Remote Estimation of Grassland Quality: Scaling from plant to pixel

By Ofer Beeri

University of North Dakota, Grand Forks, ND

This presentation is based on:Phillips, R. L., O. Beeri, and M. Liebig (2006), Landscape estimation of canopy C:N ratios under variable drought stress in Northern Great Plains rangelands, J. Geophys. Res., 111, G02015, doi:10.1029/2005JG000135

Remote Estimation of Grassland Quality: Scaling from plant to pixel

Outline

Carbon:Nitrogen Ratio (C:N)

Remote Sensing Rangeland Issues-Variable Drought Stress

Multiple-Scale Experimental Design

Plant Scale, 34 cm2, Greenhouse Conditions

Plot Scale, 1-m2, Mixed Species Rangeland Community, Field Conditions

Pasture Scale, 30-m and 15-m Pixels, Field Conditions, 4 Time Points

Carbon:Nitrogen Ratio (C:N) in Mixed Grass Prairie

Grassland quality

C:N Ratio Indicator of plant qualityIndicator of nutrient stress

Carbon-Based Compounds ~40%

Nitrogen-Based Compounds ~1.5%.

Rangeland Remote Sensing Issues

•Mixed plant species (communities)

•Mix of live and dead material

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Photosynthetically ActiveNitrogen region (Kokaly & Clark,1999)

Spectra influence by plant moisture

Remote Detection of Plant N Background

Field Detection of Plant N and Plant Moisture

Example of Advanced Remote Sensing Application

The Problem:Remote, Satellite-Based Detection of Mixed Grass Material Quality Under Variable Moisture

Approach:-Separate the spectral response of leaf water content from leaf C:N-Derive algorithm to remotely estimate canopy C:N under field conditions-Apply this algorithm to remotely detect C:N using Landsat & ASTER images -Assess application accuracy

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Photosynthetically ActiveNitrogen region (Kokaly & Clark,1999)

Multiple-Scale Experimental Design

Community level Pasture levelPlant level

1. Identify bands & indices

2. Analyzed spectra to determine influence of C:N and plant moisture

3. Calculate regression formulae

4. Test formulae under field conditions

5. Validate optimum formula for pastures

Plant level experiment

Cool season Sandburg bluegrass (Poa secunda (J. Presl)) &Warm season blue grama (Bouteloua gracilis (Willd. ex Kunth) Lag. Ex Griffiths)

24 flats, 12 flats of each, randomly assigned on greenhouse benchPlants were grown under optimum conditions until just prior to seed set, when drought stress

was induced

A spectro-radiometer Field-Spec Pro (Analytical Spectral Devices Inc. Boulder, Colorado) wasused for spectral measurements

Spectral readings were recorded over full leaf canopy, every 48-hr for 1.5 weeks

Plant material was sampled and analyzed for water content and C:N following each spectral collection

Plant level experiment

An aluminum arm was constructed to hold the recording gun at a fixed distance

Targets were shielded from diffuse sun light and clouds

A 15W halogen light source (ASD Pro-Lamp) used as a sable, controlled light

Plant level experiment: results

Statistical test for effect of water content, C:N ratio and plant species on spectral algorithms

Index Name

Water Content

C:N ratio Plant Species Algorithm

NDVI 0.0207 0.0031 <0.0001 (λ800-λ680)/( λ800+λ680)

SAVI_0.1 0.5010 0.0030 <0.0001 1.1*( λ800- λ680)/( λ800+ λ680+0.1)

WDRVI_5 0.0740 0.0030 <0.0001 (λ800-5*λ680)/( λ800+5*λ680)

EVI 0.4810 0.0040 <0.0001 2.5*( λ800- λ680)/( λ800+6* λ680-7.5* λ485+1)

ND45 0.0047 0.0569 <0.0001 (λ800-λ1675)/( λ800+λ1675)

ND47 <0.0001 0.0380 <0.0001 (λ800-λ2220)/( λ800+λ2220)

TC234 0.6506 0.0021 <0.0001 λ800 * ( λ555/λ680)

TC235 0.2377 0.0009 <0.0001 λ1675 * ( λ555/λ680)

ND23 0.0256 <0.0001 <0.0001 (λ555 - λ680)/(λ555 + λ680)

Tasseled Cap Greenness transformation 0.9090 0.0270 <0.0001λ485 x -0.27 + λ555 x -0.22 + λ680 x -0.55 + λ800 x 0.72 + λ1675 x 0.07 + λ2220 x -0.16

ND53 0.3360 0.0060 <0.0001 1-( λ1675- λ680)/( λ1675+ λ680)

ND73 0.1000 0.0300 0.0002 1-( λ2220- λ680)/( λ2220+ λ680)

ND52 0.0239 0.0008 0.1984 ( λ1675- λ555)/( λ1675+ λ555)

Tasseled Cap Wetness transformation <0.0001 0.7876 0.9050λ485 x 0.14 + λ555 x 0.18 + λ680 x 0.33 + λ800 x 0.34 + λ1675 x -0.62 + λ2220 x -0.42

SR71 0.0364 0.0623 0.0018 λ2220/λ485

SR640/485 0.3870 0.0010 0.0220 λ640/ λ485

SR31 0.8240 <0.0001 0.0090 λ680/λ485

SR700/485 0.0680 0.0030 <0.0001 λ700/ λ485

ND(640-680) 0.3740 <0.0001 0.0003 1-( λ640- λ680)/( λ640+ λ680)

Plant level experiment: results

Community level

Regression analysis were preformed to estimated C:N ratio for each indexC:N ratio = a + b*(remote sensing index)

Each formula was tested with community level measurements

Community levelThe community level test reduce the number of formulae,according to R

SAVI_0.1WDRVI_5EVITC234TC235Greenness ND53ND53 ND73ND73SR640/485SR31SR700/485ND(640-680)

Pasture level, Landsat & ASTER images

Satellite images evaluation:

Experimental grazing treatments: A = Agropyron moderately grazed H = Heavily grazed mixed-grass M = Mixed-grass moderately grazed

4 Landsat and 1 ASTER images were geometric and atmospheric corrected

Pasture level (48 obs) Field-clipped plots, (0.5 x 0.5 m) collected within 4 days of image acquisition

Apr 26

May5

July 24

Sep 17Acquisition date

Measured canopy C:N per treatment per date

Estimated canopy C:N per treatment per date

A H M A H MGrazing treatment A H M A H M

Statistical test:

Does measured C:N vary with treatment and time?

Statistical test:

Does estimated C:N vary with treatment and time?

Pasture level, Landsat & ASTER images

Results for each satellite sensor

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+−

−+=680167568016751*35.562.62 ratio N:C

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Rangeland C:N formula (RCNF)

Sensor n RMSE relative error R2

ASTER 12 1.6 9.6% 0.83Landsat 5 48 3.1 13.8% 0.69

Pasture level, Landsat & ASTER images

Mean (±STD) of plot C:N measurements and satellite C:N estimations, for each grazing treatment

ASTER, May 20, 2004 Landsat, May 5, 2004

Pasture level, Landsat images

ANOVA test results:treatment time

Measured C:N F2,9 =19.80, p < 0.0005; F1,35 = 26.93, p < 0.0001Landsat C:N estimates F2,9 =25.53, p < 0.0002; F1,35 = 73.16, p < 0.0001

Mean (±STD) of plot C:N measurements and Landsat plot C:N estimations, for each ground campaign date

Pasture level, ASTER image

ASTER estimations of C:N ratio, May 20, 2004. Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND.

Summary

•Determined effect of C:N ratio and water content•Tested using community spectra for canopies in the field•Applied model on satellite data and determined accuracy

Satellite-based RCNF tracked C:N ratio spatial & temporal variability with 10 to 14% accuracy

Landsat archived data can be used with the RCNF for land management decision making or ecosystem models

Acknowledgment:We are grateful to R. Kelman Wieder, Scott Bylin, Cory Enger, Bob Sheppard,

Blake Mozer, Jared Clayburn, Jason Gross, Gail Sage, and Becky Wald for their incredible contributions to this project.

Special thanks to Dean Smith and Mike Poellet at the University of North Dakota John D. Odegard School of Aerospace Sciences.

Thanks are owed to Wind River Seed Company for grass seed donation and to Bob Sherman from the UND Biology Department for technical support.

This work was supported by USDA Cooperative Agreement 58-5445-3-314