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.
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.
| Original language | English |
|---|---|
| Article number | 2401 |
| Journal | Remote Sensing |
| Volume | 13 |
| Issue number | 2401 |
| Number of pages | 21 |
| ISSN | 2072-4292 |
| DOIs | |
| Publication status | Published - 2021 |