Sr Dr WONG Man Sing, Charles

Associate Professor

Department of Land Surveying and Geo-Informatics,

The Hong Kong Polytechnic University

From Structural Defect to Tree Health: The Use of Remote Sensing Technologies to See Through the Urban Forestry

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Remote Sensing

Hyperspectral

Imaging

Airborne LiDAR

Thermal Imaging

Urban Planning Smart

Sensing

Presentation

Outline

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Remote Sensing

Vegetation and Habitat Monitoring Using Remote Sensing

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Multi-scale Object-Oriented Segmentation and Classification method (MOOSC) (developed by our research team in 2006)

Level 1Level 2Level 3Level 4

Overview of study area Zoom in

Level 5

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Vegetation Map from MOOSC

Nichol J.E., Wong M.S. (2008). Photogrammetric Engineering and Remote Sensing, 74(11), 1325-1334.

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Vegetation / Ecological Study

Image courtesy: the Planning Department of HKSAR

Vegetation areas over non-built up areas

Vegetation areas over the entire territories

Spectral Unmixing Model for Urban Bird Habitat Monitoring

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Nichol J. E. and Wong M. S. (2007), International Journal ofRemote Sensing, 28(5), 985-1000.

Nichol J. E., Wong M. S., Corlett R. A., Nichol D. W. (2010),Landscape and Urban Planning, 95(1-2), 54-60.

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Unmixing Model for Urban Bird Habitat

Simulated Bird flying paths

• Route A: birds have preference for denser tree cover over

• Route B: birds which only tolerate dense tree cover

• Route C: birds found where any tree cover is present e.g. 1 -100% tree fraction

• SPOT MLC: simulated with MLC classified image from SPOT (20m)

• IKONOS MLC: simulated with MLC classified images from IKONOS

Route C

Remote Sensing of Phenology Observation & Impact Assessment of a super-typhoon on Hong Kong’s secondary vegetation

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Remote Sensing of Phenology Observation

Climate-Vegetation Interaction in the Greater Bay Area

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NDVI dataset spans from 1981 to 2015

Impact Assessment of a super-typhoon on Hong Kong’s secondary vegetation

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Abbas S., Nichol J. E., Fischer G. A., Wong M. S.*, Irteza S. M. (in press). Agricultural and Forest Meteorology.

a) b) c) d)

Left: 23rd October 2017 (control image); Right: 3rd October 2018 (post-typhoon image)

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Tree Health Detection Using Remote Sensing Technique

* Supported by the Highways Department of HKSAR

Multispectral Orthophotos

Jan 2017

Pan-sharpened

Jul 2017

Pan-sharpened

Sep 2017

Native

Mar 2018

Native

Mar 2018

Pan-sharpened

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• Spectral resolution:

• Red

• Green

• Blue

• Near-infra Red

• Camera Specifications:

• UltraCam Eagle Mark 1

• Panchromatic: 20,010 x 13,080 pixels

• Multispectral: 6,670 x 4,360 pixels

• Focal Length: 210.750 mm

Image courtesy: the Lands Department of HKSAR

Machine Learning Approach

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Vegetation Indices Approach

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Tree samples

Normalization and outlier removal

24 vegetation indices

calculation

Threshold Determination

Using Distribution Plots

• Vegetation indices are normally higher in summer (Jul 2017, Sep 2017)

• Vegetation indices in winter are consistent from the pan-sharpened image

of Jan 2017 and March 2018 and the native image of March 2018

Index Family 1 Family 2 Family 4 Family 6 Family 7

BDRVI <-0.6 <-0.7 <-0.6

BR <1.1

CI

CIG <1 <1.5 <1.2 <1.5

CVI <1.3 <1.7 <1.8

EVI >0.6

gbNDVI <-0.1 <0.1 <0.1 <0.1

GLIb <0 <0 <0.025

GLIg <0.025 <0.025

GLIr >-0.1 >-0.1

grNDVI <0.2 <0.2

GRVI

N_G >0.2 >0.24 >0.25 >0.24

N_NIR <0.5 <0.6 <0.55

N_R >0.2 >0.16 >0.16

NDGR <0.1 <0.1

NDRGRI >-0.1 >-0.1

NDVI <0.4 <0.4 <0.5 <0.4 <0.5

NDVIg <0.3 <0.45 <0.35 <0.45

NIRG <2.5 <2.7 <2.5

rbNDVI <0.2 <0.2

RGI >0.8 >0.75

RVI <3 <3.5 <4.5

VARIg

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Yellow = Unhealthy, Green = Healthy

Verification:

Jan 2017 Jul 2017 Sep 2017 Mar 2018(P)

Comments from the Highways Department:

Form 2 record:

20170517 – Healthy tree regarding size, color and density of foliage

20180507 – similar to 05/2017 but a small detected dead branch above the sidewalk notes. Result consistent with change status

Consistent with change status

18Yellow = Unhealthy, Green = Healthy

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Airborne LiDAR

Basic Tree Data Extraction through LiDAR Technique

* Supported by the Highways Department of HKSAR

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Introduction

• Advantages of LiDAR

• Collect spatially-explicit data

• Finer temporal resolution

• Collect:

• Crown size

• Tree height

• Diameter at Breast Height (DBH)

• Geographic coordinates

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LiDAR for Tree Survey

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Airborne LiDAR for Tree Survey

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Airborne LiDAR for Tree Survey

Number of returns: 5

Nominal point density:

40 points / m2

Average point spacing: 0.2 m

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Tree Detection

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Tree Detection

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Hyperspectral

Imaging

Hyperspectral Imaging for Vegetation Monitoring

* Supported by the Highways Department of HKSAR

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Hyperspectral Vegetative Monitoring

Calibration by Halogen lamp ~ solar light at control environment

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Data Acquisition Procedures and Field Measurements

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Typical

Spectral

Plots

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Mean Canopy Spectral Plots

• Each species shows different plots

• Each species follows different

seasonality based on average

values over canopy

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Multivariate Regression Tree - Recursive Partitioning

• A recursive partitioning of multivariate

regression tree approach

• A two-stage procedure to build binary

trees

• Stepwise process of

finding the single variable

which best divides the pool

of species

• k-fold cross-validation of

the decision tree

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AI: Deep Learning Classification

Species 2

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Spectral Cube

One-hot Coding

Input Output

TrainSelect Points

Spectral Images

max

Output Species for each selected points

MajoritySpecies

Inference

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Smart

Sensing

Jockey Club Smart CityTree Management Project

* Funded by The Hong Kong Jockey Club Charities Trust

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IoT wireless sensor

Marked tree

After Typhoon Mangkhut

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1

3

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Rotational angle

Tree displacement

Tilting angle

SST Devices

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SST Network: System Architecture

GIS & Application Server

4G/Optical Fiber

LPWAN SIGNAL Gateway(Private / Public Network)

LPWAN SIGNALCLOUD

Mobile & Tablet

Tree Monitoring & Management Dashboard

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1. High Vehicular Traffic2. High Pedestrian Traffic

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GIS-based Tree Monitoring System

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GIS-based Tree Monitoring System

45Thermal Imaging

Detection of Structural Tree Defect Using Thermal Infrared Imaging

* Supported by the Tree Management Office, Greening, Landscape & Tree Management Section, Development Bureau of HKSAR

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Thermal Infrared Image Analysis onboard of Helicopter

Collaborated with the Government Flying Service, HKSAR

•Urban Heat Island √

•Tree ?

Thermal Infrared Assessment

• Thermal infra-red image to

identify the defected areas over

tree trunk and roots

• Structural defect and

corresponding surface

temperature of trees

• Defected area has lower

temperature

(a) True colour image

(b) Raw thermal image of a broadleaf tree48

Data Capture (Artocarpus Hypargyreus with known defect)

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Thermal Images of Trees with Defects

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Crateva unilocularis Delonix regia Delonix regiaArtocarpus

hypargyreus

Cinnamomum

camphora

Automatic Structural Defect Detection

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K-means clusteringSobel

gradient filter ResultTrue colour image Thermal image

Crateva unilocularis

Automatic Structural Defect Detection

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K-means clustering

Sobel gradient filter Result

True colourimage Thermal image

Delonix regia

Artocarpus hypargyreus

Cinnamomum camphora

Time Series Analysis of Crateva unilocularis

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Time Series Analysis of Crateva unilocularis

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Unhealthy part heats up faster in this period

(rate of change of

temperature)

Unhealthy part cools down faster in this

period (rate of change of

temperature)

Results of Tomogram Validation

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Results of Tomogram Validation

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Urban Planning

Analysis of Urban Green Management

through Remote Sensing and

Geographic Information Systems

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* Supported by the Planning Department of HKSAR

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Yuen W. M. J., …, Wong M. S., et al. (2019), Influence of Urban Green Space and Facility Accessibility on Exercise and Healthy Diet in Hong Kong, International Journal of Environmental Research and Public Health, 16, 1514.

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On-going Health Related Studies

Sr Dr Wong Man Sing, Charles 黃文聲博士

Email: Lswong@polyu.edu.hk

Tel: 3400-8959

Remote Sensing Laboratory: http://www.lsgi.polyu.edu.hk/rsl/

Research websites: