Detecting Stand-Replacing Disturbance using RapidEyeImagery: a Tasseled Cap Transformation and ModifiedDisturbance Index

摘要

The development of robust approaches for monitoring forest cover is a fundamental forest management objective. Timelyand detailed mapping of forest disturbance can be used to identify threatened habitat, estimate standing forest carbon stocks,and aid in the sustainable management of forests. Remote sensing technologies have provided new opportunities for mappingforest cover; however, various spatial and temporal resolution limitations have resulted in challenges for gathering up-to-dateinventory data. A new generation of satellite constellations may provide solutions to better meet monitoring needs. In this article,we demonstrate a quick and accurate method to locate abrupt, fine-scale forest disturbances using RapidEye 5 m, 5-band imagery.To identify disturbance, we first derived a set of tasseled-cap indices from 50 cloud-free RapidEye images. We then adapted apreviously developed disturbance index using Landsat Thematic Mapper data and estimated a simple threshold to differentiatedisturbed and unchanged regions. To test the accuracy of the disturbance index, 7 image pairs depicting change were selected andcompared with forest inventory data and manually interpreted sample locations. Overall accuracy estimates ranged from 83.65± 0.77 to 97.65 ± 0.25 for individual tiles. Across the 7 tiles, overall accuracy was 92.64 ± 0.15, using forest inventorydata, and 96.91 ± 1.16, using the manually interpreted sample locations. An assessment of producers’ and users’ accuracies fordisturbed and undisturbed regions suggested inaccuracies in the forest inventory reference data. In several instances, the modifieddisturbance index appeared to have provided a more accurate depiction of real change than the forest inventory referencedata. Results suggest that the RapidEye disturbance index was successful in identifying stand-replacing disturbances across thelandscape and could be implemented to detect disturbances in near real time.