Land Cover Area Research Articles

Spatial pattern of land use along the terrain gradient of Qinghai Tibet Plateau: a case study of Qiangtang Plateau, China

Understanding the land use and land cover (LULC) pattern of the Qinghai Tibet Plateau in China is crucial to support the structural and functional integrity of the ecological barrier of the plateau. This information has significant implications not only for the ecological environment and regional development of the plateau but also for the broader environmental impact at regional and global levels. It is critical to comprehend the spatial pattern and ascertain the underlying causes of land use and land cover in significant areas of the Qinghai Tibet Plateau. This can enable the formulation and implementation of context-specific policies and measures that can contribute to enhancing the ecological security barrier function of the plateau. This study focused on the core area of the Qinghai Tibet Plateau - the Qiangtang Plateau. Based on surface cover data from 2020 and ASTER GDEMV3 data, distribution indices and geographic detectors were used to explore the quantitative impact of land use/cover spatial patterns and terrain factors on land use/cover spatial distribution. The results showed the following: 1) The land use/cover space of the Qiangtang Plateau was dominated by grassland and bare land. Specifically, grassland had the greatest area, accounting for approximately 63.94% of the plateau area and covering an area of 449,983 km2. Grassland was widely distributed in most areas of the plateau. Bare land was the second-largest land cover type, with an area of 186,302 km2, accounting for 26.47% of the plateau area. Bare land was primarily located in the western and northern parts of the plateau. 2) The spatial differentiation of land use/cover in the Qiangtang Plateau was significant. Grasslands were primarily distributed in low mountainous areas characterized by high relief, gentle slopes, and low roughness. In contrast, bare land was mainly distributed in low mountainous areas with high relief, steep slopes, and low roughness. 3) The main drivers of the spatial distribution of land use/cover on the Qiangtang Plateau were elevation, roughness, and relief. Moreover, the interactions between these three terrain factors and other environmental variables significantly influenced the land use/cover pattern in the study area.

A Framework Using Open-Source Software for Land Use Prediction and Climate Data Time Series Analysis in a Protected Area of Portugal: Alvão Natural Park

Changes in land use and land cover (LULC) in protected areas can lead to an ecological imbalance in these territories. Temporal monitoring and predictive modeling are valuable tools for making decisions about conserving these areas and planning actions to reduce the pressure caused by activities such as agriculture. This study accordingly developed an LULC analysis framework based on open-source software (QGIS and R language) and predictive methodology using artificial neural networks in the Alvão Natural Park (PNA), a protected area in northern Portugal. The results show that in 2041, Agriculture and Open Space/Non-vegetation classes will evidence the greatest decrease, while Forest and Bushes will have expanded the most. Spatially, the areas to the west and northeast of the protected area will experience the most significant changes. The relationship of land use classes with data from the climate model HadGEM3-GC31-LL (CMIP6) utilizing scenarios RCP 4.5 and 8.5 demonstrates how through the period 2041–2060 there is a tendency for increased precipitation, which when combined with the dynamics of a retraction in classes such as agriculture, favors the advancement of natural classes such as bushes and forest; however, the subsequent climate data period (2061–2080) projects a decrease in precipitation volumes and an increase in the minimum and maximum temperatures, defining a new pattern with an extension of the period of drought and precipitation being concentrated in a short period of the year, which may result in a greater recurrence of extreme events, such as prolonged droughts that result in water shortages and fires.

ANALISIS PERUBAHAN TUTUPAN LAHAN DI HUTAN LINDUNG NGGALAK REGO PASCA KONSESI PERTAMBANGAN DI KECAMATAN REOK KABUPATEN MANGGARAI PROVINSI NUSA TENGGARA TIMUR

The mining concession in the Protected Forest Area that occurred in 1994 resulted in forest ecological damage. The damage occurs by dismantling trees and soil to take the manganese in them. This research was conducted in the Nnggalak Rego RTK 103 Protected Forest Area, Reok District, Manggarai Regency, East Nusa Tenggara Province with the aim of the research being to explain changes in land cover before and after mining in the Protected Forest Area. Data analysis used in this study is a quantitative analysis that utilizes remote sensing data using satellite imagery interpretation techniques. The method used in this research is OBIS (Object Based Image Segmentation) were analyzed to determine the area of ​​land cover in 1992, 2002, 2012 and 2022 as well as changes in land cover from 1992 to 2022 in mining concession areas. The results showed that there were changes in the area of ​​protected forest in 2002, 2012 and 2022. In 1992 the mining class was 0 Ha, the mining concession entered in 1994 the forest area changed, and in 2002 the mining class was 22.14 Ha or 3.87% , in 2012 61.69 Ha or 10.78%, in 2022, 15.45 Ha or 2.70%. The cessation of mining activities in 2013 to 2022 will result in a change in land cover class, namely the dense forest class will experience an increase in area due to succession and climate that occurs in the Manggarai district. Efforts to restore protected forests to their main function, it is suggested that reclamation and rehabilitation of post-mining land should be carried out.

Fish and crustaceans provide early indicators of success in wetland restoration

Early indicators of restoration success can inform adaptive management strategies and maintain community interest and financial investment. Coastal wetland restoration projects prioritize monitoring the succession of habitat forming communities. However, these communities often expand slowly. In contrast, fish and crustaceans can quickly occupy newly available habitats and therefore, may be early indicators of restoration success. Here, we compared the short‐term responses of fish and crustacean communities and landcover area to restoration actions at a restoring wetland and three reference wetlands in the Maroochy River in eastern Australia. Fish and crustacean communities and landcover area were surveyed every 6 months between Spring 2017 and Spring 2019 (n = 5), with two sampling events conducted before restoration actions commenced in May 2018 and three after. Fish and crustaceans were surveyed using baited underwater video stations and fyke nets. Landcover was remotely sensed using a combination of Worldview‐2 satellite imagery, object‐based image analysis, and random forest classification. Fish and crustaceans make viable indicators for early restoration success, as our findings show the composition, diversity, and abundance of species targeted in fisheries of these communities can resemble assemblages at reference sites within 1 year of restoration actions. The area of tidal inundation significantly increased overtime, but the recovery of mangroves and saltmarsh communities did not. These findings support the notion that coastal wetland restoration can promote immediate recruitment by fish and crustacean communities, thus making these taxa useful indicators for communicating early success of restoration projects.

Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles

Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location time series have been used to take advantage of spectral differences among land cover classes over time and to minimize the impact of missing observations. Deep convolutional neural networks (CNNs) have demonstrated potential for land cover classification of single date images. However, over large areas and using time series their application is complicated because they are sensitive to missing observations and they may misclassify small and spatially fragmented surface features due to their spatial patch-based implementation. This study demonstrates, for the first time, a one-dimensional (1D) CNN single pixel time series land classification approach that uses temporal percentile metrics and that does not have these issues. This is demonstrated for all the Conterminous United States (CONUS) considering two different 1D CNN structures with 5 and 8 layers, respectively. CONUS 30 m land cover classifications were derived using all the available Landsat-5 and -7 imagery over a seven-month growing season in 2011 with 3.3 million 30 m land cover class labelled samples extracted from the contemporaneous CONUS National Land Cover Database (NLCD) 16 class land cover product. The 1D CNNs and, a conventional random forest model, were trained using 10%, 50% and 90% samples, and the classification accuracies were evaluated with an independent 10% proportion. Temporal metrics were classified using 5, 7 and 9 percentiles for each of five Landsat reflective wavelength bands and their eight band ratios. The CONUS and detailed 150 × 150 km classification results demonstrate that the approach is effective at scale and locally. The 1D CNN classification land cover class boundaries were preserved for small axis dimension features, such as roads and rivers, with no stripes or anomalous spatial patterns. The 8-layer 1D CNN provided the highest overall classification accuracies and both the 5-layer and 8-layer 1D CNN architectures provided higher accuracies than the random forest by 1.9% - 2.8% which as all the accuracies were > 83% is a meaningful increase. The CONUS overall classification accuracies increased marginally with the number of percentiles (86.21%, 86.40%, and 86.43% for 5, 7 and 9 percentiles, respectively) using the 8-layer 1D-CNN. Class specific producer and user accuracies were quantified, with lower accuracies for the developed land, crop and pasture/hay classes, but no systematic pattern among classes with respect to the number of temporal percentiles used. Application of the trained model to a different year of CONUS Landsat ARD showed moderately decreased accuracy (80.79% for 7 percentiles) that we illustrate is likely due to different intra-annual surface variations between years. These encouraging results are discussed with recommended research for deep learning using temporal metric percentiles.

Using Sentinel 2A spectral-zone images for qualitative assessment of soils in the Northern Forest-Steppe of Ukraine

The research described in this paper is aimed at developing a methodology for remote determination of arable soil quality in the form of a bonita score using high-resolution spectral images. To achieve this goal, the dependencies between the values of the soil bonitet score and the spectral characteristics of satellite images of land cover areas were established. The geospatial models of soil cover scoring were verified, the results of which show a fairly high degree of correlation between the results of traditional soil scoring and remote sensing (NDSI, r = 0.91), the error value of the soil cover score for individual agricultural production groups of soils ranges from 1 to 12 points. It is found that despite the traditionally established use of vegetation indices to determine the state of crops, their use to determine the level of soil fertility is no less informative. It has been shown that the paired use of NDVI and NDSI values allows for the construction of relevant equations for remote determination of soil fertility, where the first of them acts as an auxiliary (clarifying) index, and the second as an effective working index.

Study Variability of the Land Surface Temperature of Land Cover during El Niño Southern Oscillation (ENSO) in a Tropical City

The World Health Organization has reported numerous fatalities, primarily among urban residents, during El Niño events. This study employed remote sensing technology to investigate the influence of the El Niño Southern Oscillation on temperature. The objective was to analyze the effect of ENSO on temperature across different land cover types using Landsat satellite data. Pre-processing was applied to the satellite data before converting numerical values into surface temperatures. The findings revealed that RS technology effectively captured the impact of varying ENSO intensity levels on surface temperatures. ENSO strength influenced temperature variations in the study areas. During El Niño events, urban areas exhibited higher land surface temperatures compared to vegetation, wetlands, and water bodies, a pattern consistent during La Niña. Specifically, there was a 2.5 °C temperature increase in the urban land cover area during El Niño events between 2016 and 1997. Water bodies, vegetation, and wetlands experienced respective temperature increases of 0.17 °C, 0.17 °C, and +0.7 °C during ONI value 1 events between 2016 and 1997. These findings are crucial for local authorities, providing spatial information on hot spots to enhance vigilance against potential El Niño temperatures.

Analysis of The Influence of Changing Land Area on The Value of Gross Regional Domestic Product (GRDP) (Case Study: Lampung Province)

Residents' activities will always use the land as a place to carry out activities which will affect the GRDP value. This study uses data on the GRDP values of Lampung Province in 2014–2021 obtained from BPS Lampung Province and uses land cover area data obtained from the classification results of Landsat 8 imagery for 2014–2021. The data analysis technique used in this study is correlation analysis and linear regression using panel data. The results of the correlation analysis found that the value of GRDP in total, GRDP in the agriculture, forestry, and fisheries sectors, GRDP in the processing industry sector, and GRDP in the construction sector all correlated with built-up land cover, for GRDP in the agriculture, forestry, and fisheries sectors, in addition to correlation with built-up land cover as well correlated with paddy field cover, and pond land. GRDP values that correlated with the land cover area were carried out by a linear regression analysis of panel data. The results of panel data linear regression calculations show that changes in the area of built-up land cover in Lampung Province do not affect the total value of GRDP, but the built-up land cover has a negative effect on GRDP in the agriculture, forestry, and fisheries sectors and a positive effect on GRDP in the processing industry sector and GRDP in the construction sector. The area covered by paddy fields and ponds has a positive effect on GRDP in the agriculture, forestry, and fisheries sectors.
 Keywords: Land Cover, GRDP, Panel Data Linear Regression, Correlation

Modelling transformation and its impact on water quality of the wetlands of lower Gangetic plain: a geospatial approach for sustainable resource management

ABSTRACT Over the past few decades, water quality in aquatic ecosystems has been deteriorating steadily due to population growth, urbanization and industry expansion, discharge of municipal wastewater, and agricultural runoff into surface water sources. Wetlands of the Indo-Gangetic plain are disappearing continuously by several geo-genic and anthropogenic factors. The study focuses on the most important wetland ecosystems in the Malda district of the lower Gangetic plain in terms of area and socioeconomic value. The study aims to emphasize the land transformation around the selected wetlands, and to determine the current status of the water quality of the wetlands. From 2003 to 2018, the land use land cover (LULC) and land transformation of wetland areas were computed using geospatial data. The water quality of the wetlands has been measured at different sampling sites in pre- and post-monsoon seasons. The focus of the study is to identify the causal factors of the degradation and disappearance of wetlands. For the water quality index (WQI), on-site and laboratory analyses of the chosen parameters were conducted. Over the past 15 years, the built-up area increased by+98.08%, +90.32%, and +67.17%, while waterbody experienced a considerable negative change, with percentages of −21.30%, −26.83%, and −29.63% for the Chakla Beel, Chatra wetland, and Nayabandh wetland complex, respectively. The WQI revealed poor and very poor water categories at various wetland sites, under the WQI scores with 100–200 indicating “poor water” and 200–300 “very poor water.” The study analyzed the causes of the change in land use and alteration in water quality of selected wetland ecosystems.

Spatial Evaluation of Dengue Transmission and Vector Abundance in the City of Dhaka, Bangladesh

In recent years, many urban areas in low and middle income countries have experienced major dengue epidemics, and the city of Dhaka, the capital city of Bangladesh, is one of them. Understanding models based on land cover and land use in urban areas in relation to vector abundance and possible disease transmission can be a major epidemiological tool in identifying disease incidence and prevalence. Demographic and human behavioral factors can also play a role in determining microenvironments for entomological distribution—which is a major risk factor for epidemicity. Data collected from a cross-sectional entomological survey in the city of Dhaka during the monsoon season of 2012 and two serological surveys—one pre-monsoon and another post-monsoon in 2012—were analyzed in this study. A total of 898 households and 1003 containers with water were inspected, and 1380 Ae. aegypti pupae and 4174 larvae were counted in these containers. All Stegomyia indices were found to be the highest in the central business and residential mixed zone. The odds ratios of risk factors for seroprevalence, including sex, age, self-reported febrile illness during the previous six months, and travel during the last six months, were calculated; age distribution was found to be a highly significant risk factor (p = value < 0.0001). The study offers clear patterns of dengue viral transmission, disease dynamics, and their association with critical spatial dimensions.

Seasonal Variation of the Community of Chrysomelidae (Coleoptera) in a Temperate Forest at Tamaulipas, Mexico

Seasonal variation and its effect on structure and composition of a Coleoptera community were studied, prompted by fragmentation of land cover in natural areas. Some groups of phytophagous beetles are most abundant during the rainy season, although this contrasts with a greater number of community attributes in the season with less precipitation. The hypothesis of our study was that most chrysomelid specimens and species would be observed at the end of the dry season and beginning of the rainy season, with subfamilies Galerucinae, Eumolpinae, and Cassidinae frequent between seasons. The objective was to analyze seasonal variation of Chrysomelidae in a fragment of temperate forest in northwestern Mexico. A sweep net was used in the understory to capture specimens in quadrants. Specimens captured were in six subfamilies, 24 genera, and 31 species, which varied between dry and rainy seasons. Estimated richness was less at the beginning of the rainy season, which was reflected by the greatest percentage of complementarity, although species were present throughout a season. The greatest dominance of Chrysomelidae was in the rainy season, and largest entropy values at the end of both seasons, while diversity of abundant and dominant species was notable at the end of the dry season. Correspondence analysis revealed three faunal associations, indicating the largest number of species was associated with the end of the dry season and beginning of the rainy season, showing precipitation and ambient temperature were most important variables. The community structure between seasons differed, showing that seasonality provided a wide food resource over time, which generated variation in relative abundance and therefore in composition of chrysomelid species. Results suggested temperature variation and water availability determined establishment of Chrysomelidae among months of the year.

Greener residential environment is associated with increased bacterial diversity in outdoor ambient air

In urban areas, exposure to greenspace has been found to be beneficial to human health. The biodiversity hypothesis proposed that exposure to diverse ambient microbes in greener areas may be one pathway leading to health benefits such as improved immune system functioning, reduced systemic inflammation, and ultimately reduced morbidity and mortality. Previous studies observed differences in ambient outdoor bacterial diversity between areas of high and low vegetated land cover but didn't focus on residential environments which are important to human health. This research examined the relationship between vegetated land and tree cover near residence and outdoor ambient air bacterial diversity and composition. We used a filter and pump system to collect ambient bacteria samples outside residences in the Raleigh-Durham-Chapel Hill metropolitan area and identified bacteria by 16S rRNA amplicon sequencing. Geospatial quantification of total vegetated land or tree cover was conducted within 500 m of each residence. Shannon's diversity index and weighted UniFrac distances were calculated to measure α (within-sample) and β (between-sample) diversity, respectively. Linear regression for α-diversity and permutational analysis of variance (PERMANOVA) for β-diversity were used to model relationships between vegetated land and tree cover and bacterial diversity. Data analysis included 73 ambient air samples collected near 69 residences. Analysis of β-diversity demonstrated differences in ambient air microbiome composition between areas of high and low vegetated land (p = 0.03) and tree cover (p = 0.07). These relationships remained consistent among quintiles of vegetated land (p = 0.03) and tree cover (p = 0.008) and continuous measures of vegetated land (p = 0.03) and tree cover (p = 0.03). Increased vegetated land and tree cover were also associated with increased ambient microbiome α-diversity (p = 0.06 and p = 0.03, respectively). To our knowledge, this is the first study to demonstrate associations between vegetated land and tree cover and the ambient air microbiome's diversity and composition in the residential ecosystem.

The Impact Of Urbanization on Water Quality and Riparian Land Cover Change in the Grand River Watershed

The research topic of my paper is the impact of urbanization on water quality and riparian land cover change in the Grand River watershed. Urban expansion could have unpredictable impacts on natural environment and resources. This watershed is comprised of lots of major rivers and tributaries in Canada which are significant hydrological resources to humans and nature, so it plays important roles in providing nearby residences water use and land irrigation. The watershed contains two First Nation territories and a total of 39 municipalities, including larger cities like Waterloo, Kitchener, Guelph, and Cambridge (GRCA 2008), so rapid urbanization could be one of the factors to degrade water quality and the underwater ecosystem. Moreover, due to the water quality change of the water, the riparian area land cover can correspondingly change over the years. Therefore, this study will mainly focus on studying water quality trends from 2000 to 2020, and data is collected from 12 water monitoring stations near the large cities in the watershed. The water quality will be defined by parameters like pH, temperature, conductivity, turbidity, and other chemical elements. The land cover layer of 2000 and 2020 will be used to compare the riparian land cover change, illustrating the spatial relationship of urban expansion and riparian area with significant land cover change. Furthermore, more discussions will focus on if the riparian land cover change is closely related to water quality degradation caused by urbanization. The final deliverable would be presented by ArcGIS pro layouts and statistic charts.

Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning

Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts. Synthetic aperture radar (SAR) is an all-weather, 24 h data source used to extract information about flood inundations, and its primary aim is to extract water body information for flood monitoring. In this study, we have studied the backscattering characteristics of water and non-water, combined the threshold segmentation method with Markov random fields (MRF), and embedded simulated annealing (SA) in the process of image noise reduction, resulting in the development of a water extraction method KI-MRF-SA with high accuracy in classification and high automation. Furthermore, object-scale adaptive convolutional neural networks (OSA-CNN) are introduced for the classification of optical images before the flood in order to provide reference data for flood inundation analysis. The method proposed in this study consists of the following three steps: (1) The Kittler and Illingworth (KI) thresholding algorithm is used for the segmentation of SAR images in order to determine the initial flood inundation extent; (2) MRF and SA algorithms are employed as a means to optimize the initial flood inundation extent, and the results are combined across multiple polarizations by using an intersection operation to determine the final flood inundation extent; and (3) As part of the flood mapping process, land cover types before the flood are classified using OSA-CNN and combined with flood inundation extents. According to the experimental results, it is evident that the proposed KI-MRF-SA method is capable of distinguishing water from non-water with significantly higher accuracy (3–5% improvement in the overall accuracy) than conventional thresholding methods. Combined with the classification method of OSA-CNN proposed in our earlier research, the overall classification accuracy of flood-affected areas could reach 92.7%.

Interactions between Climate and Land Cover Change over West Africa

Climate–land interaction over West Africa has often been assessed using climate simulations, although the model-based approach suffers from the limitations of climate models for the region. In this paper, an alternative method based on the analysis of historical land cover data and standardized climatic indices is used to investigate climate–land interactions, in order to establish climatic conditions and their corresponding land cover area changes. The annual variation in land cover area changes and climatic changes are first estimated separately and then linked using various spatiotemporal scales. The results show that incidences of land cover change result from abrupt changes in climatic conditions. Interannual changes of −1.0–1.0 °C, 0–1.5 °C, and −0.5–0.5 °C, and up to ±50 mm changes in precipitation and climatic water balance, lead to 45,039–52,133 km2, 20,935–22,127 km2, and approximately 32,000 km2 changes, respectively, while a ±0.5 °C and ±20 mm change represents normal climate conditions with changes below 20,000 km2. Conversely, conversions of cropland, forest, grassland, and shrubland are the main land cover change types affecting the climate. The results offer a basis for the re-evaluation of land cover change and climate information used in regional climate models simulating land–climate interactions over West Africa.

Tracking changes in coastal land cover in the Yellow Sea, East Asia, using Sentinel-1 and Sentinel-2 time-series images and Google Earth Engine

Coastal zones are essential ecosystems due to their provision of invaluable ecosystem services. However, the geomorphologic characteristics of coastal zones are becoming more complex and changeable due to global warming, sea-level rise (SLR), and the intensification of anthropogenic activities. Therefore, accurate and timely knowledge of coastal land cover types (including tidal flats, coastal vegetation, and year-long water cover) is needed for coastal research and sustainable management. To date, land cover products for coastal areas are mainly derived from moderate resolution imaging spectroradiometer images, but few studies have used Sentinel-1 synthetic aperture radar (S1) and Sentinel-2 Multispectral Instrument (S2) images, which can provide more detailed maps. We developed a Rule-based Time Series Classification (RTSC) approach to map coastal land cover types at a 10 m resolution, combining S1/S2 time-series images (2015–2019) and Google Earth Engine (GEE). These products were developed for the coastal zone of the Yellow Sea (YS), East Asia, which is an essential ecosystem protecting a coastal population of 60 million people from storms and SLR effects. Accuracy assessment showed that the annual maps of coastal land cover had high overall accuracy. The coastal land cover types for the YS in 2019 comprised 3593.42 km2 of tidal flats, 28,506.98 km2 of coastal vegetation, and 5436.92 km2 of coastal year-long water. The interannual dynamics of the coastal land cover area in the YS during 2015–2019 were smaller. This study provides a promising method that combines S1/2 time series, a RTSC approach, and GEE to map coastal land cover areas at large scales. The 10 m resolution maps generated in this study are the most current dataset of coastal land cover types for the YS, and they potentially provide a basis for the sustainable management and conservation of this important coastal zone.

Composite extraction index to enhance impervious surface information in remotely sensed imagery

Fast and accurate impervious surface extraction is important to monitor urban dynamics and ensure people's well-being. In order to improve the extraction accuracy of impervious surface, a composite impervious surface extraction index CBI based on NDBI, NDVI and MNDWI is proposed in the paper. The impervious surface extraction images in different land cover areas were compared with the NDBI and NDISI, respectively, and were assessed by using visual and statistical analysis. The results show that CBI has good robustness, less affected by the type of ground objects and sensors. The Kappa coefficients of CBI are the highest in different sensors and OLI images with different land cover areas, and the overall accuracy is above 91%. In the area with high coverage of vegetation and water, the extraction accuracy of CBI was significantly improved. CBI is more suitable for areas with high coverage of vegetation and water and has an improved Kappa coefficient of 0.1116 than that of the areas containing only a small amount of water and vegetation. As the NDISI algorithm uses the thermal infrared band, when the area of the study area is small, its extraction accuracy is low, indicating that the accuracy of the algorithm is affected by the size of the study area.

Spatial optimization of the economic benefits of the potential hydropower locations: study case upper citarum river

Hydropower is a renewable energy source that provides clean energy services and contributes to climate change mitigation by replacing fossil fuel energy production sources. The Upper Citarum Watershed in West Java, Indonesia, is described as a giant basin known as the Bandung Basin, which is type by mountainous relief with an elevation of 750-2300 meters above sea level, a slope of 30-90%, and a radiating river flow pattern. This study focuses on the potential of small hydropower with a run-of-river scheme that acts as a filler for the study of hydropower potential with a pump storage scheme by the Australian National University. Climate data in the study area were analyzed using the SWAT model to obtain Cumulative Discharge Frequency and identify potential discharge by adopting the method of previous studies. The results identified 7 SHPs with P exceeding 500 kW and below 2000 kW. BHUMI ATR BPN can predict land value and land cover in areas designated for hydropower development. The findings suggest improvements in aspects such as proper forecasting, on-the-ground valuation, and calculations relating to economically viable sustainability.

The Effects of Coal Mining on Land Use and Land Surface Temperature: A Case Study of Korba District, Chhattisgarh

Coal is one of the most important mineral which is used as a fuel for generating energy in India, with the increasing urbanization and industrialization, need for more fuel is increasing resulting in extraction of coal in more and more amount, therefore in order to fulfil the demand, mining activities has increased a lot with time, besides the extraction of coal, many other minerals are also being extracted for various purposes as well. Increase in such mining activities are leading to adverse change in air, water and land quality, changes in land use land cover pattern and variation in surface temperature, these continuous changes are therefore, im-portant to be studied in order to assess its impact on the environment for suture sustainability and better management of available resources. The current study has been done for the Korba district with special attention towards the mining areas as major area of the district after agricultural and cultivation land is occupied with mining area and related industries which is on spreading at higher pace since last many years, due to which variation in surface temperature and changes in land use pattern can be seen in its vicinity which is increasing with the time. According to the shifting patterns of land use and land cover, both urban regions and coal mine areas have grown by roughly 35 and 12%, respectively. In the central area of Korba district, the number of mines has risen, and the mining area has expanded over the last thirty years. Through a temporal analysis of the data, the study found that between 2000 and 2021, both the mining zones and adjacent urban areas witnessed increased temperatures. This temperature rise can be linked to the expansion of mining operations and the deforestation that accompanies such activities.

The Evaluation of Land Cover Change of The Watershed Area Using ASTER Image

ASTER image is already widely used by various parties in various part of the world for the phenomena observation of the environmental changes. The image processing is supervised classification of maximum likelihood technique to produce land cover into 8 main classes. This study evaluate the changes in land cover in the Lesti’s sub-watershed where is a sub-watershed priority in the Brantas river’s watershed. The result of this study is the land cover change in the upstream area which approximately 1,314 km2 (4.65%) of forest area is converted to another type of land use and its dominated by dry-farm agricultural sector which is in particular farm and field. This is due to the large area of land cover for dry-land farming in 1999 which is approximately 78.198 km2 (31,77%) for the field and 61.844 km2 (25,13 %) for the farm while in 2009 is approximately 97.219 km2 (39,50%) for the fields and 49.421 km2 (20,08%) for the farm from the total land area. The dry-land farming is one of the most factor causing erotion and landslide.