The purpose of the study was an assessment of LiDAR point clouds for automating the mapping of land use and land cover changes, mainly land abandonment and the process of secondary forest succession. Detailed information about land cover was determined based on airborne laser scanning data. The presented study focuses on the analysis of the spatial range and structure of vegetation. The study area was located in Milicz district in the voivodeship of Lower Silesia – the central west part of Poland. The areas of interest were parcels where agricultural land had been abandoned and forest succession processes had progressed. Analysis of the spatial range of the secondary forest succession was carried out using a reclassified nDSM. Reclassification of the nDSM was done using > 1 m, > 2 m and > 3 m for the pixel values, representing the height of vegetation above the ground. Parameters such as height of vegetation, standard deviation of height and cover density were calculated, to show the process of the increase in forest succession on abandoned agricultural land. The results confirmed a discrepancy between the cadastral data and the actual use of the plots. In the study area, more than three times as much forested and wooded area was detected than had been recorded in official databases. Analyses based on airborne laser scanning point clouds indicated significant diversity in the vertical and horizontal structure of vegetation. The results demonstrated gradual succession of greenery in the research area.

The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation. The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classification; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.

Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km2) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.

The purpose of this study was to determine the spatial structure of vegetation on the repository of the mine “Fryderyk” in Tarnowskie Góry. Tested area was located in the Upper Silesian Industrial Region (a large industrial region in Poland). It was a unique refuge habitat – Natura2000; PLH240008. The main aspect of this elaboration was to investigate the possible use of geotechniques and generally available geodata for mapping LULC changes and determining the spatial structure of vegetation. The presented study focuses on the analysis of a spatial structure of vegetation in the research area. This exploration was based on aerial images and orthophotomaps from 1947, 1998, 2003, 2009, 2011 and airborne laser scanning data (2011, ISOK project). Forest succession changes which occurred between 1947 and 2011 were analysed. The selected features of vegetation overgrowing spoil heap “Fryderyk” was determined. The results demonstrated a gradual succession of greenery on soil heap. In 1947, 84% of this area was covered by low vegetation. Tree expansion was proceeding in the westerly and northwest direction. In 2011 this canopy layer covered almost 50% of the research area. Parameters such as height of vegetation, crowns length and cover density were calculated by an airborne laser scanning data. These analyses indicated significant diversity in vertical and horizontal structures of vegetation. The study presents some capacities to use airborne laser scanning for an impartial evaluation of the structure of vegetation.

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.

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.

Ethiopia has lost sizable forest resources due to rapid population growth and subsequent increase in the demand for agricultural land and fuel woods. In this study, GIS and remote sensing techniques were used to detect forest cover changes in relation to climate variability in the Kafa zone, southwest Ethiopia. Landsat Thematic Mapper (TM) images of 1986 and 1990, Enhanced Thematic Mapper plus (ETM+) image of 2010 and Landsat-8 Operational Land Imager (OLI-8) image of 2018 were acquired at a resolution of 30 m to investigate spatial-temporal forest cover and land use changes. A supervised image classification was made using a maximum likelihood method in ERDAS imagine V9.2 to identify the various land use and land cover classes. Both spectral (normalised difference vegetation index – NDVI) and post classification change detection methods were used to determine the forest cover changes. To examine the extent and rate of forest cover changes, post classification comparisons were made using ArcGIS V 10.4.1. A net forest cover change of 1168.65 ha (12%) was detected during the study period from 1986 to 2018. The drop in the NDVI from 0.06–0.64 in 1986 to (–0.08)–0.12 in 2018 indicated a marked forest cover change in the study area. The correlation of NDVI values with climate data indicated the forest was not in a stable condition. The declining of the forest cover was most likely caused by climate variability in the study area.

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.