Application of Remotely Sensed Vegetation Persistence to Monitor and Improve Management for Sustainable Development Goal 15: A Case Study of Kavongo-Zambezi Transfrontier Park

Keywords: vegetation, persistence, KAZA, agriculture, management
Abstract Type: Paper Abstract

Abstract

There is an increasingly urgent need to sustainably address economic development, food production, social welfare, and environmental health. In this context, the United Nations’ Sustainable Development Goal (SDG) 15 specifically addresses life on land with multiple aims to improve the sustainability of terrestrial ecosystems.

Remote Sensing applications have been widely used to examine terrestrial ecosystems, often using classification models to assess landcover change over time. Numerous studies apply remote sensing methodologies to measure or monitor processes related to SDG15, but there exists a need to expand these techniques beyond landcover change . This paper presents an NDVI-based vegetation persistence dataset as an approach to evaluate progress on SDG15 and identify priority areas for future intervention. This metric highlights areas of improved or degraded habitat which can inform management decisions related to human-environment interaction and social welfare, like agricultural land use and conservation efforts.

By comparing pixel-level persistence values to baseline NDVI we can synthesize information on the progress toward sustainability goals. For example, we can identify areas grappling with desertification, deforestation, or habitat degradation in the baseline period that show significant improvement in vegetation health as well as those that may still need interventions. In this paper, we apply the persistence methodology to a case study of the Kavongo-Zambezi Transfrontier Park to demonstrate how this remote sensing technique can enhance efforts to sustain life on land as well as inform land use management. Additionally, we provide key instances where vegetation has been maintained, improved, or degraded in this protected area.