In the last decade, there have been a number of global/regional targets and initiatives to halt and reverse land degradation and restore degraded land. Starting in 2010, these include the CBD’s Aichi Biodiversity Targets, one of which aims to restore at least 15% of degraded ecosystems; the Bonn Challenge and its regional initiatives to restore more than 150 million hectares; and most recently the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs).

As investments in nature are needed more than ever, and are increasingly gaining traction, the challenge is to identify environmental and social risks and to demonstrate positive impacts associated with investing in nature-based projects in a standardized and comparable manner. To help create a clear track record of projects that can be assessed on risk-return-impact profiles, a certain degree of standardization is needed, while maintaining scientific rigor.

Trends.Earth (formerly the Land Degradation Monitoring Toolbox) is a platform for monitoring land change using earth observations in an innovative desktop and cloud-based system.

The three sub-indicators for monitoring achievement of Land Degradation Neutrality (LDN, Sustainable Development Goal (SDG) Target 15.3), are supported by: productivity, land cover, and soil organic carbon.

The study examines long-term land use/land cover change (LUCC) at a finer scale in a semi-arid region of India. The objectives were to study and quantify the spatiotemporal LUCC and uncover the major drivers causing the change in the Mula Pravara river basin, which is located in a semi-arid region of Maharashtra state, India.

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.

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.

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.