The Potential of Widespread Ready-Made UAVs in Quantifying Conifers and Delineating Their Crowns

With the ever-improving Earth observation capabilities, variables such as tree health status, biomass storage, or stand structure are increasingly estimated through remote sensing. While many protocols of data acquisition and satellite data processing are in place, the still novel unmanned aerial vehicles (UAVs) face some challenges during data acquisition and processing. While tree height extraction seems to be a common practice, identifying individual trees and measuring their crowns is still quite tricky. We performed several flights with three different UAVs and four different popular sensors over two sites with coniferous forests of various ages at flight levels of 100–200 m above ground level (AGL) using custom settings preset by UAV solution suppliers. Considering the success rate of the individual tree identification, casual RGB cameras provided more consistent results at all flight levels (84 − 77% for Phantom 4), while the success of tree identification decreases with higher flight levels and smaller crowns in the case of multispectral cameras (77 − 54% for RedEdge-M). In general, RGB cameras yielded the best results at 150 m AGL while multispectral cameras at 100 m AGL. Regarding the accuracy of the measured crown diameters, most datasets tended to overestimate when using automatic crown delineation within the lidR package. Only RGB cameras yielded satisfactory results (Mean Absolute Error – MAE of 0.79–0.99 m and 0.88–1.16 m for Phantom 4 and Zenmuse X5S, respectively). Multispectral cameras overestimated more than RGB cameras, especially in the full-grown forest (MAE = 1.26–1.77 m); on the other hand, they offered, in addition to the structural, also spectral information. We conclude that widespread ready-made solutions mounted with low-cost RGB cameras yield very satisfactory results for describing the structural forest information at 150 m AGL. When (multi)spectral information is needed, we recommend reducing the flight level to 100 m AGL to acquire sufficient structural forest information.

INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

INDIVIDUAL TREE CROWN DELINEATION FROM HIGH SPATIAL RESOLUTION IMAGERY USING U-NET

The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.

Individual Tree Crown Detection Using UAV Orthomosaic

Unmanned Aerial Vehicles (UAVs) are increasingly used in forestry as they are economical and flexible. This study aims to present the advantages of the drone photogrammetry method in collecting individual tree crowns, as individual tree crown detection could deliver essential ecological and economic information. The referred accuracy for individual tree crown extraction is 79.2%. Only crowns that were clearly visible were selected and manually delineated on the image because the distribution of the true crown size is significantly different from the segmented crowns. The aim of this study is to investigate UAVs orthomosaics in individual tree crown detection. The objectives of this study are to produce the orthomosaic of tree crown extraction mapping using the Pix4D software and analyze the tree crowns using tree crown delineation and the OBIA algorithm. Data processing involves the processing of aerial images using Pix4Dmapper. Automatic tree crown detection involves a tree crown delineation algorithm and OBIA operations to process the tree crown extraction. The crown delineation algorithm and OBIA algorithm operation will be compared to the actual tree crown measurement in terms of diameter and area. The tree crown delineation method obtained a 0.347m mean diameter difference from the actual tree crown diameter, while the OBIA approach obtained 4.98m. The tree crown delineation method obtained 97.26% of the actual tree crown area, while OBIA obtained 91.74%.