CLASSIFYING COMMON WETLAND PLANTS USING HYPERSPECTRAL DATA TO

IDENTIFY OPTIMAL SPECTRAL BANDS FOR SPECIES MAPPING USING A SMALL UNMANNED AERIAL SYSTEMS– A CASE

STUDY

Sathishkumar Samiappan, Gray Turnage, Lee Hathcock, Haibo Yao, Russel Kincaid, Robert Moorhead, and Steve Ashby

Outline

• Wetland Ecosystem

• Motivation

• Goals

• Hyperspectral Data Collection

• Multispectral UAS data collection

• Classification

• Results

• Summary

Wetland Ecosystem

Wetlands perform a dazzling array of ecological function.

Water purification, flood protection, shoreline stabilization, ground water recharge, fish & wildlife habitat and many more.

Wetland ecosystems are threatened by humans and invasive species.

Wetlands are not indestructible. If we want wetlands to continue to perform their ecological functions, then we have to do our part to protect them.

Wetland Threats

Triadica sebifera - commonly known as the Chinese tallow

Non-native tree from China

Shade, Frosting, Flooding and Salt Water? – No Problem

Can reproduce from 3 to 60 years

Range in the USA

Wetland Threats

Phragmites australis (Invasive)

Types: Haplotype I (Gulf coast Region)

Haplotype M (Most of the US)

Average height 15ft & Spreads rapidly

US range – 48 States

Major threat to coastal ecosystem – EPA

Navigation hazard

Decreases native biodiversity

Motivation

Quickly and accurately classifying wetland plant species is critical and challenging.

Wetland plants has similar spectral and ecological characteristics.

Current identification methods involve extensive field work.

With the increased use of unmanned aerial systems and mounted camera payloads, the surveying process is becoming easier/quicker to conduct.

Collect HSI of common wetland plants (both invasive and natives) to create library of reflectance signatures.

Identify the wavelengths or regions of the spectrum that can be useful for distinguishing these plant species.

Identify a commercially available multispectral sensor that closely matches the required specifications.

Collect multispectral imagery over a field site using a small UAS to verify the effectiveness of identified bands to make critical resource management decisions.

Goals

Hyperspectral Data Acquisition

14-bit PCO1600 HS Camera

Phragmites Australis – ((Cav.) Trin. ex Steud)

Chinese Tallow – (Triadica sebifera)

o Push broom Scanningo 200nm to 1000nm

Hyperspectral Reflectance Signatures

Selected hyperspectral signatures for native and non-native plant species commonly seen in wetland environments in the Southeastern U.S.

UAS Multispectral Data Acquisition

Study Area – Lower Pearl River Delta – Mississippi/Louisiana

UAS Multispectral Data AcquisitionPrecision Hawk fixed wing UAS

o 2.4 kg (5.2 lbs.)o 150 cm (4 ft. 11 in) wingspan. o Endurance 45 minutes at cruising

speed of 50 kph (31 mph). o Snapshots of 1280 × 960 pixels

with 12 bits per pixel per band. o Onboard GPS

MicaSense RedEdge sensor

1. Blue - C 475 nm & W 20 nm 2. Green - C 560 nm & W 20 nm3. Red – C 668 nm & W 10 nm4. Red Edge - C 717 nm & W 10 nm5. NIR - C 840 nm & W 40 nm

UAS Multispectral Data Acquisition

Camera trigger point locations (shown as Green dots) where the images were captured with MSRE camera to construct the orthomosaic

70% Frontal and Side overlaps

Altitude of 120 m (400ft)

GSD - 8 cm (3.1 inch) per pixel

Orthomosaic produced from the UAS collected data

Field Data Collection – Ground Reference

GR collected using Trimble Geo 7-X GPS device

UAS Multispectral Data Acquisition

RGB Bands

DSM

5 Band Multispectral(False color)

DSM

Use of Photogrammetry Derived DSM

Low resolution 3D rendering of RGB bands with DSM overlay (a) View from North East (b) View from East of Pearl River

80

85

90

95

100

105

Tallow

Green

Tallow Red Tallow

Yellow

RedMaple

Green

SweetGum

Green

SweetGum

Yellow

RedMaple

Red

Sycamore Hackberry WaterOak

Cla

ss A

ccura

cie

s

WWV- Classes

Classification Accuracies of Individual Wetland Woody Vegetation Using

Hyperspectral Data

PCA-MLE LDA-MLE

Results – HSI data

60

65

70

75

80

85

90

95

100

105

Phragmites RiverCane Pickerelweed Cattail Torpedograss Cogongrass GiantCutgrass Hard StemBulrush

Cla

ss A

ccura

cie

s

WHP - Classes

Classification Accuracies of Wetland Herbaceous Plants Using

Hyperspectral Data

PCA-MLE LDA-MLE

Results HSI data

Classification

Phragmites Classification map using 5 band multispectral data

Two Classes considered –Phragmites (P) and Non Phragmites (NP)

Classification of Phragmites

Source of Classification Error

Results – Classifying Invasive Phragmites Australis

# Class MS-DSM +NDVI +SAVI +AP-SD +AP-MOI

1 P(%) 88.1 88.2 90.4 91.0 91.1

2 NP(%) 88.1 89.1 88.7 88.1 89.9

OE(%) 11.3 11.8 10.8 11.8 9.5

CE(%) 10.9 9.5 10.7 9.0 8.9

OA 85.1 88.0 88.4 89.5 91.1

κ with CI (%) 57.9 (±0.4 ) 58.5 (±0.7 ) 59.2 (±0.8 ) 61.8 (±0.8 ) 63.0 (±0.6)

RoadChinese TallowWWV & PhragmitesOther WHP

Tallow Ground Reference

Crape myrtle, Bald Cypress, Holly, Oak

Pine, Red Maple, Wax Myrtle

Willow and Yopon

Results – Multispectral Data

Results – Classifying Invasive Chinese Tallow

Road

Chinese Tallow

Other Trees & Phragmites

Pasture

False Color Multispectral Classification Map

Overall Accuracy – 75 %

Kappa Statistic – 0.6 (Moderate to Substantial Agreement)

Results – Classifying Invasive Chinese Tallow

Class Matrix

N=94 (54/40) Tallow Non-Tallow

Tallow 48 6

Non-Tallow 13 27

Hyperspectral data is useful for classifying various wetland plant species.

Small UAS with commercially available has potential for mapping wetland species

Our results showed that this setup can be useful when classifying a broad group of selected wetland species

It produces subpar results when classifying individual species with subtle differences.

Summary

Questions