THE USE OF SATELLITE IMAGERY TO GUIDE FIELD PLOT SAMPLING SCHEME FOR BIOMASS ESTIMATION IN GHANAIAN FOREST
- 1PASCO Corporation, 1-1-2 Higashiyama, Meguro-ku, Tokyo 153-0043, Japan
- 2Oy Arbonaut Ltd., Latokartanontie 7 A , FIN-00700 Helsinki, Finland
- 3Forestry Research Institute of Ghana, University PO Box 63, Kumasi, Ghana
- 4The Forestry Commission of Ghana, P.O. Box MB 434, West Legon, Accra, Ghana
Keywords: LiDAR, Estimation, Land Use, Sampling Strips, Satellite Imagery, Biomass
Abstract. Accurate and reliable estimation of biomass in tropical forest has been a challenging task because a large proportion of forests are difficult to access or inaccessible. So, for effective implementation of REDD+ and fair benefit sharing, the proper designing of field plot sampling schemes plays a significant role in achieving robust biomass estimation. The existing forest inventory protocols using various field plot sampling schemes, including FAO's regular grid concept of sampling for land cover inventory at national level, are time and human resource intensive. Wall to wall LiDAR scanning is, however, a better approach to assess biomass with high precision and spatial resolution even though this approach suffers from high costs.
Considering the above, in this study a sampling design based on a LiDAR strips sampling scheme has been devised for Ghanaian forests to support field plot sampling. Using Top-of-Atmosphere (TOA) reflectance value of satellite data, Land Use classification was carried out in accordance with IPCC definitions and the resulting classes were further stratified, incorporating existing GIS data of ecological zones in the study area. Employing this result, LiDAR sampling strips were allocated using systematic sampling techniques. The resulting LiDAR strips represented all forest categories, as well as other Land Use classes, with their distribution adequately representing the areal share of each category. In this way, out of at total area of 15,153km2 of the study area, LiDAR scanning was required for only 770 km2 (sampling intensity being 5.1%). We conclude that this systematic LiDAR sampling design is likely to adequately cover variation in above-ground biomass densities and serve as sufficient a-priori data, together with the Land Use classification produced, for designing efficient field plot sampling over the seven ecological zones.