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 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.

Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

“…Remotely piloted aircrafts (RPA), popularly known as drones, have high potential in monitoring FR efficiently due to high-resolution remote sensing data [18]. For instance, RPA coupled with red-green-blue (RGB) sensors can be used to measure the structural parameters of the vegetation, such as tree cover and tree height, and such measurements are accurate especially in open canopy conditions [19][20][21][22]. RPA coupled with RGB sensors also have high potential to estimate the biomass of FR projects [18].…”

“…Despite accurately measuring the structural parameters, measuring the FR biodiversity indicators in high diverse forests is a great challenge [22,23]. Computer vision techniques, such as deep learning [24], have high potential in improving this field of research because they have revolutionized image processing [25][26][27].…”

Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images

“…Only conventional RGB sensors were chosen, similar to Albuquerque et al [34], making it possible to perform the study with any good-quality sensor equipment. No ground support points were used to produce the georeferenced mosaics because the positional accuracy obtained from the global navigation satellite system (GNSS) sensor embedded in the equipment was sufficient to reach the purpose.…”

Aplicação de aeronaves remotamente pilotadas e fotogrametria para avaliação e monitoramento de áreas em processo de restauração florestal

“…A seção A (ALBUQUERQUE et al, 2020) mostra que a nuvem de pontos incide diretamente na acurácia da detecção automática de árvores, e consequentemente na medição do indicador densidade de árvores. Já na seção B, Albuquerque et al (2021) mostram que a nuvem de pontos é importante também para os indicadores estruturais cobertura de dossel e altura de dossel. Em Albuquerque et al (2021), o ortomosaico foi necessário para mapear a ocorrência de gramíneas e de cobertura de dossel de árvores pequenas em área declivosa.…”

“…Já na seção B, Albuquerque et al (2021) mostram que a nuvem de pontos é importante também para os indicadores estruturais cobertura de dossel e altura de dossel. Em Albuquerque et al (2021), o ortomosaico foi necessário para mapear a ocorrência de gramíneas e de cobertura de dossel de árvores pequenas em área declivosa.…”