Airborne Lidar data and VHR Worldview Satellite Imagery to Support Community Based Forest Certification in Chitwan,Nepal
The sustainable management of forest (SFM) cannot be done without understanding of the ecosystem as a whole.In doing so,SFM demands accurate and up-to-date information,which usually involves a system of criteria and indictors.The certification system of SFM based on criteria and indictors has emerged as powerful tool to produce progress reports towards monitoring and assessment of SFM.However,studies have proved that there is still lack of an accurate estimation of criteria and indictor to support SFM,particularly using high resolution remote sensing techniques.This study aims therefore to explore the role of LiDAR and Worldview-2 satellite imagery using object based image analysis (OBIA) for estimating and mapping of three criteria and five indictors to assess the current community forest condition and sustainability in part of subtropical forest of Chitwan,Nepal.The LiDAR point clouds data was pre-processed to generate a Digital Surface Model (DSM) and Digital Terrain Model (DTM).The DSM was generated from LiDAR first return data and DTM was derived from LiDAR last return.A tree Canopy Height Model (CHM) was computed as a difference between the DSM and DTM.The LiDAR derived tree height was plotted against the field measured tree height for accuracy assessment which was found to be RMSE of 3.2m and R2 of 0.77.Multi-resolution segmentation was used to extract the individual tree crowns from both LiDAR and Worldview-2 images in eCognition developer.An overall segmentation accuracy of 79% in 1:1 correspondence and 69% segmentation accuracy from D value were found.The resulted segmented polygons were further used for forest cover and tree species classification using the OBIA technique.Forest cover classification was done into two classes: forest area and non-forest area with accuracy of 94% and kappa statistics of 0.75 in Devidhunga,86% accuracy and kappa of 0.72 in Janprogati and 82% accuracy and kappa of 0.7 for Nebuwater.Tree species were classified into six species and one broader classes “others and resulted with accuracy of 67% and kappa statistics of 0.52.A non-linear regression model was used to estimate and map Above Ground Biomass (AGB).The model resulted R2 of 0.71 and RMSE of 22 Mg for Shorea robusta and R2 of 0.79 and RMSE of 51 Mg for the other species.The power model was found to be best with R2 of 0.74 and RMSE of 9.2 to predict DBH and in turn to estimate timber volume.The linear regression showed R2 of 0.73 between the observed timber volume and predicted timber volume.Statistical methods were used to analyse indictors for forest condition and sustainability assessment.With regard to the rating of indictors,the species diversity and composition were comparatively low in Janprogati,and high in Devidhunga.
M.F.Asmare Y.A.Hussin M.Weir H.Gilani
Department of Natural Resources, Faculty of Geo-information Science and Earth Observation, Universit International Centre for Integrated Mountain Development, GPO Box 3226, Khumaltar, Lalitpur
国际会议
北京
英文
218-225
2013-10-09（万方平台首次上网日期，不代表论文的发表时间）