Abstract
Abstract Remote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.
Highlights
Planted forest containing Pinus spp. achieved near 2 million hectares in Brazil
As forest production is related to dynamic variables such as ecological conditions of the planting site, silvicultural treatments, and environmental/climate condition, monitoring systems based on remote sensing techniques are recommended to cover large areas (Günter et al 2009, Restrepo and Orrego 2015)
Our results suggest that normalized difference vegetation index (NDVI) can be used for almost 18 years while enhanced vegetation index (EVI) reaches the saturation after 14 years
Summary
In the South of Brazil (Rio Grande do Sul State), because of soil and climate characteristics similar to those of the southeastern region of the United States, Pinus elliottii found adequate conditions of growth (Gholz and Fisher 1982, Izumi et al 2008, Yuan et al 2013). Such plantations are mainly intended to be a raw material to pulp and paper industries, mechanical processing and resin extraction (Shimizu 2008). By monitoring forest growth, the commercial wood and resin production can be estimated, allowing a more accurate management of plantations
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