Individual Tree Detection from UAV Imagery Using Hölder Exponent

Abstract

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.

Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data

“…Although the suitability of segmentation is rarely evaluated in the classification of individual tree crowns, the results obtained from this segmentation appear to be in line with the literature [43,44,65,66].…”

“…The segmentation step plays a crucial role in ITD-based classifications, and needs to be assessed as per the classification results. A qualitative and quantitative assessment based on the works of Persello et al and Yurtseven et al [42,43] was applied, similar to the analysis proposed in Belcore et al [44].…”

“…One-hundred reference crowns were manually described using epoch III as a reference. The producer's and user's accuracy and the F1-score were calculated considering matched and omitted crowns [44]. The under-segmentation index (US), the over-segmentation index (OS), the completeness index (D), and the Jaccard index (J) were computed, in addition to the root mean square error (RMSE) over the area and the perimeter.…”

Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data

“…However, these sensors may not be accurate enough in specific cases; therefore, UAVs with sensors of different wavelength ranges, such as infrared bands, were used. The use of these bands in the environmental field has grown in recent years, particularly in the monitoring of wooded areas or regions subject to flooding [35,[38][39][40].…”

“…However, one of the main limitations regarding hydrological applications of UAVbased mapping is typically the use of RGB sensors, which limits possibilities to detect and reconstruct the properties of the submerged stream channel, as was well-described in [32,33]. Indeed, "invisible" spectral data (red-edge and near-infrared bands) represent a tremendous step forward in defining environmental, forestry, and hydraulic problems, as thoroughly demonstrated in [34][35][36][37][38]. On the other hand, the use of UAV in shallow riverbeds has provided good performance in stream hydromorphology mapping, enabling partial coverage of submerged zones of the channel [39].…”