Crop Classification Using Object-Oriented Method Based on MODIS EVI Time Series Analysis

1. Crop Classification Using Object-Oriented Method Based on MODIS EVI Time Series Analysis Ru AN Geo-informatics Department, School of Earth Sciences and Engineering, Hohai University, Nanjing 210098.Tel.: +86 025 83787578; E-mail addresses: anrunj@yahoo.com.cn; anrunj@163.com

2. Outlines • Background • Study content and technology route • study area and the study data • The spectral response of crops on the ETM+ image • The response of crops on the MODIS EVI time series • The extraction of crops and the result discussion

3. Background • It is important to derive crop type information for the assessment of cropland evaportranspiration and water management for irrigation area.. • Remote sensing is one of the most valuable technologies for this purpose.

4. Study contents • The spectral response of different crops on the ETM+ image. • The response of crops on the MODIS EVI time series. • The crop information recognition based on object-oriented method.

5. Technology route Crop classification using ETM+ and MODIS EVI Projection conversion, image fusion Update the vector database analyzethe classification result Image segmentation Revise the result by MODIS EVI and its combination Calculating NDVI by ETM+ image Accuracy evaluation and result comparing Distinguish crop and non-crop using NDVI

6. Study area • Merced county of California State in USA. • In the center of the Central Valley, north latitude is 37°18′22″, west longitude is 120°28′40″. • Mediterranean climate

7. The proportion of each crop in the study area

8. The study data • ETM+ image：2462×2547 pixels • MODIS EVI time series data • Vector field data

9. The ETM+ image and the vector field data

10. The acquiring time of the MODIS EVI time series data

11. Data preprocess • Image fusion • Projection conversion • Vector database updating

12. The spectral response of crops on the ETM+ image The color of each crop on ETM+ image

13. The spectral response of crops on the ETM+ image The spectral profile of each crop on ETM+ image

14. The response of crops on the MODIS EVI time series The growth phonological of each crop

15. The response of crops on the MODIS EVI time series The time series profile of each crop on MODIS EVI

16. The extraction of crops and the result comparing • Forming the object The result of image segmentation The original vector field data

17. Distinguish the crop and non-crop The ETM+ image Theresult :The green part is the crop

18. The first classification and the result analysis • Using 6 bands of ETM+ image, NDVI and MODIS EVI time series to process the first classification. And then , analyze the classification result. • Alfalfa->Mixed pasture • Almond，Vineyard and Mixed pasture • Cotton<->Tomato are easily mixed.

19. Improving the first classification • Extraction of corn The time series characteristics of corn

20. Extraction of alfalfa The time series characteristics of alfalfa and mixed pasture

21. Distinguish of almond、mixed pasture and vineyard The time series characteristics of almond、mixed pasture and vineyard

22. Distinguish of cotton and tomato The time series characteristics of cotton and tomato

23. The spectral characteristics of cotton and tomato

24. Extraction of winter wheat • Using the same rule of corn extraction to extract the winter wheat

25. Type of crop Rule Corn MaxT10-MinT15>2.2 Alfalfa Sum (T11~T30)>10 Almond T20-T13<0&MaxT13>0.45&Sum (T1~T30)>11 Mixed pasture T20-T13<0&MaxT13>0.45&Sum (T1~T30)>11 Vineyard T20-T13>0&MaxT13<0.35&Sum (T1~T30)<10.5 Cotton B4>112&MaxT20>0.58 Tomato B4<112&MaxT20<0.58 Winter wheat NDVI<-0.12& MaxT10-MinT15>2.2

26. samples to process the accuracy evaluation is as follows: Randomly select 142 alfalfa , 214 almond , 157 corn,129 cotton,83 winter wheat,147 mixed pasture, 57 tomato, and 50 vineyard samples. Result evaluation and comparing

27. The distribution of the random selected samples

28. Classification results of using ETM+ image only

29. The first classification result using ETM+ image and MODIS EVI time series

30. The result of the first classification improved

31. The result using maximum likelihood method based on pixels

32. The result of taking thickest value of crop types based on field data after using maximum likelihood method

33. The accuracy assess result of converting to pixels based on the result of the method this paper used

34. Conclusions • As the crops have lots of spectral similarities on ETM+ image, such as alfalfa, cotton and tomato ,so only use spectral characteristics to distinguish them is difficult.

35. Conclusions • Different crops have different growth phonology , and the vegetation index reflects the growth condition of green plants. Based on these characteristics, the paper employ ETM + image and before and after about one year's time MODIS EVI time series data to distinguish mixed classified crops, and the accuracy is high about 83.1 % . it is a much better result compared to other methods.

36. Outlooks • Segmentation algorithm is not include in the study. • The ancillary information is not considered fully. • In the study area there are also other types of crops, the paper only use a minimum membership of 0.6 threshold to distinguish them, other minimum membership threshold is not compared.

37. Thanks a lot for your attention!