Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

Abstract

The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression -kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos -Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand. KEYWORDS: Geographically Weighted Regression, Geographically Weighted Regression-Kriging, RedEdge, Carbon emissions, Ecuadorian Amazon.Avaliação de técnicas geoestatísticas na estimativa da distribuição espacial da biomassa acima do solo na Amazônia utilizando sensoriamento remoto de alta resolução espacial RESUMO A distribuição espacial da biomassa na Amazônia é heterogênea, variando temporalmente e espacialmente em relação aos diferentes tipos de formações vegetais abrangidas por este bioma. Estimativas de biomassa nesta região variam significativamente dependendo da abordagem aplicada e do conjunto de dados utilizados para sua modelagem. Assim, este estudo teve como objetivo avaliar três diferentes técnicas geoestatísticas na estimativa da distribuição espacial da biomassa acima do solo (BAS). As técnicas escolhidas foram: 1) regressão por mínimos quadrados ordinários (OLS), 2) regressão geograficamente ponderada (RGP) e, 3) regressão geograficamente ponderada -krigagem (RGP-K). Estas técnicas foram aplicadas sobre um mesmo conjunto de dados de campo, utilizando as mesmas variáveis ambientais decorrentes de dados cartográficos e de sensoriamento remoto de alta resolução espacial (RapidEye). Este trabalho foi desenvolvido na floresta amazônica da província de Sucumbíos no Equador. Os resultados deste estudo mostraram que a RGP-K, sendo uma técnica híbrida, forneceu estimativas estatisticamente satisfatórias com menor erro de predição em comparação com as outras duas técnicas. Além disso, observou-se que 75% da BAS foi explic...

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

“…The RapidEye image was chosen based on its high spatial resolution (5 m) and spectral resolution, which features a red edge band in addition to visible and near infrared bands. Located between the red and near-infrared ranges (690-730 nm), the red edge band enhances RapidEye's capabilities for vegetation aspects assessment (Benítez et al 2016).…”

“…The Cerrado vegetation has an important timber potential to provide raw material for both sawmills and energy purposes (López et al 2013, Lima et al 2016. Several studies have emphasized the productive potential of forest areas commonly focusing on the estimation of above-ground biomass and stored wood volume (Miguel et al 2015, Benítez et al 2016, Dalponte et al 2018; however, more research efforts are required to assess the energy potential of those areas for economic and environmental purposes.…”

“…More recently, the advances in remote sensing and geoprocessing technology allowed to better characterize forest structure, biomass, stored carbon, and other variables (Groot et al 2015). Several studies using remotely sensed data showed satisfactory results in the indirect estimation of the aboveground biomass in several types of forest (Miguel et al 2015, Benítez et al 2016, Dalponte et al 2018.…”

Quantifying Forest Biomass Carbon Stocks From Space

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Modelling Land Sharing and Land Sparing Relationship with Rural Population in the Cerrado

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