Modeling REDD+ Baselines using Mapping Technologies: A pilot study from Balpakram-Baghmara Landscape (BBL) in Meghalaya, India

Abstract

Reducing Emissions from Deforestation and Forest Degradation (REDD+) has emerged as a prominent potential climate change mitigation measures which aims at conservation of forests to sequester carbon. A key task of a REDD+ project is determination of historical deforestation rates and patterns, as well as the proximate causes and underlying forces of such deforestation. This baseline mapping is critical for identification of future REDD+ project scenarios to conserve carbon. The study demonstrates the potential use of mapping technologies like satellite remote sensing and Geographic Information System (GIS) in modeling REDD+ baselines for Balpakram-Baghmara Landscape (BBL) in Meghalaya, north-east region of India. The landscape is characterized by rapid deforestation and forest degradation. Temporal satellite remote sensing data of 1991, 2000 and 2011 were used to assess spatio-temporal patterns of forest cover changes. Based on quantitative analysis of the changes (1991-2011) deforestation rate was estimated to be 2.35% annually. Spatio-temporal datasets along with biotic and abiotic variables provide opportunities to model forest cover change further. Land Change Modeller (LCM) was used to predict forest cover status for 2021and 2031 using current disturbance scenarios. Comparing actual land-use land-cover (LULC) of 2011 with the predicted LULC of 2011 validated change prediction model and agreement was 65.22%. Expansion in cropland area, which has increased more than five times, has been the major force behind continuing forest loss.