Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments.

The dramatic decline of the abundance of farmland bird species can be related to the level of land-use intensity or the land-cover heterogeneity of rural landscapes. Our study area in central Europe (Hungary) included 3049 skylark observation points and their 600 m buffer zones. We used a very detailed map (20 × 20 m minimum mapping unit), the Hungarian Ecosystem Basemap, as a land-cover dataset for the calculation of three landscape indices: mean patch size (MPS), mean fractal dimension (MFRACT), and Shannon diversity index (SDI) to describe the landscape structure of the study areas.

Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs demonstrated favourable performance for Land Cover (LC) change analyses, few studies have explored or quantified the geospatial data characteristics required to utilize this method. Likewise, many studies utilize overall measures of accuracy rather than metrics accounting for the slow or sparse changes of LC that are typically observed.

Environment statistics enumerate various aspects of the environment and human interactions with it. The scope of environment statistics encompasses all dimensions of the environment, be it Earth, Water or Air, the biotic and abiotiv matter found within the natural environment, and various concerns arising out of impacts of human footprints on it. The objective of environment statistics is to provide information about the environment, its changes over time and across locations, and the main factors that influence them.

In the tropics, the domestic water supply depends principally on ecosystem services, including the regulation and purification of water by humid, dense tropical forests. The Yangambi Biosphere Reserve (YBR) landscape is situated within such forests in the Democratic Republic of Congo (DRC). Surprisingly, given its proximity to the Congo River, the YBR is confronted with water issues. As part of its ecosystem function, the landscape is expected to reduce deterioration of water quality.

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation.

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland.

In some cloudy and rainy regions, the cloud cover is high in moderate-high resolution remote sensing images collected by satellites with a low revisit cycle, such as Landsat. This presents an obstacle for classifying land cover in cloud-covered parts of the image. A decision fusion scheme is proposed for improving land cover classification accuracy by integrating the complementary information of MODIS (Moderate-resolution Imaging Spectroradiometer) time series data with Landsat moderate-high spatial resolution data. The multilevel decision fusion method includes two processes.

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia.

This study analyzed and evaluated the changes that occurred in two coastal wetlands, characterized by complex and fragmented landscape patterns, in Southern Italy, which were monitored over a period of seven years from 2007 to 2014. Furthermore, the performances of two Land Cover (LC) and habitat taxonomies, compared for their suitability in mapping the identified changes, were assessed. A post-mapping method was adopted to detect the habitat/LC changes that occurred in the study period.

The recent increase in urban areas has stimulated landscape urbanization. One of the ways to study this process is an analysis based on the structure of land cover. The aim of this paper is to assess the intensity of the urban landscape on the basis of the CORINE in the seven largest metropolitan areas in Poland and in the Ruhr Metropolis in Germany. To this end, an urban landscape intensity indicator (ULII) was used based on Corine Land Cover at three levels of detail: the metropolitan area, municipalities and hexagons.

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable.