Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion

Alves de Almeida Danilo Roberti, Broadbent Eben North, Pinheiro Ferreira Matheus, Meli Paula, Almeyda Zambrano Angélica María, Bastos Gorgens Eric, Faria Resende Angelica, Torres de Almeida Catherine, Hummel do Amaral Cibele, Dalla Corte Ana Paula, Silva Carlos Alberto, Romanelli João P., Atticciati Prata Gabriel, de Almeida Papa Daniel, Stark Scott C., Valbuena Ruben, Nelson Bruce Walker, Guillemot Joannès, Feret Jean Baptiste, Chazdon Robin, Brancalion Pedro H.S.. 2021. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sensing of Environment, 264:112582, 13 p.

Quartile : Outlier, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Outlier, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Outlier, Sujet : REMOTE SENSING

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : 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.

Mots-clés Agrovoc : télédétection, forêt tropicale, écosystème forestier, Aéronef, biodiversité, forêt tropicale humide, indice de surface foliaire, forêt, biomasse, déboisement, évaluation des technologies, écosystème, méthode statistique

Mots-clés géographiques Agrovoc : France, Brésil

Mots-clés libres : Forest landscape restoration, Tropical forests, Drones, Lidar remote sensing, Hyperspectral remote sensing, Leaf area density, Vegetation indices

Classification Agris : U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Auteurs et affiliations

• Alves de Almeida Danilo Roberti, USP (BRA) - auteur correspondant
• Broadbent Eben North, University of Florida (USA)
• Pinheiro Ferreira Matheus, IME (BRA)
• Meli Paula, Universidad de La Frontera (CHL)
• Almeyda Zambrano Angélica María, University of Florida (USA)
• Bastos Gorgens Eric, Universidade Federal dos Vales do Jequitinhonha e Mucuri (BRA)
• Faria Resende Angelica, USP (BRA)
• Torres de Almeida Catherine, USP (BRA)
• Hummel do Amaral Cibele, UFV (BRA)
• Dalla Corte Ana Paula, UFPR (BRA)
• Silva Carlos Alberto, University of Florida (USA)
• Romanelli João P., USP (BRA)
• Atticciati Prata Gabriel, University of Florida (USA)
• de Almeida Papa Daniel, EMBRAPA (BRA)
• Stark Scott C., MSU (USA)
• Valbuena Ruben, Bangor University (GBR)
• Nelson Bruce Walker, INPA (BRA)
• Guillemot Joannès, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0003-4385-7656
• Feret Jean Baptiste, INRAE (FRA)
• Chazdon Robin, University of the Sunshine Coast (AUS)
• Brancalion Pedro H.S., USP (BRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/601215/)

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