Goldbergs, G., & Upenieks, E. M.. Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach. Forests, 15(4), 644 pp. MDPI, 2024.
Bibtex citāts:
Bibtex citāts:
@article{16842_2024,
author = {Goldbergs and G. and & Upenieks and E. M.},
title = {Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach},
journal = {Forests},
volume = {15},
issue = {4},
pages = {644},
publisher = {MDPI},
year = {2024}
}
author = {Goldbergs and G. and & Upenieks and E. M.},
title = {Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach},
journal = {Forests},
volume = {15},
issue = {4},
pages = {644},
publisher = {MDPI},
year = {2024}
}
Anotācija: This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests.
Žurnāla kvartile: Q1