TREE SPECIES CLASSIFICATION OF BROADLEAVED FORESTS IN NAGANO, CENTRAL JAPAN, USING AIRBORNE LASER DATA AND MULTISPECTRAL IMAGES

This study attempted to classify three coniferous and ten broadleaved tree species by combining airborne laser scanning (ALS) data and multispectral images. The study area, located in Nagano, central Japan, is within the broadleaved forests of the Afan Woodland area. A total of 235 trees were surveyed in 2016, and we recorded the species, DBH, and tree height. The geographical position of each tree was collected using a Global Navigation Satellite System (GNSS) device. Tree crowns were manually detected using GNSS position data, field photographs, true-color orthoimages with three bands (red-green-blue, RGB), 3D point clouds, and a canopy height model derived from ALS data. Then a total of 69 features, including 27 image-based and 42 point-based features, were extracted from the RGB images and the ALS data to classify tree species. Finally, the detected tree crowns were classified into two classes for the first level (coniferous and broadleaved trees), four classes for the second level (Pinus densiflora, Larix kaempferi, Cryptomeria japonica, and broadleaved trees), and 13 classes for the third level (three coniferous and ten broadleaved species), using the 27 image-based features, 42 point-based features, all 69 features, and the best combination of features identified using a neighborhood component analysis algorithm, respectively. The overall classification accuracies reached 90 % at the first and second levels but less than 60 % at the third level. The classifications using the best combinations of features had higher accuracies than those using the image-based and point-based features and the combination of all of the 69 features

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification. (C) 2015 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [41471281]; Beijing Natural Science Foundation [4154074]; Research Fund for Doctoral Program of Higher Education of China [20130001120016]; SRF for ROCS, SEM, ChinaSCI(E)[email protected]

Fusion Approaches to Individual Tree Species Classification Using Multi-Source Remotely Sensed Data

Tree species information plays essential roles in urban ecological management and sustainable development, and thus tree species classification has been an active research topic over the years. This study investigated fusion approaches deployed with Support Vector Machine (SVM) and Random Forest (RF) algorithms to incorporating multispectral imagery (MSI), a very high spatial resolution panchromatic image (PAN), and Light Detection and Ranging (LiDAR) data for five object-based tree species classification in an urban environment. The results demonstrated that 3D structural features contributed more to tree species with broad crowns, such as honey locust and Austrian pine, whereas textural features were more effective in differentiating trees in narrow crowns, such as spruce. Among all the possible classification schemes based on multi-source features in combinations, decision fusion achieved the best overall accuracies (0.86 for SVM and 0.84 for RF), slightly outperforming the feature fusion approach (0.85 for SVM and 0.83 for RF). Both fusion approaches significantly improved tree species classifications produced by MSI (0.7), PAN (0.74), and LiDAR (0.8) individually

Fusion Approaches to Individual Tree Species Classification Using Multi-Source Remotely Sensed Data

Tree species information plays essential roles in urban ecological management and sustainable development, and thus tree species classification has been an active research topic over the years. This study investigated fusion approaches deployed with Support Vector Machine (SVM) and Random Forest (RF) algorithms to incorporating multispectral imagery (MSI), a very high spatial resolution panchromatic image (PAN), and Light Detection and Ranging (LiDAR) data for five object-based tree species classification in an urban environment. The results demonstrated that 3D structural features contributed more to tree species with broad crowns, such as honey locust and Austrian pine, whereas textural features were more effective in differentiating trees in narrow crowns, such as spruce. Among all the possible classification schemes based on multi-source features in combinations, decision fusion achieved the best overall accuracies (0.86 for SVM and 0.84 for RF), slightly outperforming the feature fusion approach (0.85 for SVM and 0.83 for RF). Both fusion approaches significantly improved tree species classifications produced by MSI (0.7), PAN (0.74), and LiDAR (0.8) individually

AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level

Remote sensing tools for the objective quantification of tree structural condition from individual trees to landscape scale assessment

Tree management is the practice of protecting and caring for trees for sustainable, defined objectives. However, there are often conflicts between maintaining trees and the obligation to protect targets, such as people or infrastructure, from the risks associated with the failure of trees and major limbs. Where there are targets worthy of protection, tree structural condition is typically monitored relative to the prescribed management objectives. Traditionally, field methods for capturing data on tree structural condition are manual with a tree surveyor taking very limited direct measurements, and only from parts of the tree that are within reach from the ground. Consequently, large sections of the tree remain unmeasured due to the logistical complications of accessing the aerial structure. Therefore, the surveyor estimates tree part sizes, approximates counts of relevant tree features and uses personal interpretation to infer the significance of the observations. These techniques are temporally and logistically demanding, and largely subjective. This thesis develops solutions to the limitations of traditional methods through the development of remote sensing (RS) tools for assessing tree structural condition, in order to inform tree management interventions. For individual trees, a proximal photogrammetry technique is developed for objectively quantifying tree structural condition by measuring the self-affinity of tree crowns in fractal dimensions. This can identify the individual tree crown complexity along a structural condition continuum, which is more effective than the traditional categorical approach for monitoring tree condition. Moving out in scale, a framework is developed which optimises the matchpairing agreement between ground reference tree data and RS-derived individual tree crown (ITC) delineations in order to quantify the accuracy of different ITC delineation algorithms. The framework is then used to identify an optimal ITC delineation algorithm which is applied to aerial laser scanning data to map individual trees and extract a point cloud for each tree. Metrics are then derived from the point cloud to classify a tree according to its structural condition, a process which is then applied to the tree population across an entire landscape. This provides information with which to spatially optimise tree survey and management resources, improve the decision making process and move towards proactive tree management. The research presented in this thesis develops RS tools for assessing tree structural condition, at a range of investigative scales. These objective, data-rich tools will enable resource-limited tree managers to direct remedial interventions in an optimised and precise way