use change detection. • Impervious land has expanded 10 times within 30 years and will increase in the future. The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. [ABSTRACT FROM AUTHOR]

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource–based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources. [ABSTRACT FROM AUTHOR]

Land is an important carrier of resource elements, and improving land green use efficiency (LGUE) is the key to achieving "smart growth" in cities. This study takes 269 cities in China from 2008 to 2020 as the research object and integrates multisource remote sensing data, GIS spatial analysis, and spatial econometric models to explore the evolutionary characteristics of LGUE and the spatiotemporal effects of urban expansion on LGUE. The results show that (1) urban LGUE increases over time and has certain gradient distribution and spatial dependence characteristics; (2) urban expansion has a significant inverted U-shaped relationship with LGUE, indicating that maintaining reasonable urban expansion is the key to improving LGUE, but excessive urban expansion has a strong negative spatial effect on LGUE. In terms of spillover effect, for every 1% increase in the square term of urban expansion, the LGUE of the local city decreases by 0.0673%, but the neighboring city increases by 0.2283%; and (3) urban expansion has significant spatiotemporal heterogeneous effects on LGUE, and spatial development boundaries must be reasonably determined to achieve "smart growth". Currently, some cities in China are expanding rapidly, and we hope to provide key support for promoting the "smart growth" of cities and improving LGUE. [ABSTRACT FROM AUTHOR]

A third of the world's ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO's SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data. [ABSTRACT FROM AUTHOR]

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department's sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is one of the most important variables in the physical processes of surface energy and water balance. The temporal behavior of LST was analyzed between the latitudes 32°00′ S and 34°24′ S (Valparaíso and Metropolitana regions of Chile) for three summer months (December, January, and February) in the 2000–2017 period, using the Terra MODIS image information and applying the Mann–Kendall test. The results show an increase in LST in the study area, particularly in the Andes mountain range in January (5240 km2), which mainly comprises areas devoid of vegetation and eternal snow and glaciers, and are zones that act as water reserves for the capital city of Santiago. Similarly, vegetated areas such as forests, grasslands, and shrublands also show increasing trends in LST but over smaller surfaces. Because this study is regional, it is recommended to improve the spatial and temporal resolutions of the images to obtain conclusions on more local scales. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LULC) exert a substantial influence on human life and environmental well-being. This research utilized the Intensity Analysis (IA) method to assess LULC change intensities spanning from 2000 to 2021 in the Muthurajawela coastal wetland (MW) and its 5 km buffer zone (MB) along the west coast of Sri Lanka. The research employed Landsat Thematic Mapper (TM) and Optical Land Imager (OLI) Remote Sensing Images. The supervised classification was used to accurately identify waterbodies, thick vegetation, other vegetation, settlements, and open areas with an average of 86% accuracy. The study revealed that the MW experienced a fast Interval level intensity shift during 2000-2010, followed by a slower pace during 2010-2021. Conversely, the MB displayed a consistently fast interval level intensity throughout both time intervals. Notably, waterbodies in MW decreased by 19.6%, while settlements witnessed a gain of 19.1% over the entire period. Transition Level (TL) analyses underscored the transformation of water bodies to open areas (6.75% in 2000-2010) and open areas to settlements (5.38%). In MB, other vegetation saw a significant decrease of 33.7%, with settlements registering a notable increase of 39.4% over the entire period. The study emphasizes urgent action for sustainable land use development in the face of rapid urbanization within the study area. [ABSTRACT FROM AUTHOR]

The Land Use and Land Cover (LULC) changes are commonly used to determine the landscape conditions and have significant impacts on the earth's surface processes. During the last three decades, there has been an acceleration of LULC changes in Eastern DR Congo. However, there is no comprehensive overview of the drivers of these changes at the territorial level in this region, even though the knowledge of these drivers is important for land use planning and spatial modeling of environmental changes. Using the Kalehe Territory as a case study, this work sought to fill this gap by analyzing the drivers of LULC changes during the 1987-2020 period. A mixed approach combining remote sensing, Geographic Information System, and logistic regression modeling was used. The results indicated that the prominent LULC changes in the study area are deforestation, built-up area expansion, cropland expansion, and shrubland expansion. These changes are significantly influenced by biophysical factors (slope, altitude, and soil type), conservation zoning, population dynamics, and accessibility factors at different levels. The occurrence of conservation zones decreases the susceptibility to deforestation, built-up land, and cropland expansions. In contrast, the proximity factors (distance to road, artisanal mining, and locality) increase the susceptibility to LULC changes. These factors can be integrated into spatial models to forecast LULC changes susceptibility in this region. Furthermore, the establishment of future land use management policy at the territorial level in eastern DR Congo should be space-specific as the susceptibility of LULC changes shows a spatial trend. [ABSTRACT FROM AUTHOR]

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space–air–ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area. [ABSTRACT FROM AUTHOR]

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. [ABSTRACT FROM AUTHOR]

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area. [ABSTRACT FROM AUTHOR]

Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest–grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest–grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest–grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development. [ABSTRACT FROM AUTHOR]

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM2.5, PM10, O3, NO2, SO2, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran's I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM2.5 and PM10 in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O3. Seasonal peaks for O3 occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) changes resulting from copper exploration in Kitwe District, Copperbelt Province has adversely impacted the environment. To understand LULC change dynamics associated with mining activities, this study mapped LULC changes using the Google Earth Engine (GEE) from 1990 to 2020. In addition, the Zambian legal framework for mine closure was assessed in terms of adequacy and comprehensiveness. A remote sensing analysis using Landsat TM (1990, 2000, and 2010) and OLI (2020) images was performed and the GEE Random Forest classifier algorithm was employed to detect LULC changes. Then, transition matrices and overall changes were calculated for each LULC class. The LULC classification had an overall accuracy and kappa coefficient of 82.47% and 0.78, respectively. In total, 45.2% of the district area (360.92 km2) experienced LULC changes from 1990 to 2020. The overall change indicates that the areas of built-up area, bare land, and grassland/pasture/agricultural land gained 35.84, 14.67, and 43.53 km2, respectively, while forest lost 95.30 km2, with the major driver being the privatization of mining companies. Several concerns regarding the mine closure process practiced in Zambia have principally been raised to the government. Although the legislation generally conformed to international best practices, a gap involving various pieces of legislation, overlapping requirements, and different interpretations of the laws by different governmental departments makes the system complex and unmanageable. An area of concern is the government's capability and competence to implement legislation. Ineffective law enforcement, that is, the inadequacy of the legislation, is to blame for LULC changes in mining areas, resulting in mining corporations not paying attention to the changes made, particularly regarding mine closures. This study provides decision-makers and land use planners with baseline knowledge on LULC changes that can be valuable for future mining legislation and how these legislations can be effectively executed to ensure sustainable mine closure. [ABSTRACT FROM AUTHOR]

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed's water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study's results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments. [ABSTRACT FROM AUTHOR]

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of "double carbon". In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB. [ABSTRACT FROM AUTHOR]

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model's understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities. [ABSTRACT FROM AUTHOR]

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia's dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes. [ABSTRACT FROM AUTHOR]

The increased availability of remote sensing products and new legislative agendas are driving a growing focus on farm‐level traceability and monitoring to tackle commodity‐driven deforestation. Here, we use data on land use change in Brazil (1985–2021) from Mapbiomas to demonstrate how analyses of the drivers of deforestation are sensitive to the scale of analysis: while pixel‐ or property‐level analyses identify proximate drivers of deforestation, analyses at larger scales (subnational regions or countries) capture more complex land use dynamics, including indirect land use change. We argue that initiatives which seek to monitor and address commodity‐driven deforestation—such as the European Union's deforestation due‐diligence regulation and the World Business Council on Sustainable Development's Greenhouse Gas Protocol—must be conscient of these wider land use dynamics. Only by measuring progress and defining success at multiple scales can initiatives for sustainable commodity sourcing create the right mix of incentives for addressing deforestation. [ABSTRACT FROM AUTHOR]

Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao's Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements. [ABSTRACT FROM AUTHOR]

With the rapid economic development and urban expansion, there has been a growing trend of ecological land being converted into non-ecological land. In order to mitigate this issue and improve the environment, the concept of national key ecological functional areas (NKEFA) has been implemented, and it is of great importance to assess the effectiveness of NKEFA in terms of land use and ecosystem services. Taking Chongqing City in China as a case study, this research utilizes remote sensing technology to analyze land use change and measure the ecosystem service value (ESV), and the NKEFA in Chongqing is regarded as a quasi-natural experiment to evaluate the protection effect on ecosystem services. Firstly, the results show that dynamic degree of that building land is the highest, with a value of 12.55% between 2010 and 2020, which is mainly due to the conversion of cultivated land into building land, and other land types transferred into building land are mainly concentrated in the southwest region of Chongqing; the dynamic degree of forest land and waters in NKEFAs is greater compared to Non-NKEFAs. Secondly, the ESV in NKEFAs is greater than that of Non-NKEFAs, and there is a significant concentration of low–low (L–L) agglomeration areas and high–high (H–H) gather areas of ESV. The former primarily lies within Chongqing's one-hour economic circle, while the latter is mainly distributed in Eastern Chongqing. Thirdly, the policy effect of NKEFA for ecosystem protection is 1.8445 higher than that of the control group. The mechanism analysis indicates that NKEFA plays a crucial role in restricting population density that improves ESV to a certain extent. It is evident that NKEFA functions through institutional constraints. [ABSTRACT FROM AUTHOR]

The diverse landscape of global land use and land cover (LULC) change studies were evaluated to uncover the current advances in data and future research potential through bibliometrics and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. A total of 2710 published articles with the search phrase “Land use and Land cover change” OR “Land-use and Land cover change” OR “Land use/Land cover change” OR “Land use and Land cover changes” were retrieved using Scopus, Web of Science (WOS), and ScienceDirect databases from 1993 to 2022. The findings indicated a 24.37% annual growth rate in LULC change studies, reflecting a rapid overall increase in published articles. China and the USA emerged as the most influential countries regarding article numbers, total citations, and single-country publications. Ethiopia, Ghana, and South Africa, among the top 20 global rankings of the most influential countries in LULC change studies, underscore the global importance of this research. However, the disparity in research output between multiple-country publications and the dominant trend of single-country publications highlights a geographical bias in LULC change studies, particularly in the Global South. This finding underscores the need for a more balanced research approach and can stimulate further investigation. The results also revealed that remote sensing, a rapidly growing field utilising advanced computing techniques, is the most prevalent keyword and has significant applications in reducing land degradation. These findings can significantly enhance research, climate policy programs, land management, and forest ecology planning, which are crucial in the face of the growing demand for agriculture and habitable land.

Rapid urbanization is restructuring landscapes across sub-Saharan Africa. This study employed post-classification comparison of multi-temporal Landsat imagery to characterize land cover changes in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022, addressing the need for empirical baselines to guide sustainable planning. Four classes were considered and images classified with overall accuracy of 95% for the year 2000 and 97% for the year 2022. Notably, 21,000 hectares of vegetation were lost, while built-up and bare land increased by 7500 and 13,700 hectares respectively. Spatial patterns revealed built-up encroachment from vegetation and bare land; this establishes the first standardized quantification of Abakaliki LGA's shifting landscape, with results supporting compact development models while conserving ecological services under ongoing transformations. The study makes a significant contribution by establishing an empirical baseline characterizing Nigeria's urbanization trajectory essential for evidence-based stewardship of regional resources and livelihoods in a period of accelerating change. [ABSTRACT FROM AUTHOR]