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Remote Sensing: Viewing Japan from Space Remote Sensing for Agriculture Monitoring

Remote Sensing: Viewing Japan from Space Remote Sensing for Agriculture Monitoring. Mirza Muhammad Waqar PhD Scholar Email: mirza.waqar@chiba-u.jp Website: https://mirzawaqar.wordpress.com/ Josaphat Microwave Remote Sensing Laboratory (JMRSL),

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Remote Sensing: Viewing Japan from Space Remote Sensing for Agriculture Monitoring

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  1. Remote Sensing: Viewing Japan from Space Remote Sensing for Agriculture Monitoring Mirza Muhammad Waqar PhD Scholar Email: mirza.waqar@chiba-u.jp Website: https://mirzawaqar.wordpress.com/ Josaphat Microwave Remote Sensing Laboratory (JMRSL), Center of Environmental Remote Sensing (CEReS), Chiba University, Chiba, Japan

  2. Islamabad Capital Territory – Raw Image Band Combination RGB:542 Vegetation: R=38% G=48% B=17% Grey Tone=17% ----------------------- R=21% G=31% B=0% Yellow Tone= 21% ----------------------- R=0% G=10% B=0% Vegetation: Green Dominating Yellow Soil: ? Snow: ? Cloud: ? Water: ?

  3. Introduction • Indices are used to enhance a particular single feature on image. • Different indices are used to enhance different features on image, like enhancement of vegetation, water , snow etc. • Indices discussed in my lecture are • NDVI (Normalized Difference Vegetation Index) • NDWI (Normalized Difference Water Index) • NBI (Normalized Built-up Index) • NDSI (Normalized Difference Snow Index)

  4. Band ratio • Aerial images commonly exhibit illumination differences produced by • Shadows • Differing surface slope angles • Slope directions. • Because of these effects, the brightness of each surface material can vary from place to place in the image. Although these variations help us to visualize the three-dimensional shape of the landscape, they hamper our ability to recognize materials with similar spectral properties. • We can remove these effects, and accentuate the spectral differences between materials, by computing a ratio image using two spectral bands.

  5. Example of Band ratio A ratio of near infrared (NIR) and red bands (TM4 / TM3) This ratio is useful in mapping vegetation and vegetation condition. Note:- Simple band ratio have some problems like sensor noise amplification and range/distortion of the calculated values.

  6. NDVI (Normalized Difference Vegetation Index) A variant method of the simple ratio calculation that avoids problems in band ratio. Corresponding cell values in the two bands are first subtracted, and this difference is then “normalized” by dividing by the sum of two brightness values. The normalization tends to reduce artifacts related to sensor noise, and most illumination effects still are removed. The most widely used example is the Normalized Difference Vegetation Index (NDVI), which is (NIR – RED)/(NIR + RED). Or (Band4 – Band3)/(Band4 + Band3)

  7. NDVI (Normalized Difference Vegetation Index)

  8. NDWI(Normalized Difference Water Index) NDWI is used to enhance water index in a given image. Formula for normalized difference water index is (GREEN – NIR)/(GREEN + NIR). Or (Band4 – Band5)/(Band4 + Band5).

  9. NDWI(Normalized Difference Water Index)

  10. NDSI(Normalized Difference Snow Index) NDSI is used to enhance snow index in a given image. Formula for normalized difference water index is (BLUE – NIR)/(BLUE + NIR). Or (Band2 – Band5)/(Band2 + Band5).

  11. NDBI(Normalized Difference built-up Index) NDBI is used to enhance built-up index in a given image. Formula for normalized difference built-up index is (Band5 – Band4)/(Band5 + Band4).

  12. NDBI(Normalized Difference built-up Index)

  13. Vegetation Condition Index • VCIj is the image of vegetation condition index values for date j; • NDVIjis the image of NDVI values for date j; • NDVImax and NDVImin are images of maximum and minimum NDVI values from all images within the data set; • VCI assesses changes in the NDVI signal through time due to weather conditions, reducing the influence of ‘geographic’ (Kogan, 1990) or ‘ecosystem’ (Kogan, 1995c) variables i.e. climate, soils, vegetation type and topography.

  14. Mean Referenced Vegetation Condition Index (MVCI) Let NDVIm(x, y), NDVImax(x, y) and NDVImin(x, y) be the mean, maximum and minimum of the time series NDVI at location (x, y) across entire time span. Let NDVIi(x, y) be the current NDVI. Then a measure of vegetation condition can be defined by the NDVI percent change ratio to the historical NDVI time series mean NDVIm(x, y) as following:

  15. NDVI Change Ratio to Previous Year Let NDVIi(x, y) be the current year NDVI value at location (x, y), and NDVIi-1(x, y) be the previous year NDVI. The current year NDVI ratio to the previous year value is given by

  16. Crop Type Mapping using Satellite Data for Punjab, Pakistan

  17. Background • This short research study resulted as a collaboration between Dept. of Space Science at IST and Center for Energy Systems (CES) at NUST. • Funding Source: International Finance Corporation (IFC), World Bank Group. • The overall goal of complete project was to analyze and map crop biomass potential for energy generation. • Pakistan has rich and vast natural resource base covering various agro -ecological zones hence the country has great potential for producing all kind of food commodity. • Crop type mapping is very useful for: • Crop identification • Crop area determination • Crop production statistics • crop rotation records • cropcondition monitoring(health and vialibility). • Crop type mapping is also used for identification of factors influencing crop stress, assessment of crop damage due to storms and droughts

  18. Goals And Objectives • The main goal of this research is to map crop type by integrating satellite data and ancillary data. • By preparing base map using time series MODIS NDVI data • By supervised classification of SPOT data using base map

  19. Dataset Used

  20. Methodology Framework FINAL CROP TYPE MAPS USING SPOT DATA BASE MAPS PREPARATION Crop type Maps MODIS NDVI SPOT Imagery Crop Calendar Ancillary Data Crop type Maps Batch processing Un-supervised Classification Plotting No. of Class vs Seperability Curves Compare with Crop Calendar and Ancillary data Selection of appropriate No. of Classes Statistics Computation (Mean NDVI of each class) Crop identification Plot Phenological NDVI Spatial Database Spatial Database Spatial Database Pre-Processing Re-projection Rescaling Supervised Classification Spot-5 Images Stacking Subset to AOI

  21. Crop Calendar

  22. Spectral Reflectance Curve Wheat and Rice curve of Gujranwala

  23. Spectral Reflectance Curve Wheat and Cotton curve of Ahmadpur

  24. Limitations • Unpredictable farmer • Insufficient survey data • Unavailability of vegetable calendar

  25. SUPARCO: Crop area under floods Crop Damage Assessment

  26. Questions & Suggestions

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