Mapping Individual Tree Species in an Urban Forest Using Airborne Lidar Data and Hyperspectral Imagery

Zhang, Caiyun; Qiu, Fang

doi:10.14358/pers.78.10.1079

Cited by 112 publications

(76 citation statements)

References 24 publications

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“…All individual tree level attributes including tree location, height, base height, crown depth, and crown diameter are stored in a tree inventory database. Species for each LiDAR-detected tree can be identified using the hyperspectral data, which has been reported in Zhang and Qiu (2012) [35]. There are three major steps in the framework: LiDAR data filtering, treetop detection, and individual tree extraction.…”

Section: A Framework To Combine Lidar and Hyperspectral Data For Urbamentioning

confidence: 99%

Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

2015

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Abstract:The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.

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Section: A Framework To Combine Lidar and Hyperspectral Data For Urbamentioning

confidence: 99%

Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

2015

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“…(Alonzo et al 2014, Brook et al, 2010, Dalponte et al 2008, Koetz et al 2008, Latifi et al 2012. In some research works, LiDAR data is used for separation of 2D and 3D objects and then hyperspectral images are applied to discriminate among different species of an object, such as roofing material (Niemann, et al, 2009;Zhang and Qiu, 2012). Sugumaran and Voss (2007), apply the object based classification where LiDAR data is used for segmentation and hyperspectral image to classify the segments.…”

Section: Related Workmentioning

confidence: 99%

Determination of Optimum Classification System for Hyperspectral Imagery and Lidar Data Based on Bees Algorithm

Samadzadega¹,

Hasani²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

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“…On the other hand, aerial photography does not directly provide 3D information of trees structure (Chen, et al, 2006) and is easily influenced by weather condition and topographical covers (Chen, et al, 2005). Therefore, somewhat insufficient information is obtainable about trees in many cities around the world, which is a major limitation for actualizing their benefits (Zhang, and Qiu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Extraction of Urban Trees From Integrated Airborne Based Digital Image and Lidar Point Cloud Datasets - Initial Results

Dogon-yaro

Kumar

Rahman

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Timely and accurate acquisition of information on the condition and structural changes of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting tree features include; ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work, a lot of financial requirement, influences by weather condition and topographical covers which can be overcome by means of integrated airborne based LiDAR and very high resolution digital image datasets. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LIDAR and multispectral digital image datasets over Istanbul city of Turkey. The above scheme includes detection and extraction of shadow free vegetation features based on spectral properties of digital images using shadow index and NDVI techniques and automated extraction of 3D information about vegetation features from the integrated processing of shadow free vegetation image and LiDAR point cloud datasets. The ability of the developed algorithms shows a promising result as an automated and cost effective approach to estimating and delineated 3D information of urban trees. The research also proved that integrated datasets is a suitable technology and a viable source of information for city managers to be used in urban trees management.

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Mapping Individual Tree Species in an Urban Forest Using Airborne Lidar Data and Hyperspectral Imagery

Cited by 112 publications

References 24 publications

Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

Determination of Optimum Classification System for Hyperspectral Imagery and Lidar Data Based on Bees Algorithm

Extraction of Urban Trees From Integrated Airborne Based Digital Image and Lidar Point Cloud Datasets - Initial Results

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