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Article

Individual Tree Detection from UAV Imagery Using Hölder Exponent

1
DIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, Italy
2
DIST, Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10125 Torino, Italy
3
Centrum Badań Kosmicznych Polskiej Akademii Nauk, 00-716 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(15), 2407; https://doi.org/10.3390/rs12152407
Received: 22 May 2020 / Revised: 17 July 2020 / Accepted: 22 July 2020 / Published: 27 July 2020
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
This article explores the application of Hölder exponent analysis for the identification and delineation of single tree crowns from very high-resolution (VHR) imagery captured by unmanned aerial vehicles (UAV). Most of the present individual tree crown detection (ITD) methods are based on canopy height models (CHM) and are very effective as far as an accurate digital terrain model (DTM) is available. This prerequisite is hard to accomplish in some environments, such as alpine forests, because of the high tree density and the irregular topography. Indeed, in such conditions, the photogrammetrically derived DTM can be inaccurate. A novel image processing method supports the segmentation of crowns based only on the parameter related to the multifractality description of the image. In particular, the multifractality is related to the deviation from a strict self-similarity and can be treated as the information about the level of inhomogeneity of considered data. The multifractals, even if well established in image processing and recognized by the scientific community, represent a relatively new application in VHR aerial imagery. In this work, the Hölder exponent (one of the parameters related to multifractal description) is applied to the study of a coniferous forest in the Western Alps. The infrared dataset with 10 cm pixels is captured by a UAV-mounted optical sensor. Then, the tree crowns are detected by a basic workflow. This consists of the thresholding of the image on the basis of the Hölder exponent. Then, the single crowns are segmented through a multiresolution segmentation approach. The ITD segmentation was validated through a two-level validation analysis that included a visual evaluation and the computing of quantitative measures based on 200 reference crowns. The results were checked against the ITD performed in the same area but using only spectral, textural, and elevation information. Specifically, the visual assessment included the estimation of the producer’s and user’s accuracies and the F1 score. The quantitative measures considered are the root mean square error (RMSE) (for the area, the perimeter, and the distance between centroids) and the over-segmentation and under-segmentation indices, the Jaccard index, and the completeness index. The F1 score indicates positive results (over 73%) as well as the completeness index that does not exceed 0.23 on a scale of 0 to 1, taking 0 as the best result possible. The RMSE of the extension of crowns is 3 m2, which represents only 14% of the average extension of reference crowns. The performance of the segmentation based on the Hölder exponent outclasses those based on spectral, textural, and elevation information. Despite the good results of the segmentation, the method tends to under-segment rather than over-segment, especially in areas with sloping. This study lays the groundwork for future research into ITD from VHR optical imagery using multifractals. View Full-Text
Keywords: individual tree detection (ITD); Hölder exponent; multifractals; unmanned aerial vehicles (UAV); VHR imagery; alpine arch; precision forestry; segmentation accuracy assessment individual tree detection (ITD); Hölder exponent; multifractals; unmanned aerial vehicles (UAV); VHR imagery; alpine arch; precision forestry; segmentation accuracy assessment
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MDPI and ACS Style

Belcore, E.; Wawrzaszek, A.; Wozniak, E.; Grasso, N.; Piras, M. Individual Tree Detection from UAV Imagery Using Hölder Exponent. Remote Sens. 2020, 12, 2407. https://doi.org/10.3390/rs12152407

AMA Style

Belcore E, Wawrzaszek A, Wozniak E, Grasso N, Piras M. Individual Tree Detection from UAV Imagery Using Hölder Exponent. Remote Sensing. 2020; 12(15):2407. https://doi.org/10.3390/rs12152407

Chicago/Turabian Style

Belcore, Elena, Anna Wawrzaszek, Edyta Wozniak, Nives Grasso, and Marco Piras. 2020. "Individual Tree Detection from UAV Imagery Using Hölder Exponent" Remote Sensing 12, no. 15: 2407. https://doi.org/10.3390/rs12152407

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