Urban Remote Sensing: Using NASA Goddard's LiDAR, Hyperspectral & Thermal Imager to map Ash and...
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Transcript of Urban Remote Sensing: Using NASA Goddard's LiDAR, Hyperspectral & Thermal Imager to map Ash and...
Rich Halletta, Jen Pontiusba , Bruce Cookc , Ryan P. Hanavand a USFS Northern Research Station, b University of Vermont ,
c NASA Goddard Space Flight Center, d USFS Forest Health Protection
Scanning/Profiling LiDAR
Urban Remote Sensing: Using NASA Goddard's LiDAR, Hyperspectral & Thermal Imager to map Ash and detect the
effects of emerald ash borer (EAB), Agrilus plannipenis, in Bowie, MD
First discovered in SE MI in 2002
Currently in 22 US states and 2 Canadian provinces
Larvae feed on all ash species (don’t discriminate between healthy or sick trees)
Emerald ash borer, Agrilus planipennis
Girdled Trap Trees Woodpecker “blonding”Purple Panel Traps
Current Detection Techniques
Hyperspectral remote sensing has been used to effectively assess ash decline associated with EAB (Hallett et al. 2008, Pontius et al. 2008)
The G-LiHT (PI Bruce Cook) imaging system provides a data fusion opportunity to increase accuracy and early detection of incipient EAB infestations.
First use of the G-LiHT sensor in an urban forest health application (summer 2012)
Introduction
Goddard’s LiDar, Hyperspectral and Thermal Airborne Imaging System
1. Off the shelf instrumentation2. Surface temperature
observations3. Downwelling irradiance
measurements4. Profiling and scanning LiDAR
instruments5. Eye-safe lasers at two
wavelengths (905, 1550 nm)6. Portability (~80lbs)7. Versatility (adaptable to many
platforms, low power8. Low operating costs (~$1/ha –
acquisition & processing)
Data Fusion – What is G-LiHT?
Data Fusion – What is G-LiHT?
Imagery collected for Bowie, MDJuly 2012
Goal: collect data from ~100 trees per AOI
Health Data – Species, DBH, Discoloration, Defoliation Class, % dieback, Vigor, Crown Class, Woodpecker evidence, Epicormic Branching
Chlorophyll Fluorescence – Hansatech PEA
Canopy Transparency – digital images
Spectrometer calibration – Handheld and tarp fly-over
Methods – Field Plots
Vigor – percent dieback on a scale of 1-5 (Millers et al. 1991)
1 = <10% dieback5 = dead tree
Discoloration – measured on a scale of 0-3
0 – no trace of discoloration
3 - >60% discoloration
Methods – Health Plots
Physiologically, a reduction in net photosynthesis is one of the earliest and most subtle signs of plant stress.
Photosynthetic capacity was directly measured as chlorophyll fluorescence (photosynthetic activity)
(Carter and Knapp 2001)
Methods – Photosynthetic Capacity
4 photos taken straight up capturing as much pure canopy as possible
Canopy Thinning – measured with digital photos
Consistent height and location
Processed as jpg’s and assessed with Gap Light Analyzer (GLA)
12 % 63 %
Automated process
Methods – Canopy Transparency
Results – Canopy Transparency
Uninfested - Healthy Infested – EAB confirmed
Putting it all together: Creating a stress index
Tree 36Stress Index -0.783
Tree 71Stress Index -.409
Tree 45Stress Index -0.191
Tree 77Stress Index 0.81
Tree 65Stress Index 1.3
Tree 75Stress Index -0.009
𝑋 (𝑧𝑃𝐼+𝑧𝐹𝑣𝐹𝑚+𝑧𝐷𝑖𝑒𝑏𝑎𝑐𝑘+𝑧𝑇𝑟𝑎𝑛𝑠𝑝𝑎𝑟𝑒𝑛𝑐𝑦+𝑧 𝐷𝑖𝑠𝑐𝑜𝑙𝑜𝑟 )
Tree Stress Index
Variable NotesPI Chlorophyll FluorscenceFvFm Chlorophyll FluorscenceTransparency Digital estimateDiscolor Visual estimateDieback Visual estimate
Early Signs
Later Signs
Pontius, J., & Hallett, R. (2014). Comprehensive methods for earlier detection and monitoring of forest decline. Forest Science, 60(2).
Field Validation Canopies
Ash species
Red MapleOak speciesNorway MapleHoney Locust
Field/Lawn
SycamoreCallery Pear
Silver Maple
Impervious
Results - Where are the ash trees?
Results - Where are the ash trees?
Overall accuracy of the 290 independent validation points resulted in 81% accuracy in distinguishing ash from non-ash polygons.
Sick ash trees don’t look like ash trees anymore!
Results – Identifying trees in decline
Visible Wavelengths
Healthy
Sick
Healthy Sick
Severe decline
Healthy
All Trees
AshTreesSevere decline
Healthy
Severe decline
Healthy
Ash Trees in Bowie
Thank You!Questions?