Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest

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Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest. / Albuquerque, Rafael; Eduardo Ferreira, Manuel; Olsen, Søren Ingvor; Ricardo Caetano Tymus, Julio; Palheta Balieiro, Cintia; Mansur, Hendrik; Jos  Ribeiro Moura, Ciro; Vitor Silva Costa, Jo o; Ruiz Castello Branco, Maur cio; Henrique Grohmann, Carlos.

I: Remote Sensing, Bind 13, Nr. 2401, 19.06.2021.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Albuquerque, R, Eduardo Ferreira, M, Olsen, SI, Ricardo Caetano Tymus, J, Palheta Balieiro, C, Mansur, H, Jos  Ribeiro Moura, C, Vitor Silva Costa, J, Ruiz Castello Branco, M & Henrique Grohmann, C 2021, 'Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest', Remote Sensing, bind 13, nr. 2401. https://doi.org/10.3390/rs13122401

APA

Albuquerque, R., Eduardo Ferreira, M., Olsen, S. I., Ricardo Caetano Tymus, J., Palheta Balieiro, C., Mansur, H., Jos  Ribeiro Moura, C., Vitor Silva Costa, J., Ruiz Castello Branco, M., & Henrique Grohmann, C. (2021). Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing, 13(2401). https://doi.org/10.3390/rs13122401

Vancouver

Albuquerque R, Eduardo Ferreira M, Olsen SI, Ricardo Caetano Tymus J, Palheta Balieiro C, Mansur H o.a. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing. 2021 jun 19;13(2401). https://doi.org/10.3390/rs13122401

Author

Albuquerque, Rafael ; Eduardo Ferreira, Manuel ; Olsen, Søren Ingvor ; Ricardo Caetano Tymus, Julio ; Palheta Balieiro, Cintia ; Mansur, Hendrik ; Jos  Ribeiro Moura, Ciro ; Vitor Silva Costa, Jo o ; Ruiz Castello Branco, Maur cio ; Henrique Grohmann, Carlos. / Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest. I: Remote Sensing. 2021 ; Bind 13, Nr. 2401.

Bibtex

@article{196a4a85cf7e4b76a29ac244e8a3942c,
title = "Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest",
abstract = "Traditional forest restoration (FR) monitoring methods employ spreadsheets and photostaken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolutionand georeferenced view of the entire area of interest, this technology has high potential to improvethe traditional FR monitoring methods. This study evaluates how low-cost RPA data may contributeto FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of TreeDensity, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The pointcloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters.The orthomosaic was used for a Random Forest classification that considered trees and grasses as asingle land cover class. The Grass Infestation parameter was mapped by the difference between thisland cover class (which considered trees and grasses) and the Vegetation Cover results (obtained bythe point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameterspresented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by theError Percentage considering the traditional fieldwork and the RPA results. The Error Percentagewas equal to 0.13 and was considered accurate because it estimated a 13% shorter height for treesthat averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accuratelymeasured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accuratelymeasuring the structural parameters, this study reinforced the challenge of measuring the Biodiversityparameter via remote sensing because the classification of tree species was not possible. After all, theBrazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectralresponses in the visible spectrum and similar geometric forms. Therefore, until improved automaticclassification methods become available for tree species, traditional fieldwork remains necessary fora complete FR monitoring diagnostic.",
author = "Rafael Albuquerque and {Eduardo Ferreira}, Manuel and Olsen, {S{\o}ren Ingvor} and {Ricardo Caetano Tymus}, Julio and {Palheta Balieiro}, Cintia and Hendrik Mansur and {Jos  Ribeiro Moura}, Ciro and {Vitor Silva Costa}, Jo o and {Ruiz Castello Branco}, Maur cio and {Henrique Grohmann}, Carlos",
year = "2021",
month = jun,
day = "19",
doi = "https://doi.org/10.3390/rs13122401",
language = "English",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "M D P I AG",
number = "2401",

}

RIS

TY - JOUR

T1 - Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest

AU - Albuquerque, Rafael

AU - Eduardo Ferreira, Manuel

AU - Olsen, Søren Ingvor

AU - Ricardo Caetano Tymus, Julio

AU - Palheta Balieiro, Cintia

AU - Mansur, Hendrik

AU - Jos  Ribeiro Moura, Ciro

AU - Vitor Silva Costa, Jo o

AU - Ruiz Castello Branco, Maur cio

AU - Henrique Grohmann, Carlos

PY - 2021/6/19

Y1 - 2021/6/19

N2 - Traditional forest restoration (FR) monitoring methods employ spreadsheets and photostaken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolutionand georeferenced view of the entire area of interest, this technology has high potential to improvethe traditional FR monitoring methods. This study evaluates how low-cost RPA data may contributeto FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of TreeDensity, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The pointcloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters.The orthomosaic was used for a Random Forest classification that considered trees and grasses as asingle land cover class. The Grass Infestation parameter was mapped by the difference between thisland cover class (which considered trees and grasses) and the Vegetation Cover results (obtained bythe point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameterspresented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by theError Percentage considering the traditional fieldwork and the RPA results. The Error Percentagewas equal to 0.13 and was considered accurate because it estimated a 13% shorter height for treesthat averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accuratelymeasured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accuratelymeasuring the structural parameters, this study reinforced the challenge of measuring the Biodiversityparameter via remote sensing because the classification of tree species was not possible. After all, theBrazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectralresponses in the visible spectrum and similar geometric forms. Therefore, until improved automaticclassification methods become available for tree species, traditional fieldwork remains necessary fora complete FR monitoring diagnostic.

AB - Traditional forest restoration (FR) monitoring methods employ spreadsheets and photostaken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolutionand georeferenced view of the entire area of interest, this technology has high potential to improvethe traditional FR monitoring methods. This study evaluates how low-cost RPA data may contributeto FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of TreeDensity, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The pointcloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters.The orthomosaic was used for a Random Forest classification that considered trees and grasses as asingle land cover class. The Grass Infestation parameter was mapped by the difference between thisland cover class (which considered trees and grasses) and the Vegetation Cover results (obtained bythe point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameterspresented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by theError Percentage considering the traditional fieldwork and the RPA results. The Error Percentagewas equal to 0.13 and was considered accurate because it estimated a 13% shorter height for treesthat averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accuratelymeasured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accuratelymeasuring the structural parameters, this study reinforced the challenge of measuring the Biodiversityparameter via remote sensing because the classification of tree species was not possible. After all, theBrazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectralresponses in the visible spectrum and similar geometric forms. Therefore, until improved automaticclassification methods become available for tree species, traditional fieldwork remains necessary fora complete FR monitoring diagnostic.

U2 - https://doi.org/10.3390/rs13122401

DO - https://doi.org/10.3390/rs13122401

M3 - Journal article

VL - 13

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 2401

ER -

ID: 272412399