Artificial neural networks and remote sensing for volumetric prediction in a Eucalyptus sp. plantation

Argemiro José Moreno Arteaga,Monica Fabiana Bento Moreira Thiersch,Alessandro Araujo Amaral De Almeida,Lucas Kröhling Bernardi,Franciane Andrade De Pádua,Claudio Roberto Thiersch

doi:10.33448/rsd-v10i12.20466

Argemiro José Moreno Arteaga, Monica Fabiana Bento Moreira Thiersch + Show 4 more

Open Access

https://doi.org/10.33448/rsd-v10i12.20466

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Abstract

Forest inventory is an important tool for estimating the production of forest stands and normally employs traditional methods for volume estimation. However, as a result of technological advancements, artificial neural networks and remote sensing have assumed a prominent role in the forestry sector since satellite images have different components that correlate with the dendrometric variables and can be used as auxiliary variables. The objective of this work was to evaluate the performance of artificial neural networks regarding the estimation of volume in a Eucalyptus sp. plantation with the use of satellite images. Pre-cut inventory data were used with ages varying between 5.3 and 6.3 years. The variables used were volume, age, 4 bands of the satellite image with a 10 m spatial resolution from Sentinell-2 satellite, ratio between the bands, NDVI, and genetic material. All processing was performed using the free software R. The evaluation criteria for the neural network were percentage of residual standard error and graphical analysis of the residues. The best neural network configuration for volume estimation presented a residual standard error of 10.63% and 12.00% for training and validation, respectively. The methodology proposed in this work proved to be efficient in estimating the volume of the stand.

Highlights

In the last two decades, the majority of research conducted within the scope of satellite images together with ArtificialNeural Network (ANN) has been focused on the estimation of biomass in forest stands, being estimated in units of mass (Frazier et al, 2014; López-Serrano et al, 2016; Lu et al, 2016; Sarker & Nichol, 2011; Wang et al, 2011)
Almeida et al (2009) carried out a study in an area located in the Amazon Rainforest, estimating the forest biomass processing ArtificialNeural Network (ANN) with spectral bands and vegetation indexes derived from Landsat 5 satellite images
This study aims to evaluate the efficiency of an artificial neural network methodology, associated with Sentinel-2 satellite images, in estimating the volume of a Eucalyptus sp. stand

Summary

Introduction

In the last two decades, the majority of research conducted within the scope of satellite images together with ArtificialNeural Network (ANN) has been focused on the estimation of biomass in forest stands, being estimated in units of mass (Frazier et al, 2014; López-Serrano et al, 2016; Lu et al, 2016; Sarker & Nichol, 2011; Wang et al, 2011). In India, Nandy et al (2017) in the Barkot forest and Deb et al (2017) in the Bundelkhand region, measured forest biomass using ANN, integrating field inventory data, spectral bands, texture and vegetation indexes from the Resourcesat 1 and 2 satellite images. Almeida et al (2009) carried out a study in an area located in the Amazon Rainforest, estimating the forest biomass processing ANN with spectral bands and vegetation indexes derived from Landsat 5 satellite images. Foody et al (2001) using TM-Landsat 4 and 5 images, and Cutler et al (2012) with SAR and TM-Landsat images, have used ANN to estimate biomass of tropical forests using spectral bands and vegetation indexes, in the first case, and bands and textures, in the second case, as input variables. In India, Nandy et al (2017) in the Barkot forest and Deb et al (2017) in the Bundelkhand region, measured forest biomass using ANN, integrating field inventory data, spectral bands, texture and vegetation indexes from the Resourcesat 1 and 2 satellite images. Almeida et al (2009) carried out a study in an area located in the Amazon Rainforest, estimating the forest biomass processing ANN with spectral bands and vegetation indexes derived from Landsat 5 satellite images. Foody et al (2001) using TM-Landsat 4 and 5 images, and Cutler et al (2012) with SAR and TM-Landsat images, have used ANN to estimate biomass of tropical forests using spectral bands and vegetation indexes, in the first case, and bands and textures, in the second case, as input variables. Ferraz et al (2014) performed a study in a Tropical

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