Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop –...

Pierre C. Sibiry Traoré & al. © ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004

Upscaling carbon model predictions using very high resolution satellite imagery: evaluation of contextual approaches to land cover classification and crop identification

P.C.S. Traoré, W.M. Bostick, A. Yoroté,J.W. Jones, M.D. Doumbia

in partnership with : with funding from :


Overview

The problem A (slow) process need for (flawed) time models need for adjustments

using (inaccurate) measurements need for (problematic?) spatial surrogates The options

Remote sensing data assimilation Very high resolution: potential, challenges Contextual classification & texture analysis

Methods Quickbird imagery Study site: Oumarbougou Ground truthing Digital image processing

Results (preliminary) Contour tillage, biophysical parameters Land cover classification, crop identification

Next steps…


The problem


Challenges of quantifying C sequestration

Long integration time – requires models (e.g. DSSAT-CENTURY), which are not perfect and need both internal improvement and external adjustment to correct deviations over time

Uncertainty of point estimates - High model and measurement errors relative to annual changes in soil C (standard deviation of 0.058 to 0.21% on a mass basis) potential of data assimilation techniques (e.g., Kalman Filtering)

Scale - On the order of 10,000 ha (100 km2) of cropland may be needed to sequester a tradable amount of C potential of remote sensing techniques (e.g., QuickBird)


Specificity of sudano-sahelian agricultural systems

Local ecotypes are different (photoperiod sensitivity, biomass partitioning, …)

Agricultural intensification lower harvest indices, cash crops (cotton), …

Dr. H

ooge

nboo

m (2

m)

x 2

x 3


The options


Ensemble Kalman Filter (EnKF)

Data assimilation (or optimal control): deals with the inclusion of measured data into numerical models for the forecasting or analysis of the behavior of a system

EnKF: combination of model estimates and measurements from multiple sources (e. g. remote sensing or direct measurements) to estimate system states and parameters in an optimal way

takes into account uncertainty of model estimates and measurements and provides estimates of uncertainty of filtered results

can be used for both spatial and temporal modeling applications


time

X

predictionstep

t

updatestep

EnKF

Zt+1, z

t+1

EnKF for one state variable X with std. dev.

Monte Carlo methods used to initialize ensemble of equally-

likely initial conditions.

initializationstep

Model is propagated forward in time with each realization

of the ensemble.

When state variable measurement occurs EnKF updates model state variables and parameters and measures of uncertainty thereof.


RS data assimilation in process based models

Process scales vs. spatial resolutions : from weather / climate to hydrological to crop modeling

Different information available from optical, thermal, microwave observations but…

… always a need to resolve ambiguities in the remote sensing signal : empirical vs. mechanistic approaches (VIs, SAIL/DART, SVATS…)

Different approaches to data assimilation (Moulin & al., 1998)


Model forcing

(reproduced from

Moulin et al., 1998)


Model re-initialization / re-parameterization (a)

(reproduced from



Model re-initialization / re-parameterization (b)

(reproduced from



Very high resolution imagery

Potential: sub-meter pixel sizes open unprecedented perspectives for land cover studies in patchy, heterogenous landscapes, such as the sudano-sahelian zone…

… but: [more spatial detail = lower spectral dimensionality] 1. decrease in performance of pixel-by-pixel classifiers 2. more emphasis should be put on higher level image primitives(segments, regions as opposed to pixels) = contextual operators

The spatial context

familiar process for photo-interpreters (intuitive) Feature extraction (e.g. geology, glaciology,…), pattern recognition (e.g., forestry) Again, applications function of sensor resolution Increasingly employed for agricultural applications


1st, 2d, 3d order measures – diversity, variance, etc. Grey Level Co-occurrence Matrix (GLCM) = probability of occurrence of couple of

values in 2 neighboring pixels Measures of contrast: contrast, (dis)similarity, homogeneity (IDM) Measures of orderliness: energy (ASM), entropy Descriptive statistics: mean, variance, correlation

Measures of texture


The methods


QuickBird imagery: specifications

(excerpts fromwww.digitalglobe.com)


RS data assimilation methodology

DDATAATA

Measurement

Soil Sampling

Biomass

Soil C

WeatherManagement

Soil Properties

Parameters

BiomassMeasured

Soil CMeasured

Soil CSimulated

Optimized Soil-C Estimation

Optimized BiomassEstimation

Optimized Parameter Estimation

MMODELODEL DDATA ATA AASSIMILATIONSSIMILATION

ENSEMBLE

KALMAN

FILTERBiomassSimulatedDSSAT

-CENTURYCrop/Soil C

Model


Study site: Oumarbougou


Processing chain

3. Ground truthing for land cover classification, crop & ridge tillage identification

1. GCPs for improved locational accuracy

2. LAI & biomass sampling for inversion of remote sensing signal

4. Digital image processing (LC, texture) + inversion of remote sensing signal

5. Assimilation of remote sensing for model re-initialization / re-calibration


Results (preliminary)


Resolution: the proof (panchromatic)


False color composite (R4G2B1)


‘Natural’ color composite (R3G2B1)

Class Number of samples

Bare Soil 10Cotton 154Grass + pasture + fallow 32Groundnut / legumes 32Maize 51Millet 104Rock Outcrops 2Sorghum 51Wetland + ponds 15Wild vegetation 21

total 472


Ridge tillage detection – preliminary results

ridges (‘ados’)

87% of proposed ridge tillage fields confirmed by survey

7% of total actual ridge tillage fields missed

Real potential for simple operational detection method


(raw) NDVI=NIR-VIS/(NIR+VIS)


LAI=f(NDVI) – preliminary results

millet y = 7.4x - 1.6 R2= 0.78

0

1

2

3

4

0.3 0.4 0.5 0.6 0.7NDVI

LAI

0

1

2

3

4

5

0.3 0.4 0.5 0.6 0.7NDVI

cotton y = 9.7x - 2.9 R2= 0.65

LAI

0

0.5

1

1.5

2

2.5

0.4 0.45 0.5 0.55 0.6

LAI

NDVI

maize y = 7.8x - 2.1 R2= 0.71

0

1

2

3

0.2 0.3 0.4 0.5 0.6

LAI

sorghum y = 5.2x - 0.5 R2= 0.54

NDVI


Land cover classification (spectral only)

Spectral information taken alone in pixel-by-pixel classifiers cannot separate agricultural fields from wild vegetation when crop stands are established, and cannot discriminate amongst crops


Land cover classification (spectral only)


NDVI sub-plots (~34x34m)

Cotton

Maize

Millet

Sorghum

Wild vegetation


Panchromatic sub-plots (~34x34m)

Cotton

Maize

Millet

Sorghum

Wild vegetation


7x7 variance sub-plots (~34x34m)

Cotton

Maize

Millet

Sorghum

Wild vegetation


7x7 variance image (1st order)


Textural analysis on stratified sample subset N S E W min max ASM Contrast Correlation Variance IDMcohi1 1347202.96 1347168.64 270598.84 270564.53 48.00 77.00 0.011 12.090 0.649 18.680 0.401cohi2 1346265.35 1346231.03 263933.67 263899.36 59.00 79.00 0.011 9.483 0.688 16.370 0.416colo1 1346984.52 1346950.20 270586.38 270552.07 51.00 60.00 0.055 1.842 0.703 4.125 0.678colo2 1348740.77 1348706.46 267695.46 267661.14 49.00 67.00 0.037 4.920 0.635 7.857 0.581comd1 1347062.21 1347027.90 271236.14 271201.82 54.00 74.00 0.016 6.099 0.713 11.670 0.437comd2 1348112.79 1348078.47 266336.89 266302.57 56.00 71.00 0.017 5.503 0.604 7.982 0.437mihi1 1348060.78 1348026.46 266168.90 266134.59 53.00 66.00 0.028 4.657 0.510 5.818 0.482mihi2 1349394.20 1349359.88 268640.23 268605.91 62.00 81.00 0.015 10.880 0.557 13.510 0.415milo1 1348183.97 1348149.66 269942.10 269907.78 60.00 74.00 0.026 4.412 0.747 9.815 0.600milo2 1349180.54 1349146.23 270318.88 270284.57 64.00 71.00 0.067 1.886 0.562 3.186 0.666mimd1 1349383.05 1349348.72 267621.24 267586.95 53.00 67.00 0.050 3.498 0.451 4.234 0.572mimd2 1348342.29 1348307.98 267973.65 267939.34 55.00 69.00 0.037 3.053 0.529 4.269 0.529mzhi1 1348738.63 1348704.31 267227.83 267193.52 53.00 64.00 0.040 3.905 0.339 3.990 0.506mzhi2 1347329.71 1347295.40 264190.66 264156.34 50.00 70.00 0.011 13.280 0.367 11.590 0.330mzlo1 1349254.16 1349219.85 268289.63 268255.31 45.00 75.00 0.019 11.580 0.536 13.900 0.462mzlo2 1349180.52 1349146.21 267969.91 267935.59 55.00 68.00 0.028 4.232 0.560 5.879 0.496mzmd1 1349503.31 1349468.99 267950.27 267915.96 51.00 70.00 0.020 7.446 0.558 9.534 0.437mzmd2 1348526.66 1348492.35 266738.13 266703.82 54.00 66.00 0.048 2.205 0.599 3.762 0.574sghi1 1350097.19 1350062.87 266841.16 266806.84 51.00 64.00 0.035 3.460 0.615 5.540 0.543sghi2 1347206.42 1347172.11 270144.30 270109.98 53.00 67.00 0.021 6.179 0.505 7.330 0.451sglo1 1349222.54 1349188.22 270113.56 270079.25 68.00 80.00 0.035 2.901 0.770 7.371 0.662sglo2 1348426.23 1348391.91 266594.57 266560.25 48.00 56.00 0.094 1.391 0.497 2.402 0.674sgmd1 1348403.21 1348368.90 269590.33 269556.02 54.00 65.00 0.043 3.302 0.409 3.830 0.549sgmd2 1350106.23 1350071.92 265987.52 265953.20 48.00 64.00 0.044 3.131 0.644 5.451 0.581wv1 1347066.53 1347032.21 268722.14 268687.82 37.00 73.00 0.006 27.780 0.588 35.240 0.320wv2 1349543.44 1349509.13 267355.20 267320.88 41.00 75.00 0.007 17.480 0.574 21.770 0.317wv3 1350226.25 1350191.94 265956.07 265921.76 38.00 74.00 0.004 30.000 0.663 46.060 0.287wv4 1349235.31 1349201.00 266284.48 266250.17 40.00 73.00 0.007 26.110 0.520 28.700 0.312wv5 1346073.44 1346039.13 270269.77 270235.46 34.00 75.00 0.004 39.390 0.649 57.680 0.269wv6 1347268.01 1347233.69 269293.54 269259.23 41.00 74.00 0.004 35.080 0.493 36.070 0.260


Objects mean Variance Inverse Diff Moment Angular Second Moment(increases with homogeneity) (increases with orderliness)

cotton 11.114 0.492 0.025

millet 6.805 0.544 0.037maize 8.109 0.468 0.028sorghum 5.321 0.577 0.045cereals 6.715 0.522 0.036

wild vegetation 37.587 0.294 0.005

Objects SD Variance Inverse Diff Moment Angular Second Moment

cotton 5.558 0.112 0.018

millet 4.028 0.089 0.019maize 4.208 0.082 0.014sorghum 1.952 0.083 0.025cereals 3.450 0.097 0.022

wild vegetation 14.228 0.022 0.002

Textural analysis on stratified sample


Panchromatic sub-plots (~34x34m)

Cotton

Maize

Millet

Sorghum

Wild vegetation


Next steps…


Next steps

Explore the potential of directional operators and variogram modeling for enhancing separability between cotton and cereals

Increase the sample size for texture subsets to improve statistical significance of calculated separability signatures

Overcome file size limitations to generate texture images over the entire study area as inputs to Bayesian / maximum likelihood classifiers


Conclusions

QuickBird can detect ridge tillage management practices with high accuracy (panchromatic)

QuickBird can estimate within-field LAI with high accuracy, but this can be improved by quantifying texture (a surrogate of canopy structure)

A single QuickBird scene on any date can separate natural vegetation from cropland with high accuracy (contextual)

A single QuickBird scene during the growing season can discriminate cotton and cereals with reasonable accuracy (contextual) – additional work is needed though

A single QuickBird scene cannot distinguish cereals from one another : multitemporal imagery would be required

All remote sensing estimates need to be characterized in error terms for data assimilation purposes (LAI in particular : temporal uncertainty)

Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop –...

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Transcript of Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop –...