Linda Gulbe, Juris Zarins, Ints Mednieks. Automated delineation of microstands in hemiboreal mixed forests using stereo GeoEye-1 data. Remote Sensing, 14(6), 1471 pp. MDPI, 2022.

Bibtex citāts:
author = {Linda Gulbe and Juris Zarins and Ints Mednieks},
title = {Automated delineation of microstands in hemiboreal mixed forests using stereo GeoEye-1 data},
journal = {Remote Sensing},
volume = {14},
issue = {6},
pages = {1471},
publisher = {MDPI},
year = {2022}

Anotācija: A microstand is a small forest area with a homogeneous tree species, height, and density composition. High-spatial-resolution GeoEye-1 multispectral (MS) images and GeoEye-1-based canopy height models (CHMs) allow delineating microstands automatically. This paper studied the potential benefits of two microstand segmentation workflows: (1) our modification of JSEG and (2) generic region merging (GRM) of the Orfeo Toolbox, both intended for the microstand border refinement and automated stand volume estimation in hemiboreal forests. Our modification of JSEG uses a CHM as the primary data source for segmentation by refining the results using MS data. Meanwhile, the CHM and multispectral data fusion were achieved as multiband segmentation for the GRM workflow. The accuracy was evaluated using several sets of metrics (unsupervised, supervised direct assessment, and system-level assessment). Metrics were calculated for a regular segment grid to check the benefits compared with the simple image patches. The metrics showed very similar results for both workflows. The most successful combinations in the workflow parameters retrieved over 75 % of the boundaries selected by a human interpreter. However, the impact of data fusion and parameter combinations on stand volume estimation accuracy was minimal, causing variations of the RMSE within approximately 7 m3/ha.


Žurnāla kvartile: Q1

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