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Introduction: Modern forestry increasingly relies on the management of large datasets, such as forest inventories and land cover maps. Governments are typically in charge of publishing these datasets, but they typically employ disparate data formats (sometimes proprietary ones) and published datasets are commonly disconnected from other sources, including previous versions of such datasets. As a result, the usage of forestry data is very challenging, especially if we need to combine multiple datasets. Methods and results: Semantic Web technologies, standardized by the World Wide Web Consortium (W3C), have emerged in the last decades as a solution to publish heterogeneous data in an interoperable way. They enable the publication of self-describing data that can easily interlink with other sources. The concepts and relationships between them are described using ontologies, and the data can be published as Linked Data on the Web, which can be downloaded or queried online. National and international agencies promote the publication of governmental data as Linked Open Data, and research fields such as biosciences or cultural heritage make an extensive use of Semantic Web technologies. In this study, we present the result of the European Cross-Forest project, addressing the integration and publication of national forest inventories and land cover maps from Spain and Portugal using Semantic Web technologies. We used a bottom-up methodology to design the ontologies, with the goal of being generalizable to other countries and forestry datasets. First, we created an ontology for each dataset to describe the concepts (plots, trees, positions, measures, and so on) and relationships between the data in detail. We converted the source data into Linked Open Data by using the ontology to annotate the data such as species taxonomies. As a result, all the datasets are integrated into one place this is the Cross-Forest dataset and are available for querying and analysis through a SPARQL endpoint. These data have been used in real-world use cases such as (1) providing a graphical representation of all the data, (2) combining it with spatial planning data to reveal the forestry resources under the management of Spanish municipalities, and (3) facilitating data selection and ingestion to predict the evolution of forest inventories and simulate how different actions and conditions impact this evolution. Discussion: The work started in the Cross-Forest project continues in current lines of research, including the addition of the temporal dimension to the data, aligning the ontologies and data with additional well-known vocabularies and datasets, and incorporating additional forestry resources. [ABSTRACT FROM AUTHOR]

A study of land use and land cover (LULC) mapping using satellite data was conducted in the Congo Basin rainforest in the Sangha area of the northern Congo Republic, which is crucial for sustainable land management. However, the heterogeneous nature of LULC features makes data classification difficult in this area because persistent cloud cover is challenging for observing the land surface and tropical humid conditions characterizing the study area. This study compares and evaluates the potential of two of the most widely used and popular satellite image sources, the recently launched Landsat 9 and Sentinel-2, to generate land-use land cover (LULC) maps in the Sangha area using pixel-based supervised machine learning algorithms, Random Forest (RF), for classification. All procedures were performed using ArcGIS Pro. The potential to improve Landsat 9 performance was tested using the nearest resampling technique, and different bands were resampled from 30 × 30 to 10 × 10 m. Owing to the heterogeneous characteristics of the Sangha study area, six classes were defined: dense forest, open forest, wetland forest, water bodies, urban areas, and bare soils. To classify and compare Sentinel-2 and Landsat 9, the shortwave infrared (SWIR), Near Infrared (NIR), and Red, Green, Blue (RGB) bands were used. These two satellite images were compared to test the quality of their results, particularly for assessing the accuracy of the LULC classifications and identifying which dataset had the highest accuracy. Results show that overall accuracy was 93.80% for Sentinel-2 while it was 91.60% for Landsat 9, similarly, the Kappa coefficient was calculated 0.89 and 0.85 for Sentinel-2 and Landsat 9, respectively. Therefore, Sentinel-2 exhibits significant classification ability compared to the nearest resample technique Landsat 9, despite the improved resolution of the latter, which offers a scientific basis for choosing the appropriate satellite imagery to create accurate LULC maps. [ABSTRACT FROM AUTHOR]

China leads in the greening of the world, with a nearly doubled increase in its forest area since the 1980 s revealed by the National Forest Inventory (NFI). However, a significant challenge persists in the absence of consistent and reliable remote sensing data that align with the NFI, hindering a comprehensive understanding of the spatiotemporal patterns of terrestrial ecosystem changes driven by afforestation and reforestation efforts over recent decades in China. Moreover, conventional binary thematic maps and land use and land cover (LULC) maps encounter difficulties in providing a thorough assessment of canopy cover at the subpixel level and trees extending beyond officially designated forest boundaries. This limitation creates substantial gaps in our comprehension of their invaluable contributions to ecosystem services. To confront these challenges, this study presents a systematic framework integrating time-series Landsat satellite imagery and random forest-based ensemble learning techniques. This framework aims to generate China's inaugural annual tree cover dataset (CATCD) spanning from 1985 to 2023 at a 30 m spatial resolution. Evaluation against multisource reference data shown high correlations ranging from 0.70 to 0.96 and reasonable RMSE values ranging from 5.6 % to 25.2 %, highlighting the reliability and precision of our approach across different years and data collection methodologies. Our analysis reveals that China's forested area has doubled, expanding from 1.04 million km2 in 1985 to 2.10 million km2 in 2023. Notably, 33 % of this growth can be attributed to a shift from non-forest to forest land categories, primarily observed in the three-north and southwest regions. However, the majority, contributing 67 %, results primarily from crown closure in central and southern China. This realization underscores the limitations of conventional binary thematic maps and LULC maps in accurately quantifying forest gain in China. Furthermore, China's tree population structure has undergone a transformative shift from 83 % forest trees and 17 % non-forest trees in 1985 to 92 % forest trees and 8 % non-forest trees in 2023, signifying a transition from afforestation to established forests. Our study not only enhances the understanding of tree cover variations in China but also provides valuable data for ecological investigations, land management strategies, and assessments related to climate change. [ABSTRACT FROM AUTHOR]

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Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions. [ABSTRACT FROM AUTHOR]

Critical discussions are developing to combine geographic information systems (GIS) and qualitative methods as an alternative approach to traditional quantitative GIS, but the possibility of integrating spatial information technology and phenomenology is still unknown. This study explores a new qualitative and humanistic GIS approach called geo-phenomenology. It attempts to improve the ability to validate and authenticate findings and to gain a holistic understanding of the complex interplay among space, place, and human emotion through a combination of phenomenology, natural language processing methods, and GIS. To illustrate this approach, this study focuses on international students and examines the effects of geographic mobility on individuals' sense of attachment and belonging. Importantly, it allows for a more sensitive approach to the spatial scale of material culture as a mediator of sense of place. The overlay of emotional mapping and land use provided a perspective for narrative interpretation and hypothesis building, ultimately leading to the discovery of key evidence determining the main themes of international students' sense of place attachment and belonging. By combining quantitative emotion detection with qualitative phenomenological interpretation in GIS, this study concludes that geo-phenomenology can help us consider the rich nuances of lifeworlds from multiple perspectives. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the spatio-temporal dynamics of land vulnerability and pasture areas in the dairy basins of the states of Pernambuco and Alagoas, which are part of the Ipanema River Watershed (IRW) in the Northeast Region of Brazil. Maps of the Land Use and Land Cover (LULC); the Index of Vulnerability to Degradation (IVD); the Land Vulnerability Index (LVI); time series of Effective Herd (EH), Milked Cows (MC), and Milk Production (MP); and Pasture Cover (PC) and Quality (PCQ) were created as parameters. An opposite pattern was observed between the land use classes of Livestock, Agriculture, and Forest. The IRW area has predominantly flat terrain with a very high risk of degradation. The analysis of MC was consistent with the information from the EH analysis as well as with MP. When assessing Pasture Quality, Severe Degradation areas increased from 2010 to 2014, decreased after 2015, and rose again in 2020. Moderate Degradation areas remained high, while Not Degraded pasture areas were consistently the lowest from 2012 to 2020. Over the 10 years analyzed (2010–2020), the area showed a strong degradation process, with the loss of approximately 16% of the native vegetation of the Caatinga Biome and an increase in pasture areas and land vulnerability. [ABSTRACT FROM AUTHOR]

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future. [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]

1-Introduction In recent years, the increasing trend of the population has caused urban areas to face a lot of physical development, the share of developing countries has been higher than that of developed countries. Physical development in many areas has caused rapid changes in land use and increased environmental degradation. Therefore, the physical development of cities is considered as one of the main factors that change the surface of the earth. Studies have shown that the urban areas of Iran, due to their unlimited and unplanned expansion, have encroached on the natural environments and destroyed them, which has increased the potential for natural hazards such as floods in these areas. Has been considering the mentioned cases, more supervision should be done on the physical development of cities, especially flood-prone cities. Saghez city in Kurdistan province is one of the flood-prone cities that have faced a lot of physical development in recent years. Due to its economic and social situation, this city has faced a large population growth in recent years, which has caused the significant physical development of this city and as a result, the development of residential areas into flood-prone areas. Considering the importance of the topic, in this research, the physical development of Saghez city towards flood-prone areas has been investigated. 2-Methodology In this research, in order to achieve the desired goals, descriptive-analytical methods have been used. The research data includes topographic maps 1:50000 and geological maps 1:100000, a digital elevation model of 30 meters, as well as Landsat satellite images (Table 1). Research tools include Arc GIS, ENVI and IDRISI. The models used in the research also include the integrated model of fuzzy and AHP (in order to identify flood-prone areas) as well as the LCM model (in order to evaluate the physical development process of Saghez city towards flood-prone areas). This research has been done in two stages. In the first stage, in order to prepare the flood potential map of the study area, 6 parameters of height, slope, slope direction, distance from the river, and type of lithology and land use have been used. In the second stage, in order to evaluate the process of physical development of Saghez city towards flood-prone areas, the LCM model has been used. At this stage, after preparing the land use maps, the prepared maps were entered into the IDRISI software and using the LCM model, the trend of land use changes in the study area was evaluated. 3-Results The results of the flood zoning of Saghez city have shown that the middle areas of the range have the highest flood potential due to the proximity to the Saghez River, low altitude and slope. Also, the results obtained from the satellite images have shown that the city of Saghez had an area of about 6.5 square kilometers in 1369, which increased by 1.1 square kilometers to 7.6 square kilometers in 2000. In continuation of the physical development process of this city, the area of residential areas increased to 10.2 and 16.6 square kilometers by 2019 and 2019, respectively. Based on this, the maximum amount of physical development with 5.5 square kilometers was related to the time period of 1389 to 1399. Also, the results of the physical development of cities towards flood-prone areas show that in 1369, 3.8 square kilometers of residential areas were exposed to the risk of flooding, and from this amount to 1379, 1389 and 1399, respectively, 3 It has increased by 4.1, 5.1 and 2.7 square kilometers. Based on this, the highest amount of development towards flood-prone areas with 1.9 square kilometers was related to the time period from 2010 to 2020. 4-Discussion & Conclusions The increasing trend of population and physical development of urban areas has caused a large part of population areas to move towards flood-prone areas in recent years. The results of this research have shown that the middle parts of the study area have a high flood potential due to their proximity to the river, low altitude and slope. The results of the evaluation of the physical development of the city of Saghez also indicate that this city has faced a lot of development during the years 1369 to 1399, and based on the results, the city of Saghez had an area of about 6.5 square kilometers in 1369, which is 2019 has increased to 16.7 square kilometers. The results of the evaluation of the physical development process of Saghez towards flood-prone areas indicate that in 1369, about 3.8 square kilometers were exposed to floods, which increased to 2.7 square kilometers in 1399. Also, the results of the evaluation of land use changes indicate that during the studied time period, 8.8 square kilometers of pastures, 0.6 of agricultural lands and 0.8 of pastures have been converted into residential areas, the highest amount of which is related to It was the eastern areas of Saghez city. According to the mentioned cases, a large part of the city of Saghez has moved towards flood-prone areas in recent years, which indicates the lack of monitoring of the physical development of this city. [ABSTRACT FROM AUTHOR]

Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization. [ABSTRACT FROM AUTHOR]

In the era of information and communication technology (ICT), the advancement of science and technology has led to a trend of diversification in map representation. However, the lack of professional knowledge means that there is still a challenge in determining the appropriate type of thematic map for land use expression. To address this issue, this paper proposes a knowledge recommendation method for land use thematic maps based on the theory of visualization dimensions. Firstly, we establish a knowledge ontology of land use thematic maps centered on spatial data, data characteristics, visualization dimensions, thematic map forms, and application scenarios. A land use thematic map knowledge graph is constructed through knowledge extraction and storage operations. Secondly, knowledge embedding is performed on the knowledge graph to enable the knowledge-based expression of map visualization elements. Finally, based on the knowledge elements of land use thematic expression, a similarity calculation model is established to calculate the similarity between input data and the spatial data characteristics, visualization dimensions, and application scenarios within the knowledge graph, deriving a comprehensive similarity result to achieve precise recommendation for land use thematic map forms. The results show that the method can provide a more accurate visualization reference for the selection of land use themes, meeting the diversified needs of land use thematic expression to a certain extent. [ABSTRACT FROM AUTHOR]

This study examined the impact of climate change on streamflow in the Andit Tid watershed using climate models of dynamically downscaled Ethiopia's CORDEX. The Arc SWAT and ArcGIS 10.5 software assessed the spatial and temporal distribution of streamflow, incorporating geospatial data like land use maps, digital elevation models, soil maps, and climate data. The SWAT model was calibrated and validated using SWAT-CUP with the SUFI-2 algorithm. The Canadian Centre for Climate Modeling and Analysis, Canada (CCCma (RCA4) model was selected for future projections after validation. From 1991 to 2021, the average streamflow rate was 0.0374 m3/s (247 mm), with R2 values of 0.83 for calibration and 0.72 for validation. Hotspots with active gullies and slopes over 20% were identified mainly in cultivated lands. Future projections indicated a comparable streamflow rate to current conditions at 0.0322 m3/s (212.6 mm). A decline in streamflow is projected: 7.2% and 30.2% decreases in the near and far future under RCP 4.5, and 32.3% decreases and 5% increases under RCP 8.5 scenarios. These variations were attributed to differences in catchment characteristics and climate variability. Further research is needed to validate these findings by incorporating additional biophysical variables. This study provides insights into hydrological planning and management in the Andit Tid watershed and similar regions facing climate variability. [ABSTRACT FROM AUTHOR]

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents a challenge that traditional plant breeding alone struggles to address. In response to this pressing need, the infusion of intelligent technologies emerges as a pivotal solution, poised not only to boost production but also to meet the burgeoning demand. This transformative approach encompasses a spectrum of cutting-edge tools, including Remote Sensing and GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. The synergistic integration of these technologies will enhance agricultural monitoring by facilitating precise crop surveillance, early detection and mitigation of diseases and pests, optimization of water resources, accurate mapping of land use and crop types, comprehensive environmental monitoring, real-time weather and climate tracking, efficient nutrient management, precise irrigation and spraying practices, reliable yield prediction, advanced demand forecasting, genetic analysis, and informed decision-making processes. The amalgamation of intelligent technologies with modern plant breeding methodologies signifies a significant advancement towards achieving more efficient and sustainable agricultural practices. This convergence not only addresses the immediate need for increased food production but also sets the stage for a resilient and future-ready agricultural landscape. In this era of integration, we witness the harmonious coexistence of tradition and innovation, paving the way for a more abundant and secure agricultural future. [ABSTRACT FROM AUTHOR]

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies. [ABSTRACT FROM AUTHOR]

The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the produced thematic maps revealed that the proposed method minimizes the salt-and-pepper effect associated with pixel-based classification while mitigating segmentation problems arising from object-based classification. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning. [ABSTRACT FROM AUTHOR]

Climate change leads to changes in climatic variables, with rainfall being one of them. Changes in rainfall influence rainfall erosivity and subsequently erosion rates. This study analysed rainfall data from 1901 to 2017 in Odisha, focusing on different agro-climatic zones to discern annual rainfall pattern, its spatial variation, and trend, particularly concerning the rainfall erosivity factor and its impact on soil erosion and agricultural productivity. Notably, the Eastern Ghats Highland region received the highest average annual rainfall of 1578.5 mm, while the Western Undulating Zone received the lowest (1308.4 mm). The rainfall distribution showed spatial variability largely influenced by topography, with areas experiencing orographic lifting receiving higher rainfall. The study observed significant trend in annual rainfall, noting a maximum decline of 1.2 mm yr−1 in the North Western Plateau, Western Central Table Land, and Western Undulating Zone, whereas the East and South Eastern Plain, Mid Central Table Land, North Eastern Coastal Plain, North Eastern Ghats, and South Eastern Ghats exhibited a noteworthy increase in annual rainfall (0 to 3.9 mm yr−1). The decline in rainfall can result in the drying up of water bodies and reduced soil water availability to crop, thereby influencing agricultural production. On the other hand, areas with increased rainfall, may face extreme events which can aggravate soil erosion and thereby loss of soil fertility. Considering the scarcity of pluviographic data in countries like India, Modified Fournier Index (MFI) may be considered as one of the useful methods to capture rainfall's aggressiveness towards soil erosion through rainfall erosivity (R-factor). Therefore, to evaluate potential soil erosion levels, the Modified Fournier Index method was employed, revealing varying degrees of soil erosiveness across different regions. The Eastern Ghats Highlands exhibited the highest erosion potential. The R-factor, aligned with these spatial patterns, with the Eastern Ghats Highland (12,965.4 MJ mm ha−1 h−1 yr−1) and South Eastern Ghats (12,242.3 MJ mm ha−1 h−1 yr−1) regions displaying the highest R-factor values. Furthermore, the research identified areas prone to soil erosion by overlaying R-factor, slope, and land use maps, highlighting vulnerable regions such as Eastern Ghats Highlands, North Eastern Ghats, South Eastern Ghats, and Western Undulating Zone. This comprehensive analysis allows for informed prioritization of conservation efforts and the implementation of appropriate measures like strip cropping of finger millet with groundnut, intercropping finger millet with hedgerows of Gliricidia and Leucaena, bio-engineering measures such as earthen or stone bunds with broom grass in arable land and growing of aromatic grasses like lemon and citronella grass, construction of staggered trenches in non-arable land to mitigate erosion in these susceptible areas. [ABSTRACT FROM AUTHOR]

Semiarid regions and seasonally dry tropical forests play a critical role in global carbon exchange. In the Southern Hemisphere, these areas can contribute over 80% of positive global carbon storage in wet years due to their sensitivity to precipitation. It is therefore vital that climate models accurately represent land surface processes in these regions, including the contribution of vegetation seasonality to the water budget and carbon cycle. However, the simulation of phenological processes introduces new uncertainties associated with vegetation parameters, especially in biomes with a lack of field experiments, such as the Caatinga biome, a seasonally dry tropical forest. Furthermore, the global land cover maps used in land surface models and their associated parameters do not accurately reflect the diversity of vegetation in these regions. In this study, we improved the Noah-MP leaf area index simulation through parameter calibration and sensitivity analysis for the Caatinga biome in Brazilian semiarid region. To assess parameter uncertainty, we applied the generalized likelihood uncertainty estimation (GLUE) method with MODIS LAI as reference data. The best-performing models from GLUE improve the LAI simulation for BSA natural vegetation. The results indicated that the most sensitive parameters in LAI simulation are the field capacity, the specific leaf area index, and the leaf turnover rate. These parameters regulate water stress, conversion from leaf mass to LAI, and leaf carbon allocation to leaves. Additionally, it was observed that some vegetation parameters exhibit seasonal behavior, suggesting that allowing parameters to vary within the year could enhance the simulations. [ABSTRACT FROM AUTHOR]

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) mapping in the Brazilian Cerrado is challenging due to its vastness, seasonal vegetation changes, and spectral class confusion. Artificial intelligence, particularly deep learning (DL), offers promising solutions through semantic segmentation of Cerrado images, though it traditionally requires manual image annotation. This paper introduces the AI4LUC (Artificial Intelligence for Land Use and Land Cover Classification) method, leveraging DL and automated mask labeling to address LULC in the Brazilian Cerrado. AI4LUC includes pre-processing modules and the Smart Mask©Labelling (SML) module, which combines morphological operations with the CerraNetv3 deep convolutional neural network to automatically generate training masks for DL models like U-Net and DeepLabv3+. Additionally, we present the Cerrado Dataset (CerraData-v3), a high-resolution image dataset with 80,000 labeled samples. Our experiments show the SML module achieved an F1-score of 0.6647 and mIoU of 0.5068, demonstrating significant potential for automated semantic segmentation in this large biome. Furthermore, DeepLabv3+ outperformed U-Net by 69.86%. Source code and dataset: . [ABSTRACT FROM AUTHOR]

Wetlands, which serve as transitional zones between terrestrial and aquatic environments, play a crucial role in providing diverse ecosystem services for the sustenance of wildlife and mankind. Unfortunately, these delicate systems have experienced illicit human interventions in recent times, resulting in degradation and deterioration. Despite being designated as protected areas, wetlands are being exploited beyond their ecological capacity, posing a threat to their sustainability and potential existence in the near future. The present study aims to analyse the historical spatial changes in three wetlands located in Kerala, India, both before and after their declaration as Ramsar sites. Ashtamudi Lake, Sasthamkotta Lake, and Vembanad Lake were the focus areas, and the study employed multi-temporal satellite data sets from 1988 to 2023 to generate land use and land cover maps. The objective was to assess the ecological transitions, particularly the expansion of urbanization and subsequent reclamation of wetlands. By analysing the changes in aerial extent of each land cover class within the study area, the study identified spatio-temporal transitions and modifications occurring in and around the Ramsar sites. A comparison of the thematic maps from 1988 to 2023 revealed a significant increase in built-up areas and a noticeable reduction in vegetation, agriculture, and water bodies, both in terms of extent and quality. The study identified various socio-economic drivers that are responsible for these changes and emphasized the urgent need for regular monitoring and management of these wetland ecosystems. It stresses the importance of treating wetland conservation as a priority. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study's novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas. [ABSTRACT FROM AUTHOR]

The current study focuses on the devastating floods along the Kosi River in Bihar in July 2020, which were recorded by Sentinel-1A satellite images. Three flood-affected districts in Saharsa, Supaul, and Madhepura have been selected for this study. Pre- and post-flood images from the Sentinel-1A satellite were utilized to generate the flood-inundated map, and pre-flood Landsat-8 datasets were used to generate the land use and land cover maps. Finally, damage assessment was done by superimposing land use and land cover on a flood-inundated map. The results showed that Madhepura was the most affected district with 57.6% of its barren land experiencing inundation. Furthermore, a comparison of cloud cover percentages during the flood was undertaken using the Sentinel-1A, Sentinel-2B, and Landsat-8 datasets. The findings indicate Sentinel-1A has less than 1% cloud cover, making it suitable for flood monitoring. Due to urbanization and industrialization, land use/cover (LULC) varies rapidly, and the effect on the flood plain must be determined. TerrSet's Land Change Modeler uses cellular automata–Markov modeling to project LULC maps for 2030 and their implications for subsequent flooding. The findings indicate that Saharsa will see an increase in the built-up area of 2.7%, Supaul with a gain of 13.4%, and Madhepura will see a growth of 10.3%. The growth of built-up regions in the near future will encourage greater impermeable layers and more discharge. Therefore, land use planners, environmentalists, and lawmakers should consider these LULC changes in water resource planning. [ABSTRACT FROM AUTHOR]

The Madurai local planning authority, encompassing a land area of 726.34 sq. km, has encountered challenges of droughts and flash floods during the north–east monsoon season. These issues have arisen as a result of notable alterations in land use and the swift pace of urbanization. This comprehensive study aims to assess the effects of land use changes on direct runoff within the study area over a span of 40 years, from 1980 to 2020. This study attempted to understand the evolution of land use and land cover change in the Madurai LPA region over the past 2 decades and its corresponding impact on runoff generation. The study also predicted the trend of LULC in 2040. LANDSAT images from different years were acquired to create land use and land cover maps using ERDAS IMAGINE 9.1 and Arc GIS Version 10.1. Four hydrological soil groups were determined using data from the Madurai Atlas, and the surface runoff was calculated using the soil conservation service–curve number. The accuracy of the land use and land cover maps was evaluated using an error matrix and kappa index. LULC predictions for 2040 were made using the cellular automata and artificial neural network model. The analysis showed that agricultural land increased by 5.9% between 1980 and 2020, while forest cover decreased by 0.2% and urban settlements grew by 7.4% in the D hydrological soil group. The predicted land use for 2040 indicates that agricultural land will account for 54.1%, followed by 1% forest cover and 15.8% urban areas. The accuracy of the predicted land use map was validated using the 2020 map, with a 91% accuracy and a kappa coefficient of 0.8. The Madurai region has experienced a notable surge in urbanization, highlighting the urgency for effective flood management and the implementation of urban development strategies that prioritize the creation of green spaces and efficient storm water drainage systems. [ABSTRACT FROM AUTHOR]

Rail transit system plays an important role in connecting the urban center, new towns and suburbs in the metropolitan area. Exploring the methods of integrating rail transit and land use in different locations is essential to improve trip rates and promote transit-oriented development (TOD). Taking Wuhan, China, as a case study, this research uses a cellular automaton-based random forest (CA-RF) model to simulate land use change surrounding rail transit by capturing the impact differences in mother city (MC) and new towns (NT). An impact area identification model is proposed to recognize the most sensitive threshold of land use change in both MC and NT stations, which are 1000m and 1200m, respectively. The characteristics of land use change from 2010 to 2020 are analyzed and compared. The CA-RF model calibration shows that transportation and spatial accessibility present wide variations in driving land use change surrounding the MC and NT stations. The high accuracy of CA-RF model, which is 88.1% for MC and 82.1% for NT, indicating its effectiveness in quantifying the non-linear relationship of land use change and spatial attributes. The forecasted land use maps for proposed rail stations provide a reference for governments and planners in policy intervention and decision-making. This research framework can be applied to other metropolitans to explore the regulations of land use development and promote transit-oriented sustainable development. • Explore the driving mechanisms for land use change within rail transit stations area in different metropolitan areas. • Examine the non-linear relationship between land use change and spatial attributes by proposing a CA-RF model. • Develope a simulation framework to visualize future land use maps considering the impacts of rail transit. [ABSTRACT FROM AUTHOR]

Air quality concerns have become increasingly serious in metropolises such as Tehran (Iran) in recent years. This study aims to assess the contribution of urban trees in Tehran toward mitigating air pollution and to evaluate the economic value of this ecosystem service using the i‐Tree Eco model. To accomplish this objective, we utilized Tehran's original land use map, identifying five distinct land use categories: commercial and industrial, parks and urban forests, residential areas, roads and transportation, and urban services. Field data necessary for this analysis were collected from 316 designated plots, each with a radius of 11.3 m, and subsequently analyzed using the i‐Tree Eco model. The locations of these plots were determined using the stratified sampling method. The results illustrate that Tehran's urban trees removed 1286.4 tons of pollutants in 2020. Specifically, the annual rates of air pollution removal were found to be 134.8 tons for CO; 299.7 tons for NO2; 270.3 tons for O3; 0.7 tons for PM2.5; 489.4 tons for PM10 (particulate matter with a diameter size between 2.5 and 10 µm); and 91.5 tons for SO2, with an associated monetary value of US$1 536 619. However, despite this significant removal capacity, the impact remains relatively small compared with the total amount of pollution emitted in 2020, accounting for only 0.17%. This is attributed to the high emissions rate and low per capita green space in the city. These findings could serve as a foundation for future research and urban planning initiatives aimed at enhancing green spaces in urban areas, thereby promoting sustainable urban development. Integr Environ Assess Manag 2024;20:2142–2152. © 2024 SETAC Key Points: Tehran's urban trees removed 1286 tons of pollutants in 2020, and yet their contribution toward improving air quality was relatively small, accounting for less than 1% of the overall pollution reduction and unable to offset local emissions due to dormancy in winter.Compared with other preferred approaches, such as replacing eroded diesel buses and trucks with newer models, urban trees showed the lowest cost‐effectiveness in improving air quality but were insufficient on their own.Urban planners and managers must consider the combined impact of urban trees with other approaches to significantly mitigate air pollution and ensure city sustainability.Future research should investigate the role of shrubs alongside trees in reducing air pollution and quantify the combined air pollution removal by vegetation canopies to fully understand urban green infrastructure's effectiveness in mitigating air pollution. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Archaeologists have developed tools to reconstruct the locations of farming and animal herding using ecological and digital modeling of ancient landscapes. The determination of where on a landscape farming and herding took place, however, can remain elusive in environments with evidence for substantial geomorphological and/or ecological change since the period of occupation. Archaeobotanical and geoarchaeological evidence from the site of Gordion, in central Anatolia, indicates substantial landscape change over the last 4000 years, including deforestation, overgrazing, erosion, and alluviation. These have been inferred to be the result of past agricultural practices, but no firm evidence has pointed to specific locations (geographic and temporal) where ancient farming and herding may have caused these changes. Integrating extant archaeobotanical, zooarchaeological, and geoarchaeological evidence with new isotopic data provides a more detailed reconstruction of the sequence of agricultural practices that shaped the present landscape and ecology of the region, offering a model for future archaeological research within substantially transformed landscapes. [ABSTRACT FROM AUTHOR]

Nature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change. [ABSTRACT FROM AUTHOR]

Regional development is one of the efforts to improve socioeconomic development, reduce regional disparities and preserve the environment. The Gross Regional Domestic Product (GRDP) of Banyumas Regency has increased over the past 5 years, with the largest contributor to the Gross Value Added category being the Agriculture and Forestry sector. Arabica coffee varieties are one of the mainstay commodities in the region and a source of income for the people of Kabupaten Banyumas. Arabica coffee is a plantation commodity that has been determined to be developed in Kabupaten Banyumas in accordance with the plantation commodity development plan in the RTRW Kabupaten Banyumas 2012-2032. The main objective of this research is to develop a strategy in planning the development of arabica coffee plantation area in Kabupaten Banyumas. The research area covers all 29 subdistricts starting from January to April 2024. In looking at the actual distribution of arabica coffee plantations, overlay analysis techniques were used using data from land use maps, elevation maps. Land availability was analyzed through overlay and matching techniques with ArcGIS software and direct measurement. Arabica coffee agribusiness system was analyzed through R/C ratio descriptively. The development strategy of Arabica coffee plantation area in Banyumas Regency was conducted using AWOT analysis. The results showed that the actual distribution of arabica coffee plantations in the cultivation area in Banyumas Regency covers several sub-districts and the area reaches 1,214 ha. Available and suitable land for the development of arabica coffee plantation in the cultivation area is indicated to reach an area of 20,471 ha, of which the largest area is located in Lumbir Sub-district with an area of 3,726 ha or 18.09% of the total available land area. [ABSTRACT FROM AUTHOR]

Changes in policy and new plans can significantly influence land use and trigger land use change in the long term. The data for pre- and post-policy implementation is necessary to assess the specific policy's impact on land use. In the early nineties, Germany started promoting renewable energy production, including bioenergy, which changed the agricultural landscape. Remote sensing (RS) image-based machine learning models can be beneficial for mapping agricultural land use in the present and the past. However, machine learning classification models trained on RS data from specific training sites and time may not be able to predict data for unknown sites and unknown temporal points due to changes in crop phenology, field features, or ecological site circumstances because most of the models are limited in their performances according to variations of the training data set. Therefore, this study aims to assess the spatial–temporal transferability of Landsat-based agricultural land use type classification. The study was developed to map agricultural land cover (5 classes: maize, grasslands, summer crops, winter crops, and mixed crops) in two regions in Germany (North Hesse and Weser-Ems) between the years 2010 and 2018 using Landsat archive data (i.e., Landsat 5, 7, and 8). Two machine learning models (random forest − RF and 2D convolution neural network – 2DCNN) were trained and evaluated according to no transferability (reference) scenario and three spatial–temporal scenarios using mF1 and class level F1 values. Three model transferability scenarios were evaluated: a) temporal – S1, b) spatial – S2, and c) spatiotemporal – S3. The reference scenario, without transferability, achieved an overall accuracy of 89.1% and a macro F1 score of 0.74 for RF and 89.9% and 0.75 for CNN, respectively. Under three transferability scenarios (S1, S2, and S3), the macro F1 scores decreased to 0.67, 0.66, and 0.62 for RF, and 0.68, 0.62, and 0.58 for CNN, respectively. The dissimilarity between the data employed to train the model and data from the new domain indicated a clear link that could explain the reduction in model predictability. Moreover, the performance degradation could be attributed to the disparity in environmental, climatic, and crop calendar conditions between the two domains. Understanding the extent of model performance degradation during transferability is crucial for developing effective strategies to mitigate these issues and enhance the generalisability of machine learning models for agriculture land cover mapping. [ABSTRACT FROM AUTHOR]

Spectral indices developed to extract features from satellite images are simple and fast methods that reduce processing time compared to traditional satellite image classification. In this paper, the effectiveness of the normalized difference building index (NDBI), the normalized difference vegetation index (NDVI), and the Normalized Difference Water Index (NDWI) were evaluated in land cover mapping and detecting its changes in the period from 2013 to 2023 in Fayoum Governorate, Egypt, using Landsat 8-OLI images. The results of supervised classification showed a decrease in green areas between 2013 and 2023 by 76.03 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.8 square kilometers and an increase in built-up areas by 79.83 square kilometers, at the expense of green lands. On the other hand, the results of using spectral indices showed a decrease in green areas between 2013 and 2023 by 91.12 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.35 square kilometers. Meanwhile, it was noticed that, increasing in the built-up areas by 94.47 square kilometers at the expense of green areas. The results showed a general convergence in the changes in the studied classes during the study period, with a great convergence in the change in the area of the water bodies resulted from the classification and from the NDWI and a convergence in the effectiveness of both the NDVI and NDBI. Finally, the effectiveness of the spectral indices in land cover mapping using check points was evaluated. The results showed a high percentage of the number of correctly identified check points for the NDWI index, with this percentage being noticeably low in the case of the NDVI and NDBI. [ABSTRACT FROM AUTHOR]

The Baner-Balewadi was a rural area that transformed into a residential suburb and is now metamorphosing into a commercial and educational hub of Pune city. This area is now a part of Pune Municipal Corporation and is occupied by various schools, universities, and IT companies. The decadal population growth rate of the Baner-Balewadi area is 275%; it is much higher than the Pune district's and city's growth rate, which makes it a fascinating area for this study. A cross-sectional survey was conducted in this area covering three major regions: Balewadi Highstreet, Baner-Balewadi road, and NICMAR University. The data for 15 parameters were gathered, a total of 38 survey samples were collected using stratified random sampling, and land use was mapped to understand the grassroots reality. Thereafter, the parameters were analysed using a descriptive statistical technique, and spider mapping was used for data presentation. The strengths and challenges regarding infrastructural services, transportation, socio-economic factors, environment, and land use were identified. The results show that the waste collection is strong, but there are drinking water issues in the area. The recent development is causing stress on the infrastructural services and unregulated land-use change leading to environmental degradation. The metro construction is causing a lot of noise and air pollution, and people will still prefer buses as a transportation medium in the future because of good connectivity. The study is concluded with area-wise parametric recommendations made after examining the advancement, challenges, and gaps identified at the grassroots from both stakeholders' and experts' perspectives. [ABSTRACT FROM AUTHOR]

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest–grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar–optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region. [ABSTRACT FROM AUTHOR]

Worldwide, the majority of countries are actively devising strategies to address the challenges associated with unregulated and unmanageable development, the decline in environmental quality and the depletion of valuable agricultural land. This has led to a growing emphasis on understanding land use and land cover. In order to determine a better land use policy, legislators and planners need to know the current distribution of agricultural and urban lands, as well as information about changes in their proportions. Our approach combines data centred on main four themes—geology, slope gradient, hydrographic network and land use—in order to exploit classifier complementarities in our targeted agricultural study area of Tamlouka Basin, Algeria. Landsat 8 OLI-TIRs multispectral imagery and Shuttle Radar Topography Mission (SRTM-1arc v3) were used experimentally for classification and Digital Elevation Model (DEM) analysis. The classification's accuracy is confirmed by comparing the results of the decision tree classification with the validation samples. Results of the combination of several maps of classifications from the different methods show that the Tamlouka alluvial plain, having an area of 19,300 ha and an average slope gradient of less than 2°, drains the elevated reliefs that surround it via hydrographic network. The plain occupies 37% of the total basin area, with over of 60% being used for crop cultivation, regardless of fallow land areas in agricultural rotation at that time. The slope has been identified as a crucial factor determining land use patterns in the study area. This result can be used in prospective watershed management. [ABSTRACT FROM AUTHOR]

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In this study, we investigated the sensitivity of landscape metrics to varying grain sizes in a rural, forested landscape in Japan, contributing to a broader understanding of landscape metric behavior across different scales. We analyzed six class-level and two landscape-level metrics on a land use map at grain sizes of 5, 10, 20, 30, 40, 50, 75, and 100 m. Our results indicate that the effect of increasing grain size on landscape metrics varies depending on the specific metric and land use type. Key metrics, such as the percentage of land use types in a landscape, and two landscape-level metrics showed minimal change across the range of grain sizes. Conversely, patch density and cohesion decreased, whereas Euclidean nearest neighbor distance increased. In addition, the patch area and radius of gyration showed variable responses across different land use types, influenced by their characteristic dimensions. This study highlights the limitations of using coarse-resolution data for detailed landscape analysis, as it may not fully capture landscape change or the relationship between landscape patterns and ecological processes. We propose an optimal grain size of 5–50 m for analyzing rural forested landscapes in Japan, which effectively captures fine-scale elements critical for biodiversity conservation. This range allows accurate comparisons between different regions and land use plans, especially in satoyama landscapes. This research highlights the importance of selecting appropriate grain size in landscape analysis and interpretation of landscape metrics as well as urges researchers and policymakers to ensure accurate ecological assessments and informed decision-making. [ABSTRACT FROM AUTHOR]

In the present study, we propose a transdisciplinary investigation aimed at developing an index to assess the vulnerability to landslides in the Brazilian municipality of São José dos Campos. The proposed Socio-Geoenvironmental Vulnerability Index (SGeoVI) was developed using a hybrid modeling approach that integrates socioeconomic data with landslide susceptibility mapping. Landslide susceptibility was derived from the FS FIORI deterministic model, which calculates the Safety Factor (FS) using the concept of limit equilibrium. The mapping of land use and land cover enabled the inclusion of new parameters related to certain anthropogenic conditions and vegetation cover, such as the overabundance of buildings and the presence or absence of tree vegetation, which are significant factors influencing landslide occurrence. Socioeconomic indicators were extracted from Brazilian Institute of Geography and Statistics (IBGE) census data, and variables were selected and spatially represented for each of São José dos Campos's 1073 census tracts. Socioeconomic indicators were taken into account for the SGeoVI proposal and categorized into five thematic groups: economic; educational; housing infrastructure; social dependency; e family structure. Based on the Socio-geoenvironmental vulnerability indicators, several neighborhoods in São José dos Campos, SP, were selected for a detailed SGeoVI assessment. In Pinheirinhos, a neighborhood situated in the southernmost part of São José dos Campo, the highest SGeoVI value reached to 0.91. In contrast, Jardim Apolo I, an established neighborhood located in the Central Region of São José dos Campos, exhibited a considerably lower SGeoVI value of only 0.04, for instance. The verification results of SGeoVI revealed socioeconomic disparities that align with geomorphological variations. The northern and southern parts of the municipality exhibit high susceptibility to landslides, coupled with low-income communities and inadequate housing structures, contributing to elevated socio-geoenvironmental vulnerability. In contrast, central regions feature flat terrain, reduced natural susceptibility, higher income levels, and improved access to housing infrastructure, resulting in lower vulnerability values. Given its ability to assess intra-municipal socio-geoenvironmental vulnerability, SGeoVI holds potential for extrapolation to other municipalities. Consequently, it can serve as a valuable tool for municipal authorities in formulating public policies aimed at landslide risk management. [ABSTRACT FROM AUTHOR]

Since the 1970s, there has been a long history of assessing the accuracy of remotely sensed land cover types. Various accuracy assessment methods have been proposed, including the method of bootstrap resampling. However, the potential of the bootstrap resampling method to diagnose the accuracy of remote sensing monitoring of land cover has not been fully explored. Here, we used the land cover map classified in the Gannan Tibetan Autonomous Region as an example and investigated the impact of two parameters in the bootstrap resampling method on the assessment of land cover classification accuracy: the number of validation points and the number of bootstrap resampling times. Varying these two parameters can help diagnose the accuracy of remote sensing classifications of land cover. The main conclusions are as follows. First, the number of validation points required to achieve a stabilized accuracy estimate varies among land cover types. It indicates not only the level of homogeneity of the land cover type but also the level of difficulty associated with classifying it. Second, it is not necessary to have a very large number of bootstrap resampling times to estimate the classification accuracy – 50 was sufficient. In summary, we recommend that various numbers of validation points be employed in bootstrap to objectively and comprehensively assess the overall, producer's, and user's accuracies of remote sensing classifications of land cover. [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]

Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence on water quality is vital for the sustainable development of inland lakes. In this study, based on Landsat5/8 images, three types of land features, namely spectral features, index features, and texture features, and five machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), k-nearest neighbor (KNN), and Gaussian naive Bayes (GNB), were combined to identify aquaculture ponds and some other primary land use types around a typical inland lake of China. The results demonstrated that the XGBoost algorithm that integrated the three features performed the best among all groups of the five machine learning algorithms and the three features, with an overall accuracy of up to 96.15%. In particular, the texture features provided additional useful information besides the spectral features to allow more accurately separation of aquaculture ponds from other land use types and thus improve the land use mapping ability in complex inland lakes. Next, this study examined the tendency of aquaculture ponds and found a segmented increase mode, namely sharp increase during 1984–2003 and then slow elevation since 2003. Further positive correlation detected between the area of aquaculture ponds and the phytoplankton population dynamics suggest a likely influence of aquaculture activity on the lake water quality. This study provides an important scientific basis for the sustainable management and ecological protection of inland lakes. [ABSTRACT FROM AUTHOR]

The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth's ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products. [ABSTRACT FROM AUTHOR]