• Mads Christensen
The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG’s) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased urbanisation, deforestation and rapid economic development has decidedly modified the surface of the earth, resulting in dramatic land cover changes, which continue to cause significant degradation of environmental attributes and threaten planetary boundaries. In order to reshape policies and management frameworks, conforming to the objectives of the SDG’s, it is paramount to understand the driving mechanisms of land use changes and determine future patterns of change.
The Virunga National Park is located in the surrounding area of the contentious North Kivu province in the north-eastern part of the Democratic Republic of the Congo and has been the scene of near-constant conflict, exploitation and extreme poverty. While contributing to the livelihoods of millions of people in one of the most densely populated regions in Africa, efforts to conserve this globally significant ecosystem and its catchment areas is threatened by uncontrolled agricultural expansion, natural resource extraction and deforestation. Thus, the Virunga National Park catchment has experienced significant land cover changes, which continues to undermine, not just the integrity of the national park, but the foundation of millions of livelihoods who depends on its ecosystem services.
This study aims to assess and quantify future land cover changes in the Virunga catchment by simulating a future landscape for the SDG target year of 2030, in order to provide evidence to support data-based decisionmaking processes conforming to the requirements of the SDG’s. The study follows six sequential steps: (1) Creation of three land cover maps from 2010, 2015 and 2019 derived from satellite images; (2) Land change analysis by cross-tabulation of land cover maps; (3) Sub-model creation and identification of explanatory variables and dataset creation for each variable; (4) Calculation of transition potentials of major transitions within the case study area using machine learning algorithms; (5) Change quantification and prediction using Markov Chain analysis; (6) prediction of a 2030 land cover.
The model was successfully able to simulate future land cover and land use changes and dynamics and goes on to conclude that agricultural expansion and urban development is expected to significantly reduce Virunga’s forest and open land areas in the next 11 years. Accessibility in terms of landscape topography and proximity to existing human activities are concluded to be primary drivers of forest cover change. Drawing on these conclusions, the discussion provides recommendations and reflections on how the predicted future land cover changes can be used to support and underpin policy frameworks towards achieving the SDG’s and the 2030 Agenda for Sustainable Development.
Publication date5 Jun 2019
Number of pages72
ID: 305168074