Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data

Autor(es)

Silva, Carlos Alberto ^{[1, 2]} ; Hudak, Andrew Thomas ^[1] ; Klauberg, Carine ^[1] ; Vierling, Lee Alexandre ^[2] ; Gonzalez-Benecke, Carlos ^[3] ; Chaves Carvalho, Samuel de Padua ^[4] ; Estraviz Rodriguez, Luiz Carlos ^[5] ; Cardil, Adrian ^[6]

Afiliação do(s) autor(es)

^[1] US Forest Serv, USDA, RMRS, 1221 South Main St, Moscow, ID 83843 - USA

^[2] Univ Idaho, Dept Nat Resources & Soc, Coll Nat Resources, 875 Perimeter Dr, Moscow, ID 83843 - USA

^[3] Oregon State Univ, Dept Forest Engn, 269 Peavy Hall, Corvallis, OR 97331 - USA

^[4] Univ Fed Mato Grosso, Coll Forestry, Av Fernando Correa da Costa 2367, BR-78060900 Cuiaba, MT - Brazil

^[5] Univ Sao Paulo, Dept Forest Sci, Coll Agr Luiz de Queiroz ESALQ, Av Padua Dias 11, BR-13418900 Piracicaba, SP - Brazil

^[6] Tecnosylva, Parque Tecnol Leon, Leon 24009 - Spain

• Tipo de documento: Artigo Científico
• Fonte: Carbon Balance and Management; v. 12, JUN 7 2017.
• Citações Web of Science: 14

Resumo

Background: LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m(-2) (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. Results: The results show that LiDAR pulse density of 5 pulses m(-2) provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m(-2) in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. Conclusion: LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m(-2) and a grid cell size of 5 m. The promising results for AGC model ing in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory. (AU)

• Processo FAPESP: 12/03176-0 - O uso do sensoriamento remoto por meio da tecnologia aerotransportada laser (ALS) e de imagens aéreas de alta resolução multiespectral para obtenção do estoque de carbono na biomassa arbórea em povoamentos de Eucalyptus spp
• Beneficiário: Carlos Alberto Silva
• Modalidade de apoio: Bolsas no Brasil - Mestrado