Quantifying the urban forest environment using dense discrete return LiDAR and aerial color

198 Pages·2016·9.2 MB·English

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Madhurima Bandyopadhyay
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Bandyopadhyay, Madhurima, "Quantifying the urban forest environment using dense discrete return LiDAR
and aerial color imagery for segmentation and object-level biomass assessment" (2015). Thesis.
Rochester Institute of Technology. Accessed from
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Quantifying the urban forest environment using dense discrete return
LiDAR and aerial color imagery for segmentation and object-level
biomass assessment
by

Madhurima Bandyopadhyay

B.SC. Electronic Science, University of Calcutta, 2004
B.Tech. Optics and Optoelectronics, University of Calcutta, 2007

A dissertation submitted in partial fulfillment of the
requirements for the degree of Doctor of Philosophy
in the Chester F. Carlson Center for Imaging Science
College of Science
Rochester Institute of Technology

April 27, 2015

Signature of the Author: ____________________________________________

Accepted by: _____________________________________________________
Coordinator, Ph. D. Degree Program Date
i
CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE
COLLEGE OF SCIENCE
ROCHESTER INSTITUTE OF TECHNOLOGY
ROCHESTER, NEW YORK

CERTIFICATE OF APPROVAL

Ph.D. DEGREE DISSERTATION

The Ph.D. Degree Dissertation of Madhurima Bandyopadhyay
has been examined and approved by the
dissertation committee as satisfactory for the
dissertation required for the
Ph.D. degree in Imaging Science

Dr. Jan van Aardt, Dissertation Advisor

Dr. David Messinger

Dr. Emmett Ientilucci

Dr. Karl Korfmacher, Outside Chair

Date

ii
This dissertation work is dedicated to my parents Arabinda Banerjee and
Kalpana Banerjee for their endless love, support, and encouragement.

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Acknowledgements

The arduous journey of earning the doctorate degree would not be possible without the guidance
of my committee members, support from my family and husband, and selfless help of my
friends, professors, and staff members at the Imaging Science department at RIT. I would like
to express my deepest gratitude to Dr. Jan van Aardt, my mentor and dissertation adviser, for
his constant encouragement, support, and guidance. His insight helped me to improve my
understanding about the subject and to think clearly and logically. He always patiently corrected
my writing and helped to improve my dissertation. I am really fortunate to have him as my
adviser.
I would like to thank my dissertation committee, Dr. David Messinger, Dr. Emmett
Ientilucci, and Dr. Karl Korfmacher. Their insight, feedback, and advice were very helpful for
my research. My special thanks to Dr. Harvey Rhody who helped and guided me whenever
required. I want to thank Dr. Peter Bajorki who helped me to solve statistics-related problems.
My thanks to all of the professors who gave their time and provided me with knowledge which
were essential to my research.
I am grateful to all my friends at RIT, Dr. Sanjit Maitra, Shagan Sah, Bikash Basnet,
Ritu Basnet, and all others for their selfless help.
Thanks to everyone in the DIRS group for their helpful feedback and suggestions during
my presentations in DIRS meetings. Also I want to thank my office mates and our LiDAR
group, Paul Romanczyk, Wei Yao, David Kelbe, Colin Axel, Dr. Kerry Cawse-Nicholson, and
Dr. Martin van Leeuwen for their valuable suggestions, feedback, and help.
I want to thank all the staff members, especially Cindy Schultz and Susan Chan, for all
of their administrative support and help.
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My sincere thanks goes to the Chester F. Carson Center for Imaging Science at RIT and
the National Science Foundation for providing financial support throughout my study.
I want to thank all my close friends for their support and help to survive the stress during
the course of my study.
I express my gratitude to my father, Arabinda Banerjee, and my mother, Kalpana
Banerjee for their encouragement, unconditional support, and countless sacrifices they have
made for me. They are one of the keys to my success.
I would like to thank my husband, Dr. Saugata Sinha, for his constant support, help, and
understanding. He always stood by me through good and bad times. I count myself lucky to
have him in my life.
Last, but not least, there are so many people whose names might not appear here, but
my gratitude towards them will always remain in my heart.

Thank you all.
Madhurima Bandyopadhyay
Rochester Institute of Technology
Rochester, NY
April 2015

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Abstract

The urban forest is becoming increasingly important in the contexts of urban green space and
recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition
to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources,
and mitigate adverse climate changes, among other benefits. A great deal of attention recently
has been paid to urban forest management. However, the comprehensive monitoring of urban
vegetation for carbon sequestration and storage is an under-explored research area. Such an
assessment of carbon stores often requires information at the individual tree level, necessitating
the proper masking of vegetation from the built environment, as well as delineation of
individual tree crowns. As an alternative to expensive and time-consuming manual surveys,
remote sensing can be used effectively in characterizing the urban vegetation and man-made
objects.
Many studies in this field have made use of aerial and multispectral/hyperspectral
imagery over cities. The emergence of light detection and ranging (LiDAR) technology,
however, has provided new impetus to the effort of extracting objects and characterizing their
3D attributes - LiDAR has been used successfully to model buildings and urban trees. However,
challenges remain when using such structural information only, and researchers have
investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to
extract objects, thereby allowing the complementary characteristics of the two modalities to be
utilized.
In this study, a fusion-based classification method was implemented between high
spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify
urban vegetation and buildings from other urban classes/cover types. Structural, as well as
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spectral features, were used in the classification method. These features included height,
flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-
calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064
nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused
difference vegetation index (LDVI). Classification results indicated good separation between
buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85.
A multi-tiered delineation algorithm subsequently was developed to extract individual
tree crowns from the identified tree clusters, followed by the application of species-independent
biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-
based biomass assessments were conducted for individual trees, as well as for clusters of trees,
in cases where proper delineation of individual trees was impossible. The detection accuracy of
the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated
against allometry-based biomass estimates that were computed from field-measured tree data.
It was found out that LiDAR-derived tree volume, area, and different distribution parameters
of height (e.g., maximum height, mean of height) are important to model biomass. The best
biomass model for the tree clusters and the individual trees showed an adjusted R2 value of 0.93
and 0.58, respectively.
The results of this study showed that the developed fusion-based classification approach
using LiDAR and aerial color (RGB) imagery is capable of producing good object detection
accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a
substitute for the normalized difference vegetation index (NDVI), when near-infrared
multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the
urban forest and associated biomass was proven. This work could have significant impact on
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the rapid and accurate assessment of urban green spaces and associated carbon monitoring and
management.

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Table of Content Page
Abstract .............................................................................................................................. vi
Table of Contents ............................................................................................................... ix
List of Figures .................................................................................................................... xi
List of Tables................................................................................................................... xvii
List of Acronyms ............................................................................................................. xix

Chapter 1 Introduction ........................................................................................................ 1
1.1. Objectives .............................................................................................................. 5
1.2. Light Detection and Ranging (LiDAR) Remote Sensing ...................................... 7
1.3. Digital Surface Model (DSM), Digital Terrain Model (DSM) and
Normalized Digital Surface Model (nDSM) ........................................................ 10
1.4. Literature review .................................................................................................. 12
1.4.1. Ground point selection and DEM generation ............................................. 12
1.4.2. Building extraction ...................................................................................... 15
1.4.3. Tree extraction ............................................................................................ 18
1.4.4. Biomass estimation ..................................................................................... 24
1.4.5. Registration of aerial imagery with LiDAR point clouds ........................... 26

Chapter 2 Methods - data collection by LiDAR and the Wildfire Airborne Sensing Program
(WASP) sensor ............................................................................................................ 33
2.1. Study area............................................................................................................. 33
2.2. Dataset.................................................................................................................. 34
2.2.1. LiDAR data ................................................................................................. 35
2.2.2. Wildlife airborne sensor program (WASP) imagery .................................. 35
2.2.3. National agriculture imaging program (NAIP) imagery ............................. 37

Chapter 3 Classification of urban vegetation and buildings using a fusion-based
approach ...................................................................................................................... 38
3.1. Methods................................................................................................................ 40
3.1.1. Study sites ................................................................................................... 40
3.1.2. LiDAR point cloud preprocessing: Statistical noise removal ..................... 42
3.1.3. Registration of LiDAR and WASP imagery ............................................... 44
3.1.4. DEM and nDSM generation from LiDAR point clouds and establishing a height
threshold Normalized height image ............................................................... 49
3.1.5. Normalized height image ............................................................................ 53
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