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

Author(s):Show less -

de Almeida, Danilo Roberti Alves ^{[1, 2]} ; Broadbent, Eben North ^[2] ; Ferreira, Matheus Pinheiro ^[3] ; Meli, Paula ^[4] ; Zambrano, Angelica Maria Almeyda ^[5] ; Gorgens, Eric Bastos ^[6] ; Resende, Angelica Faria ^[1] ; de Almeida, Catherine Torres ^[1] ; do Amaral, Cibele Hummel ^[7] ; Corte, Ana Paula Dalla ^[8] ; Silva, Carlos Alberto ^{[9, 10]} ; Romanelli, Joao P. ^[1] ; Prata, Gabriel Atticciati ^[2] ; Papa, Daniel de Almeida ^[11] ; Stark, Scott C. ^[12] ; Valbuena, Ruben ^[13] ; Nelsonn, Bruce Walker ^[14] ; Guillemot, Joannes ^{[1, 15, 16]} ; Feret, Jean-Baptiste ^[17] ; Chazdon, Robin ^[18] ; Brancalion, Pedro H. S. ^[1]

Affiliation:Show less -

^[1] Univ Sao Paulo USP ESALQ, Luiz de Queiroz Coll Agr, Dept Forest Sci, Piracicaba, SP - Brazil

^[2] Univ Florida, Sch Forest Resources & Conservat, Spatial Ecol & Conservat Lab, Gainesville, FL 32611 - USA

^[3] Mil Inst Engn IME, Cartog Engn Dept, Rio De Janeiro, RJ - Brazil

^[4] Univ La Frontera, Landscape Ecol & Conservat Lab LEPCON, Temuco - Chile

^[5] Univ Florida, Ctr Latin Amer Studies, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL - USA

^[6] Fed Univ Jequitinhonha & Mucuri Valleys UFVJM, Dept Forestry, Diamantina, MG - Brazil

^[7] Univ Fed Vicosa, Dept Forest Engn, Vicosa, MG - Brazil

^[8] Univ Fed Parana, Dept Forest Engn, Curitiba, Parana - Brazil

^[9] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 - USA

^[10] Univ Florida, Sch Forest Fisheries & Geomat Sci, Gainesville, FL - USA

^[11] Embrapa Acre, Rio Branco, Acre - Brazil

^[12] Michigan State Univ, Dept Forestry, E Lansing, MI 48824 - USA

^[13] Bangor Univ, Sch Nat Sci, Bangor, Gwynedd - Wales

^[14] Natl Inst Amazon Res INPA, Manaus, Amazonas - Brazil

^[15] CIRAD, UMR ECO & SOLS, F-34398 Montpellier - France

^[16] Univ Montpellier, Inst Agro, CIRAD, INRAE, Eco & Sols, IRD, Montpellier - France

^[17] Univ Montpellier, CNRS, AgroParisTech, CIRAD, INRAE, TETIS, Montpellier - France

^[18] Univ Sunshine Coast, Trop Forests & People Res Ctr, Sippy Downs, Qld 4556 - Australia

• Document type: Journal article
• Source: REMOTE SENSING OF ENVIRONMENT; v. 264, OCT 2021.
• Web of Science Citations: 2

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 highresolution 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. (AU)

• FAPESP's process: 19/24049-5 - Monitoring São Paulo State restoration forests: application of new remote sensing tools and subsidies for public policies
• Grantee: Angelica Faria de Resende
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 19/08533-4 - Understanding ecological and social aspects of restoration actions in tropical regions through systematic reviews and meta-analyses
• Grantee: João Paulo Romanelli
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/18416-2 - Understanding restored forests for benefiting people and nature - NewFor
• Grantee: Pedro Henrique Santin Brancalion
• Support Opportunities: BIOTA-FAPESP Program - Thematic Grants
• FAPESP's process: 19/14697-0 - Monitoring the demography and diversity of forests undergoing restoration using a drone-lidar-hyperspectral system
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor