Evaluation of models to determine LAI on poplar stands using spectral indices from Sentinel-2 satellite images

Paulo Cañete-Salinas, Francisco Zamudio, Marco Yáñez, John Gajardo, Héctor Valdés, Cristian Espinosa, Jaime Venegas, Luis Retamal, Samuel Ortega-Farias and César Acevedo-Opazo

Ecological Modelling, 2020, vol. 428, issue C

Abstract: The Leaf Area Index (LAI) is one of the most important structural and functional attributes of forests. This is determined by indirect methods such as digital hemispheric photography (DHP), because is simple to use at the field level, but it does not reflect the spatial variability of forest stands at a larger scale. Therefore, there is increasing interest for using satellite images to estimate LAI. This study aims to calibrate and validate models for LAI in Populus × canadensis Moench. (P. deltoides × P. nigra) stands based on two spectral indices, the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) obtained from Sentinel-2. The experiment was carried out in poplar stands established in the Maule Region, Chile. The reference LAI was obtained using DHP and was regressed against the spectral index through a linear, quadratic and sum of sine models. Within the study, the linear models for both indices presented the lowest adjustment parameters with a Root Mean Square Error (RMSE) close to 0.3 and R2 less than 0.5. On the other hand, the quadratic models and sum of sine for the SAVI were higher than NDVI, with R2 of 0.57, Mean Absolute Error (MAE) of 0.22, RMSE of 0.25 and for the 1:1 ratio, the slope of the curve is 0.99. Despite the homogeneity observed at the field level and the sources of error, this study showed a good degree of adjustment for the quadratic and sum of sine models for determining LAI. These models are presented as an excellent tool to estimate LAI large area of poplar.

Keywords: Leaf Area Index (LAI); Sentinel-2; Hemispherical photographs; SAVI; NDVI (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:428:y:2020:i:c:s0304380020301307

DOI: 10.1016/j.ecolmodel.2020.109058

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