Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

The present study assessed the potential of sediment loading in Beteni, Lauruk, Andheri, and Harpan sub-watersheds of Phewa Lake and estimated the sediment yield in the year 2020. Morphometry, land use/land cover, geology, climate, and human and development factors of the sub-watersheds were studied to assess the potential of sediment loading in the sub-watersheds. SRTM DEM was used for the computation of morphometric parameters and land use/land cover maps were prepared by using Landsat imagery. Geology, rainfall data, census data, and road maps were collected from various secondary sources. The sediment yields of the four sub-watersheds in the year 2020 were estimated by measuring the sediment volume deposited in the sediment retention ponds at the outlet of each sub-watershed. Results indicated that Beteni had the highest potential for sediment loading, while Harpan had the lowest. Likewise, the sediment yields for Beteni, Lauruk, Andheri, and Harpan sub-watersheds in 2020 were estimated at 1,420.67 m³/km²/year, 2,280.14 m³/km²/year, 1,666.77 m³/km²/year, and 766.42 m³/km²/year, respectively. To reduce sedimentation in Phewa Lake, it is recommended to regularly maintain siltation dams and construct check dams along the drainage slopes, alongside other soil conservation measures and appropriate land use practices in the upstream areas of the sub-watersheds.

Land use or land cover (LU/LC) mapping serves as a kind of basic information for land resource study. Detecting and analyzing the quantitative changes along the earth’s surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, economic and industrial growth. The LU/LC pattern in Madurai City, Tamil Nadu, has undergone a significant change over the past two decades due to accelerated urbanization. In this study, LU/LC change dynamics were investigated by the combined use of satellite remote sensing and geographical information system. To understand the LU/LC change in Madurai City, different land use categories and their spatial as well as temporal variability have been studied over a period of seven years (1999-2006), by analyzing Landsat images for the years 1999 and 2006 respectively with the help of ArcGIS 9.3 and ERDAS Imagine 9.1 software. This results show that geospatial technology is able to effectively capture the spatio-temporal trend of the landscape patterns associated with urbanization in this region.

The Cisadane Watershed is in a critical state, which has expanded residential areas upstream of Cisadane. Changes in land use and cover can impact a region's hydrological characteristics. The Soil and Water Assessment Tool (SWAT) is a hydrological model that can simulate the hydrological characteristics of the watershed affected by land use. This study aims to evaluate the impact of land use change on the hydrological characteristics of the Cisadane watershed using SWAT under different land use scenarios. The models were calibrated and validated, and the results showed satisfactory agreement between observed and simulated streamflow. The main river channel is based on the results of the watershed delineation process, with the watershed boundary consisting of 85 sub-watersheds. The hydrological characteristics showed that the maximum flow rate (Q max) was 12.30 m³/s, and the minimum flow rate (Q min) was 5.50 m³/s. The study area's distribution of future land use scenarios includes business as usual (BAU), protecting paddy fields (PPF), and protecting forest areas (PFA). The BAU scenario had the worst effect on hydrological responses due to the decreasing forests and paddy fields. The PFA scenario yielded the most favourable hydrological response, achieving a notable reduction from the baseline BAU in surface flow, lateral flow, and groundwater by 2%, 7%, and 2%, respectively. This was attributed to enhanced water infiltration, alongside increases in water yield and evapotranspiration of 3% and 15%, respectively. l Therefore, it is vital to maintain green vegetation and conserve land to support sustainable water availability.