change detection of soil states

Use of dense time series of high resolutionimages for change detectionand land use classification

J. Inglada, B. Beguet, J.-F. Dejoux, C. Marais-Sicre, D. Ducrot, M. Huc,O. Hagolle, F. Baup, G. Dedieu

CESBIO, UMR 5126, Toulouse, France - [email protected]

Real Time Land Cover Map Production

CESBIO’s Sud-Ouest Project: the goal of this project is to contribute to the understanding and the modeling of the continental surfaces at the landscape and regional levels and toincrease knowledge and develop generic methods. Yearly monitoring and regular satellite image acquisitions since 2006, 3 permanent instrumented sites.

Land-cover map production: as a means (input for models) but also as a research goal in itself.

Real time land cover map production: know as soon as possible in the agricultural season which is the crop which is going to be grown.

Satellite DataFormosat-2: 11 images in 2008.

• For soil work:

– 29th August to 12th November 2008

– 5 Formosat-2 images

Image pre-processing: all data are geometrically corrected and ra-diometrically calibrated; cloud screening is performed [3]

(a) February 11, 2008 (b) July 10, 2008 (c) October 26, 2008

Ground Data2008 campaign:

• 14 terrain surveys

• 650 plots revisited

• only 501 plots were kept for the land-cover classifications

• accuracy of the surveys allowing for diachronic studies

Soil work: 7 field surveys for 300 plots

• Each ground sample is associatedwith a confidence index

• Some soil states are visible on theground before being detected onthe images

Supervised Classification

Yearly classification: using thedata for the whole season.

Advanced methods: describedin [2, 4]

class accuracy (%)broad leaf forest 97.88needle leaf forest 97.05eucalyptus 74.53rape 99.33barley 99.06maize 99.60sunflower 99.12sorghum 100.00soybean 97.36fallow 97.75grassland 95.16

Soil Work

Problem PositionMain goal: improve real-time crop classification; soil work can give hints on the type of crop

Soil map: is also interesting in itself as a product

Classes of interest:Inter-crop Stubble disking Deep ploughing Harrowing Sowing preparation Emergence

Crops (C): Sunflowers, which are mostly dry in September and harvested in September or October. Irrigatedsoybean and maize, which are green in September and begin to dry in October (harvest in October andNovember).

Inter-crop (IC): Begin after harvest. No recent or visible soil tillage. Stubble stands often right, cropresidues may be visible on top soil. Some green plants can grow, like volunteers (or regrow) and weeds, ifclimatic conditions are favorable (rain, etc.).

Stubble disking (SD): Superficial (5 to 15 cm) soil tillage in order to mix crop residues and soil and todestroy green vegetation (weeds). Soil surface is irregular, has some small clods and a small roughness.Stubble and crop residues are partly visible.

Deep ploughing (DP): Mainly mouldboard ploughing between 20 to 45 cm deep. More than 95% baresoil: no visible crop residues. Visible clods and strong roughness.

Harrowing (H): Secondary or superficial tillage. More than 95% bare soil. There are medium sized clods.Improper for seedling. Various tillage operations are possible: rotary harrowing, chiseling, superficial plough-ing (less than 20 cm deep).

Remark: Some green plants (volunteers, weeds) may be visible for the 4 previous categories, only if climaticconditions are favorable and duration between each stage or soil tillage is sufficient. In the present poster,it was sometimes the case only in inter-crops or after stubble disking.

Sowing preparation (SP): More than 95% bare soil. Soil ready for seedling. Regular surface. Small clods.

Emergence (E): Germination. Plants are visible from field borders and are at cotyledons or first leavesdevelopment stages. Plant height lower than 5 cm.

ApproachRadiometry only: only the reflectances and combi-

nations of them (indexes) are used; no texture, statis-tics, nor object-based features.

Statistical analysis: the temporal evolution of thereflectances and the indexes – globally and per class– are studied.

2 kinds of analysis:

1. Identification of the soil state: classification

2. Identification of the transitions between states:change detection

SVM classification: Support Vector Machines [1]are both used as separability measure and as clas-sification tool.

Index Formula

NDVI NIR−RNIR+R

Color R−BR

Brightness√G2 + R2 + NIR2

Shape 2R−G−BG−B

Redness R−VR+V

Classification

Direct approach: each soil state isconsidered a class and a supervisedclassification is performed

Crop class: not so easy to classify,since it corresponds to several croptypes

Errors:

• IC can be confused with MT, sincethe amount of green vegetationbefore tilling varies very much;

•many confusions between bare soilclasses;

• germination is correctly detected

Grouping soil classes improves theclassification

C IC SD DP H SP EC 66.4 9.54 7.08 4.67 0.35 7.53 4.43

IC 6.54 64.67 14.14 0.95 4.71 3.89 5.1S 4.08 6.6 63.5 1.5 13.6 6.94 3.78

DP 6.36 2.76 2.64 57.54 16.51 10.53 3.66H 1.53 1.35 6.9 20.85 44.09 23.17 2.11

SP 3.6 0.0 6.17 23.1 13.12 41.52 12.49E 1.28 5.85 1.67 0.08 1.72 1.6 87.8

Overall Accuracy = 0.6085Kappa = 0.541

C IC SD Soil EC 65.65 10.47 8.9 7.12 7.86

IC 6.19 65.22 16.16 6.23 6.2SD 5.29 6.61 67.88 15.43 4.79

Soil 3.92 2.16 8.18 77.27 8.47E 2.98 6.39 2.32 2.31 86.0


Change DetectionClasses are transitions: supervised classification is used in order to detect transitions between soil states.

C→ IC SD DP H SPIC 73.49 16.23 0.03 1.07 9.18SD 6.93 53.17 5.63 12.73 21.54DP 0.65 2.14 83.07 3.54 10.6H 2.47 5.93 10.7 74.97 5.93SP 5.08 1.63 8.96 2.5 81.83


IC→ SD DP H SPSD 75.99 11.54 10.97 1.5DP 3.58 89.67 6.75 0.0H 14.32 15.87 62.15 7.66SP 0.58 0.0 2.92 96.5


SD→ DP H SP EDP 74.1 14.02 3.2 8.68H 23.96 32.88 28.59 14.57SP 7.51 13.69 65.91 12.89E 6.82 5.69 7.25 80.24


Transition D→H H→SP H→E SP→EAccuracy (%) 97.0 88.74 87.91 96.76

• The number of transitions is very low for somecases (between 12 and 50 plots; or between 1000and 10000 pixels)

•Many transitions between states can’t be de-tected accurately

•However, some changes are well detected (about90% and more)

ConclusionsSoil work knowledge is needed to improve real-time land-cover map production; soil maps

are also useful in themselves

Soil states are difficult to identify using direct classification and optical radiometry only

Soil state changes can be detected in some cases, but many transitions seem difficult to identify

References

[1] C.J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998.

[2] D. Ducrot, A. Masse, C. Marais-Sicre, J-F. Dejoux, and F. Baup. Multisensor and multitemporal image fusion methods to improve remote sensing image classification. In Recent Advances in Quantitative RemoteSensing, September 2010.

[3] O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment,114:1747–1755, August 2010.

[4] S. Idbraim and D. Ducrot. An unsupervised classification using a novel ICM method with constraints for land cover mapping from remote sensing imagery. International Review on Computers and Software (IRECOS),March 2009.

change detection of soil states

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