From REDD+ MRV Perspective: Comparison of Two Different Forest Management Regimes Using Geospatial Techniques in Ludi Khola Watershed, Gorkha District, Nepal

Abstract

This study presents a practical example of using remote sensing data and methods for forest management in Ludi Khola watershed (5750 ha) Gorkha District, a REDD+ (Reducing Emissions from Deforestation and Forest Degradation) pilot project site in Nepal. The study area consists of 1888 ha that are assigned to 31 community forests (CFs) and 3862 ha that belong to non-community forests such as governmental and private forests (Non-CFs). By using high-resolution GeoEye-1 (2009 and 2012) satellite images and forest inventory data, temporal dynamics of land cover transitions, tree canopy size classes (crown projection area), Above-Ground Biomass (AGB) were estimated and compared for the two forest regimes (CFs and Non-CFs). Geographic Object-Based Image Analysis (GEOBIA) segmentation and classification techniques were performed. By using the change matrix method, forest area conversion to non-forest (forest loss) of only 1 ha (0.05%) in CF and 27 ha (0.7%) in Non-CF was observed over 2009–2012. On the other hand, change from non-forest to forest (forest gain) occurred on 12 ha (0.6%) in CF and 60 ha (1.5%) in Non-CF. According to land cover information from 2009 to 2012, closed broadleaved forest concealed almost 87% of total CFs’ forest area and 59% of total Non-CFs’ forest area, while open broadleaved forest occupied almost 12 and 20% in CFs and Non-CFs, respectively. The community-based inventory revealed an annual increment of 3.7 t/ha AGB, whereas remote sensing-based modelling estimated 4.5 t/ha AGB. The integration of remote sensing and field data can demonstrate and endorse a much more efficient REDD+ Measurement, Reporting, and Verification (MRV) system in terms of information content, reliability, cost, transparency, verifiability, and scalability.