Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects

Author:

Ghosh Amit1ORCID,Rambaud Pierrick1,Finegold Yelena1ORCID,Jonckheere Inge1ORCID,Martin-Ortega Pablo1,Jalal Rashed1ORCID,Adebayo Adebowale Daniel2ORCID,Alvarez Ana3ORCID,Borretti Martin3,Caela Jose4,Ghosh Tuhin5ORCID,Lindquist Erik1ORCID,Henry Matieu1

Affiliation:

1. Food and Agriculture Organization of the United Nations, 00153 Rome, Italy

2. Department of Geographical Science, University of Maryland, College Park, MD 20742, USA

3. Ministerio de Vivienda y Ordenamiento Territorial, Montevideo 11000, Uruguay

4. Food and Agriculture Organization of the United Nations, Huambo 298, Angola

5. School of Oceanographic Studies, Jadavpur University, Kolkata 700032, India

Abstract

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.

Publisher

MDPI AG

Reference55 articles.

1. Monitoring land degradation using remote sensing and GIS techniques in an area of the middle Nile Delta, Egypt;Baroudy;CATENA,2011

2. FAO (2022). The State of the World’s Land and Water Resources for Food and Agriculture—Systems at Breaking Point. Main Report, FAO.

3. Montanarella, L., Scholes, R., and Brainich, A. (2018). The IPBES Assessment Report on Land Degradation and Restoration., Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.

4. GEF (2022). Combating Land Degradation, GEF.

5. UNCCD (2014). Desertification: The Invisible Frontline, UNCCD. [2nd ed.].

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