Land use land cover change (LULC) has become part of the global science agenda and the understanding of LULC change is vital for planning sustainable management of natural resources. The study has employed multi- temporal satellite imagery to examine the LULC change in the Abbottabad District from 1989 to 2019. Images from Landsat-5, Landsat-7, and Landsat-8 Thematic Mapper (TM) for the same season were acquired from the USGS for the years of 1989, 1999, 2009 and 2019. The images were pre-processed by atmospheric correction, extraction of the study area and band composite. The supervised image classification using Maximum Likelihood Classifier and accuracy assessment were applied to prepare LULC maps of the Abbottabad District. In the last three decades, the study area witnessed number of changes in the pattern of LULC due to population growth, rapid urbanization and increased development of infrastructure, which cumulatively led to the emergence of new patterns being employed for land use. Results of the analysis involving the classified maps show that agricultural land and bare land have decreased, respectively 15.73% and 3.81%, whereas water resources have decreased significantly by 0.58%. This study reveals that GIS can be used as an informative tool to detect LULC changes. However, for planning and management, as well as to gain better insight into the human dynamics of environmental variations on the regional scale, it is crucial to have information about temporal LULC transformation patterns in the study area.

Human activities on land have grown significantly changing the entire landscape, while most of the changes have occurred in the tropics. The change has become a serious environmental concern at the local, regional and global scales. The intensity, speed, and degree of land use / land cover (LULC) changes are nowadays quicker compared to the past because of the development of society. Moreover, the rapid increase in population resulted in disturbing a large number of landscapes on the Earth. The main objective of this study was to detect historical (1990– 2020) and predicted (2020–2050) LULC changes in the Welmel River Watershed, which is located in the Genale-Dawa Basin, South Eastern Ethiopia. The dataset of 1990, 2005, and 2020 was generated from Landsat 5, Landsat 7 and Landsat 8 respectively to determine the historical LULC map. The result of this study revealed that agriculture/ settlement increased by 6.85 km ²∙y ^–1, while forestland declined by 9.16 km ²∙y ^–1 over the last 31 years between 1990 and 2020. In the coming 31 years (by 2050), if the existing trend of the LULC change continues, agriculture/settlement land is expected to increase from 290.64 km ² in 2020 to 492.51 km ² in 2050 at the rate of 6.73 km ²∙y ^–1, while forestland is expected to shrink from 690.48 km2 in 2020 to 427.01 km ² in 2050 by a rate of 8.78 km ²∙y ^–1.

Despite many studies on the hydrological responses to forest cover changes in micro and mesoscale watersheds, the hydrological responses to forest cover alterations and associated mechanisms through the large spatial scale of the river watershed have not been comprehensively perceived. This paper thus reviews a wide range of available scientific evidence concerning the impacts exerted by the forest removal on precipitation, water yield, stream flow, and flow regimes. It is concluded that there is no statistical correlation between forest cover and precipitation and water yield at the micro and mesoscale. In contrast, there is a relative correlation coefficient ( r = 0.77, p < 0.05) between forest cover and water yield at large scales (>1000 km2). These findings help our understanding of the hydrological response to forest disturbance at large and regional scale and provide a scientific perception to future watershed management in the context of human activities and natural hazards.

Many countries, including Indonesia, face severe water scarcity and groundwater depletion. Monitoring and evaluation of water resources need to be done. In addition, it is also necessary to improve the method of calculating water, which was initially based on a biophysical approach, replaced by a socio-ecological approach. Water yields were estimated using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. The Ordinary Least Square (OLS) and geographic weighted regression (GWR) methods were used to identify and analyze socio-ecological variables for changes in water yields. The purpose of this study was: (1) to analyze the spatial and temporal changes in water yield from 2000 to 2018 in the Citarum River Basin Unit (Citarum RBU) using the InVEST model, and (2) to identify socio-ecological variables as driving factors for changes in water yields using the OLS and GWR methods. The findings revealed the overall annual water yield decreased from 16.64 billion m3 year-1 in the year 2000 to 12.16 billion m3 year-1 in 2018; it was about 4.48 billion m3 (26.91%). The socio-ecological variables in water yields in the Citarum RBU show that climate and socio-economic characteristics contributed 6% and 44%, respectively. Land use/Land cover (LU/LC) and land configuration contribution fell by 20% and 40%, respectively.The main factors underlying the recent changes in water yields include average rainfall, pure dry agriculture, and bare land at 28.53%, 27.73%, and 15.08% for the biophysical model, while 30.28%, 23.77%, and 10.24% for the socio-ecological model, respectively. However, the social-ecological model demonstrated an increase in the contribution rate of climate and socio-economic factors and vice versa for the land use and landscape contribution rate. This circumstance demonstrates that the socio-ecological model is more comprehensive than the biophysical one for evaluating water scarcity.

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen’s Kappa coefficient). A case study of the “Parkowo-Lesne” housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management.