Google Earth Images Research Articles

Identification of Beach Erosion in Colombo- North Coast Line: Effectiveness of the Engineered Solutions

Coastal erosion is a silent disaster in Sri Lanka due to the loss of valuable land. Already, millions of rupees have been spent on coastal protection. This study assessed the long-term effectiveness of those engineered solutions, especially focusing on the Colombo- North coastline. The effectiveness was assessed by comparing erosion rates in the last 20 years with available historical data. Shorelines of the last 20 years were observed and digitized by Google Earth image analysis and erosion rates were calculated by GIS and Digital Shoreline Analysis (DSAS) tools. The evolution of the structures was observed and analysed by satellite image analysis and other available data from reliable sources such as reports produced by the Government. Cost estimation for the observed structures was carried out using BOQ analysis and discounted methods. Results showed that more than 60 numbers of structures have been constructed during the last 20-30 years along the study area. Uswetakeyyawa to Estuary of Kelani River stretch was identified as the region with the highest structure density. When analyzing the results, it was revealed that the structures built in that region have reduced the erosion rate (0.43 m/year) compared to the 1996 rates (2.5 m/year). Digital shoreline analysis further indicated effectiveness as most of the protected areas from Negombo to Maha Oya estuary have been accreted during the last 20 years while unprotected areas from Uswetakeyyawa to Negombo lagoon, have been subjected to erosion. According to cost estimations, approximately Rs 1.23 billion have been spent in the last 20-30 years on the entire study area. Further, the cost analysis of the Uswetakeyyawa to Kelaniya stretch revealed that the investment for 1m accretion was Rs 28.5 million. However, the cost-effectiveness of the structures should be further assessed against the land loss value with an integrated Coastal Vulnerability Index.

Factors Influencing Ephemeral Gullies at the Regional Scale: Formation and Density

Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. The present study visually interpreted the EGs based on Google Earth images in 137 small watersheds uniformly distributed in the Loess Plateau, compared them with measured results, and analyzed the factors influencing EG formation and density using GeoDetector. The results showed that visually interpreting EGs from Google Earth images was suitable for EG regional studies. Out of the 137 small watersheds, 33.6% had EG occurrence with an average density of 3.41 km/km2. Rainfall (R) and slope gradient (S) were the primary factors influencing the formation of EGs, while the area proportion of sloping farmland (APSF) and soil erodibility (K) were the main factors affecting EG density. The interaction of dual factors had a greater influence compared to single factors, with the interaction between S and Normalized Difference Vegetation Index (NDVI) having the greatest impact on EG formation and the interaction between K and NDVI on EG density. Although natural forces significantly influence whether EGs can form in a specific area, human activities greatly affect the density of the gullies that develop. This underscores the importance of proper land management in controlling gully erosion. These findings could provide theoretical support for EG prediction models and a scientific basis for soil and water loss control strategies at the regional scale.

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

Vectorized dataset of silted land formed by check dams on the Chinese Loess Plateau

Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer’s accuracy and user’s accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.

Rock glacier distribution and kinematics in Shigar and Shayok basins based on radar and optical remote sensing

AbstractRecent studies have demonstrated the rock glacier destabilisation and permafrost thawing induced by warming climate represent a continuous threat to life, infrastructure and socio‐economic development in the mountainous regions of the Hindu Kush Himalaya. This study presents the first systematic rock glacier inventory for the Shigar and Shayok basins, quantifying rock glacier geomorphology and kinematics based on morphological evidence using Google Earth images and interferometric synthetic aperture radar (InSAR). The certainty index of each inventoried rock glacier is recorded, along with its geomorphological properties and kinematic attributes. The rock glacier velocity is estimated through the InSAR time series analysis of Sentinel‐1 images from 2020 to 2021, with temporal baselines at 12‐day intervals. We developed a rock glacier inventory consisting of 84 rock glaciers covering an area of 29 km2 for the Shigar Basin and 2206 rock glaciers encompassing 369 km2 for the Shayok Basin. Among these rock glaciers, 69% and 52% are categorised as active rock glaciers, respectively. Rock glaciers in both catchments are confined to elevations between 3600 and 5875 m a.s.l., with a mean area of 0.22 km2. The maximum recorded velocity for active rock glaciers in the Shigar Basin is 101 ± 9 cm year−1, with a median of 27 ± 10 cm year−1, and in the Shayok Basin 114 ± 10 cm year−1 (median of 29 ± 9 cm year−1). Temporal variations in the surface velocities of the rock glaciers reveal that they increase with rising temperatures in both catchments, highlighting the seasonality in the rock glacier surface velocity. In total, we recorded the kinematic attributes of 98% of the inventoried rock glaciers in the study area.

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

Comparison of SWAT and WEPP for Modeling Annual Runoff and Sediment Yield in Agewu - Mariyam Watershed, Northern Ethiopia

The Soil and Water Assessment Tool (SWAT) and the Geographic Water Erosion Prediction Project (Geo-WEPP) were applied to compare modeling of annual runoff and sediment yield in the Agewmariam watershed, eastern Amhara Region, Ethiopia. Spatial and temporal data distributions were required as inputs to run both models. Soil texture and other soil properties were measured in the field and in the laboratory, and soil maps were generated from global digital soil maps. Land use maps were created by manually digitizing Google Earth images. Watersheds were defined using watershed DEMs and gradient maps were created for each runoff event. Runoff samples were collected and analyzed for sediment concentrations in the laboratory; average annual runoff and sediment volumes were estimated using the WEPP and SWAT models. The results were satisfactory compared to the observed values, with R2 values of 0.86 and 0.91 for the SWAT and WEPP models, respectively, and NSE values of 0.54 and 0.71 for the monthly runoff. The estimated annual mean runoff and sediment yield at the watershed outlets were 65.54 mm, 146.14 mm, 43t/ha/yr and 41.7t/ha/yr for the WEPP and SWAT models, respectively. Several sub watersheds were determined to be susceptible to soil erosion and were prioritized, so more attention was given to this area to reduce runoff and soil erosion. Therefore, the SWAT and WEPP models were suitable for estimating annual runoff and sediment volumes. Sediment yields simulated from both models were high and alarming and far exceeded the allowable rate of soil loss.

Analyzing the spatial relationship between land surface temperature and elevation in three gullies locations in southeastern Nigeria

Gully erosion is a dangerous phenomenon that threatened human existence, plants and animals communities. Families are being separated from their ancestral home and livelihood due to gully erosion. Also, available agricultural lands to combat food insecurity are contently depleted on yearly basis across the globe as a result of gullies menace. Southeastern Nigeria is highly vulnerable to gully erosions due to it geology and studies on spatial relationships between gully LST and elevation has not been carried out, particular by applying remote sensing and GIS technique to investigate it variations. Available studies where limited to mapping and causes of gully erosion in the region. The study applies LST and elevation data to investigate correlation on three gullies locations. LST was derived from Landsat data and was first converted to radiance image while the elevation data was obtained from Google Earth image. Elevation data was used to produce raster surface in that cell values equivalent to LST at every point can be extracted. Three sets of paired sample points of LST and elevation was extracted from the three gullies locations and used to compute correlation coefficient (r). The study observed at Nanka gully a strong negative correlation of -0.665. This implies an increase in LST correspond to decrease in elevation within the gully. At Ekwolobia and Aguluezechukwu gullies sites, positive correlation coefficient of 0.692 and 0.910 were obtained. The positive correlation coefficients implied that LST and elevation increase or decrease within the gullies. This might be due to unequal distribution of radiation within gully surface as a result of three canopies. For further study, it is recommended that improve elevation data source like LiDAR elevation data should be use to investigate spatial correlation between LST and elevation at gully location.

Overview of Fault Zones Based on Remote Sensing Data as Contribution to the Safety of Infrastructure and Land Use in Southern Egypt

Part of a requisite for natural hazard awareness and damage prevention in Egypt is the detailed monitoring of surface fault zones. The inventory of active faults and their risk assessment is an essential contribution to the safety of settlements, land use and infrastructure (railroads, highways, pipelines) and to damage prevention. Thus, this study aims to contribute to the detection, inventory, and documentation of fault zones. Surface faulting hazard assessment considers any surface consequences caused by surface near faults such as abrupt horizontal and vertical displacements and rotations, or mass movements after stronger earthquakes. Long-term, aseismic, slow-creeping movements along fault zones have to be considered as well. Southern Egypt offers unique and optimal conditions for the research of fault zones and their different structural expressions and conditions because of the dry desert climate conditions and relatively low human influence on the landscape in extended areas, especially for the investigation of different types of faults, their interactions with each other and the outcropping rocks, and fault-related deformation structures. Volcanic activity has been influenced in the geologic past often by larger fault zones as dike intrusions, volcanic cones and plugs or craters occur concentrated along these zones of weakness facilitating the uprise of magma. Larger fault zones are crossing reservoir areas. Surface water intrusions into deep-seated faults have played a role in triggering earthquake swarms in the reservoir areas during the last decades. Mapping of active faults is not only an important but also a cost-intense task when carried out in the field or when using geodetic and geophysical data. In the scope of this study, fault detection was carried out based on different open-source satellite data (Landsat 8 and 9, Sentinel 2 optical data, Sentinel 1 and ALOSPASAR radar data, and Google Earth and Bing Map high-resolution satellite images) from the southern part of Egypt. Faults were digitized using ArcGIS and QGIS software. An inventory of fault-related structural features (depressions, ridges, rotation structures, etc.) and rift zones was elaborated based on remote sensing data. An overview of different types of faults and their related structures as well as their interactions with their host rock conditions could be achieved. By merging the inventory results with infrastructural and land use data, critical areas with potential damage risk were pointed out.

Glacial geomorphology of the Vanch River basin, the western Pamirs, Tajikistan

ABSTRACT Geomorphological mapping of glaciers and glacial landforms helps describe the current environment and investigate past glacial changes. Here we present the first detailed 1:80,000 glacial geomorphological map of the Vanch River basin (about 2100 km2), the western Pamirs, Tajikistan [38.6667°N, 72.0000°E]. Mapping was performed using DEM (30 m), Landsat 8 imagery (15 m), and Google Earth images (∼1 m). The resultant map provides the spatial distribution of glaciers and associated erosional and depositional landforms such as cirques, glacial valleys, rock glaciers, and moraines. Morphology of the lower section of the main valley outlines the limits of Vanch Glacier in the Middle Pleistocene. Comparison with former glacier inventories indicates that the glaciers have generally been stable, but the proportion of debris-covered areas has increased since 2007. The identified 184 intact rock glaciers represent hidden water storage, providing a new basis for estimating water resources in the basin.

Agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province, Nepal

Agricultural land abandonment is the crucial issue of land use /land cover change in many parts of the world and it is directly related to soil erosion in the mountain areas. Nepal has also faced heavy agricultural land abandonment from the mountain regions in the last two decades. This study is concerned with agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province of Nepal. This study used high-resolution aerial photography and Google Earth images to map cultivated land and its abandonment in 1995 and 2020. The Revised Universal Soil Loss Equation (RUSLE) model has been used to estimate soil erosion. Studies revealed that more than 40% of the agricultural land was abandoned from 1995 to 2020 and there is higher spatial variability of abandonment intensity. High altitude and steeper slope areas have a higher intensity of abandonment than in lower altitude and lower slope gradients. There is a significant impact of abandonment of agricultural land on reducing soil erosion from 1995 to 2020. The average rate of soil erosion was quite high in 1995 but decreased to 51.5 percent in 2020. There are higher rates of reduction in soil erosion at higher altitudes and marginal lands like steeper slopes and the areas having lower solar radiation because of the higher proportion of agricultural land abandonment. Thus, it can be concluded that agricultural land abandonment during the last 25 years significantly impacts soil erosion control in the study area.

Hydro-sediment responses to management options under current and future climate change scenarios in Maybar watershed, South Wollo Zone, Ethiopia

Climate change coupled with inappropriate land use management is a serious environmental challenge in the highland part of Ethiopia. The aim of this study was to investigate hydro-sediment responses to management options under current and future climate change scenarios in Maybar watershed, Northern Ethiopia. We employed soil and water assessment tool (ArcSWAT 2012.10_7.24) for hydrologic modeling and CORDEX-climate data under RCP 4.5 and RCP 8.5 emission scenarios for climate projections. Soil map was prepared by digitizing soil characterization map of the watershed generated by Weigel in 1986. DEM data with 2 m spatial resolution was obtained from water and land resource center and land use classification was done through digitization of google earth images. Better agreement between calibrated SWAT simulation and observed variables was achieved. Delta change bias correction method was employed to improve the climate simulation in reproducing the observed climate variables. We found a considerable increase in annual precipitation by 18.71 % and 22.33 % for RCP 4.5 and RCP 8.5, respectively compared with the current climate conditions. Climate change induced hydro-sediment results were observed to be consistent with predicted precipitation. Under the current climatic conditions, average sediment yield at the sub-basin scale varies from negligible (under terrace complementation) to approximately more than 50 t ha−1 yr−1 with a basin average of 32 t ha−1 yr−1 (under no-terrace conditions). The implementation of management alternatives gets the dry season's hydrological behavior of the catchment improved and climate change induced sediment yield reduction. Generally, the information could possibly support decision makers and planners to implement best-fitted management options and thereby reduce current and expected aggressive sediment loss situations.

Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application: Study case of rural area in Thailand

It is necessary to ensure security and community safety around High Voltage Transmission Poles (HVTP). Legislation requires a safety perimeter around HVTP and the High Voltage Lines (HVL) where no building and tree can be located. However, surveying thousands of kilometers of circuit is an expensive and challenging task that is currently performed by human inspection. Therefore, the use of automatic detection methods enables to facilitate the inspection is necessary to reduce time and cost. Convolutional Neural Network (CNN) is proposed in this work to detect, from Google Earth images, buildings and trees within the safety perimeter of HVTP. A dedicated 3 class (House, forest and HVTP) dataset of approximately 1 million tiles with a resolution of 0.09 m/pixel is created. Tiles size for trees and building classes is 64 × 64 pixels while for the HVTP 128 × 128 pixels is used. Three CNN models are built and optimized to classify each of these classes. Models validation shows that, except for houses where the accuracy is only 84 %, the other two classes have an accuracy of over 89 %. Moreover, by analyzing the classified HVTP, type can be identified. Finally, buildings and trees within the safety perimeter around the HVTP can be identified and displayed on the image demonstrating the usefulness of the tool.

Enhanced detection of freeze‒thaw induced landslides in Zhidoi county (Tibetan Plateau, China) with Google Earth Engine and image fusion

Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a geological disaster leading to soil erosion. These landslides reduce biodiversity and intensify landscape fragmentation, which in turn are strengthen by the persistent climate change and increased anthropogenic activities. However, conventional techniques for mapping FTILs on a regional scale are impractical due to their labor-intensive, costly, and time-consuming nature. This study focuses on improving FTILs detection by implementing image fusion-based Google Earth Engine (GEE) and a random forest algorithm. Integration of multiple data sources, including texture features, index features, spectral features, slope, and vertical‒vertical polarization data, allow automatic detection of the spatial distribution characteristics of FTILs in Zhidoi county, which is located within the Qinghai‒Tibet Engineering Corridor (QTEC). We employed statistical techniques to elucidate the mechanisms influencing FTILs occurrence. The enhanced method identifies two schemes that achieve high accuracy using a smaller training sample (scheme A: 94.1%; scheme D: 94.5%) compared to other methods (scheme B: 50.0%; scheme C: 95.8%). This methodology is effective in generating accurate results using only ∼10% of the training sample size necessitated by other methods. The spatial distribution patterns of FTILs generated for 2021 are similar to those obtained using various other training sample sources, with a primary concentration observed along the central region traversed by the QTEC. The results highlight the slope as the most crucial feature in the fusion images, accounting for 93% of FTILs occurring on gentle slopes ranging from 0° to 14°. This study provides a theoretical framework and technological reference for the identification, monitoring, prevention and control of FTILs in grasslands. Such developments hold the potential to benefit the management of grassland ecosystem, reduce economic losses, and promote grassland sustainability.

A global dataset of the shape of drainage systems

Abstract. Drainage basins delineate Earth's land surface into individual water collection units. Basin shape and river sinuosity determine water and sediment dynamics, affecting landscape evolution and connectivity between ecosystems and freshwater species. However, a high-resolution global dataset for the boundaries and geometry of basins is still missing. Using a 90 m resolution digital elevation model, we measured the areas, lengths, widths, aspect ratios, slopes, and elevations of basins over 50 km2 globally. Additionally, we calculated the lengths and sinuosities of the longest river channels within these 0.67 million basins. We built a new global dataset, Basin90m, to present the basins and rivers, as well as their morphological metrics. To highlight the use cases of Basin90m, we explored the correlations among morphological metrics, such as Hack's law. By comparing with HydroSHEDS, HydroATLAS, and Google Earth images, we demonstrated the high accuracy of Basin90m. Basin90m, available in shapefile format, can be used on various GIS platforms, including QGIS, ArcGIS, and GeoPandas. Basin90m has substantial application prospects in geomorphology, hydrology, and ecology. Basin90m is available at https://doi.org/10.5880/GFZ.4.6.2023.004 (He et al., 2023).

A Study on Crenulate - Shaped Bays in Coastline of Sri Lanka

Various research studies have been reported in the literature investigating the crenulate-shaped bays that are frequently seen in coastlines all over the world and these coastal bays are recognized as the most stable naturally forming beaches under oblique wave attacks mostly due to swell dominant seas. Although many crenulated bays exist in Sri Lanka coastline, no considerable attention has been given so far to investigate their characteristics. The authors have studied the temporal and spatial variations of crenulated bays in many parts of the coastline of Sri Lanka using Google Earth images. Based on the detailed analysis carried out to investigate the shape of the bay profiles using empirical formulations, a hyperbolic tangent formula has been identified as the best fit for crenulated bays in the coastline of Sri Lanka. Considering various coastal sectors and monsoon seasons, different relationships have been derived between the coefficients a and b of the hyperbolic tangent formula. A relationship that has been developed co-relating the coefficient a with few coastal parameters is also discussed. Since many parts of the Sri Lanka coastline are subjected to severe erosion despite the construction of a variety of shore protection structures in traditional ways, a new approach is suggested for carrying out such work where the coastal structures will be aligned in predetermined directions depending on the coastal stretch so that stable crenulated bays will be naturally formed in between these structures, stabilizing the entire coastal stretch. This approach will be very useful in managing coastal erosion problem in the future.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Integrated analytic hierarchy process and fuzzy analytic hierarchy process for Sahel watershed flood susceptibility assessment, Algeria

Abstract Effective disaster management heavily relies on accurate flood susceptibility mapping. The fuzzy analytic hierarchy process (FAHP) is adept at considering the imprecise nature of decision-making criteria. This study assesses FAHP's effectiveness in flood susceptibility mapping, comparing it with the conventional analytic hierarchy process (AHP). By using Geographic Information System-analyzed remotely sensed data, the research systematically evaluates flood risk southeast of Algiers. Various datasets, including Digital Elevation Model, slope, precipitation, and land use maps, were collected via remote sensing. A linear fuzzy membership function transformed the data into fuzzy values. AHP determined the importance of each dataset, with calculated weights multiplied by corresponding fuzzy values. Fuzzy analysis combined these characteristics into a five-category flood risk map, verified with Google Earth and satellite images. Results indicate a high potential for flood hazard mapping, categorizing 30% of frequently flooded regions as high risk. Maps reveal north basin areas are more flood prone due to excessive precipitation, and urban areas in floodplains are vulnerable. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) assessments demonstrate AHP and FAHP's effectiveness. AUC values of 88.40 and 92% indicate that both models accurately predict flood-prone areas. FAHP excels, reducing subjectivity and ambiguity in human judgments.

Analysis of the temporal variations in glaciers’ surface area in Alaknanda River Basin, Uttarakhand

Abstract This study presented detailed analysis of glacier surface extent in the Alaknanda river basin, Western Himalaya, using Landsat series data, land surface temperature and digital elevation model (DEM). The clean glaciers were delineated using automatic glacier extraction index (AGEI) and the debris-covered glaciers were extracted by utilizing slope and land surface temperature. The generated glaciers map was compared and cross-checked with Randolph Glacier Inventory (RGI v5.0) and sharp google earth image. The reduction in glacier surface area was observed by 367.364 sq. km from 1994 (1150.471 sq. km) to 2015 (783.107 sq. km) at the shrinkage rate of 1.45% per annum. The shrinkage rate was higher from 2006 to 2015 (2.43% per annum) than from 1994 to 2006 (0.78% per annum). The glacier count expanded from 314 (1994) to 360 (2015) due to the fragmentation of individual glaciers. The elevation range of 5000–5500 m above m.s.l. hosted maximum number of glaciers and glacial coverage. The debris-covered glaciers showed deglaciation at the rate of 0.59% per annum from 1994 to 2015. The total area occupied by glacier extent was about 25% of the total basin in the year 1994, 23% in the year 2006 and 17% in the year 2015.