Land as an essential resource is becoming increasingly scarce due to population growth. In the case of the Kenyan coast, population pressure causes land cover changes in the Arabuko Sokoke Forest, which is an important habitat for endangered species. Forest and bushland have been changed to agricultural land in order to provide livelihood for the rural population who are highly dependent on small-scale farming. Unclear land rights and misbalanced access to land cause uncontrolled expansion and insecure livelihoods.

Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas.

Language and technology barriers are a very serious constraint to effectively exchange and learn from land data, information and technologies across the globe. We would like to explore whether we can gain inspiration from how semantic web technologies have overcome knowledge-sharing challenges in other sectors, such as the agriculture sector. With emerging technologies, new tools and ever-growing amounts of land data, we face a very real risk of losing the overview. Without this overview, data is much less likely to be used and thus be useful.

Inappropriate land management leads to soil loss with destruction of the land’s resource and sediment input into the receiving river. Part of the sediment budget of a catchment is the estimation of soil loss. In the Ruzizi catchment in the Eastern Democratic Republic of the Congo (DRC), only limited research has been conducted on soil loss mainly dealing with local observations on geomorphological forms or river load measurements; a regional quantification of soil loss is missing so far. Such quantifications can be calculated using the Universal Soil Loss Equation (USLE).

The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks (CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of problems pertaining to land coverage. Taking advantage of the two phenomena, we propose a machine learning model for the classification of land abandonment.

This report provides a summary of an online workshop on March 16th 2020, organised in place of a planned fringe meeting of the World Bank Land and Poverty Conference which was cancelled due to the COVID-19 pandemic. The 2-hour digital workshop brought together over 40 participants from across the world to discuss key data and key open data use-cases for land governance. This report is written based on workshop recordings and shared notes.

This September, the Land Portal hosted an online dialogue on ‘Open Land Data in the Fight Against Corruption’. This responded to a dual recognition that corruption remains a major issue in land governance, and that open data has been identified as a powerful tool in the fight against corruption. At the same time, gaps remain between the promise and the reality of open data in the land sector. Poor data availability, underdeveloped theories of change, and a lack of implementation support have all contributed to slowerthan-desired progress in data publication and use over the last decade.

Participatory mapping is a valuable approach for documenting the influence of human activities on species, ecosystems, and ecosystem services, as well as the variability of human activities over space and time. This method is particularly valuable in data-poor systems; however, there has never been a systematic approach for identifying the total number of respondents necessary to map the entire spatial extent of a particular human activity. Here, we develop a new technique for identifying sufficient respondent sample sizes for participatory mapping by adapting species rarefaction curves.

Recent studies assessing agricultural policies, including the EU’s Agri-Environment Scheme, have shown that these have been successful in attaining some environmental goals. In Finland, however, the economic situation of farms has dramatically fallen and hence, the actions do not result in social acceptability. Sustainable intensification is a means to combine the three dimensions of sustainability: environmental, economic and social. Here we introduce a novel land use optimization and planning tool for the sustainable intensification of high-latitude agricultural systems.

Anthropogenic activities have substantially changed natural landscapes, especially in regions which are extremely affected by population growth and climate change such as East African countries. Understanding the patterns of land-use and land-cover (LULC) change is important for efficient environmental management, including effective water management practice. Using remote sensing techniques and geographic information systems (GIS), this study focused on changes in LULC patterns of the upstream and downstream Wami River Basin over 16 years.

The present study revealed how local socioecological knowledge elucidated during participatory rural appraisals and historical remote sensing data can be combined for analyzing land use change patterns from 1954 to 2007 in northwestern Vietnam. The developed approach integrated farmer decision rules on cropping preferences and location, visual and supervised classification methods, and qualitative information obtained during various forms of participatory appraisals.

This study analyzed and assessed spatio-temporal dynamics of land-use change (LUC) and urban expansion (UE) within the Greater Accra Metropolitan Area (GAMA) of Ghana. This region serves as a case to illustrate how a major economic hub and political core area is experiencing massive spatial transformations, resulting in uneven geographies of urban land expansion. Quickbird/Worldview-2 images for the years 2008 and 2017 were segmented and classified to produce LUC maps. LUC and UE were analyzed by post-classification change detection and spatial metrics, respectively.