Primary forest characteristics estimation through remote sensing data and machine learning: Sakhalin case study
Currently, remote sensing techniques assist in various environmental applications and facilitate observation and spatial analysis. Machine learning algorithms allow researchers to find dependencies in satellite data and vegetation cover properties. One of the significant tasks for ecological assessm...
| Published in: | E3S Web of Conferences |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English French |
| Published: |
EDP Sciences
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.1051/e3sconf/202454204003 https://doaj.org/article/dfd867d13d9a40af87f9370665a171cc |
| Summary: | Currently, remote sensing techniques assist in various environmental applications and facilitate observation and spatial analysis. Machine learning algorithms allow researchers to find dependencies in satellite data and vegetation cover properties. One of the significant tasks for ecological assessment is associated with estimating forest characteristics and monitoring changes over time. In contrast to the general computer vision domain, remote sensing data and forestry measurements have their own specific requirements and necessitate tailored approaches that involve processing multispectral satellite data, creating feature spaces, and selecting training samples. In this study, we focus on extracting primary forest characteristics, including forest species groups, height, basal area, and timber stock. We utilise Sentinel-2 multispectral data to develop a machine learning-based solution for vast and remote territories. Timber stock is calculated using empirical formulas based on measurements of forest species groups, height, and basal area. These intermediate forest parameters are estimated using individually trained machine learning algorithms for each parameter. As a case study, we examine the Sakhalin region (Russia), which encompasses several forestries with varying vegetation properties. In Nevelskoye forestry, we achieved a mean absolute error (MAE) of 1.6m for height, 0.084 for basal area, and 47.8 m3/ha for timber stock. The results obtained demonstrate promise for further integrating artificial intelligencebased solutions into forestry decision-making processes and natural resources management. |
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