multi-temporal FORMOSAT-2 images Mapping paddy rice agriculture using Kang-Tsung Chang Shou-Hao Chiang Tzu-How Chu Yi-Shiang Shiu

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1 Mapping paddy rice agriculture using multi-temporal FORMOSAT-2 images Yi-Shiang Shiu Shou-Hao Chiang Tzu-How Chu Kang-Tsung Chang Department of Geography, National Taiwan University

2 Outline Introduction Brief description of the study area Data used and methodology Results and discussion Conclusions

3 Introduction Paddy rice fields account for over 11% of global cropland area (Maclean et al. 2002; Xiao et al. 2006). Monitoring and mapping of paddy rice agriculture in a timely and efficient manner is therefore important for agricultural and environmental sustainability, food and water security, and greenhouse gas emissions (Xiao et al. 2006).

4 Introduction In East and South-East Asia, paddy fields are usually small and fragmentary Getting more precise mapping for cultivated paddy fields is hard Use artificial tasks have some deficiencies: Time and energy consuming Getting proper growing time aerial photographs is difficult because cloudy climate hinders the photography work Aerial photographs have good resolution, but small areas can be covered during every shot

5 Introduction Satellite images are good alternatives to aerial photographs because satellite images cover wide areas and even have higher temporal resolution. Supervised and unsupervised classifications are commonly used for interpreting land features.

6 Introduction The main objectives in this research Use geographic information system (GIS) overlay mapping with the cultivation data to pick up the training areas. Generate normalized difference vegetation index (NDVI) and differential NDVI (dndvi) from primary images for improving classification. Apply region-based classification using cultivation parcels data

7 Outline Introduction Brief description of the study area Data used and methodology Results and discussion Conclusions

8 Brief description of the study area In Taiwan, there are two cultivating periods First rice crops period Second rice crops period Like most regions of East Asia, paddy rice goes through flooding, transplanting, tillering, flowering, and harvesting stages in each period Transplanting and the most active tillering stages bring special spectral characteristics in remotely sensed imagery different from other land features

9 Brief description of the study area Yunlin County Study Area Chayi County Yunlin City

10 Brief description of the study area Classify an area of 30 km 2 in Yunlin County for the first rice crops in 2006 Paddy rice fields account for half of this study area, hence the paddy rice plays an important role for inhabitants livelihood.

11 Outline Introduction Brief description of the study area Data used and methodology Results and discussion Conclusions

12 Data used in research FORMOSAT-2 The first remote sensing satellite developed by National Space Organization (NSPO) Successfully launched on May 21, 2004 The main mission of FORMOSAT-2 is to conduct remote sensing imaging over Taiwan and on terrestrial and oceanic regions of the entire earth.

13 General Specification of FORMOSAT-2 PAN 0.45~0.90μm MS 0.45~0.52μm(Blue) 0.52~0.60μm(Green) 0.63~0.69μm(Red) 0.76~0.90μm(Near Infrared) Remote Sensing Ground Resolution Image Swatch 24 kilometers Mission life 5 years PAN ( Black/white ) Image 2 meters MS (color) Image 8 meters

14 FORMOSAT-2 image in transplanting stage March 10, 2006 FORMOSAT-2 image in most active tillering stage April 7, 2006

15 Data used in research Cultivation parcels data

16 Data used in research The Agriculture and Food Agency in Taiwan has more than ten-year cultivation data produced by the Chinese Society of Photogrammtry and Remote Sensing (CSPRS) The data from 1996 to 2005 were used to produce training areas of supervised classification. And the data of 2006 were used to validate the classification results.

17 Methodology Four different combinations of imagery materials Original 8-layer data Combinations of original 8-layer data and NDVI data in transplanting stage Combinations of original 8-layer data and NDVI data in the most active tillering stage Combinations of original 8-layer data and differential NDVI data for two stages

18 Research framework

19 Methodology Extract training sites with GIS analysis Farmers usually don t easily change their cultivating crops in the same periods for the next year, i.e. the longer the farmers have cultivated rice, the higher potential the farmers will cultivate rice in the following year

20 Methodology This research supposed Fields which had been paddy fields for past ten years will still be paddy fields for this year. Fields which had been non-paddy fields for past ten years will still not be paddy fields for this year. Based on these supposition, we use GIS overlay mapping with the cultivation data to pick up the training areas

21 Training sites extraction Inside 16-meter Buffer Rice Paddy Fields Intersect Dissolve Adjacent Paddy Fields Erase Buffercovered Areas in Dissolved Results Rice Paddy Training Sites for 2006 Cultivation Data from 1996 to 2005 Non-rice Fields Intersect Dissolve Adjacent Non- Rice Fields Erase Buffercovered Areas in Dissolved Results Non-rice Paddy Training Sites for 2006 Inside 16-meter Buffer

22 Mask

23 Unsupervisedly classify into 20 categories and produce signature information Unsupervisedly classify into 20 categories and produce signature information 40-category signature information

24 Maximum likelihood classification 40-category signature information Different combination of images 40 categories Recode 2 categories Maximum likelihood classification

25 Region-based classification pre-classification Average of first-stage NDVI: Average of dndvi: Average of red band: 78 Average of first-stage NDVI: Average of dndvi: Average of red band: 95

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27 Region-based classification post-classification This parcel is classified as rice This parcel is classified as non-rice

28 Outline Introduction Brief description of the study area Data used and methodology Results and discussion Conclusions

29 Results and discussion Accuracy assessment for pixel-based classification Use cultivation data for the first rice crops in (ArcInfo coverage) Rasterize coverage to 8m 8m raster file (the same as the resolution of FORMOSAT-2 images) Use overall, producer s, user s and kappa value

30 Results and discussion Accuracy assessment for region-based classification Use cultivation data for the first rice crops in 2006 (ArcInfo coverage) Parcel by parcel Use overall, producer s, user s and kappa value

31 Accuracy assessment for pixel-based classification (% of area) Producer's Rice Non-rice User's Producer's User's Overall Kappa Original 8-layer 8 data 86.42% 90.52% 77.04% 69.10% 83.77% Combinations of original 8-layer 8 data and first-stage stage NDVI data Combinations of original 8-layer 8 data and second-stage stage NDVI data Combinations of original 8-layer 8 data and two-stage differential NDVI data 86.86% 91.16% 78.62% 70.22% 84.53% % 91.47% 79.59% 69.55% 84.38% % 90.92% 77.99% 70.02% 84.34%

32 Accuracy assessment for region-based classification Pre-classification % of area Producer's Rice Non-rice User's Producer's User's Overall Kappa Original 8-layer 8 data 97.27% 87.17% 63.17% 90.01% 87.73% Combinations of original 8-layer 8 data and first-stage stage NDVI data 97.47% 83.56% 51.16% 88.81% 84.41% Compare with pixel-based results

33 Accuracy assessment for region-based classification Post-classification % of area Producer's Rice Non-rice User's Producer's User's Overall Kappa Original 8-layer 8 data 94.07% 93.63% 83.73% 84.76% 91.15% Combinations of original 8-layer 8 data and first-stage stage NDVI data 94.07% 93.81% 84.21% 84.83% 91.29% Compare with pixel-based results

34 Ground truth-results The classification result for combinations of original 8-layer data and first-stage NDVI data

35 The pre-classification result for original 8-layer data The post-classification result for combinations of original 8-layer data and first-stage NDVI data

36 Results and discussion Many thin and long parcels are non-rice, but they are easily classified erroneously because of mixed pixels or improper image registration Enforce thin and long parcels as non-rice

37 Accuracy assessment for region-based classification Pre-classification % of area Producer's Rice Non-rice User's Producer's User's Overall Kappa Original 8-layer 8 data 97.05% 89.04% 69.62% 90.26% 89.30% Combinations of original 8-layer 8 data and first-stage stage NDVI data 97.25% 85.81% 59.22% 89.44% 86.49% Compare with pixel-based results

38 Accuracy assessment for region-based classification Post-classification % of area Producer's Rice Non-rice User's Producer's User's Overall Kappa Original 8-layer 8 data 93.92% 95.11% 87.76% 85.05% 92.18% Combinations of original 8-layer 8 data and first-stage stage NDVI data 93.91% 95.34% 88.36% 85.12% 92.34% Compare with pixel-based results

39 Outline Introduction Brief description of the study area Data used and methodology Results and discussion Conclusions

40 Conclusions The results of region-based are better than pixel-based As for region-based classification, the results of post-classification are better than preclassification, and the data processing is much simpler

41 Factors which may influence the results Homogeneity of land features Discrepancy of cultivation habits Similarity of growing pattern between different crops

42 Thank you for your attention!!