Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

Xiandie Jiang,Dengsheng Lu,Guiying Li,Emilio Moran,Mateus Batistella

doi:10.3390/rs12203330

Abstract

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

Highlights

Forests, which cover approximately 30% of the Earth’s land surface, produce about 75% of the terrestrial gross primary production and contain 80% of total plant biomass [1], thereby playing important roles in the global carbon cycle and global climate changes
DTh7i5shraessuthlteinhdigichaetsetsrthvaaltuSeWoIfR−20.a6n8,dfosplleocwtreadl binydNicDesIIc7oanntadinailnbgedSoW, wIRi2thhravvaeluheigshoefr 0.66 and −0.63, respectively. This result indicates that SWIR2 and spectral indices containing SWIR2 have higher correlations with aboveground carbon density (ACD) than other spectral bands or spectral indices, which is similar to results from previous studies based on Landsat imagery in the Brazilian Amazon [67]
random forest (RF) based on spectral indices alone provided the best modeling performance, for ACD values greater than 25 kg C/m2, the linear regression (LR) model based on spectral indices alone provided better estimation than RF-based models, implying different effects of datasets and modeling algorithms on ACD estimations and a relationship between ACD ranges and performance

Summary

Introduction

Forests, which cover approximately 30% of the Earth’s land surface, produce about 75% of the terrestrial gross primary production and contain 80% of total plant biomass [1], thereby playing important roles in the global carbon cycle and global climate changes. The Brazilian Amazon has the largest rainforest area, and has significant deforestation rates. The newly released data from Brazil’s National Institute for Space Research (INPE) indicate that deforestation in the Brazilian Amazon since 2014 reached its highest rate in 2018–2019 due to illegal occupation for economic benefit, resulting in a large area of rainforest being deliberately destroyed [4]. Accurate estimation of biomass or carbon density is critical for quantifying carbon stocks and dynamics, and for sustainable forest management in the Amazon [5]. Remote sensing techniques enable rapid mapping of forest distribution and assessment of biomass over large areas at relatively low cost, and have become the most important tools for quantifying biomass at scales ranging from local to regional and global [6,7]

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