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Potential use of AVHRR NDVI in estimating crop aboveground biomass and carbon inputs from crop residues. Erandi Lokupitiya 1,2 , Michael Lefsky 3, and Keith Paustian 1,2 - PowerPoint PPT Presentation
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Potential use of AVHRR NDVI in estimating crop aboveground biomass and carbon inputs from crop residues
Erandi Lokupitiya1,2, Michael Lefsky3, and Keith Paustian1,2
1 Natural Resource Ecology Laboratory, 2 Department of Soil and Crop Sciences, 3 Department of Forest, Rangeland and Watershed Stewardship,
Colorado State University, Fort Collins, CO
• The current study was carried out as part of a project that assesses potential carbon sequestration in agricultural soils in the conterminous U.S.
• Crop yields can be used to estimate crop aboveground biomass and carbon inputs from crop residues; one limitation of using the available national crop yield statistics for biomass (and carbon inputs) estimation is missing data in certain counties and certain years.
• This study was designed to evaluate the potential use of remote sensing in estimating crop aboveground biomass when no yield information is available; model relationships between biweekly NDVI and biomass of the main crops (estimated from the crop yields) in Iowa were developed using the available data from 1992 and 1997.
Introduction
A BF
GK
ML
R
S
E
Data analysis and imputation procedure
Methods
• Biweekly NDVI images of Iowa in 1992 and 1997 were concatenated separately, and non-crop areas were excluded using the recoded NLCD coverage for Iowa; average biweekly NDVI pixel values for each county were calculated.
• Crop area within each county, and the crop area occupied by each major crop (i.e. corn, soybean and oats), and crop aboveground biomass in the two years were estimated using the Crop yields and acreages reported by NASS; county level yield data reported by NASS were converted to aboveground biomass using equations derived from past studies (Paustian and Williams, submitted).
Analyses to find NDVI- biomass relationships
Best subset multiple regression analyses
Independent variables- avg. pixel values from biweekly NDVI images during the crop growth period in Iowa (altogether 15 in 1992, and 16 those in 1997)
Dependent variables : a. Above ground biomass kg ha-1 for each crop b. Area fraction (area of the particular crop as a fraction of the total crop area) for
each crop• Area weighted biomass kg ha-1; the sum of the aboveground biomass of each crop
weighted by their area fraction)
• Each dependent variable was regressed against the best subset of the biweekly NDVI pixel bands
Canonical correlation analyses
• Since the simple best subsets multiple regression analyses yielded some un-interpretable negative coefficients (as shown in the Results section) in the models, which can be attributed to autocorrelation among biweekly NDVI values, canonical correlation analyses (CANCORR, SAS 9.0) were performed using the above independent and dependent variables for the data in 1992 and 1997.
• Canonical correlation creates new canonical variables from the original independent and dependent variables, and maximizes the correlation between each set of canonical variables.(Highly correlated variables in the original data are combined together, thus reducing the number of variables, but still retaining the information from the original data.)
Iowa Composite NDVI image
- 1992
NDVI image for crops- after being masked using the NLCD image
- 1992
Some results from the simple best subsets multiple regression analyses- models between biweekly NDVI and corn aboveground biomass
____________________________________________________ Year regression model R-sq ___________________________________________________________________________________________
1992 Corn AGBM kg/ha = - 15426 + 316 (Apr03-16) - 166 (May01-14) 78%
- 59.6 (May29-Jun11)+ 71.6 (Jun12-25) + 241 (Aug21-Sep03) - 163 (Sep18-Oct01)
1997 Corn AGBM kg/ha = 15755 - 89.5 (Mar28-Apr10) + 85.2 (Jun06-19) 74%
- 76.1 (Jul04-17)+ 117 (Jul18-31) + 44.1 (Sep12-25) - 91.4 (Sep26-Oct09) - 32.7 (Oct10-23)
_____________________________________________________________ Simple best subsets multiple regression analyses yielded certain
un-interpretable negative coefficients in the models
Results
100110120130140150160170180
94-107
108-121
122-135
136-149
150-163
164-177
178-191
192-205
206-219
220-233
234-247
248-261
262-275
276-289
290-303
time (day of the year)
NDVI
pixe
l valu
es
92
97
Change of NDVI over time- Iowa
Best subset multiple regression models between
canonical variables (cv) from NDVI and corn aboveground biomass-
________________________________________________ Year Model with NDVI canonical variables R-sq
____________________________________________________________________________________
1992 =16653 + 1105 cv1 - 266 cv2 - 283 cv3 - 281 cv5 + 192 cv6 78% 1997 =15723 + 873 cv1 + 234 cv2 + 311 cv4 82%
__________________________________________________________________
Correlations between aboveground biomass and NDVI pixel values in 1992
-1
-0.5
0
0.5
1
Apr03-16
May15-28
Jun26-Jul09
Aug07-20
Sep18-Oct01
Biweekly NDVI pixel bands
Cor
r. C
oeffi
cien
t w
ith b
iom
ass
soycornoats
Loadings on canonical variables from original NDVI- 1992
-1.2
-0.8
-0.4
0
0.4
0.8
1.2
Apr03-16
May01-14
May29-Jun11
Jun26-Jul09
Jul24-Aug06
Aug21-Sep03
Sep18-Oct01
Oct16-29
Biweekly NDVI band
Can
onic
al load
ing
cv1
cv2
scatter plot predicted vs observed corn biomass - 1992
13000
15000
17000
19000
13000 15000 17000 19000
CAGBM_obs
CAG
BM
_pre
d 92 Model97 Model
Image and data processing
Simple best subsets multiple regression analyses
Canonical correlation analyses (CCA)
Corn aboveground biomass observed (CAGBM_obs) in 1992 vs predicted (CAGBM_pred) using the above models for 1992 and 1997.
• Our results confirmed earlier studies that NDVI can be used to predict crop aboveground biomass, making its use feasible in estimating crop residue carbon inputs. • Both simple best subset multiple regression analyses and CCA yielded good model relationships between NDVI and biomass. But CCA improved the interpretability of the results, and gave better predictability in the models, as they better dealt with the autocorrelation associated with biweekly NDVI. CCA found that NDVI and crop biomass are well correlated during the middle of the crop growth from mid June to end August, and using the canonical variables from original biweekly NDVI pixel values in subsequent best subset multiple regression analyses was important in determining model relationships with biomass of individual crops.
Discussion and conclusion Acknowledgements
Sunil Kumar and Kay Dudek from Colorado State University. This work was financially supported by the Consortium for Agricultural Soil Mitigation of Greenhouse Gases.
ReferencesDoraiswamy, P.C., Hartfield, J.L., Jackson, T.J., Akhmedov, B., Prueger, J., & Stern, A. 2004. Crop condition and yield simulations using Landsat and MODIS. Remote sensing of environment 92: 548-559Doraiswamy, P. C., T. R. Sinclair, S. Hollinger, B. Akhmedov, A. Stem, and J. Prueger. 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sensing of Environment 92:192-202.Timm, N.H. 2002. Applied multivariate Analysis. Springer texts in statistics. Springer. New York.
• CCA produced 8 canonical variables from various combinations of raw independent variables (biweekly NDVI), with most highly correlated variables forming the first canonical variable (cv1). Similarly, canonical variables were formed from the dependent variables (biomass & area fraction), too. • Correlations between the raw dependent and independent variables, each canonical variable and the raw (both independent and dependent) variables were produced.
CCA contd.