Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
2021
Almeida, Danilo Roberti Alves De | Broadbent, Eben North | Ferreira, Matheus Pinheiro | Meli, Paula | Zambrano, Angelica Maria Almeyda | Gorgens, Eric Bastos | Resende, Angelica Faria | de Almeida, Catherine Torres | Do Amaral, Cibele Hummel | Corte, Ana Paula Dalla | Silva, Carlos Alberto | Romanelli, João | Prata, Gabriel Atticciati | de Almeida Papa, Daniel | Stark, Scott | Valbuena, Ruben | Nelson, Bruce Walker | Guillemot, Joannes | Féret, Jean-Baptiste | Chazdon, Robin | Brancalion, Pedro H.S. | Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ) ; Universidade de São Paulo = University of São Paulo (USP) | University of Florida [Gainesville] (UF) | Instituto Militar de Engenharia=Military Institute of Engineering (IME) | Universidad de la frontera [Tecumo, Chile] = University of La Frontera [Tecumo, Chile] = Université de La Frontera [Tecumo, Chili] (UFRO) | Universidade Federal dos Vales do Jequitinhonha e Mucuri = Federal University of Jequitinhonha and Mucuri Vallays (UFJMV) | Universidade Federal de Viçosa [Brasil] = Federal University of Viçosa [Brazil] = Université fédérale de Viçosa [Brésil] (UFV [Brésil]) | Universidade Federal do Parana [Curitiba] (UFPR) ; Universidade Federal do Paraná [Curitiba, Brasil] = Federal University of Paraná [Curitiba, Brazil] = Université fédérale du Paraná [Curitiba, Brésil] (UFPR) | University of Maryland [College Park] (UMD) ; University System of Maryland | Brazilian Agricultural Research Corporation = Empresa Brasileira de Pesquisa Agropecuária (Embrapa) | Michigan State University [East Lansing] ; Michigan State University System | Bangor University | Instituto Nacional de Pesquisas da Amazônia = National Institute of Amazonian Research (INPA) | Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | University of the Sunshine Coast (USC) | Sao Paulo Research Foundation (FAPESP), (grants #2018/21338-3, #2018/18416-2, #2019/14697-0, #2019/08533-4 and #2019/24049-5) | Fondecyt (project 11191021) | Brazilian National Council for Scientific and Technological Development (CNPq) (grant #306345/2020-0 | Brazilian National Council for Scientific and Technological Development (CNPq) (#302891/2018-8; #408785/2018-7) | National Science Foundation (NSF) DEB-1754357, DEB-1950080, EF1340604, and EF-1550686 | ANR-17-CE32-0001,BioCop,Suivi de la biodiversité tropicale avec les satellites Sentinel-2 du programme Copernicus(2017)
International audience
Mostrar más [+] Menos [-]Inglés. 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.
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Información bibliográfica
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