Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

Abstract

Commission VI, WG VI/4KEY WORDS: GLCM, Getis statistic, random forest, forest health condition, Robinia pseudoacacia ABSTRACT:In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. The different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen's neighborhood rule; and (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.

Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

“…Spaceborne imagery is the most cost-efficient option to cover larger areas, as it is comparably easy to process and has been widely used to monitor stress responses in tree canopies [35][36][37][38][39][40][41][42]. However, satellite images often lack the spatial and spectral resolution for an assessment on the individual tree crown level and are bound to certain over-flight times [19,39].…”