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

Rafael Walter Albuquerque,Søren Ingvor Olsen,Julio Ricardo Caetano Tymus,Maurício Ruiz Castello Branco,João Vitor Silva Costa,Cintia Palheta Balieiro,Carlos Henrique Grohmann,Hendrik Mansur,Manuel Eduardo Ferreira,Ciro José Ribeiro Moura

doi:10.3390/rs13122401

Abstract

Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional FR monitoring methods. This study evaluates how low-cost RPA data may contribute to FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of Tree Density, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The point cloud 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 a single land cover class. The Grass Infestation parameter was mapped by the difference between this land cover class (which considered trees and grasses) and the Vegetation Cover results (obtained by the point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameters presented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by the Error Percentage considering the traditional fieldwork and the RPA results. The Error Percentage was equal to 0.13 and was considered accurate because it estimated a 13% shorter height for trees that averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accurately measured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accurately measuring the structural parameters, this study reinforced the challenge of measuring the Biodiversity parameter via remote sensing because the classification of tree species was not possible. After all, the Brazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectral responses in the visible spectrum and similar geometric forms. Therefore, until improved automatic classification methods become available for tree species, traditional fieldwork remains necessary for a complete FR monitoring diagnostic.

Highlights

Piloted aircraft (RPA), popularly known as drones, present notable technical advantages in several fields, such as journalism [1] and agriculture [2]
Traditional Forest Restoration (FR) monitoring methods employ sheets and photos taken at the ground level that do not register the whole area of an FR project, e.g., the methods described in the FR monitoring protocol of the Brazilian Atlantic Forest biome [3]
This study focused on the FR structural parameters, which play an important role in FR monitoring [7]

Summary

Introduction

Piloted aircraft (RPA), popularly known as drones, present notable technical advantages in several fields, such as journalism [1] and agriculture [2]. In Forest Restoration (FR) projects, the real benefits that RPA can provide still demand more studies. Traditional FR monitoring methods employ sheets and photos taken at the ground level that do not register the whole area of an FR project, e.g., the methods described in the FR monitoring protocol of the Brazilian Atlantic Forest biome [3]. Viani et al [4], the Atlantic Forest FR monitoring protocol is excellent because it provides data collection standards to avoid biases and subjectivity. As the scope of future studies, the authors stated that an automatic feedback report would improve the FR monitoring protocol. It would be interesting to investigate whether RPA is capable of generating an automatic feedback report to efficiently support FR monitoring

Objectives

Methods

Results

Discussion

Conclusion