Estimates of Forest Canopy Height and Aboveground Biomass Using Airborne Lidar and Landsat-8 OLI in Mai Ndombe Province, Democratic Republic of Congo

Authors: HERVE KASHONGWE*, South Dakota State University
Topics: Remote Sensing, Geographic Information Science and Systems, Africa
Keywords: Forest canopy height, Tropical Forest, Above ground biomass
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download

Forest structure at tree level is a key component to comprehend to certify an accurate estimate of carbon stock. Tropical rainforests constitute the most forested ecosystems that harbor the largest biodiversity on the Earth and stores a large amount of carbon. Several scientists have devoted their research to mapping the forest structure of tropical forests, but the complexity of this biome and the limitation of remote sensing imagery available to determine vertical structure of the forest were the main obstacles in determining the forest structure. This study aims to develop a predictive model to estimate tree height from Landsat-8 imagery over the dense Central Africa tropical rainforest. This research will develop an approach that combines the multispectral satellite Landsat-8 with the airborne LiDAR remote sensing products in order to circumvent the difficulties encountered in mapping forest structure with optical sensors such as Landsat and MODIS. The analysis will use a variety of software including FUSION for the treatment of LiDAR, ENVI for Landsat-8 imagery, and R software for the classification and the accuracy assessment. This new approach will provide a tree height distributions classification and above ground biomass estimates for the Congo Basin tropical forests. This classification will help to establish an accurate estimate of carbon flux over the area and to fill the gaps

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