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Monthly night-time light (NTL) data can be very useful for studying intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines started providing monthly NTL imagery for free since 2021; based on Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data. In the current study, an attempt has been made to produce an improved monthly DMSP product from 1992 to 2013. The monthly DMSP product suffers from several drawbacks such as spatial inconsistency, random fluctuations in the data for consecutive years, pixel saturation in bright cores, and blooming effect around settlements. As a result, geometric errors, background noise, and radiometric errors persist in the monthly DMSP images. This research tries to address each of these errors by applying various techniques to produce a vigorously tested and improved monthly DMSP dataset consisting of 216 images (1992–2013). Automatic intensity-based registration has been used to register the monthly images to their corresponding annual composite stable light image. Thresholding and land cover data have been used to remove the background noise. Finally, ridgeline regression is implemented considering F14 December 2003 as the reference image. Each technique has been verified by qualitative analysis of the intermediate outputs. The quantitative assessments of the improved monthly DMSP product reveal that it is a good proxy measure when split from 1992 to 2002 and 2003–2013 with an R2 of 0.84 & 0.82 w.r.t GDP and 0.83 & 0.80 w.r.t population in contrast to the original DMSP data with R2 of 0.70 & 0.75 w.r.t GDP and 0.73 and 0.80 w.r.t population. Transect analysis across six urban cities verifies that the improved product shows reduced saturation in urban areas and increased contrast in sub-urban regions. Finally, a consistent monthly DMSP product is delivered to the research community which will be useful for studying time-series intra-year changes in urbanization, economic growth, and other socio-economic dynamics. [ABSTRACT FROM AUTHOR]

Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes. [ABSTRACT FROM AUTHOR]

The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia. [ABSTRACT FROM AUTHOR]

The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles. [ABSTRACT FROM AUTHOR]

This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance. [ABSTRACT FROM AUTHOR]

The digital number of a given pixel in a satellite image does not only measure the radiance originating from the area of the Earth’s surface represented by this pixel, but also measures radiance originating from surrounding areas represented by adjacent pixels. This adjacency effect, also known as image blurring, is a fundamental limitation of satellite images, and introduces errors in all image processing techniques that quantify the properties of the Earth’s surface on a per-pixel basis. Image deblurring via deconvolution, is a traditional technique to reduce the adjacency effect, which has proven effective in a variety of remote sensing applications. For example, it has provided an almost fourfold reduction of the adjacency error, when using support vector machine classifiers to estimate land cover proportion at a subpixel level. The remote sensing literature usually assumes that satellite images should be deconvolved with a kernel defined by the sensor’s Point Spread Function PSF(x, y), overlooking the results of three seminal empirical studies which systematically demonstrated that the optimum deconvolution kernel is a shrunk PSF of the form PSF(x/β, y/β), where β < 1 is the optimum shrink factor. The current empirical procedure to find the optimum shrink factor is laborious and restrictive since it requires suitable satellite images of much higher resolution, such that their Ground Sampling Distance (GSD) is equal to the GSD of the sensor of interest divided by an integer number much greater than one. A new theoretical procedure that uses synthetic edge images to find the optimum shrink factor is proposed and applied to the same cases considered by the current empirical procedure, showing that the same results are obtained. The new procedure is much simpler to apply and more accurate and versatile, opening a variety of research paths on satellite image deblurring. [ABSTRACT FROM AUTHOR]

There is a need to assess whether ecological resources are being protected on large, federal lands. The aim of this study was to present a methodology which consistently and transparently determines whether two large Department of Energy (U.S. DOE) facilities have protected valuable ecological lands on their sites compared to the surrounding region. The National Land Cover Database (2019) was used to examine the % shrub-scrub (shrub-steppe) and other habitats on the DOE’s Hanford Site (HS, Washington) and on the Idaho National Laboratory (INL), compared to a 10-km and 30-km diameter band of land surrounding each site. On both sites, over 95% is in shrub-scrub or grassland, compared to the surrounding region. Approximately 70% of 10 km and 30-km bands around INL, and less than 50% of land surrounding HS is located in these two habitat types. INL has preserved a significantly higher % shrub/scrub habitat than HS, but INL allows grazing on 60% of its land. HS has preserved a significantly higher % grassland than INL but no grazing on site is present. The methodology presented may be used to compare key ecological habitat types such as grasslands, forest, and desert among sites in different parts of the country. This methodology enables managers, resource trustees, and the public to (1) make remediation decisions that protect resources, (2) assess whether landowners and managers have adequately characterized and protected environmental resources on their sites, and (3) whether landowners and managers have protected the integrity of that land as well as its climax vegetation. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to improvements in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labelled data is still a challenge, hindering the large-scale application of land cover classification methods. In this study, a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data is proposed. First, to capture long-range dependencies without overlooking the fine-grained details of objects, a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention is designed. Second, an anti-noise loss calculation module based on optimal transport (OT) is proposed. The anti-noise loss calculation identifies confident areas and vague areas based on the OT matrix, which relieves the effect of noises on outdated land cover products. By introducing a weakly supervised loss with weights and using an unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolutions and the corresponding ground truths of six states in the United States were used to validate the performance of the proposed method. The experiments used outdated land cover products with 30 m resolutions from 2013 as training labels and produced land cover maps with 1 m resolutions from 2017. The results showed the superiority of the proposed method over state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former. [ABSTRACT FROM AUTHOR]

Land cover change research is salient in providing information for land use planning and management in urban areas. This study therefore analysed land cover change in four local government areas in Ogun State, Nigeria. Using remote sensing and geographic information system, Landsat images were analysed between 2001, 2013 and 2020 with both unsupervised and supervised image classifications. Results showed that between 2002 and 2013, there was a 41.14% decrease in forest area which further reduced by 41.92% by 2020. Agricultural land increased by 66.47% between 2002 and 2013 but reduced by 43.24% by 2020. Urban areas increased by 108.66% between 2002 and 2013 while by 2020 they had gained 42.19% additional land. It is concluded that the 2005–2025 Ogun State regional planning policy has so far been less than effective due to massive ecological change caused by urban expansion in the region. [ABSTRACT FROM AUTHOR]

During floods, updated and accurate information on affected human settlements helps save lives and reduces time to rescue. Therefore, approaches that can provide reliable information during floods via the use of all-weather and real-time functional technology is highly needful. The study presented here attempts to efficiently and precisely map villages in the Indian sub-continent during floods via a three-stage approach which uses PolSAR data. However, an accurate segregation of villages in India even with PolSAR data is challenging because the built-up structures in the villages of rural India are closely placed and are randomly oriented w.r.t. each other. This condition either hinders their segregation or otherwise induces false alarms during extraction. More descriptive land cover characterization features and powerful feature classifiers may address this challenge. The study in this paper proposes a novel approach to efficiently detect and map flood affected villages which utilize polarization signatures from PolSAR imagery, ensemble-of -dilated-convolutions based CNNs, apriori knowledge and image morphology. The approach broadly involves three stages: first, built-up area extraction from a PolSAR image: second, detection of villages in a built-up area image and third, identification and mapping of villages that are affected by the flood. In the first stage, an ensemble of varying dilated-convolutions based novel CNN classifier which directly utilizes PolSAR-2 polarization signatures (PSs) in window-mode as features are developed to extract built-up areas. The second stage provides a novel village detection filter based on apriori knowledge and image morphology to detect actual villages and mask out the false objects. Finally, in the third stage, flood affected villages are mapped via a series of morphological operations based degree-of-intersection measure. Experiments are conducted on both simulated and natural flooded area datasets. Experimental results show 81% detection accuracy and 100% mapping performance of the proposed approach which indicates its potential as an effective flood affected village mapping system. [ABSTRACT FROM AUTHOR]

From land degradation and desertification to cyclones and tropical storms, and so on, the repercussions of global change have become increasingly severe in recent years. Such environmental impacts require continuous assessment and monitoring. Thus, to study and analyse these impacts, a variety of time-series forecasting approaches have been developed, including statistical ones [i.e. Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), etc.], and machine-learning approaches such as Recurrent Neural Networks (RNN), Long-Short-Term Memory Network (LSTM) and Convolutional Neural Networks (CNN). In this study, accuracy of the most used forecasting approaches (i.e. MA, LSTM and Conv-LSTM) has been quantified, and three impacts (i.e. climate regions, land cover and satellite sensors) have been brought to light. Firstly, eight Mediterranean regions were selected (i.e. two hot arid regions, two cold arid regions, two regions with temperate hot summers and two regions with temperate warm summers) based on Köppen climate classification. Secondly, 654 hyperspectral images retrieved from three different satellites (i.e. Sentinel-2, Landsat-8 and MODIS) from 2016 to 2022 were computed in order to predict 29 vegetation biophysical indices (i.e. NDVI, GNDVI, EVI, CVI, etc.). Accordingly, more than 18,000 images were computed, resulting in 696 time series forecasted using the aforementioned approaches. Finally, 2088 forecasted time series have been determined, and their accuracy has been compared. As a result, Landsat-8 and Sentinel-2 images had the highest forecasting accuracy in the three approaches, reaching 86% of the computed indices being over 50% accurately predicted in LSTM model, while MODIS data had the highest forecasting accuracy only in MA model, with a percentage of 72% of the computed indices being over 50% accurately predicted. Furthermore, we have identified that region climate impacts vegetation forecasting accuracy. For instance, arid regions showed low accuracies across all models, while temperate regions showed higher accuracies in the Mediterranean region. [ABSTRACT FROM AUTHOR]

A significant strategy for achieving a balance between spatial and spectral resolution is to combine hyperspectral remote sensing images with low spatial resolution and multispectral remote sensing images with high spatial resolution. For the issues of spectrum distortion and lack of the ability of gaining high frequency information when the Fuvar algorithm employs the Vertex Component Analysis (VCA) technique, this thesis presents a modified Fuvar (MFuvar) algorithm that utilizes the Maximum Distance Analysis (MDA) method instead of the VCA approach. The two subsets from the hyperspectral remote sensing image of GF-5 and the multispectral remote sensing image of Sentinel-2A, representing different land cover types were used as test data. The spectral fidelity and the ability of gaining high frequency information were assessed by using visual and statistical analysis. Fused images are compared with eight fusion methods, including SFIMHS, GLPHS, MAPSMM, CNMF, Hysure, SpaFusion, LTTR, and Fuvar, respectively. The results show that the MFuvar algorithm can keep the best balance between spectral fidelity and the ability of gaining high frequency information, and it is generally better than the compared eight algorithms. And it fulfils the automatic selection of end elements without manual intervention and increases the efficiency of algorithm operation. [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]

In remote sensing applications, image classification algorithms normally require parameter optimization strategies to adapt to the complexities of the data and determine the model's parameters for optimal performance. However, a unique set of hyper-parameters is generally chosen, without acknowledging the presence of subsets of classes that exhibit different levels of spectral similarity. A model used to distinguish between well-separable classes might not be optimal for delineating other classes. This study explores a classification method that extends the standard One-Against-One (OAO) and One-Against-All (OAA) strategies for Support Vector Machines (SVMs), based on agglomerative hierarchical clustering of spectrally-similar classes. The main objective is to provide additional classification scenarios where more detailed per-class analyses can be performed, rather than simple comparisons of global accuracies, particularly for hard-to-distinguish classes. Clusters of classes closely located in the feature space are initially classified using an OAA strategy, while each cluster undergoes further subdivision using an OAO approach. This strategy aims to identify the most effective sequence for incorporating subgroups of classes into the tuning process. The experimental results indicate that the proposed method can be efficiently applied to classify remote sensing data, producing varying levels of enhancement in per-class accuracies at each stage of the agglomerative process. [ABSTRACT FROM AUTHOR]

Soil erosion and sediment yield in basins are influenced by a combination of land use/land cover changes and climatic factors. The existing empirical sediment yield index (SYI) model does not consider the spatiotemporal non-stationarity of the parameters and its application is limited in data-deficient basins. To address these issues, a novel framework was proposed to extend the application of the empirical SYI model, and to identify the spatiotemporal heterogeneity of the driving factors using the geographically and temporally weighted regression (GTWR) model, and it was demonstrated in the Dongjiang River basin, South China. The constructed multi-factor GTWR model explains 87% of the variation in SYI. Spatially, population density and urban land are the main driving factors of SYI, and there are significant spatial differences in their effects. Temporally, urban land is the most significant driving factor for SYI, and its effect increased over time. [ABSTRACT FROM AUTHOR]

The growth of African cities is characterised by sprawl, significantly impacting social, economic and environmental systems. The change in land use due to urban sprawl often dispossesses farmers in peri-urban communities, resulting in income loss. Focusing on social and ecological changes in peri-urban communities of Ghana, this paper sheds light on land use and land cover change stemming from urban sprawl. It is argued that land governance systems that shape resource access in peri-urban areas have been transformed with farming being the most impacted. Emerging livelihoods like sand winning further compounds a precarious situation. Women and youth are the most affected, with limited options for alternative occupations and uncertain futures. It is thus imperative that policy interventions prioritise sustainable land governance and urban planning, taking into account the needs and vulnerabilities of peri-urban communities, particularly women and youth. [ABSTRACT FROM AUTHOR]

The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively. [ABSTRACT FROM AUTHOR]

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of freshwater, contributing significantly for drinking and other domestic activities around the world. However, its quality is deteriorating over time due to overexploitation and anthropogenic activities. Rib watershed is located in the Tana basin (Ethiopia), is intensively cultivated and urbanizing area. Therefore, this study attempted to assess groundwater vulnerability to pollution using the GIS-based Modified DRASTIC Model. The modified DRASTIC model was selected due to it incorporates both hydrogeological parameters and anthropogenic factors for assessment. The necessary data were collected from the field, and downloaded from websites and laboratory experiments (No3-). The results showed that more than 73.24% of the watershed is under medium to high vulnerability. Highly vulnerable areas of the watershed (22.48%) was confined to the Southern parts of the watershed (under built-up and cultivated areas). Based on single parameter sensitivity analysis, its vulnerability was highly influenced by aquifer media (24.1%), net recharge (21.75%), land use/land cover (15.1%), and depth of groundwater table (13.6%). The Modified drastic model was validated based on the observed data of nitrate concentration in groundwater. Based on the observed data of nitrate concentration, high-vulnerable areas were more contaminated than medium and low-vulnerable areas. The modified drastic index has a strong correlation with nitrate concentration with (r = 0.76) and (P < 0.043). Therefore, the result indicated that the area is vulnerable to contamination calling for appropriate groundwater management. Hence, this finding helps to plan and minimize future contamination of groundwater by considering its vulnerability before high-risk activities are allowed. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

According to the 2018 report of the United Nations Office for Disaster Risk Reduction (UNISDR), the total number of recorded flood events between 1998 and 2017 was 3,148. This means there is an average of 13 monthly floods on our Earth. Satellite monitoring using Sentinel-1 synthetic aperture radar (SAR) can be effectively used to map floods. It provides a short revisit time, a high spatial resolution of 5 m, and a swath of up to 400 km. In this study, we compare two promising flood mapping algorithms to map floods which are common phenomena in the An Giang area, and to monitor the mapping difficulty of flood peaks. The two mapping algorithms are Global Flood Mapper (GFM), an open-source Google Earth Engine (GEE) application; and the other algorithm implemented the Otsu threshold method and change detection approach. They were visually compared using Sentinel-2 optical data and a land-use land cover (LULC) map of An Giang, Vietnam. The results show that the GFM overcomes the misclassification of flooded and non-flooded areas. A good statistical agreement showed that the performance of flood maps derived from the modified GFM algorithm on Sentinel-1 VH-VV-VH RGB composite and Sentinel-2 images have better accuracy than the Otsu Sentinel-1 technique by $\sim20\% $ ∼ 20 %. [ABSTRACT FROM AUTHOR]

This study comprehensively analyses spatiotemporal land use/land cover (LULC) dynamics within a 10-km buffer zone surrounding Volcanoes National Park (VNP), Rwanda, from 2000 to 2019, integrating biophysical and socio-economic drivers. It aims to quantify LULC change extent, rate, and type around VNP and inside the park; identify socio-economic, demographic, and policy drivers of LULC change; and evaluate the impact of LULC changes on VNP's biodiversity via habitat fragmentation, connectivity, and species diversity. Employing remote sensing, spatial modelling, and field data, we reveal profound transformations within the park's periphery. Forests and woodlots have significantly declined, replaced by agriculture, grasslands, and built-up areas, particularly in the 5.1–10 km belt (92.29% forest loss). While reforestation efforts exist, they are outpaced by deforestation, creating a stark deforestation-to-reforestation ratio of 12:1 in the 5.1 10 km belt and 6:1 in the 0–5 km belt. This alarming trend threatens VNP's biodiversity as resource dependent communities continue to extract firewood, construction materials, and beanpoles from the park. To mitigate these impacts, we propose integrated strategies encompassing strengthened law enforcement, sustainable land management practices, community-based conservation initiatives, and alternative livelihood development. [ABSTRACT FROM AUTHOR]

The decline in air quality is thought to be due to rapid land use/land cover (LULC) changes near metropolitan areas that are undergoing rapid urbanisation. Here, we quantify the characteristics and relationship between the aerosol optical depth (AOD) index and LULC changes in Hangzhou, China, since the 21st century using multispectral satellite remote sensing data. The regional study results show that the correlation between the operational AOD observations (MOD04_3K) at 3 km resolution and the in situ AOD measurements available in Hangzhou ranges from 0.48 to 0.80, and their root mean square error (RMSE) < 0.30. During the past two decades, Hangzhou has been in the stage of rapid urbanisation, and the area of construction land has increased significantly, mainly converted from cultivated land and forests. The correlation coefficients between the AOD and the changes of cultivated land, forest land, grassland and construction land in Hangzhou were 0.36, 0.86, 0.61 and 0.63, respectively, indicating a strong correlation except for the cultivated land. Finally, the temporal evolution of the correlation coefficients between the cultivated land and building land are positively correlated with AOD, while from the trend of the correlation curve, grassland, woodland and AOD show negative correlations. [ABSTRACT FROM AUTHOR]

Urban expansion in Ethiopia, particularly informal construction, has led to complex and dynamic changes in livelihoods and wetlands. This study examined the effects of urban expansion on the Cheleleka Wetland and the livelihood of the peripheral community in Hawassa. A mixed research design was used to quantitatively analyze Landsat imagery (1991, 2001, 2011, 2021) and socioeconomic data. Key informant interviews and focus group discussion data were narrated qualitatively. The built-up area substantially increased from 718.11 ha in 1991 to 4820.71 ha in 2021. However, agricultural and wetland areas decreased from 8807.58 ha and 8177.04 ha in 1991 to 6382.44 ha and 7030.26 ha in 2021, respectively. Water and forest areas fluctuated. Built-up areas had a significant advantage over other land use and cover classes, with agricultural, wetland, and forest areas being converted into built-up areas throughout the study period. As a result, the area of Lake Hawassa expanded. Currently, the Cheleleka Wetland has been negatively impaired by overgrazing, cutting of grass, animal dung dumps, eucalyptus plantations, agricultural expansion, and urban expansion. Urban expansion has exacerbated social and economic divisions, resulting in the concentration of poor-quality neighborhoods in the peripheral community. To protect the wetlands, alternative livelihoods and participatory land-use planning are crucial. [ABSTRACT FROM AUTHOR]

In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity. [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]

This study aims to investigate the changes in landscape metrics with varying spatial resolution from Sentinel-2 (10 m), SPOT 7 (1.5 m), Pleaides (0.5 m), and Worldview-4 (0.3 m) images. We implemented Geographic Object-Based Image Analysis (GEOBIA) techniques to all images to identify 21 land use and land cover (LULC) classes, which were then used to calculate several landscape metrics. We performed the Welch hypothesis testing on the class-level landscape metrics and applied Standardized Principal Component Analysis (PCA) with the correlation matrix to reveal the multivariate pattern of landscape metrics. Our results showed that 10 m and even the 1.5 m spatial resolutions cannot guarantee the identification of all LULC classes, and class areas change with varying spatial resolution (sometimes with 200% differences). Sentinel-2 images have some limitations, specifically from the landscape ecological planning perspective; on the other hand, Pleaides and Worldview-4 seem good alternatives to understand habitats' viability and landscape isolation/connectivity. [ABSTRACT FROM AUTHOR]

The enhancement of spatial resolution has improved the spatial information conveyed by remote sensing images. Nevertheless, the impact of spectral-texture combined features on classification accuracy in deep learning remains understudied. This study develops five spectral texture features (Spe-GLCM, Spe-FRS, Spe-Gabor, Spe-CS-LMP, and Spe-FD) using GID15 and Vaihingen datasets. These features were compared with U-Net++, Deeplabv3+, and PSPNet models, incorporating fine-tuned model structures. The results indicate that integrating texture features enhances accuracy in both overall and sparsely sampled land cover classifications. This highlights the potential of deep learning combined with texture features to recognize land cover from high-resolution remote sensing imagery. [ABSTRACT FROM AUTHOR]

Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field data can be very rare for multi-temporal analysis, many studies use samples visually interpreted from Remote Sensing images. This subjective process is prone to errors in interpretation, leading to uncertainties not fully quantified in the reference sample collection of historical time series. We evaluated the agreement rate among independent interpreters in the identification of land cover classes assigned to pixels for yearly Landsat observations from 1984 to 2020, in the Lower Tapajós Basin region (within the Brazilian Amazon). Considering 10 classes, we found that sets of three interpreters completely disagreed on the label of 14.8% of the pixels for each year, and completely agreed in less than half (39.6%). These values change to 2.7% and 68.8% when we consider only five classes in the analysis. We observed time-dependent variations in agreement for specific land cover classes: whereas forest areas tend to present an increase in the disagreement rate over time, land cover types that happen only in previously deforested areas tend to present a higher proportion of pixels with better agreement rates. Disagreements tend to be higher in pixels identified as borders or near borders between targets. However, there is a non-negligible amount of disagreements in pixels identified as not borders caused by limitations derived from the unit of analysis, image composition techniques, and intrinsic similarities among classes. We highlight problems with the identification of classes that happen in complex spatiotemporal patterns (Secondary Vegetation, Small-scale Agriculture, and Degraded Forest). These results alert to possible inconsistencies in reference samples collected for the accuracy assessment of land cover time series and the correspondent values of the calculated accuracy indexes. [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]

Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9’s potential in LULC classifications. [ABSTRACT FROM AUTHOR]

The recent deployment of new satellite-based hyperspectral imaging sensors, along with advances in classification methods, has enhanced the acquisition of information on the spatiotemporal patterns of Land Use/Land Cover (LULC).. With this context, the study presented in this paper seeks to assess the combined utilization of the Pixxel's technology demonstrator hyperspectral satellite mission with the Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers for LULC mapping in an agricultural area located in Queensland, Australia. Accuracy assessment of the resulting LULC maps has been done based on relevant classification accuracy metrics. It has been observed that SVMs outperformed RF in terms of both overall classification and urban vegetation cover mapping accuracy. An overall accuracy of 92.34% with kappa of 0.90 was reported for the SVMs' results, whereas for RF, the accuracy was 89.65% with 0.89 kappa, respectively. Both classifiers produced similar results for the classes of mango tree and sugarcane crops, whereas minor differences were observed in the depiction of water bodies and artificial surfaces. Our study findings provide insightful perspectives on the potential of Pixxel datasets for delineating the spatiotemporal distribution of LULC in agricultural landscapes, showcasing Pixxel's hyperspectral sensors' promising potential in this field. [ABSTRACT FROM AUTHOR]

This study modelled the drivers of satellite-derived Net Primary Productivity (NPP). The NPP drivers used were human foot print (HFP), soil organic carbon (SOC), Total Nitrogen (TN), Total Phosphorus (TP), Potential evapotranspiration, (PET), and Aridity Index (AI). The results showed that inclusion of non-vegetated land cover types (LCT) in the modelling leads to establishment of wrong relationships between the NPP and its drivers, and wrong relationships among the drivers. Inclusion of the non-vegetated LCT revealed that 1) HFP had no relationship with NPP, CUE, and SOC; 2) PET had no relationship with all the three soil variables (SOC, TN, and TP), AI and the CUE; 3) PET and NPP were strongly and positively correlated; and 4) there was no relationship between the NPP and all the three soil variables and the AI. While exclusion of the non-vegetated LCT revealed that 1) HFP had negative relationship with NPP, CUE, and SOC; 2) PET had negative relationship with the CUE, AI and all the three soil variables; 3) PET and NPP were strongly negatively correlated; and 4) there was positive correlations between the NPP and all the three soil variables and AI. However, results of both the inclusion and exclusion of the non-vegetated LCT indicated that: 1) HFP did not correlate with TN, TP, PET, and AI; and 2) there was strong and positive correlation among all the three soils and AI. It is worth noting that exclusion of the non-vegetated LCT in the modelling established the most accurate results on the relationship between the NPP and its drivers; and on the relationships among the drivers. However, there is a need to replicate this study in different geographical regions to guarantee global generalization of the results. [ABSTRACT FROM AUTHOR]

Herbivorous insects are predicted to have higher densities in larger patches, while their natural enemies will be more affected by the edge of these patches. In this paper, we investigate the effect of the relationship between brassica horticultural plot shape and the adjacent land use on the abundance of P. xylostella and its chances to be parasitised. Samples were collected from brassica plots during spring. Landscape metrics of the study area were estimated using Landsat images. We found a negative relationship between the pest abundance and the perimeter-to-area ratio of the brassica plot, and a positive relationship with land cover estimated from Landsat images. The proportion of parasitised immatures showed opposite relationship with the same variables. Implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area's exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey's Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes. [ABSTRACT FROM AUTHOR]

Previous studies suggest that the risk of human infection by hantavirus, a family of rodent-borne viruses, might be affected by different environmental determinants such as land cover, land use and land use change. This study examined the association between land-cover, land-use, land use change, and human hantavirus infection risk. PubMed and Scopus databases were interrogated using terms relative to land use (change) and human hantavirus disease. Screening and selection of the articles were completed by three independent reviewers. Classes of land use assessed by the different studies were categorized into three macro-categories of exposure ('Agriculture', 'Forest Cover', 'Urban Areas') to qualitatively synthesize the direction of the association between exposure variables and hantavirus infection risk in humans. A total of 25 articles were included, with 14 studies (56%) conducted in China, 4 studies (16%) conducted in South America and 7 studies (28%) conducted in Europe. Most of the studies (88%) evaluated land cover or land use, while 3 studies (12%) evaluated land use change, all in relation to hantavirus infection risk. We observed that land cover and land-use categories could affect hantavirus infection incidence. Overall, agricultural land use was positively associated with increased human hantavirus infection risk, particularly in China and Brazil. In Europe, a positive association between forest cover and hantavirus infection incidence was observed. Studies that assessed the relationship between built-up areas and hantavirus infection risk were more variable, with studies reporting positive, negative or no associations. [ABSTRACT FROM AUTHOR]

The study area is part of the Biosphere Reserve (BR), which, along with adjacent caves in the Aggtelek Karst in Hungary, was inscribed on the UNESCO World Heritage List in 1995. This article focuses on the assessment of temporal changes in land cover and high nature value farmland. Changes over the past three decades were assessed in the operational areas of transtermitant farms on karst plains and in three BR zones. The results help to identify factors responsible for the progressive succession of woody plants on pastures. Karst microreliefs were excluded from pastoral activities because of the inherent nature of transtermitance, conservation measures, natural conditions, and type of farmed animal. The future preservation of valuable habitats, visually attractive karst landscape characteristics, and clean underground water in a cross-border cave system hinges on adequate grazing conditions being agreed upon by farmers and the Slovenský kras BR administration. [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]

There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage's cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces. [ABSTRACT FROM AUTHOR]

On the basis of remote sensing images, land cover change detection (LCCD) has gained popularity. Even though several techniques have been promoted lately, their effectiveness and usability still need to be improved. To assist urban sustainable development, evaluate natural disasters, and analyze the environment, very high-resolution (VHR) remote sensing images can be used to detect land cover change. Planning professionals can evaluate changes in land cover using remote sensing and geographic information systems, which have a reputation for excellence. A fully convolutional two-stream architecture is used in this paper to first extract highly representative deep features from bi-temporal images. After that, a Lenet5 is given the deep features to segment the land changes and detect changes. With the help of Faster-RCNN the land change classes are classified. We implement the proposed model in Python and conduct experiments on the land cover dataset. The outcomes of the experiments demonstrate that the proposed method outperformed previous deep learning algorithms, achieving the best accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

This research focuses on exploring and mapping Ankara's urban points of interest and their values on different urban infrastructure types using geolocated data from Flickr. All publicly available data dated between 2005 and 2021 from Flickr were obtained through application programming interface and the infrastructure types were reclassified as green, blue, grey, and yellow, using the land use and land cover data based on Corine 2018. Spatial patterns for cultural service flow of landscape features and/or landscapes on different urban infrastructure types were mapped using Kernel density estimation in QGIS 3.21. The hotspots were assessed in terms of the cultural services they offered based on the Millennium Ecosystem Assessment (MA) framework, site visits, and observations. The findings showed that the city core mostly provided cultural heritage, identity, and recreational values. Results for the rural and peri-urban areas indicated that natural landscapes and protected areas stood out for their provision of cultural services, particularly for their recreational values. Our research identified user-intensive geographical clusters and hotspots that could benefit from interventions and enhancement for a more sustainable urban environment. [ABSTRACT FROM AUTHOR]

The conversion of forest to agricultural land has been a major worldwide concern and a cause of deterioration of soil quality. The study examined the influence of land uses on the soil quality index (SQI) using minimum datasets (MDS) constructed from soil profiles through two methods namely, additive (SQIa) and weighted (SQIw). In this study, 29 geo-referenced soil profiles (116 layers) up to a depth of 150 cm were collected in Northeastern India, representing forest, jhum, jhum fallow, and agricultural land uses. Principal component analysis (PCA) with multiple correlation analysis is found to be a suitable statistical method for selecting the key soil quality indicators. From PCA, organic carbon (OC), sand, clay, available P and available S were selected as soil quality indicators in MDS. Our results showed that these MDS significantly varied within the profiles depending on the land-uses. The estimated values of SQIs showed that SQIa was higher than SQIw across the soil depths and land-uses. For the surface soil, SQIa values were in the order of forest (2.97) > jhum fallow (2.82) > agriculture (2.68) > jhum land (1.97) and for profile soil jhum fallow (2.38) > forest (2.37) > agriculture (2.33) > jhum (1.88). Similarly, SQIw followed the trend of forest > jhum fallow > agriculture > jhum for all the three studied depths. The statistical analysis showed that the surface, subsurface, and profile soil had no significant impact on SQIw for agriculture, fallow jhum, and forest. The overall data indicated that jhum land had a significantly lower SQI than other land-uses for all soil depths. From this study, the information generated can be useful to assess the soil quality in different land-uses for managing soil health in similar areas of the Northeastern region of India. The findings are also useful in the ecosystems in which soils develop in an acid edaphic environment. [ABSTRACT FROM AUTHOR]

Ethiopia, marked by diverse geography and rapid land use/land cover (LULC) changes, grapples with formidable water resource management challenges. Understanding the hydrological response to these changes is paramount for sustainability. This review paper provides an overview of studies that investigated the hydrological impacts of LULC changes in Ethiopian basins. The review discusses methodologies, key findings, implications for sustainable land and water resource management, research gaps, and future directions. Emphasizing results from diverse studies that utilized hydrological and geospatial modelling, LULC significantly affects surface runoff, streamflow patterns, and groundwater recharge. The observed consequences are increased surface runoff, modified streamflow timing, and reduced groundwater recharge. The review underscores the urgency for integrated land and water management strategies to alleviate adverse effects. Future research priorities include basin-specific studies, long-term monitoring, integrated modelling, consideration of socio-economic factors, and incorporating climate change scenarios to enhance understanding and guide sustainable water resource management in Ethiopian basins. [ABSTRACT FROM AUTHOR]

Rapid urbanization currently poses a threat to cropland areas. Therefore, exploring the pattern of change in cropland areas and its driving mechanism is of great significance in safeguarding regional economic development and food security. In this study, data regarding cropland in the Changsha-Zhuzhou-Xiangtan city group (CZTCG) from 2000 to 2020 were collected, and dynamic changes in cropland in the past 20 years were analyzed. Based on this, the mechanisms that drive cropland change were explored in the context of multiple dimensions. Furthermore, an Inertial Development Scenario and a Cropland Priority Scenario were designed to simulate land use/land cover (LULC) changes in the CZTCG in 2025 and 2035, and, in particular, to analyze the characteristics of future spatiotemporal changes of cropland. The results demonstrated that the cropland area in the CZTCG in the past 20 years has shown a two-stage trend of increasing (2000-2015, increase of 8.25%) and decreasing (2015-2020, decrease of 2.91%), and the main reasons for cropland loss in the whole process of change were forest and building, with serious loss of cropland area in Changsha County and Wangcheng District. Between 2000 and 2020, socio-economic factors (GDP, total agricultural output), topography (elevation, slope), and climate change (average annual temperature, annual precipitation) were highly correlated with changes in cropland area, exhibiting significant temporal and regional variability. The simulation results indicate that until 2035, the Cropland Priority Scenario shows a significant increase in the area of cropland in the study area compared to the Inertial Development Scenario. Consequently, the implementation of a strategy that prioritizes cropland can effectively protect it, while helping to promote ecological development and regional socio-economic development. [ABSTRACT FROM AUTHOR]

Human activities, including agricultural expansion, urbanization, and industrial advancement, have led to land use/cover change (LULCC). These changes have had negative consequences, such as the loss of species and the degradation of forest areas. The Yayo Coffee Forest Biosphere Reserve (YCFBR) is undergoing changes due to forest fires and encroachment from coffee plantations, which are predicted to increase in the future. The objective of this study was to simulate the dynamics of LULCC and its impact on habitat quality in the area over the next three decades. The study used classified land cover (LC) maps of 1992, 2022, and 2052 (predicted), as well as factor and constraint maps of the area. In addition, household surveys, key informant interviews (KII), and focus group discussions (FGD) were conducted to understand the factors influencing changes in habitat quality. Habitat quality was simulated under three different future scenarios (Business as Usual; S1), conservation scenario (S2), and development scenario (S3) using CA-Markov, Future Land Use Simulation (FLUS), and Integrated Valuation of Environmental Services Tradeoffs (InVEST) models. The result showed a 28% increase in open forest and a 35% increase in agricultural area under S1, with a minimal increase in built-up area. The mediumlevel habitat quality increased from 11.3% in 1992 to 27.8% in 2052 S3. The highest average haitat quality value was 0.62 in S2, while the lowest was 0.51 in S3. If S3 is the case, this value is expected to fall below 40%, indicating the lowest possible level of habitat quality in the future. The model also predicts a 7% increase in high forest areas in S2, indicating the possibility of an alternative path to save high forest loss in the area. The main causes of habitat quality deterioration include population growth, agricultural land expansion, a lack of diverse incomes, land tenure issues, and settlement expansion. Based on the findings, S2 appears to be the best future scenario for maintaining habitat quality. This study provides useful information that will help planners and decision-makers effectively prepare future conservation strategies. [ABSTRACT FROM AUTHOR]

Satellite-based monitoring provides insights on synergies and trade-offs between energy production and land use and land cover (LULC) changes (LULCC) around hydropower dams. Using Landsat data, we mapped LULCC related to hydropower expansion to understand change dynamics of cash crop production in the Magdalena River basin (Colombian Andes). We leveraged secondary map products and an active learning routine to produce thematically detailed LULC maps, for 2009, 2015, and 2020, including key cash crops (rice and oil palm). Our area-adjusted accuracy assessment revealed high overall and class-specific user's and producer's accuracies, exceeding 80% for oil palm, rice, non-agricultural vegetation, wetlands, and grasslands, and accuracies of ~60% for other temporary and permanent crops. We identified substantial losses of rice (10,6%) and grasslands (25,9%) in El Quimbo due to reservoir flooding. For Hidrosogamoso, there was a loss of wetlands downstream. Our products provide the basis for balancing social, economic, and environmental trade-offs related to conflicting land uses. [ABSTRACT FROM AUTHOR]

Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrated to enhance the accuracy of mapping forest resources. This study presents a novel method for mapping the natural distribution of highland bamboo (Oldeania alpina) using spectral bands and three machine learning algorithms, namely random forest, gradient tree boosting, and classification and regression tree. First, spectral bands, vegetation indices and textural features including contrast, entropy and inverse difference moment were derived from the Sentinel-2 elevation data. Second, observations were categorized as the dry season (December-February), short rainy season (March-May), main (long) rainy season (June-August), wet season (September-November) and annual composite. Third, the machine learning algorithms were tested using 1882 ground control points collected from field and high spatial resolution images. Finally, the best-performing machine learning algorithm was used to classify and map bamboo forests. The five land cover categories identified were bamboo stands, natural forest, other vegetation, non-vegetated areas and water bodies. The result shows that the random forest classifier is a robust algorithm with an overall accuracy of 94% considering all the annual composite's spectral, vegetation and textural variables. The highland bamboo coverage (91 km2) in the Andracha district provided valuable insights. Bamboo stands were mostly distributed in the southern and southeastern parts of the district. Here, we show that image compositing and multiple input parameters using machine learning techniques can overcome challenges facing land cover classification, which can hinder accurate mapping of Afromontane forests. We conclude that the incorporation of vegetation indices and textural features in land cover classification increases accuracy of mapping natural highland bamboo coverage and distribution. The application of this new method is a promising prospect not only in Ethiopia but also in Afromontane forests elsewhere in Africa. [ABSTRACT FROM AUTHOR]

The purpose of the presented study is to evaluate the comprehensive impact of different land cover types on the temperature development in the entire Shandong Province by a 20-year-long (MODIS) LST time series from 2003 to 2022. To find out the primary influencing factors, methods such as the Pearson correlation, stepwise analysis, and best-subset selection were applied. The results revealed that the average temperatures had been rising in summer during day- and night-time by 2.32 ℃ and 1.22 ℃, respectively and in winter during day- and night-time by 3.25 ℃ and 1.33 ℃, whereby a significant contribution can be attributed to the period 2014-2022. Substantial variations in LSTs emerge between built-up and vegetated areas and landlocked and coastal regions. Moreover, we could identify a contribution of 0.089 ℃, caused merely by the extension of built-up areas of 1.65% in the entire Shandong Province. Modeling the combined effects of further relevant variables/factors, the percentage of cropland area (crop-per) and the number of landscape patches (NPl) indicate considerable influence on the daytime temperature in the temporal domain. [ABSTRACT FROM AUTHOR]

There are two major debates on the discussion of urban sprawl; those who maintain that it has positive implications on development and sustainability of cities and those who are otherwise. This study contributes to this intellectual debate by examining both sides of the phenomenon in a single investigation, based on the perspectives of urbanites of a secondary city. The mixed-methods research approach was adopted for the inquiry. Using Tamale Metropolis as a case, primary data were collected from 386 household respondents and three agencies for the cross-sectional survey and key informant interviews, respectively. The results revealed that urbanisation is rapid with a correspondingly high rate of sprawl. The majority of the respondents (98%) reported an increased urban concentration in the area over the past decade. There has been a 60.7% increase in population increase between 2010 and 2021. Land cover analysis revealed that in 2010, approximately 9% of the total land area of the metropolis was built up, attributed to increasing population and corresponding increased demand for land for development. This increased to 16% and 26% in 2016 and 2022, respectively. The sprawling phenomenon was largely associated with rapid population growth, economic growth, availability of automobiles, improvement in road infrastructure and family nuclearisation. Though the study unravelled some benefits associated with sprawl, Tamale is still not an exception in terms of the documented adverse implications of sprawl. Relevant recommendations were thus made for capitalising on the positive impacts of sprawl while mitigating the associated challenges. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) assessment is essential for monitoring naturally available land resources through spatio-temporal changing pattern to protect land resources sustainably. The primary objective is to identify the changing pattern in LULC over last 10 years in Imphal-Iril catchment. Multi-spectral datasets of Landsat 7 and 8 were used for five different time periods (2011, 2013, 2015, 2017 and 2019). Supervised classification method was employed to generate the classified LULC maps using the maximum likelihood classifier approach in ArcGIS 10.3. In this assessment, five major land use classes have been classified as vegetation, agriculture, settlement, barren land and water body, and the dominant land cover is vegetation. The classified images obtained from this technique were checked for their accuracy using statistical kappa co-efficient and overall accuracy. According to this study, settlement and barren areas have been significantly increased by 72.25% (39.03 km2) and 22.60% (118.29 km2) during the last 10 years, whereas areas assigned to agriculture, vegetation and water bodies have decreased by 27.26% (72.16 km2), 7.59% (73.22 km2) and 60.39% (11.94 km2), respectively. LULC map for the year 2030 was also predicted using MOLUSCE model in QGIS. The results of this assessment will be significant to the policy implicates for sustainable LULC management in the region. [ABSTRACT FROM AUTHOR]