Extraction of multilayer vegetation coverage using airborne LiDAR discrete points with intensity information in urban areas: A case study in Nanjing City, China

Han, Wenquan; Zhao, Shuhe; Feng, Xiao; Chen, Lei

doi:10.1016/j.jag.2014.01.016

Cited by 23 publications

(9 citation statements)

References 24 publications

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“…The majority of the existing literature of lawn cover in cities is based on either aerial photos (orthophotos; Akbari et al 2003;Attwell 2000) or LiDAR data (a surveying method that measures distance to a target by illuminating that target with a laser light, the acronym stands for LIght Detection And Ranging; Han et al 2014). However, many studies seem to combine different techniques such as aerial photos with other remote sensing data (Robbins 2003;Milesi et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Milesi et al (2005) argue that turf grass rarely can be identified using satellite data due to low resolutions. However, since 2005 remote sensing techniques, including high intensity of LiDAR data where multilayers of urban vegetation can be detected, has developed a lot (Han et al 2014). However, Han et al (2014) argue that LiDAR data need to be validated in field and that laser data varies in intensity and thus also varies in potential to be used for mapping of urban greenery.…”

Section: Introductionmentioning

confidence: 99%

“…However, since 2005 remote sensing techniques, including high intensity of LiDAR data where multilayers of urban vegetation can be detected, has developed a lot (Han et al 2014). However, Han et al (2014) argue that LiDAR data need to be validated in field and that laser data varies in intensity and thus also varies in potential to be used for mapping of urban greenery. In a review of satellite remote sensing in urban settings, Patino and Duque (2013) conclude that many scientists working on regional levels remain skeptical that satellite remote sensing will provide useful information on local scales.…”

Section: Introductionmentioning

confidence: 99%

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Estimating urban lawn cover in space and time: Case studies in three Swedish cities

et al. 2017

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Lawns are considered monocultures and lesser contributors to sustainability than diverse nature but are still a dominating green area feature and an important cultural phenomenon in cities. Lawns have esthetical values, provide playground, are potential habitat for species, contribute to carbon sequestration and water infiltration, but also increase pesticides, fertilization, are monocultures and costly to manage at the same time. To evaluate the potential impact of lawns, whether positive or negative, it is of interest to estimate the total lawn cover in cities and its change over time. This is not a straightforward process, e.g., because many lawns are small and covered by trees. In this study we review the existing literature of lawn cover in cities and the different methodologies used for cover estimation. We found both pros and cons with NDVI and LiDAR data as well as manually interpreted aerial photos. The total cover of lawns in three case study cities was estimated to 22.5%. By extrapolating these percentages to all Swedish cities lawn cover was estimated to 2589 km 2 (0.6% of the terrestrial surface). The approximated total municipal management cost of lawns in all Swedish cities was 910,000,000 USD/ year. During 50 years lawn area almost doubled in relative cover and 56% of them were continuously managed. Since lawns constitute large parts of the urban greenery and are costly to manage it is highly relevant to consider their social, ecological and cultural value compared to alternatives, e.g., meadows with less intensive management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating urban lawn cover in space and time: Case studies in three Swedish cities

et al. 2017

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“…During the last decades, many filtering algorithms have been explored and developed for classifying top-view LiDAR point cloud in order to extract some key components of urban features, e.g. land covers (Yan et al, 2015), trees (Alonzo et al, 2014;Han et al, 2014;Chen et al, 2015), buildings (Kabolizade et al, 2010;Awrangjeb et al, 2013;Mongus et al, 2014;Song et al, 2015;Ferraz et al, 2016), roads (Li et al, 2015;Ferraz et al, 2016), or even vehicles (Yao et al, 2010). When a set of criteria has been characterised, essential information embedded in point cloud can be extracted and classified into particular segments.…”

Section: Top-view Lidar Point Cloud Extractionmentioning

confidence: 99%

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Meesuk¹

2017

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Although all care is taken to ensure the integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons Attribution-Non Commercial 4.0 International License, http://creativecommons.org/licenses/by-nc/4.0/Published by: CRC Press/Balkema PO Box 11320, 2301 EH Leiden, the Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com -www.taylorandfrancis.com to my dear family SummaryModelling urban flood dynamics requires detailed knowledge of a number of complex processes. Numerical simulation of flood propagation requires proper understanding of all processes involved. Also, special care has to be taken to adequately represent the urban topography, taking into account typical urban features like underpasses and hidden alleyways. Incorrect input data will affect the numerical model results, which may lead to inadequate flood-protection measures or even catastrophic flooding situations. This thesis explores how to include particular urban topographic features in urban flood modelling.Aerial Light Detection and Ranging (LiDAR) systems offer opportunities for achieving good quality topographic data, with less fieldwork on the ground. Even though aerial LiDAR data have long been used in many applications, conventional top-view LiDAR data can not quite capture some hidden urban features. However, recent improvements in Structure from Motion (SfM) techniques provide opportunities to achieve improved quality topographic data by using multiple viewpoints, including side-view data.This dissertation explores insights into the capabilities of assimilating SfM point cloud data by using multi-source views as input for enhancing 2D urban flood modelling. Side-view SfM point cloud data are collected and merged with conventional top-view LiDAR point cloud data to create novel Multi-Source Views (MSV) topographic data. The main objectives of this research are (i) to provide insight into the capabilities of using computer-based environments; (ii) to explore the benefits of using the new MSV data; (iii) to enhance 2D model schematizations; (iv) to compare simulated results using new MSV data and conventional top-view LiDAR data as input for urban flood models; and (v) to help developing flood-protection measures. Findings showed that simulation results using conventional top-view LiDAR-DSM as input show the least flood inundation areas and results contained many dry areas. This is because conventional LiDAR-DSM does not capture hidden urban features like underpasses, high trees, overarching structures, which behave as obstacles in 2D model schematics. When applying extended top-view LiDAR-DBM+ and the newly developed MSV-DEM as input, simulation results showed m...

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“…In recent years, the need for vegetation observation has increasingly gained interest because of the development of precision agriculture and forest conservation [2] [3]. Most airborne systems employ light detection and ranging (LiDAR) to perform vegetation observation [4]. LiDAR measures distance by illuminating a target with a laser and analyzing the reflected light.…”

Section: Introductionmentioning

confidence: 99%

Real-time middle wave infrared aerial image capturing and stitching system for vegetation observation

Lin

Hwang

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Over the last decades, several studies have demonstrated the reflectance effectiveness of middle wave infrared (MWIR) for discriminating among different types of vegetation, and estimating the total and leaf biomass of several forest ecosystems. Therefore, a MWIR aerial image capturing system for vegetation observation is urgently required. Furthermore, stitching those MWIR aerial images together is necessary for a panorama of the region of interest (ROI). Most traditional stitching algorithms for aerial images are designed for visible images, and are not suitable for infrared images that are noisy, blurry, or lack detail. In this paper, a novel real-time MWIR aerial image capturing and stitching system for vegetation observation is proposed. The proposed stitching algorithm for aerial infrared images improved from scale invariant feature transform (SIFT) and random M-least square can find a sufficient number of feature points easily and perform rapid calculations. Therefore, the proposed MWIR aerial image capturing and stitching system for vegetation observation can operate in real time.

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Extraction of multilayer vegetation coverage using airborne LiDAR discrete points with intensity information in urban areas: A case study in Nanjing City, China

Cited by 23 publications

References 24 publications

Estimating urban lawn cover in space and time: Case studies in three Swedish cities

Estimating urban lawn cover in space and time: Case studies in three Swedish cities

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Real-time middle wave infrared aerial image capturing and stitching system for vegetation observation

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