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 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.

Analyzing ecosystem service values (ESV) is crucial for achieving sustainable development. The main objective of this study was to assess the ecosystem services of the Cisadane watershed in Indonesia, with specific goals: (i) examining the spatiotemporal dynamics of ESV using multi-year land use and land cover (LULC) data from 2000 to 2021, (ii) exploring trade-offs and synergies among various ecosystem services, and (iii) investigating the sensitivity of ESV to changes in LULC. The results unveiled a significant decrease in forested areas (21.2%) and rice fields (10.2%), leading to a decline in ESV of $196.37 billion (33.17%) from 2010 to 2021. Throughout the period from 2000 to 2021, interactions between ESV were mainly synergistic. Projected from the baseline year (2021), the decline in ESV is expected to persist, ranging from $24.78 billion to $124.28 million by 2030 and from $45.78 billion to $124.28 million by 2050. The total estimated ecosystem values exhibited an inelastic response in terms of ecosystem value coefficients. The study also emphasizes an inelastic response in total estimated ESV coefficient concerning ecosystem value coefficients. These findings underscore the urgent need for targeted conservation efforts and sustainable land management practices to mitigate the further decline in ecosystem services and safeguard the long-term well-being of the Cisadane watershed and its inhabitants.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

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.