Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data

Abstract

Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm), 5 (i.e., SWIR: 1.55–1.75 µm) and 7 (SWIR: 2.08–2.35 µm) were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications.

Keywords

• segmentation
• object-based
• Landsat
• hyperspectral
• LiDAR
• heterogeneity
• extrapolation