Identifying forest harvesting practices: clear-cutting and thinning in diverse tree species using dense Landsat time series

Date

2024-12-07

Director

Publisher

Elsevier
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107386RB-I00/ES/ recolecta
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-152885OB-I00/ES/ recolecta
Impacto
OpenAlexGoogle Scholar
No disponible en Scopus

Abstract

Forest monitoring plays a critical role in achieving sustainable forest management practices. The ability to identify ongoing harvesting activities is crucial for developing targeted strategies to maintain forest health. Traditional monitoring methods, which rely on field inventories, are often expensive and time-consuming. Remote sensing offers an interesting alternative, leveraging dense time series of satellite imagery and various algorithms for disturbance detection. This study presents and assesses a novel methodology for identifying forest harvesting practices (clear-cutting and thinning) using Continuous Change Detection and Classification (CCDC) algorithm, available in Google Earth Engine. The methodology comprises two steps. In the first step, performed at the pixel level, the CCDC algorithm was used to detect changes in the vegetation cover by considering Landsat 8 spectral bands, vegetation indices, and different combinations thereof. In the second step, two optimal thresholds were determined to identify forest harvesting practices based on the proportion of pixels flagged as change. This study was conducted in forest stands consisting of different conifer and broadleaf species. Accuracy was assessed using an independent set of photo-interpreted samples. The results indicated that the short-wave infrared 2 was the best individual band for forest harvesting practices identification, with an average F-score of 0.77 ± 0.06, overperforming vegetation indices. The combination of all spectral bands was the most effective to identify both clear-cuts and thinning (F-score = 0.85 ± 0.05). This combination was used to evaluate the accuracy of this approach for identifying harvesting practices over different tree species. Poplar (Populus sp.) had the highest identification rate (F-score = 0.99 ± 0.02), while black pine (Pinus nigra J.F. Arnold) stands had the lowest F-score (0.74 ± 0.05). These results highlight the ability to accurately identify forest harvesting practices even in heterogeneous forests with a high diversity of tree species using dense time series of Landsat imagery.

Description

Keywords

Forest harvesting, Change detection, Forest monitoring, Google Earth Engine, CCDC, Forest management

Department

Ingeniería / Ingeniaritza / Institute on Innovation and Sustainable Development in Food Chain - ISFOOD

Faculty/School

Degree

Doctorate program

item.page.cita

Giambelluca, A. L., Hermosilla, T., Álvarez-Mozos, J., González-Audícana, M. (2025) Identifying forest harvesting practices: clear-cutting and thinning in diverse tree species using dense Landsat time series. Forest Ecology and Management, 578, 1-16. https://doi.org/10.1016/j.foreco.2024.122442.

item.page.rights

© 2024 Published by Elsevier B.V. This is an open access article under the CC BY-NC license.

Licencia

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