After more than a decade of meeting the designated objective of increasing productivity in agriculture, the South West coastal polders of Bangladesh have ended up as different man-made disasters. The failure of the polders to deliver the intended outcome is basically attributed to the lack of understanding of their hydro-morphological characteristics, inadequacy in their operation and maintenance, and failure to take into account their social relationship and culture roles. Changes in socioeconomic settings have also forced changes in the designated functions of the polders, but now the emerging context of climate change has become a major issue in rationalizing the coastal polders. In this context, this study is an attempt to review the historical and ongoing process of rationalization of the South West coastal polders, revealing that it is essential to take an integrated view of the hydrologic cycle and the interactions of human interventions. Finally, this paper recommends that an extended cost–benefit analysis with a multi-objective focus or a multi-criteria analysis, if monetizing is not possible, should be an option in rationalizing this multi-functional infrastructure. Proper macro-planning would require development of an institution capable of dealing with a task which is multi-dimensional and multi-disciplinary in nature.

In response to prevailing classification inconsistency between land cover maps, developed by different organizations in different times at different scales, an object-based National Land Representation System (NLRS) for Bangladesh has been developed. The process, which began in 2013 and was completed in 2016, brought together several national organizations and involved an extensive process of consultation, data collection, translation, and analysis of existing land cover/use classification systems. The process focused on the interpretation of three legends from historic national land cover/use maps. Field inventory data were collected from over 1000 sites across the country to assist the process of land characterization and the development of a dynamic and representative overview of land cover and land use in Bangladesh. The system has been applied to the development of a wall-to-wall national land cover map for the year 2015. In this article, the methodological process and results of NLRS formulation and land cover map 2015 are presented. We also provide examples of how this interoperable system and the land cover dataset are being used for variety of applications including national forest resources assessment, estimation of REDD+ activity data, integration of biophysical and socioeconomic information, and semantic similarity assessment.

A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data.

Information on land cover is vital to numerous United Nations (UN) missions, including achieving the Sustainable Development Goals (SDGs). Because land cover data are developed by a variety of organizations for a range of objectives, they are based on different classification schemes and have discrepancies. In addition, the sustainability for land cover is hampered by limited access to information and documentation. Accordingly, international standards for land cover are developed to improve interoperability between different land cover datasets. However, the use and development of land cover datasets are limited by various factors including availability of properly documented land cover legends in support of different applications including change assessment, comparison, and international reporting. The purpose of this article is to highlight the importance of land cover in achieving several goals and to introduce the first international platform for land cover legend, named Land Cover Legend Registry (LCLR). This registry is a contribution to the international land cover community and the UN in effort to promote and support data harmonization processes and interoperability from local to global level, and vice versa. Users can not only use the registry for preparing consistent datasets, but also contribute to it by providing the latest data to ensure the long-term availability of both updated and existing datasets around the world. Moreover, building on the experience developing land cover legends with different nations, a brief explanation on the preparation of legends is also provided. Additionally, it is more important than ever to develop land cover registers to support the use, expansion, integration, and use uptake of land cover data, particularly for innovative remote sensing, machine learning, and information and communication technologies and techniques that build on existing and national contexts.

The high dependency of households on woodfuels in Uganda is a renowned driver of forest degradation. Refugee settlements might aggravate the pressure on the environment caused by woodfuel consumption in the absence of impact assessments and measures to improve environmental management and build sustainable livelihoods. In collaboration with the Government of Uganda, UNHCR, and the World Bank, FAO conducted assessments on woodfuel consumption at the household level in displacement settings in Uganda, as well as its impact on the surrounding forests, through field forest inventories, household surveys, and geospatial analysis. The results of these assessments highlight the impact of woodfuel consumption and other drivers on forest degradation, and provide guidance for the development of comprehensive interventions on landscape management and improvement of household cooking fuels and technologies, targeting both refugee and host communities.

The influx of nearly a million refugees from Myanmar’s Rakhine state to Cox’s Bazar, Bangladesh, in August 2017 put significant pressure on the regional landscape leading to land degradation due to biomass removal to provide shelter and fuel energy and posed critical challenges for both host and displaced population. This article emphasizes geospatial applications at different stages of addressing land degradation in Cox’s Bazar. A wide range of data and methods were used to delineate land tenure, estimate wood fuel demand and supply, assess land degradation, evaluate land restoration suitability, and monitor restoration activities. The quantitative and spatially explicit information from these geospatial assessments integrated with the technical guidelines for sustainable land management and an adaptive management strategy was critical in enabling a collaborative, multi-disciplinary and evidence-based approach to successfully restoring degraded landscapes in a displacement setting.