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Article

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

1
AGRESTA Sociedad Cooperativa, 28012 Madrid, Spain
2
iuFOR-EiFAB, Campus Duques de Soria, 42004 Soria, Spain
3
Department of Geography and Environment, School of Geoscience, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
4
Laboratorio de Topografía y Geomática, Departamento de Ingeniería y Morfología del Terreno, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Peter Krzystek and Juan Guerra Hernandez
Remote Sens. 2021, 13(18), 3611; https://doi.org/10.3390/rs13183611
Received: 31 July 2021 / Revised: 26 August 2021 / Accepted: 4 September 2021 / Published: 10 September 2021
(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)
Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations. View Full-Text
Keywords: BFAST; clear-cutting; cutting with seed-trees; thinning; Spain BFAST; clear-cutting; cutting with seed-trees; thinning; Spain
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MDPI and ACS Style

Esteban, J.; Fernández-Landa, A.; Tomé, J.L.; Gómez, C.; Marchamalo, M. Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data. Remote Sens. 2021, 13, 3611. https://doi.org/10.3390/rs13183611

AMA Style

Esteban J, Fernández-Landa A, Tomé JL, Gómez C, Marchamalo M. Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data. Remote Sensing. 2021; 13(18):3611. https://doi.org/10.3390/rs13183611

Chicago/Turabian Style

Esteban, Jessica, Alfredo Fernández-Landa, José Luis Tomé, Cristina Gómez, and Miguel Marchamalo. 2021. "Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data" Remote Sensing 13, no. 18: 3611. https://doi.org/10.3390/rs13183611

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