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Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. [ABSTRACT FROM AUTHOR]

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive analysis of the spatial patterns and temporal dynamics of land use and land cover changes in South Kashmir from 2000 to 2022 leveraging remote sensing technologies revealing significant transformations in various land cover classes. The study used maximum likelihood classification, a supervised classification method, to analyze Landsat satellite imagery and identify ten major land use categories. The findings demonstrate notable increases in, barren land by 18.78 km2 (0.35%), built-up areas by 72.28 km2 (1.33%), forests by 274.76 km2 (5.05%), grasslands by 68.06 km2 (1.25%), scrubland by 307.82 km2 (5.66%). horticulture experienced a significant rise of 419.17 km2 (7.70%), Conversely, several land use classes reported decline, agriculture by 757.21 km2 (13.91%), exposed rockmass by 258.58 km2 (4.75%), glaciers and snow by 136.83 km2 (2.51%), and water bodies contracted by 8.23 km2 (0.15%). The primary drivers of land use change in the region are identified as climate change, population growth, and economic factors. Climate change has altered precipitation patterns affecting agricultural productivity and leading to the retreat of glaciers. Population growth and economic reasons, including the rise of horticulture and changes in land use policies, have also played a significant role in shaping the landscape dynamics of South Kashmir. These changes underscore the dynamic nature of land use in South Kashmir, with significant implications for regional planning and environmental management. The study underscores the cost-effectiveness and efficacy of geospatial technologies in conducting spatiotemporal analyses and formulating evidence-based policies for the sustainable management of natural resources.

Copyright of Journal of Arts & Social Sciences (JASS) is the property of Sultan Qaboos University 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.)

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area.

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC's impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle. [ABSTRACT FROM AUTHOR]

Evolution of glacial lakes in the Himalayan and Karakoram (H–K) mountain ranges is an important indicator of glacier changes in response to climatic warming. The study utilized multi-temporal Landsat 4, 5, 7, and 8 images accessible in the cloud-based Google Earth Engine platform to analyse the spatiotemporal variations of the supraglacial lake (SGL) in the H–K regions from 1990 to 2020 at a decadal interval. It is observed that 61% (4.79 km2) of the SGL area increased from 1990 to 2020, while 223 new lakes formed in a similar time period. The most significant increase in the area of SGLs (30.15%; 2.93 km2) was observed between 2010 and 2020, while the slowest growth was observed between 1990 and 2000 (1.13%; 0.09 km2). The results indicate heterogeneity in SGL area changes in different regions. The region of Central Himalaya (CH) experienced the highest increase of 160% (3.8 km2) in the SGL area from 1990 to 2020 with most of the rise in the SGL area was observed in the Everest region, while a decrease of 9.4% (0.12 km2) was observed in the Eastern Himalaya (EH) region. During the study period, some SGLs converted into proglacial lakes in the EH region, which may be responsible for reducing the SGL area. The rise of SGL in the CH region can be attributed to higher mass loss, decreased glacier surface velocity, and increased rainfall in the CH region. We also identified 15 glaciers that have SGLs near the terminus of the glaciers. If the same trend continues, these SGLs may soon be converted into proglacial lakes. The current inventory of SGL at a decadal scale shall be useful as baseline data for other hydro-glaciological models. [ABSTRACT FROM AUTHOR]

Understanding the LULC change is essential to adopt the new development activities in the particular study area. The study reveals the LULC change for four different decades (1990, 2000, 2011, and 2022) of the Sulur-Singanallur region of the Coimbatore district. In this study, the Landsat satellite data of four decades were used to assess the regional LULC changes and identified the area under Built-up land increased from 6 to 25.77% and Waterbodies decreased by 5.48% (6–0.52%). Similarly, the current scenario of agriculture has decreased as Crop lands by 12.77% (30–17.23%). At the same time, the fallow land has risen and fall from starting to the ending years. Hence, the CA-ANN method has been adopted using the Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. The spatial entities such as DEM, slope, distance to river, and distance to road were utilized as drivers to predict the future LULC of 2030, 2040, and 2050. The study's findings reveal that the crop land has decreased to 10.90%, 8.89% and 8.87%, respectively. Similarly, the waterbodies will be decreased by upto 1%, and the prediction findings portrays the increasing trend was observed in Built-up area and the better kappa statistics results identified as 0.81, 0.76, and 0.76 for 2030, 2040, and 2050. The observation of post classification shows that MLC is one of the best suited method and producing realistic result in the study for the change detection analysis. Overall, the outcome of the study showcased the continuous increase Built-up land were declined the natural land environment. If it continues, the agriculture ecosystem will be affected, and minimizes the cropping pattern and further anthropogenic influences will also reduce the agricultural activities in future. This modelling study results serves the benefit to land managers, stakeholders, farmers, and researchers to take possible measures immediately to manage the land sustainably. [ABSTRACT FROM AUTHOR]

In the current study, remote sensing techniques and geographic information systems were used to detect changes in land use / land cover (LULC) in the city of Al Hillah, central Iraq for the period from 1990 - 2022. Landsat 5 TM and Landsat 8 OLI visualizations, correction and georeferencing of satellite visuals were used. And then make the necessary classifications to show the changes in LULC in the city of Al Hillah. Through the study, the results showed that there is a clear expansion in the urban area from 20.5 km2 in 1990 to about 57 km2 in 2022. On the other hand, the results showed that there is a slight increase in agricultural areas and water. While the arid (empty) area decreased from 168.7 km 2 to 122 km 2 in 2022. Long-term urban planning, which is based on LULC analysis, is an effective tool for decision makers to study future patterns in urban expansion in parallel with the expected rise of the population in Iraq in the coming years. . Therefore, it is necessary to use equivalent systematic plans to take into account the theory of urban and population expansion of cities by providing the best places for the population to be concentrated in appropriate ways in the future.

Drought has become more common problem and more severe that widespread over the world in recent decades, increasing their negative effects on the environment. The most vulnerable communities can be warned and prepared for the negative effects of droughts. This study aims to assess and predict the drought vulnerability using Landsat satellite images from 2014 to 2022 over El- Dammam City. Using remote sensing, drought phenomenon was simulated and predicted through Perpendicular drought index (PDI) and Normalized Difference Drought Index (NDDI). In the present study, some indices were selected to sasses vegetation as Normalized Difference Vegetation Index (NDVI), water as Normalized Difference Water Index (NDWI) and salinity as salinity index (SI) and Normalized Difference Salinity Index (NDSI) with the aid of Machine Learning (ML) techniques, Support Vector Machine (SVM) and Partial Least Square (PLS). Results showed that NDDI and PDI were negatively correlated with NDVI and NDWI; respectively where NDDI and PDI were positively correlated with NDSI and SI; respectively. Principal Component Analysis (PCA), PC-1, PC-2 and PC-3 are contributed about 98% of all data. Finally, PLS and SVM are two good multivariate analysis for prediction of drought with R2 0.95 and 0.98 respectively. The predicted model showed the severity of drought far away from the sea along the barren area in the southern part. Finally, it should be noted that NDVI, NDSI, SI, and NDWI were better indicators for tracking and assessing changes in drought. The results can be utilized to quickly evaluate data produced from remote sensing and water-related indices and make wise decisions. This study contributes to our understanding of droughts and sheds information on how ML technology could be used to monitor droughts. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Physics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Mangroves in Zanzibar have a high value for biodiversity and scenic beauty. However, mangroves are susceptible to anthropogenic and natural environmental disturbances. Although global mangrove monitoring systems exist, it is the practitioners focused on small mangrove areas who are knowledgeable about the area in which they work. This study examined the change in mangrove cover in Chwaka Bay and Menai Bay, between 1973 and 2020, using Landsat satellite data. The transformed divergence (TD) index and Jeffries–Matusita (J-M) distance were used to calculate separability of land cover classes before classification. The overall accuracies ranged between 82.5 and 92.7% for Chwaka Bay and 85.5 and 94.5% for Menai Bay. The kappa coefficients (ĸ) were in the range of 0.72–0.90, while the producer's and user's accuracies were between 72 and 100%. Chwaka Bay's annual decrease in mangrove cover between 1973 and 2020 was 48.5 ha, compared to Menai's 6.8 ha. The loss of mangroves in Zanzibar is linked to illegal timber/pole cutting, firewood collection, charcoal burning, unsustainable aquaculture, and agricultural and urban development. Others are changes in environmental conditions that are suitable for mangroves and climate change. This study is crucial in providing a basis for the ecological restoration and management of Zanzibar's mangrove ecosystem. [ABSTRACT FROM AUTHOR]

Opencast mining has significantly triggered vegetation degradation in many ecologically sensitive regions across the globe. The detailed spatio-temporal information on mining-induced vegetation degradation and associated primary production loss are crucial inputs to authorities and policymakers to frame and implement sustainable development programs in the mining regions to conserve vegetation ecology. Thus, the present study aimed to decipher the mining-induced vegetation cover and subsequent productivity losses over the Rajmahal Hills in Jharkhand (India). The Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Vegetation Transpiration (VT) datasets were used for analyzing the mines-induced losses in vegetation cover and associated productivity. The key findings indicated a loss of vegetation cover by ~340 km2 and an expansion of the mining area by ~54 km2 over the Rajmahal Hills during 1990–2020. The change detection analysis at the decadal period revealed that ~3.06 km2, 8.10 km2, and 22.29 km2 of vegetation cover were lost only due to the mining activity during 2000–2010, 2010–2020, and 2000–2020, respectively. The replacement of vegetation cover by mining area has caused GPP loss of 0.01 tonnes carbon (tC) per day, 0.04 tC/day, and 0.09 tC/day; NPP loss of ~1.25 tC, 2.77 tC, and 7.27 tC; VT loss of 5200 mm/day, 13,630 mm/day, and 30,190 mm/day during 2000–2010, 2010–2020, and 2000–2020, respectively. Hence, the present study revealed that the mining-induced vegetation losses have caused an alteration of carbon sequestration, carbon stock, and VT over the Rajmahal Hills. [ABSTRACT FROM AUTHOR]

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown a lack of spatial and temporal stability due, for instance, to variability between individual pixels and changes in vegetation condition, respectively. Machine learning methods that consider spatial patterns in addition to reflectance can address some of these issues. In this study, a convolutional neural network (CNN) model, U-Net, was trained for a 500 km × 500 km region in southeast Australia using annual Landsat geomedian data for the relatively dry and wet years of 2018 and 2020, respectively. The label data for model training was an eight-class classification inferred from a static land-use map, enhanced using forest-extent mapping. Here, we wished to analyse the benefits of CNN-based land cover mapping and reporting over 34 years (1987–2020). We used the trained model to generate annual land cover maps for a 100 km × 100 km tile near the Australian Capital Territory. We developed innovative diagnostic methods to assess spatial and temporal stability, analysed how the CNN method differs from pixel-based mapping and compared it with two reference land cover products available for some years. Our U-Net CNN results showed better spatial and temporal stability with, respectively, overall accuracy of 89% verses 82% for reference pixel-based mapping, and 76% of pixels unchanged over 33 years. This gave a clearer insight into where and when land cover change occurred compared to reference mapping, where only 30% of pixels were conserved. Remaining issues include edge effects associated with the CNN method and a limited ability to distinguish some land cover types (e.g., broadacre crops vs. pasture). We conclude that the CNN model was better for understanding broad-scale land cover change, use in environmental accounting and natural resource management, whereas pixel-based approaches sometimes more accurately represented small-scale changes in land cover. [ABSTRACT FROM AUTHOR]

Inspection data can be used to comprehend and plan effective maintenance of bridges. In particular, the year of initial construction is one of the most important criteria for formulating maintenance plans, making budget allocations, and estimating soundness. In an initial survey of bridges in Cambodia, it was concluded that the year of construction of only 54% of 2439 bridges surveyed is known, with the remaining 46% remaining unknown. In this research, Landsat satellite data is used to estimate the year of construction of these bridges. Landsat provides spatial spectral reflectance information covering more than 30 years, and for longer bridges this can be used to estimate the year of construction by visual judgement. However, limited image resolution means this is not possible for shorter bridges. Instead, a method using the Landsat Normalized Difference Water Index (NDWI) is used to estimate the year of construction. Three pixels are selected from Landsat image data in such a way that one lies on the current location of a bridge and two other reference pixels are placed on similar terrain at a certain distance perpendicular to the bridge axis. NDWI values are plotted over time for the three pixels and the difference in value between the bridge pixel and the two reference pixels is then compared. Before the bridge is constructed, all three pixels should have similar NDWI values, but after construction the value of the target bridge pixel should differ from the other two because the NDWI value of a bridge surface is different from that of the surrounding vegetation. By looking for this change, the year of construction of a bridge can be estimated. All the bridges in the Cambodian database are classified into three categories based on length (which affects their visibility in Landsat images) and year of construction is estimated. The results show that estimated year of construction has the same accuracy in all three categories. [ABSTRACT FROM AUTHOR]

Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g. , dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. [ABSTRACT FROM AUTHOR]

Monitoring the changes in forest cover has become an important tool for forest management due to its impact on climate change, desertification, soil erosion, and flooding. The Zagros Mountains forest-steppe is a temperate broadleaf and mixed forest ecoregion in Western Asia, and the present study analyzes the changes in Zagros forests from 1986 to 2019 using remote sensing techniques. Three Landsat images, acquired in 1986, 2002, and 2019, were used to map land cover classification in the study area. Classification kappa coefficients were 85.2, 94.1, and 94.7 for three images, respectively. Focusing on the class level, the results show that between 1986 and 2002, the primary cause of forest loss in the study area was the conversion of 7.8 thousand ha of oak forests to agricultural land. Since 2002, 0.8 thousand ha of oak forest cover have been changed to barrenlands. The decrease of Zagros forests is a main threat to the rich biodiversity found in the West of Iran. To avoid worsening soil erosion, restoration programs should prioritize planting patterns on rainfall and hill slopes, texturally dissimilar soils, and regeneration in forest gaps, particularly at high elevations and steep slopes. It is recommended that future research should use higher resolution datasets and encompass more expansive landscape units, such as the whole Zagros Mountain forests. [ABSTRACT FROM AUTHOR]

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC’s impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle.

Vietnam is the world’s second-largest coffee exporter. Information on coffee-growing areas is thus important for production estimation. This research developed an approach for coffee mapping and change detection using Landsat and DEM data in Central Highlands, Vietnam. The data were processed for 1995, 2005, and 2020 using random forests (RF). The mapping results, verified with reference data, showed overall accuracy and Kappa coefficient higher than 86.9% and 0.74, respectively. These findings were reaffirmed by a close relationship between the satellite-based coffee area and official statistics, with the root mean squared percentage error (RMSPE) and mean absolute percentage error (MAPE) greater than 9.9% and 7.6%, respectively. From 1995 to 2020, the newly planted coffee area increased by 135,520 ha, due to the conversion of forests to coffee plantations. Such distributions of coffee areas and decadal changes in farming activities are critically useful for policymakers to formulate successful crop planning strategies.

Vietnam is the world's second-largest coffee exporter. Information on coffee-growing areas is thus important for production estimation. This research developed an approach for coffee mapping and change detection using Landsat and DEM data in Central Highlands, Vietnam. The data were processed for 1995, 2005, and 2020 using random forests (RF). The mapping results, verified with reference data, showed overall accuracy and Kappa coefficient higher than 86.9% and 0.74, respectively. These findings were reaffirmed by a close relationship between the satellite-based coffee area and official statistics, with the root mean squared percentage error (RMSPE) and mean absolute percentage error (MAPE) greater than 9.9% and 7.6%, respectively. From 1995 to 2020, the newly planted coffee area increased by 135,520 ha, due to the conversion of forests to coffee plantations. Such distributions of coffee areas and decadal changes in farming activities are critically useful for policymakers to formulate successful crop planning strategies. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature's flood buffers for economic advancement. • A dual-level attention based vision transformer is proposed for processing multi-spectral satellite images. • Open-source satellite imagery based land-cover classification of stream bed is proposed. • A new labeled data set is developed for three study sites in India — Bhalswa lake, Yamuna river, and Anasagar lake. • The proposed model outperforms other considered transformer based and convolution based models. [ABSTRACT FROM AUTHOR]

Khanh Hoa coal mine (Thai Nguyen province) is one of the largest coal mines in the north of Vietnam. For many years, this area suffered from underground fires at coal mine waste dumps, seriously affecting production activities and the environment. This paper presents the results of classification of underground fire areas at Khanh Hoa coal mine using Normalized Diference Coal Fire Index (NDCFI). 03 Landsat 8 OLI_TIRS images taken on December 2, 2013, December 10, 2016, and December 3, 2019 were used to calculate NDCFI index, and then classify the underground fire areas by thresholding method. In the study, the land surface temperature was also calculated from Landsat 8 thermal infrared bands data, and then compared with the results of underground coal fire classification at Khanh Hoa coal mine. The obtained results showed that the NDCFI index can be used effectively in detecting and monitoring underground fire areas at coal mines. The use of the NDCFI index also has many advantages due to its calculation simplicity and rapidness compared to other methods for classifying underground coal fire areas.

Major development projects along rivers, like reservoirs and other hydraulic structures, have changed not only river discharges but also sediment transport. Thus, changes in river planforms can be observed in such rivers. In addition, river centerline migrations can be witnessed. The Mahaweli River is the longest in Sri Lanka, having the largest catchment area among the 103 major river basins in the country. The river has been subjected to many development projects over the last 50 years, causing significant changes in the river discharge and sediment transport. However, no research has been carried out to evaluate the temporal and spatial changes in planforms. The current seeks to qualitatively analyze the river planform changes of the Lower Mahaweli River (downstream to Damanewewa) over the past 30 years (from 1991 to 2021) and identify the major planform features and their spatiotemporal changes in the lower Mahaweli River. Analyzing the changes in rivers requires long-term data with high spatial resolution. Therefore, in this research, remotely sensed Landsat satellite data were used to analyze the planform changes of Lower Mahaweli River with a considerably high resolution (30 m). These Landsat satellite images were processed and analyzed using the QGIS mapping tool and a semi-automated digitizing tool. The results show that major changes in river Mahaweli occurred mainly in the most downstream sections of the selected river segment. Further, the river curvature was also comparatively high downstream of the river. An oxbow lake formation was observed over time in the most downstream part of the Mahaweli River after 2011. Centerline migration rates were also calculated with the generated river centerlines. It was found that the rates were generally lower than about 30 m per year, except for at locations where river meandering was observed. The main limitations of this study were the possible misclassifications due to the resolution of images and obstructions caused by cloud cover in the Landsat images. To achieve more accurate estimates, this study could be developed further with quantitative mathematical analysis by also considering the sediment dynamics of the Mahaweli River. [ABSTRACT FROM AUTHOR]

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster. [ABSTRACT FROM AUTHOR]

As the main form of the new urbanization strategy, urban agglomeration promotes inter-regional economic development, population flow, and industrial cooperation. It is not only an important platform for the country to participate in international competition, but also an important carrier to explore the multiple influencing factors of urbanization. Based on the Beijing-Tianjin-Hebei urban agglomeration, the temporal and spatial characteristics of its evolution from 1990 to 2020 are analyzed by using the models of urban evolution and industrial transfer. The results show that at the level of urban land use evolution intensity, Beijing > Tianjin > Hebei, and the evolution of urban land use showed an obvious pattern of concentration of resources in central cities (districts) and weak development in sub-central cities (counties). The overall level of urban sprawl showed a downward trend, and only 0.50% of districts and counties were in the stage of high sprawl. Before 2010, the urban sprawl of Beijing, Tangshan, and southern Hebei province remained at a high level, and after 2010, the urban sprawl of central Hebei province remained at a high level. In terms of comprehensive development, the index of cities decreased, including Beijing, Tianjin, and Tangshan, while the index of cities increased including Shijiazhuang, Handan, Xingtai, and Baoding. There is a trend of agglomeration and diffusion of regional industries in geographical space. Middle- and high-end manufacturing industry and service industry gather in the first city, and middle- and low-end processing industry, labor-intensive industry, and agriculture move to surrounding cities. The research results can provide policy suggestions for the coordinated development of regional integration in developing countries. [ABSTRACT FROM AUTHOR]

The unplanned and uncontrolled urbanization of Indian cities has put them under different ecological and environmental threats. Urban heat island (UHI) is one such critical ecological hazard, whereby an urban area is experiencing higher land surface temperature (LST) as compared to the surrounding rural area. In the present study, the relationship of LST and surface urban heat island (SUHI) with the degree of impervious surface (IS) and green spaces (GS) in four rapidly growing Indian cities is presented. This study utilizes different geospatial techniques, including urban-rural gradient analysis, surface urban heat island estimation using Landsat OLI/TIRS data. The results signify a strong negative correlation of LST with the IS for Ahmedabad, Jodhpur, and Nagpur, while a positive correlation is seen over Guwahati. The negative correlation is the manifestation of the urban cool island, pertaining to higher LST over rural areas. On the other hand, Guwahati is surrounded by green vegetation, which provides natural cooling and thus lowers the LST, resulting in positive SUHI. The density of GS is found to be a significant contributor of SUHI in Guwahati city, whereas in the other three cities, its impact is insignificant due to its presence in very less amount in rural surroundings. [ABSTRACT FROM AUTHOR]

The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km2 of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts. [ABSTRACT FROM AUTHOR]

Lagos State, which is home to Lagos metropolis and the eighteenth largest urban agglomeration in 2018 in the world, has been characterized by rapid urbanization. An earlier study of the eastern segment of the state revealed dramatic urban growth in previously rural local councils where it replaced mostly ecological assets. For a statewide view of the landscape pattern changes, this study examined the western segment of the state to characterize the magnitude and trend of these changes. This was done with Landsat images for 1984, 2006 and 2015 along with ENVI 5.0 software and FRAGSTATS v.4.2 spatial pattern analysis program. Results show that cumulatively, the ecological assets, which comprised 75% of the area in 1984, have been whittled down to about 34% by 2015 having been lost through urban development. At the council level in 2015, the highest growth areas, in decreasing order of magnitude, were Alimosho, Badagry, Ojo and Amuwo Odofin local government areas. Both class and landscape-level metrics confirmed the dominance and fragmentation of the resultant landscape in 2015 by urban development. In 1984, vegetation dominated the landscape at 55% coverage and was the largest feature with the highest area-weighted contiguity or connectedness index. Thirty-one years later in 2015, built-up areas were the dominant patch with 54% coverage and the highest contiguity and area-weighted contiguity index, higher than vegetation. These findings have narrowed the information gap about the spatial and temporal changes in the ecological assets of the western segment of Lagos State as a result of rapid urban expansion.

Land cover mapping from satellite images has progressed from visual and statistical approaches to Random Forests (RFs) and, more recently, advanced image recognition techniques such as convolutional neural networks (CNNs). CNNs have a conceptual benefit over RFs in recognising spatial feature context, but potentially at the cost of reduced spatial detail. We tested the use of CNNs for improved land cover mapping based on Landsat data, compared with RFs, for a study area of approximately 500 km × 500 km in southeastern Australia. Landsat 8 geomedian composite surface reflectances were available for 2018. Label data were a simple nine-member land cover classification derived from reference land use mapping (Catchment Scale Land Use of Australia—CLUM), and further enhanced by using custom forest extent mapping (Forests of Australia). Experiments were undertaken testing U-Net CNN for segmentation of Landsat 8 geomedian imagery to determine the optimal combination of input Landsat 8 bands. The results were compared with those from a simple autoencoder as well as an RF model. Segmentation test results for the best performing U-Net CNN models produced an overall accuracy of 79% and weighted-mean F1 score of 77% (9 band input) or 76% (6 band input) for a simple nine-member land cover classification, compared with 73% and 68% (6 band input), respectively, for the best RF model. We conclude that U-Net CNN models can generate annual land cover maps with good accuracy from proxy training data, and can also be used for quality control or improvement of existing land cover products. [ABSTRACT FROM AUTHOR]

Accurate simulations of the spatial and temporal changes in vegetation gross primary production (GPP) play an important role in ecological studies. Previous studies highlighted large uncertainties in GPP datasets based on satellite data with coarse spatial resolutions (>500 m), and implied the need to produce high-spatial-resolution datasets. However, estimating fine spatial resolution GPP is time-consuming and requires an enormous amount of computing storage space. In this study, based on the Eddy Covariance-Light Use Efficiency (EC-LUE) model, we used Google Earth Engine (GEE) to develop a web application (EC-LUE APP) to generate 30-m-spatial-resolution GPP estimates within a region of interest. We examined the accuracy of the GPP estimates produced by the APP and compared them with observed GPP at 193 global eddy covariance sites. The results showed the good performance of the EC-LUE APP in reproducing the spatial and temporal variations in the GPP. The fine-spatial-resolution GPP product (GPPL) explained 64% of the GPP variations and had fewer uncertainties (root mean square error = 2.34 g C m−2 d−1) and bias (−0.09 g C m−2 d−1) than the coarse-spatial-resolution GPP products. In particular, the GPPL significantly improved the GPP estimations for cropland and dryland ecosystems. With this APP, users can easily obtain 30-m-spatial-resolution GPP at any given location and for any given year since 1984. [ABSTRACT FROM AUTHOR]

Most lakes have undergone significant changes on the Tibetan Plateau in recent decades, affecting water resources on the Tibetan Plateau and its surrounding areas. In this paper, we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery. We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery, and then calculated the changes in lake area, level, and volume in 1972 to 2019. We also analyzed the potential impacts of temperature, precipitation, glacial and permafrost melting in lake changes during this period. The results show that the lakes tended to shrink until 2010 in southern and western plateau, after which they began to expand gradually but the overall trend is still shrinking. Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage. The lakes in the central plateau, northwest plateau and northeast plateau tend to expand overall. The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost. Overall, the lake changes have gone through 3 phases, namely a slight decrease during 1972–2000, a rapid increase during 2000–2010, and a slowdown in the last decade (2010–2019). Multiple factors such as temperature, precipitation, the state of glaciers and permafrost have contributed to the changes in the lake.

• First long-term dynamic maps of intensive forage acreage are generated. • Dynamic monthly composite images (DMCI) improve intensive forage mapping accuracy. • DMCI- and phenology-based intensive forage mapping approach is robust and extendable. • Intensive forage is harvested twice or more a year in single-cropping regions. • Intensive forage expansion is remarkable in Arhorqin Banner during 2008–2022. The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm's efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions. [ABSTRACT FROM AUTHOR]

Nepal's forest cover nearly doubled over the last three decades. While Community Forest (CF) management and agricultural abandonment are primary drivers of forest cover expansion, the contribution of afforestation on privately managed land is not well documented. We mapped forest cover change from 1988 through 2016 in 40 privately managed sites that transitioned from agriculture to forest and assessed how agricultural abandonment influenced private land management and afforestation. We used a mixed method analysis to integrate our 29-year Landsat satellite image-based record of annual forest cover with interview data on historical land cover and land use dynamics from 65 land managers in Bagmati Province. We find that privately managed land accounted for 37% of local forest cover gain, with mean forest area within private forests growing from 9% to 59%. Land managers identified two factors driving these gains on private land: implementation of CF management in adjacent government forests and out-migration. These previously undocumented linkages between forest cover gain on private land and CF management merits further research in community forests and calls for greater policy and technical support for small-scale timber growers and rural households who rely on private forests for income generation. • 29-year remote sensing analysis links forest change to migration & community Forestry. • CF management inspired farmers to grow forest resources on their private land. • Out-migration induced agricultural abandonment was followed by smallholder forestry. • Land managers actively shifted land use in response to community forestry & migration. [ABSTRACT FROM AUTHOR]

Remote sensing images are an essential tool for mapping the amount of flood inundation after flood events. For early flood estimation, flood mapping is a crucial component. This study used an integration of geospatial techniques to evaluate the flood extent in District Dera Ghazi Khan, Pakistan. The modified normalized difference water index (MNDWI) was utilized to estimate the flood extent using Landsat data. For a thorough flood investigation, pre-flood, during, and post-flood images were obtained. The analysis enabled us to delineate flood extent as well as flood duration. The result showed that the flood continued for nearly 5 weeks in the study area. This proposed geospatial technique provides a framework for the identification of inundated areas, which allows emergency responses to be focused on newly flooded areas. Hence, the current study offers a novel perspective on flood mapping and significantly contributes to flood monitoring.

Inspection data can be used to comprehend and plan effective maintenance of bridges. In particular, the year of initial construction is one of the most important criteria for formulating maintenance plans, making budget allocations, and estimating soundness. In an initial survey of bridges in Cambodia, it was concluded that the year of construction of only 54% of 2439 bridges surveyed is known, with the remaining 46% remaining unknown. In this research, Landsat satellite data is used to estimate the year of construction of these bridges. Landsat provides spatial spectral reflectance information covering more than 30 years, and for longer bridges this can be used to estimate the year of construction by visual judgement. However, limited image resolution means this is not possible for shorter bridges. Instead, a method using the Landsat Normalized Difference Water Index (NDWI) is used to estimate the year of construction. Three pixels are selected from Landsat image data in such a way that one lies on the current location of a bridge and two other reference pixels are placed on similar terrain at a certain distance perpendicular to the bridge axis. NDWI values are plotted over time for the three pixels and the difference in value between the bridge pixel and the two reference pixels is then compared. Before the bridge is constructed, all three pixels should have similar NDWI values, but after construction the value of the target bridge pixel should differ from the other two because the NDWI value of a bridge surface is different from that of the surrounding vegetation. By looking for this change, the year of construction of a bridge can be estimated. All the bridges in the Cambodian database are classified into three categories based on length (which affects their visibility in Landsat images) and year of construction is estimated. The results show that estimated year of construction has the same accuracy in all three categories.

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown a lack of spatial and temporal stability due, for instance, to variability between individual pixels and changes in vegetation condition, respectively. Machine learning methods that consider spatial patterns in addition to reflectance can address some of these issues. In this study, a convolutional neural network (CNN) model, U-Net, was trained for a 500 km × 500 km region in southeast Australia using annual Landsat geomedian data for the relatively dry and wet years of 2018 and 2020, respectively. The label data for model training was an eight-class classification inferred from a static land-use map, enhanced using forest-extent mapping. Here, we wished to analyse the benefits of CNN-based land cover mapping and reporting over 34 years (1987–2020). We used the trained model to generate annual land cover maps for a 100 km × 100 km tile near the Australian Capital Territory. We developed innovative diagnostic methods to assess spatial and temporal stability, analysed how the CNN method differs from pixel-based mapping and compared it with two reference land cover products available for some years. Our U-Net CNN results showed better spatial and temporal stability with, respectively, overall accuracy of 89% verses 82% for reference pixel-based mapping, and 76% of pixels unchanged over 33 years. This gave a clearer insight into where and when land cover change occurred compared to reference mapping, where only 30% of pixels were conserved. Remaining issues include edge effects associated with the CNN method and a limited ability to distinguish some land cover types (e.g., broadacre crops vs. pasture). We conclude that the CNN model was better for understanding broad-scale land cover change, use in environmental accounting and natural resource management, whereas pixel-based approaches sometimes more accurately represented small-scale changes in land cover.

For 43 years, multi-dated satellite imagery is used to examine shifts in shorelines and potential location on shorelines for Dr. Abdul Kalam Island, in the Bay of Bengal, India. A Normalized Difference Water Index with object-based classification system and automatic shoreline extraction tool is used to retrieve the shoreline automatically. To calculate the rates of shoreline change and future positions, based on empirical observations, the simple statistical model, such as EPR and LR models are used. The erosion/accretion scenario is also analyzed for the 1976, 1989, 1997, 2008 and 2018 shoreline of Landsat imaging.Results showsnorth of Dr. Abdul Kalam Island is subjected to high deposition rates and the south part having high erosion rates in the vicinity of the Dhamara Estuary and Maipura River. The short-term (2023) and long-term (2028) shoreline positions are predicted on the basis of the delineated shoreline. Around 63% of transect's RMSE values varies between ±10 m, showing better agreement among the estimated shoreline positions and satellite-based ones. The results of the study can be very useful for the quantification of shoreline changes, shoreline forecasting and precautionary measures for anti-satellite missile launching centre. [ABSTRACT FROM AUTHOR]

Long‐term and fine‐scale monitoring of surface urban heat island (SUHI) is critical for the design of heat mitigation strategies. Landsat series offer long‐term (since the 1980s) and fine‐scale land surface temperature (LST) observations for such SUHI analysis. However, Landsat data are characterized by a long revisit period (16 days) and are seriously impacted by cloud contamination and stripe gaps, making the Landsat‐derived long‐term SUHI trends across cities incomparable. To address this issue, here we applied the Prophet model to reconstruct temporally consistent long‐term (∼1985–2019) clear‐sky Landsat LSTs over four megacities in China (Beijing, Chongqing, Shanghai, and Shenzhen). The results show that the mean absolute error of the SUHI intensity (SUHII) estimated using the reconstructed LSTs ranges from 0.2 to 0.6°C. The reliability of the reconstructed LSTs is further evidenced by the general consistency between the reconstructed Landsat and original Moderate Resolution Imaging Spectroradiometer LSTs. Our analysis further demonstrates that the overall SUHII has been increasing slowly over Beijing but has been increasing rapidly in the other three megacities since the 1980s. Local SUHII trends also varied between urban regions. Urban core is characterized by an initial increase and then a slight decrease in the local SUHII, while a continuously increasing trend is observed for the urban fringe. For the new urban development, the interannual SUHII trend lies somewhere between these trends. Overall, our study provides a practical approach to reconstruct long‐term clear‐sky Landsat LSTs, and delivers a better understanding of long‐term and fine‐scale SUHI variations across cities. Key Points: Landsat land surface temperatures since the 1980s are reconstructed in four Chinese megacities using the Prophet modelReconstructed Landsat land surface temperatures match well Moderate Resolution Imaging Spectroradiometer land surface temperatures in depicting interannual trendsLong‐term and fine‐scale variations of the overall surface urban heat island intensity (SUHII) and region‐based local SUHII are compared over four megacities [ABSTRACT FROM AUTHOR]

This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables. The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region. [ABSTRACT FROM AUTHOR]

Summary: Precisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like‐desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)‐based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi‐temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN‐based detector is constructed based on desertification‐free features and then employed to identify atypical events associated with desertification changes. The GAN‐detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN‐based desertification detector's performance has been assessed via multi‐temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state‐of‐the‐art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN‐based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN‐based method for the analysis and detection of desertification changes. Results also revealed that the GAN‐driven desertification detection approach outperforms the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Detecting spatiotemporal dynamics of urbanization is essential to study the patterns and processes of urban ecosystems. However, urban areas are difficult to classify, and migrating static classification algorithms to dynamic detection is also challenging. Here, we develop a time-series approach for the detection, in which urbanization processes are represented by the temporal trajectories of biophysical indicators. According to the spectral analysis of land-cover classes, the indicators of normalized difference vegetation index, automated water extraction index, biophysical composition index, and shortwave infrared index are selected to characterize urbanization. The temporal trajectories of these indicators are calculated using Landsat data on the google earth engine platform; and their long-term trends and abrupt changes are estimated by the Theil-Sen estimator and Mann–Kendall test, as well as the Pettitt test. The urbanized pixel classes are identified from the possible trend combinations of the indicators, in which the falsely alarmed pixels are excluded by the time-series analyses of the indicators. Three Chinese cities—Fuzhou, Nanchang, and Xining—in various physical environments are selected to demonstrate the proposed approach. The accuracy assessments using very high resolution images achieve the overall accuracies above 90% in the three areas, showing comparable performance to supervised techniques whereas no ancillary data are required. By the comparisons with two other urban products, the advantages of the proposed approach in detecting urbanization processes in the early stage and weak impervious situation are demonstrated. Due to little human intervention involved in the proposed approach, it could be readily adapted to other regions.

This study aimed to compare the trophic status assessments of the western coastal waters of Suez Bay, Gulf of Suez, Egypt. The Trophic Index (TRIX) that depends on the lab-based water quality measurements of nutrients, chlorophyll-a (Chl-a) and dissolved oxygen, was compared with the Carlson trophic state index that integrates lab-based data such as NH4 or PO4 concentration with Landsat data and geographic information system (GIS) techniques. The physicochemical properties were determined, which indicated that Suez Bay was in an eutrophication state limited by both N and Si. It was suggested that the most influential activities be the industrial and domestic drains that raised the TRIX value to 5.59 with a mesotrophic state on average. Likewise, phosphorus trophic status mapping applying Carlson trophic state index showed high activity nearby drain outlets, especially at the Cabanon drain. The regression statistical model indicated a relationship between lab data of nutrients and band values extracted from Landsat images. Accordingly, the integration of remote sensing and GIS techniques with laboratory data is useful for predicting the state of water quality in the coastal areas with a single lab-based water quality parameter. [ABSTRACT FROM AUTHOR]

Land use / land cover (LULC) has been considered as one of the important bio-physical parameters and have significant affect on local environmental change, particularly increasing anthropogenic temperature. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. In the present investigation, long term changes in LULC and its negative impact on land surface temperature (LST) were analyzed using multi-temporal Landsat satellite images between 2000 to 2016. firstly LULC of the study area has been classified and temporal changes in land use classes were quantify, and observed that in most of the land use classes such as vegetation (-1.28 %), water bodies (-1.65 %), agriculture (-3.52) and open land (-2.43 %) have shown negative change, however large scale positive changes in built-up area (+8.87 %) has been observed during the analysis, which is mainly due to continuous urbanization and growth of population in the area. The classified thermal images from the same period also show mean temperature of the area has increased by 1.60 °C since last 16 years. The observation from the present study reveals that due to the large-scale land use change practices in urban and peri-urban area witnessed for the rising temperature due to loss natural vegetation and other natural resources.

Abstract With the increasing demands for water due to growing population, urban expansion, agricultural development and groundwater resources are gaining much attention, particularly in Gopiballavpur block, Paschim Medinipur district (West Bengal, India). Present study used cohesive approach of remote sensing and geographic information system to deliver an appropriate platform for convergent study of multi-disciplinary data and decision building for artificial groundwater recharge. Thematic maps were generated from Landsat8-Operational land Imager (OLI) and Shuttle Radar Topographic Mission (SRTM), and aquifer parameter thematic layers were organized from conventional field data. The weighted linear combination method was used to determine the weights of various themes and their classes, namely ‘very good’, ‘good’, ‘poor’ and ‘very poor’ for identifying recharge potential zone. It has been concluded that 14.58% area has very good groundwater recharge potentiality of the study area. The area having ‘good’, ‘moderate’ and ‘low’ recharge potential is about 32.77%, 39.67% and 12.98%, respectively.

Most lakes have undergone significant changes on the Tibetan Plateau in recent decades, affecting water resources on the Tibetan Plateau and its surrounding areas. In this paper, we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery. We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery, and then calculated the changes in lake area, level, and volume in 1972 to 2019. We also analyzed the potential impacts of temperature, precipitation, glacial and permafrost melting in lake changes during this period. The results show that the lakes tended to shrink until 2010 in southern and western plateau, after which they began to expand gradually but the overall trend is still shrinking. Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage. The lakes in the central plateau, northwest plateau and northeast plateau tend to expand overall. The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost. Overall, the lake changes have gone through 3 phases, namely a slight decrease during 1972–2000, a rapid increase during 2000–2010, and a slowdown in the last decade (2010–2019). Multiple factors such as temperature, precipitation, the state of glaciers and permafrost have contributed to the changes in the lake. [ABSTRACT FROM AUTHOR]

Major development projects along rivers, like reservoirs and other hydraulic structures, have changed not only river discharges but also sediment transport. Thus, changes in river planforms can be observed in such rivers. In addition, river centerline migrations can be witnessed. The Mahaweli River is the longest in Sri Lanka, having the largest catchment area among the 103 major river basins in the country. The river has been subjected to many development projects over the last 50 years, causing significant changes in the river discharge and sediment transport. However, no research has been carried out to evaluate the temporal and spatial changes in planforms. The current seeks to qualitatively analyze the river planform changes of the Lower Mahaweli River (downstream to Damanewewa) over the past 30 years (from 1991 to 2021) and identify the major planform features and their spatiotemporal changes in the lower Mahaweli River. Analyzing the changes in rivers requires long-term data with high spatial resolution. Therefore, in this research, remotely sensed Landsat satellite data were used to analyze the planform changes of Lower Mahaweli River with a considerably high resolution (30 m). These Landsat satellite images were processed and analyzed using the QGIS mapping tool and a semi-automated digitizing tool. The results show that major changes in river Mahaweli occurred mainly in the most downstream sections of the selected river segment. Further, the river curvature was also comparatively high downstream of the river. An oxbow lake formation was observed over time in the most downstream part of the Mahaweli River after 2011. Centerline migration rates were also calculated with the generated river centerlines. It was found that the rates were generally lower than about 30 m per year, except for at locations where river meandering was observed. The main limitations of this study were the possible misclassifications due to the resolution of images and obstructions caused by cloud cover in the Landsat images. To achieve more accurate estimates, this study could be developed further with quantitative mathematical analysis by also considering the sediment dynamics of the Mahaweli River.

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.

Monitoring of land use/cover (LULC) change is very important for sustainable development planning study. This research work is to understand natural and environmental situation and its cause such as intensity, distribution and socio and economic effects in Moscow, Russia based on remote sensing and Geographical Information System techniques. A model was developed by following thematic layers: land use/cover, vegetation, soil, geomorphology and geology in ArcGIS 10.2 software using multi-spectral satellite data obtained from Landsat 7 and 8 for the years of 1995, 2005 and 2016 respectively. Increasing scientific and political interest in regional aspects of global environmental changes, there is a strong stimulus to better understand the patterns causes and environmental consequences of LULC expansion in the elevation of Moscow state, one of the areas in the nation with fast economic growth and high population density. A 70 to 300 m inundation land loss scenarios for surface water and sea level rise (SLR) were developed using digital elevation models of study site topography through remote sensing and GIS techniques by ASTER GDEM and Landsat OLI data. The most severely impacted sectors are expected to be the vegetation, wetland and the natural ecosystem. Improved understanding of the extent and response of SLR will help in preparing for adaptation.