LANDSCAPE IMPACT ASSESSMENT OF SDG2 DEVELOPMENT PROJECTS USING REMOTE SENSING AND UNSUPERVISED CONTROL SITE SELECTION

Abstract

As part of its objective to achieve Zero Hunger under SDG2 the United Nations World Food Programme, in partnership with Governments, NGOs and other UN agencies, supports food insecure communities to increase natural resource availability and improve their management. This is done mostly through the building and rehabilitation of soil and water conservation assets (e.g., small dams, weirs, landscape restoration) and structures that increase productivity (e.g., vegetable gardens, irrigation canals). To adequately monitor these activities around the globe simultaneously, remote sensing was found to be an adequate tool. This study introduces the use of high-resolution satellite imagery, and more specifically NDVI derived from the Landsat series to verify and quantify the impact of such development projects. In total 121 projects in 10 countries and six different climate zones were analyzed using a pre- and post-implementation comparison and a Before-After Control Impact (BACI) study considering randomly selected control sites. Both approaches were found to show robust results throughout the different countries, project types and climate zones. 67% of all projects showed significant improvements in vegetation conditions during the wet seasons only three years after the implementation. Using the proposed workflow based on Python scripting and cloud computing of satellite data, fast and robust analyses can be achieved, while assuring constant data quality.