Use este identificador para citar ou linkar para este item: https://repositorio.inpa.gov.br/handle/1/37978
Título: Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
Autor: Almeida, Danilo Roberti Alves De
Broadbent, Eben N.
Ferreira, Matheus Pinheiro
Meli, Paula
Zambrano, Angélica María Almeyda
Görgens, Eric Bastos
Resende, Angelica Faria
de Almeida, Catherine Torres
do Amaral, Cibele Hummel
Dalla Corte, Ana Paula
Silva, Carlos Alberto
Romanelli, João Paulo R.
Prata, Gabriel Atticciati
de Almeida Papa, Daniel
Stark, Scott C.
Valbuena, Rubén
Nelson, Bruce Walker
Guillemot, Joann?s
Féret, Jean Baptiste
Chazdon, Robin L.
Brancalion, Pedro Henrique Santin
Palavras-chave: Drones
Forest landscape restoration
Hyperspectral remote sensing
Leaf area density
Lidar remote sensing
Tropical forests
Vegetation indices
Data do documento: 2021
Revista: Remote Sensing of Environment
É parte de: Volume 264, Número 264
Abstract: Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data—canopy height, leaf area index (LAI), and understory LAI—and eighteen variables derived from hyperspectral data—15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale. © 2021 Elsevier Inc.
DOI: 10.1016/j.rse.2021.112582
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