Monitoring Montane-Subalpine Forest Ecotone in the Pyrenees: Integrating Sequential Forest Inventories and Landsat Imagery - DATASET
This dataset is a valuable component of the article titled "Monitoring Montane-Subalpine Forest Ecotone in the Pyrenees: Integrating Sequential Forest Inventories and Landsat Imagery." The dataset provides comprehensive information necessary for implementing the analysis and models described. These analyses encompass studying the variations in Abies alba Mill. and Pinus uncinata Ramond. basal area and Replacement Index over three reference years (1991, 2002, and 2015). Additionally, the study applies linear mixed-effects models, considering altitude, aspect, total basal area, year, and protection level (National Park vs. protection buffer zone) as fixed effects, and plot as a random effect. By utilizing the reflectance values from the Landsat composites of 1991, 2002, and 2015, a Support Vector Machine binary classifier can be trained using presence/absence indicators for A. alba and P. uncinata, enabling the prediction of species’ distribution throughout the entire study area. All methods are thoroughly described in the manuscript.
Main Authors: | , , , , , , , |
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Other Authors: | |
Format: | dataset biblioteca |
Language: | English |
Published: |
DIGITAL.CSIC
2022
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Subjects: | Pinus uncinata, Forest ecotone monitoring, Field inventory data, Landsat reflectance values, Abies alba, |
Online Access: | http://hdl.handle.net/10261/311639 |
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Summary: | This dataset is a valuable component of the article titled "Monitoring Montane-Subalpine Forest Ecotone in the Pyrenees: Integrating Sequential Forest Inventories and Landsat Imagery." The dataset provides comprehensive information necessary for implementing the analysis and models described. These analyses encompass studying the variations in Abies alba Mill. and Pinus uncinata Ramond. basal area and Replacement Index over three reference years (1991, 2002, and 2015). Additionally, the study applies linear mixed-effects models, considering altitude, aspect, total basal area, year, and protection level (National Park vs. protection buffer zone) as fixed effects, and plot as a random effect. By utilizing the reflectance values from the Landsat composites of 1991, 2002, and 2015, a Support Vector Machine binary classifier can be trained using presence/absence indicators for A. alba and P. uncinata, enabling the prediction of species’ distribution throughout the entire study area. All methods are thoroughly described in the manuscript. |
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