HYPERTEMPORAL REMOTE SENSING Dr Ahmed DOUAIK Research Unit on - - PowerPoint PPT Presentation

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May 04, 2023 309 likes •451 views

RECOVERY OF SPATIAL INFORMATION FOR CROP STATISTICS FROM HYPERTEMPORAL REMOTE SENSING Dr Ahmed DOUAIK Research Unit on Environment and Conservation of Natural Ressources Regional Center of Rabat INRA ahmed_douaik@yahoo.com Outline

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Dr Ahmed DOUAIK

Research Unit on Environment and Conservation of Natural Ressources

Regional Center of Rabat

INRA

ahmed_douaik@yahoo.com

RECOVERY OF SPATIAL INFORMATION FOR CROP STATISTICS FROM HYPERTEMPORAL REMOTE SENSING

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Outline

 Introduction  Material and Methods  Results and Discussion  Conclusions

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Introduction

 Conventional methods of land use and land cover mapping and monitoring are laborious and expensive  Time series of NDVI used to discriminate between vegetation and

ther land uses, and between different vegetation types

 Crop statistics not informing about the spatial extent within administrative units

Objective

adding spatial information to crop statistics using hypertemporal RS data (temporal NDVI profiles).

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Material and Methods

Study area

West Nizamabad

6 Mandals or sub-districts Total area: 1300 km2 Cropland: 90000 Ha

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NDVI: 147 Spot Vegetation composite images

* spatial resolution: 1 km2 * decadal * period: April 1998 - April 2002

Land cover map at 1/50000 scale

* images acquired in 1994/1995 * IRS-C (Liss-III sensor, spatial resolution: 23 m) * original 18 legend entries reduced to 7

Crop statistics: cropped areas by administrative units

Data

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Methods

 NDVI = (IR- R) / (IR + R)  Unsupervised classification: ISODATA algorithm (2 to 30 clusters)  Cropland areas masked using land cover map  Stepwise multiple linear regression:





n i i i

r NDVIcluste c CA

 Generating maps showing cropped fractions by map units  Softwares: ArcGIS, ERDAS Imagine and SPSS  DN = (NDVI + 0.1) / 0.004

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Results and Discussion

Number of clusters

Average Divergence

2000 4000 6000 8000 10000 12000 14000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Number of clusters Divergence

Minimum Divergence

80 100 120 140 160 180 200 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Number of clusters Divergence

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Average spectral signatures

Mean Signature Profile 50 100 150 200 250 20 40 60 80 100 120 140 160 Decade Mean Signature Series1 Series2 Series3 Series4 Series5 Series6 Series7 Series8 Series9 Series10 Series11 Series12 Series13 Series14 Series15 Series16 Series17 Series18 Average signature (8 clusters) 50 100 150 200 250 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Decade Average signature Class 1 Class 2 Class 030405060710 Class 08091114 Class 1213 Class 15 Class 16 Class 1718

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NDVI-unit map

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NDVI units Kharif Adjusted R2 3 4 6 7 Area (Ha) Cotton 87.5 15.6 6860 Maize 81.3 4.1 482 Pulses 96.9 48.0 64.1 29121 Rice 95.0 50.3 75.3 22774 Sugarcane 89.9 26.0 2395 Rabi Groundnut 80.3 53.2 5942 Pulses 80.9 5.5 2824 Rice 99.8 1.8 69.1 25.0 11481 Sorghum 86.1 32.5 15454 Sugarcane 85.9 21.6 1960 Total Area (Ha) both seasons 42409 13488 8920 18216

Stepwise multiple linear regression

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Estimated maps for rice

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Conclusion

Benefit of integrating hypertemporal remote sensing data with crop

statistics to: * delineate NDVI profile clusters with their land cover map units * link these statistics to geographical locations

These map units used for future monitoring of natural resources (crop

growth, forecasting crop production, risk awareness like drought, etc.)

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Outline

Introduction

Material and Methods

Results and Discussion

Tha Thank nk Yo You For u For Yo Your ur Kin Kind d Att Atten ention tion