TY - JOUR
T1 - Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome
AU - Zimbres, Barbara
AU - Rodríguez-Veiga, Pedro
AU - Shimbo, Julia Z.
AU - Da conceição bispo, Polyanna
AU - Balzter, Heiko
AU - Bustamante, Mercedes
AU - Roitman, Iris
AU - Haidar, Ricardo
AU - Miranda, Sabrina
AU - Gomes, Letícia
AU - Alvim carvalho, Fabrício
AU - Lenza, Eddie
AU - Maracahipes-Santos, Leonardo
AU - Abadia, Ana Clara
AU - Do prado júnior, Jamir Afonso
AU - Mendonça machado, Evandro Luiz
AU - Dias gonzaga, Anne Priscila
AU - De castro nunes santos terra, Marcela
AU - De mello, José Marcio
AU - Soares scolforo, José Roberto
AU - Rodrigues pinto, José Roberto
AU - Alencar, Ane
PY - 2021/11/1
Y1 - 2021/11/1
N2 - The Brazilian Cerrado biome consists of a highly heterogeneous tropical savanna, and is one of the world’s biodiversity hotspots. High rates of deforestation, however, place it as the second-largest source of carbon emissions in Brazil. Due to its heterogeneity, biomass and carbon stocks in the Cerrado vegetation are highly variable, and mapping and monitoring these stocks are not a trivial effort. To address this challenge, we built an aboveground woody biomass (AGWB) model for the Cerrado biome using 30-m resolution optical satellite imagery (Landsat-5 and Landsat-8), 25-m resolution SAR imagery (ALOS and ALOS-2), and a set of plot-based and LiDAR-derived AGWB estimates (n = 1858) from a wide network of researchers in Brazil. We implemented both a Classification and Regression Tree (CART) and a Random Forest (RF) algorithm to model AGWB over the native vegetation in the year 2019 (as classified by MapBiomas) in the Cerrado. The RF algorithms resulted in a slightly better result (R2 = 53%; rel. RMSE = 57%) than the CART model (R2 = 45%; rel. RMSE = 63%), but our map shows an underestimation of very high AGWB (negative bias over 200 t ha−1) and a slight overestimation of low AGWB (positive bias), especially in the RF model (bias of 1.19 t ha−1 against 0.86 t ha−1 for the CART model). We believe we have contributed to knowledge on the woody biomass stocks in the biome, especially in the predominant savanna woodlands, which is where the highest current rates of conversion take place in the Cerrado.
AB - The Brazilian Cerrado biome consists of a highly heterogeneous tropical savanna, and is one of the world’s biodiversity hotspots. High rates of deforestation, however, place it as the second-largest source of carbon emissions in Brazil. Due to its heterogeneity, biomass and carbon stocks in the Cerrado vegetation are highly variable, and mapping and monitoring these stocks are not a trivial effort. To address this challenge, we built an aboveground woody biomass (AGWB) model for the Cerrado biome using 30-m resolution optical satellite imagery (Landsat-5 and Landsat-8), 25-m resolution SAR imagery (ALOS and ALOS-2), and a set of plot-based and LiDAR-derived AGWB estimates (n = 1858) from a wide network of researchers in Brazil. We implemented both a Classification and Regression Tree (CART) and a Random Forest (RF) algorithm to model AGWB over the native vegetation in the year 2019 (as classified by MapBiomas) in the Cerrado. The RF algorithms resulted in a slightly better result (R2 = 53%; rel. RMSE = 57%) than the CART model (R2 = 45%; rel. RMSE = 63%), but our map shows an underestimation of very high AGWB (negative bias over 200 t ha−1) and a slight overestimation of low AGWB (positive bias), especially in the RF model (bias of 1.19 t ha−1 against 0.86 t ha−1 for the CART model). We believe we have contributed to knowledge on the woody biomass stocks in the biome, especially in the predominant savanna woodlands, which is where the highest current rates of conversion take place in the Cerrado.
KW - ALOS-2 PALSAR-2
KW - Savanna
KW - Landsat
KW - LiDAR
KW - machine learning
KW - Synthetic Aperture Radar (SAR)
U2 - 10.1016/j.foreco.2021.119615
DO - 10.1016/j.foreco.2021.119615
M3 - Article
SN - 0378-1127
VL - 499
SP - 1
EP - 15
JO - Forest Ecology and Management
JF - Forest Ecology and Management
M1 - 119615
ER -