In Indonesia, land cover change for agriculture and mining is threatening tropical forests, biodiversity and ecosystem services. However, land cover change is highly dynamic and complex and varies over time and space. In this study, we combined Landsat-based land cover (change) mapping, pixel-to-pixel cross tabulations and expert knowledge to analyze land cover change and forest loss in the West Kutai and Mahakam Ulu districts in East Kalimantan from 1990-2009.

In Indonesia, land cover change for agriculture and mining is threatening tropical forests, biodiversity and ecosystem services. However, land cover change is highly dynamic and complex and varies over time and space. In this study, we combined Landsat-based land cover (change) mapping, pixel-to-pixel cross tabulations and expert knowledge to analyze land cover change and forest loss in the West Kutai and Mahakam Ulu districts in East Kalimantan from 1990-2009.

We recognize the economic and social significance of good land management, including soil, We stress that desertification, land degradation and drought are challenges of a global dimension and continue to pose serious challenges to the sustainable development of all countries, We recognize the need for urgent action to reverse land degradation. In view of this, we will strive to achieve a land-degradation neutral world in the context of sustainable development. Measuring Land degradation is a challenge ①Declining land-productivity ≠ Land degradation e.g.

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.

From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output.

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices.

There is a trend to acquire high accuracy land-cover maps using multi-source classification methods, most of which are based on data fusion, especially pixel- or feature-level fusions. A probabilistic graphical model (PGM) approach is proposed in this research for 30 m resolution land-cover mapping with multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer (MODIS) data. Independent classifiers were applied to two single-date Landsat 8 scenes and the MODIS time-series data, respectively, for probability estimation. A PGM was created for each pixel in Landsat 8 data.

This one-pager provides details on the LAND-at-scale project in Mozambique. This project is implemented by Centro Terra Viva and Terra Firma, and financed by the Ministry of Foreign Affairs via the Netherlands Enterprise & Development Agency. 

For the estimated 70% of the world population that lives on property without a formal land title, life can be precarious. The absence of ownership documentation raises families’ vulnerability to forced eviction and conflict; it precludes the use of the property to access financial services and other economic benefits; and it diminishes the value of property by restricting its transfer to an informal, opaque market. And yet, in many parts of the world, the process of obtaining a land title is not only expensive but also complicated and sometimes nearly impossible.

This guide presents the 'Mapping for Rights' approach, which combines participatory mapping techniques, facilitation and hands-on support with the application of relevant and available technology. This combination of tools, guidance and equipment enables communities to identify advocacy goals that their maps could support. The maps are designed to provide a snapshot of the communities’ reality, including information on their history and socio- economic situation and demonstrate occupation and use of specific areas by communities.

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.

Le présent rapport constitue un diagnostic du secteur minier artisanal dans l’ouest de la République de Centrafrique (RCA) et présente les enjeux de l’exploitation, du commerce et de l’exportation de l’or et des diamants. D’avril à juillet 2019, l’International Peace Information Service (IPIS), en partenariat avec les autorités locales, a réalisé une cartographie des sites miniers artisanaux dans l’ouest de la RCA.  Au total, 322 sites ont été visité dans 7 préfectures.