Análise de estratégias para identificação de copas de árvores em áreas arbóreas utilizando imagens de Aeronaves Remotamente Pilotadas
Abstract
The monitoring of forest areas is an essential tool for environmental conservation, either
by controlling the suppression of native vegetation or by monitoring the restoration
processes, with constant improvements in the strategies and techniques used in their
execution. Therefore, it is necessary to know the structure of these areas in order to have
a better understanding of their dynamics and distribution. In this scenario, the use of
geoprocessing tools, especially through the technique of segmentation of the treetops,
appears as essential tool to study vegetation in detail, assisting in the study of the structure
of the forest and its conservation. However, its applicability is often limited by the quality
of the images available for analysis and, sometimes, by the techniques used for data
interpretation. Remotely piloted aircraft (RPAs) are becoming an excellent alternative in
terms of data quality, allowing the acquisition of high-resolution images at low cost. In
addition, with easier access to machine learning techniques, it is possible to use neural
network models to process data in an innovative and efficient way. In this work, images
obtained through RPA were used to evaluate different techniques for segmentation of tree
crowns, aiming to find the most suitable to identify and count tree individuals. The following
segmentation methods were evaluated: mode filter, multiresolution, watershed and
segmentation with neural networks for deep learning. The images were obtained in areas
with different plant physiognomies (Pinus silviculture and riparian forest), in order to assess
their impact on the results. The results were compared using a set of validation data
(terrestrial truth) generated from independent samples of these same images, to observe
the accuracy of the techniques. The results showed that the polygons from the mode filter
do not correspond to the crowns of the trees, possibly due to the intense homogenization
of the images, which emphasizes its use as an intermediate segmentation tool. The
multiresolution and watershed segmentations, on the other hand, showed good potential
to detect treetops in the Pinus area, however, its parameters must be adjusted according
to the target area, which makes it difficult to use and requires the algorithm to be executed
multiple times. Ultimately, the segmentation generated by the deep learning model (U-Net)
had problems connecting the edges of the treetops and showed a result without individual
polygons for each tree. However, with the labeling used in this project, the tool showed
good potential for detect gaps within the forest. In all cases, results were better in
Silviculture compared to the area of Riparian Forest, a situation that may be related to the
difference in the density of the treetops between these regions. Finally, additional
experiments are necessary, using other sensors (such as LiDAR) and methods, to check
if there is an improvement in the quality of the segmentation.
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