High Spatial Resolution Remote Sensing: Data, Analysis, and Applications

407 Pages·2018·34.874 MB·English

by  Yuhong He(Editor), Qihao Weng(Editor)

High Spatial Resolution
Remote Sensing
Data, Analysis, and Applications
Taylor & Francis Series in Imaging Science
Series Editor
Qihao Weng
Indiana State University
Published Titles
Remote Sensing Time Series Image Processing
Qihao Weng
High Spatial Resolution Remote Sensing: Data, Analysis, and Applications
Yuhong He and Qihao Weng
For more information about this series, please visit: www.crcpress.com
High Spatial Resolution
Remote Sensing
Data, Analysis, and Applications
Edited by
Yuhong He
Qihao Weng
CRC Press
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Library of Congress Cataloging-in-Publication Data
Names: He, Yuhong, 1978- editor. | Weng, Qihao, editor.
Title: High spatial resolution remote sensing : data, analysis, and
applications / edited by Yuhong He and Qihao Weng.
Description: Boca Raton, FL : Taylor & Francis, 2018. | Includes
bibliographical references.
Identifiers: LCCN 2018006261 | ISBN 9781498767682 (hardback : alk. paper)
Subjects: LCSH: Remote sensing. | Optical radar. | Environmental
sciences--Remote sensing.
Classification: LCC G70.4 .H53 2018 | DDC 621.36/78--dc23
LC record available at https://lccn.loc.gov/2018006261
Visit the Taylor & Francis Web site at
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Contents
Preface.......................................................................................................................ix
Editors .....................................................................................................................xix
Contributors ............................................................................................................xxi
Section i Data Acquisition and Preprocessing
Chapter 1 High-Resolution UAS Imagery in Agricultural Research:
Concepts, Issues, and Research Directions ..........................................3
Michael P. Bishop, Muthukumar V. Bagavathiannan,
Dale A. Cope, Da Huo, Seth C. Murray, Jeffrey A. Olsenholler,
William L. Rooney, J. Alex Thomasson, John Valasek,
Brennan W. Young, Anthony M. Filippi, Dirk B. Hays,
Lonesome Malambo, Sorin C. Popescu, Nithya Rajan,
Vijay P. Singh, Bill McCutchen, Bob Avant, and Misty Vidrine
Chapter 2 Building a UAV-Hyperspectral System I: UAV and Sensor
Considerations ....................................................................................33
Cameron Proctor
Chapter 3 Building a UAV-Hyperspectral System II: Hyperspectral
Sensor Considerations and Data Preprocessing .................................49
Cameron Proctor
Chapter 4 LiDAR and Spectral Data Integration for Coastal Wetland
Assessment .........................................................................................71
Kunwar K. Singh, Lindsey Smart, and Gang Chen
Chapter 5 Multiview Image Matching for 3D Earth Surface Reconstruction ......89
Chuiqing Zeng and Jinfei Wang
Chapter 6 High-Resolution Radar Data Processing and Applications .............119
Joseph R. Buckley
v
vi Contents
Section ii Algorithms and techniques
Chapter 7 Structure from Motion Techniques for Estimating the Volume
of Wood Chips ..................................................................................149
Travis L. Howell, Kunwar K. Singh, and Lindsey Smart
Chapter 8 A Workflow to Quantify the Carbon Storage in Urban Trees
Using Multispectral ALS Data .........................................................165
Xinqu Chen and Jonathan Li
Chapter 9 Suitable Spectral Mixing Space Selection for Linear Spectral
Unmixing of Fine-Scale Urban Imagery .........................................187
Jian Yang
Chapter 10 Segmentation Scale Selection in Geographic Object-Based
Image Analysis .................................................................................201
Xiuyuan Zhang, Shihong Du, and Dongping Ming
Chapter 11 Computer Vision Methodologies for Automated Processing of
Camera Trap Data: A Technological Review ...................................229
Joshua Seltzer, Michael Guerzhoy, and Monika Havelka
Section iii case Studies and Applications
Chapter 12 UAV-Based Multispectral Images for Investigating Grassland
Biophysical and Biochemical Properties ..........................................245
Bing Lu and Yuhong He
Chapter 13 Inversion of a Radiative Transfer Model Using Hyperspectral
Data for Deriving Grassland Leaf Chlorophyll ................................261
Alexander Tong, Bing Lu, and Yuhong He
Chapter 14 Wetland Detection Using High Spatial Resolution Optical
Remote Sensing Imagery .................................................................283
Amy B. Mui
Contents vii
Chapter 15 Geomorphic and Biophysical Characterization of Wetland
Ecosystems with Airborne LiDAR: Concepts, Methods, and a
Case Study ........................................................................................307
Murray Richardson and Koreen Millard
Chapter 16 Fraction Vegetation Cover Extraction Using High Spatial
Resolution Imagery in Karst Areas ..................................................347
Xiangkun Qi, Chunhua Zhang, Yuhong He, and Kelin Wang
Chapter 17 Using High Spatial Resolution Imagery to Estimate Cherry
Orchard Acreage in Michigan ..........................................................361
Kin M. Ma
Index ......................................................................................................................375

Preface
1 INTRODUCTION
High spatial resolution data provide a novel data source for addressing environmental
questions with an unprecedented level of detail. These remote sensing data are a result
of significant advances in image acquisition platforms and sensors, including satellite,
manned aircraft, and unmanned aerial vehicle (UAV) platforms. Furthermore, the
recent development of commercially operated satellite platforms with high spatial
resolution sensors allows for the collection of a large amount of images at regular
time intervals, with relatively large footprints (i.e., image swathes). For example, the
WorldView series, with the WorldView-1 satellite launched on September 18, 2007,
and WorldView-4 launched on November 11, 2016, are capable of resolving objects
at 31 cm in the panchromatic band and at 1.24 m in 4 (or 8)-band multispectral over a
13.1-km-wide swath. For a specific study site, these image data can be easily searched
and acquired at a reasonable cost through service companies, such as DigitalGlobe.
In addition, the recent proliferation of UAVs has made it possible to collect images at
spatial and temporal scales that would be impossible using traditional platforms. Many
recent works have focused on collecting imagery using UAV-equipped multispectral,
hyperspectral, and thermal sensors as well as laser scanners at centimeter resolutions.
High (meters) and ultra-high (centimeters) spatial resolution images open a
door for fine-scale analysis of objects at the Earth’s surface. A number of scientific
journal articles have highlighted the usefulness of high spatial resolution remote
sensing, including the use of remote sensing in studying the physical environmental
system, the human system, and the interactions between them. Examples in physical
environmental studies include fine-scale forest inventory (Mora et al., 2013), wetland
plant community identification (Zweig et al., 2015), grassland mapping (Lu and He,
2017), and water resources (Debell et al., 2016). In terms of the human system, high
spatial resolution remote sensing has been used to study urban impervious surfaces
(Yang and He, 2017), public health (Hartfield et al., 2011), and epidemics (Lacaux
et al., 2007). As for human-environment interactions, high-resolution remote sensing
has been used for land degradation (Wiesmair et al., 2016), precision farming (Zarco-
Tejada et al., 2013), water and air pollution (Yao et al., 2015), and natural hazards
(e.g., earthquakes, typhoons, floods, landslides) (Joyce et al., 2009).
This increased spatial resolution exasperates the intraclass variability found in an
image. For example, in a grassland scene, vegetation leaves, gaps, shadows, and stems
are all visible in the pixels of a high spatial resolution image. While this information
is potentially useful for mapping purposes, the added details in a high-resolution
image pose challenges for image segmentation and feature selection. Furthermore, the
number of detectable entities or classes increases with spatial resolution. Traditional
information extraction techniques may not operate well at high spatial resolutions due
to large data volume and heterogeneous spectral information (Wulder et al., 2004),
spurring the need for the development of innovative image processing techniques. To
ix