Assessing the Potential of Image Segmentation on Google Earth Images for Carbon Estimation across Rubber Plantations of Different Ages

Abstract

The evolution of remote sensing technologies has improved scientific study and research. The often high cost of satellite images has led to research into alternative remotely sensed data for various analysis. Google earth data being cheap and readily available has been employed in many analyses including textural analysis with positive results. The application of OBIA to google earth image was employed in this study to assess the predictive ability of rubber tree diameter at breast height towards carbon modelling. Out of a total of 190 manually delineated tree crowns, 102 trees were found to have a 1 to 1 matching with segmented crowns on the Google Earth images were used. For the whole study area over- segmentation value was 0.43 (43% error) and the under-segmentation was 0.32 (32% error) with the D-Value computed as 0.38 (38% error) which means that the segmentation accuracy is 62%. Models developed from the segmentation process and field data were linear, quadratic and cubic models with R² of 0.014, 0.137 and 0.139 respectively. Primarily, these poor R² values are due to the fact that Google earth images have poor spectral values, red and infrared portions are absent which affect the clear crown detection of the tree canopies. The tree canopies are equally highly clustered, therefore with poor spectral values individual tree detection using OBIA procedure achieves very little success in the diameter at breast height prediction.