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.

Over the past half-century, the risk of urban flooding in Dar es Salaam has increased due to changes in land cover coupled with climatic changes. This paper aimed to quantify the impacts of climate and land-cover changes on the magnitudes and frequencies of flood runoffs in urban Dar es Salaam, Tanzania. A calibrated and validated SWAT rainfall-runoff model was used to generate flood hydrographs for the period 1969–2050 using historical rainfall data and projected rainfall based on the CORDEX-Africa regional climate model.

This State of Land Information (SOLI) report is an analysis of the current state of land data in Senegal, assessing the availability of land information and the compliance of this information with open data standards. 

Armenia is a landlocked country in the South Caucasus. After gaining independence from the former Soviet Union, a very difficult socio- economic situation developed in Armenia, with a current high level of poverty. Land privatization has led to excessive land fragmentation and a small average farm size. Most of the country (87 percent) is mountainous with a vertical zonation of diverse landscapes. Armenia is hotspot of biodiversity in the region, facing human and climate induced land degradation. Water erosion affects most forest and croplands across the country.

This publication synthesizes climate characteristics and projections, vulnerability to natural hazards, sectoral climate change impacts, and adaptation priorities in Armenia. It outlines rapid onset and long-term changes in key climate parameters, as well as the impact of these changes on communities, livelihoods, and economies—many of which are already underway. The publication is part of a series of climate risk country profiles published jointly by ADB and the World Bank Group.

Artisanal and small-scale mining affects the quality and components of the environment in the Batouri area in different ways. Activities interfere with air, soil, water, fauna and forest resources. This study seeks to assess the impacts of mining on the environment using a combination of spatial analysis, questionnaires administration and Leopold’s grid of impact assessment. The impacts of mining on physical environment include air pollution by emission of dusts and fumes from engines, soil and subsoil degradation by earthworks and release of wastewater containing chemicals from companies.

Land Use and Climate change are interrelated to each other. This change influences one another at various temporal and spatial scales; however, improper land uses are the primary causal factor on climate change. It studies relevant literature and Nepal’s case to assess the relationship between land use and climate change. Similarly focuses on how land-use impacts climate change and vice versa. In recent centuries land-use change significant effects on ecological variables and climate change.

context and backgroundDespite the robustness of various land instruments and legal and institutional regulations on various sectors, Tanzania’s land distribution-related policy implementation and practice is facing numerous challenges. Tanzania is not only experiencing poor land conditions, but also poor land management and ineffective policy implementation. Areas of concern includes public policy actions on land conflicts; settlements and housing problems; and conflicting demand for land for mining, large scale agriculture, conservation, tourism, ranching and pastoral nomadism.

BackgroundStudies which have been conducted so far have focused on the processes of land use/cover changes in different areas at regional and global scale. Little is known about factors that influence land use/ cover dynamics at finer scales such as Melela ward in Mvomero district, Morogoro, Tanzania. Goals and ObjectivesThe overall objective of the study therefore was to evaluate the land use land cover dynamics and their impacts on pastoralism in Melela.