Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal - Jamal, Juhaida (2021)
Rainforest was a type of forest that is rich in flora and fauna species. Over the last few decades, forests have been the victims of over logging and deforestation due to forest transformation activities and high demand for a piece of wood. Uncontrolled of this activity gave an impact to the tree species to be endangered and some of the species experienced in the extinction phase. A detailed inventory of tree species is needed to manage and plan the forest on a sustainable basis. Many techniques had been done to identify the tree species, but in the recent three decades, remote sensing technique was widely used to study the distribution of tree species for the large and inaccessible area. In this study, an object-based image analysis (OBIA) with a combination of high-resolution multispectral satellite imagery (WV-2) and airborne laser scanning (LiDAR) data was tested for classification of individual tree crowns of tropical tree species at Forest Research Institute Malaysia (FRIM) forest, Selangor. LiDAR data was taken using fixed-wing aircraft with Gemini Airborne Laser Terrain Mapper (ALTM) laser with 0.15m and 0.25 resolution for horizontal and vertical. WV-2 was captured with a 0.5m spatial resolution. In this study, hyperspectral data captured using bayspec sensor mount at UAV with height 220m from the ground and have 0.3 resolution was used to extract the spectral reflectance of tree species. Study area with total area 3.5 hectare were grid into 35 rectangular subplots with each subplot have 500m2 area. Segmentation of the image was performed using multi-resolution segmentation in eCognition software. Accuracy assessment for segmentation was done by measure the ‘goodness fit’ (D value) between training object and output segmentation. The overall accuracy of the segmentation was 86%. For species classification, the accuracy assessment was performed using the error matrix confusion technique to 7 classes of tree species. The result had shown the overall accuracy classification was 64%.