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Poster Session 1
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Combined Use of SPOT and GIS Data to Detect Rice Paddies
1) Single image
Table 1 shows the range of NDVI to define rice paddy and the corresponding overall accuracy. The accuracies are low at both transplanting and harvesting stages and a highest value at the reproducing stage.
Table 1. NDVI ranges and accuracies using single image
| Stages |
NDVI Range |
Overall Accuracy |
| Transplanting |
-0.01<NDVI<0.14 |
74.06% |
| Growing |
0.25<NDVI<0.45 |
87.74% |
| Growing |
0.29<NDVI<0.56 |
89.01% |
| Mellowing |
0.20<NDVI<0.42 |
84.49% |
| Harvesting |
-0.33<NDVI<0.05 |
76.01% |
On transplanting stage, the paddy was flooded with water and cover by rice germ. The NDVI make user confuse paddy with other landuse parcel. On growing and mellow stages, the crop was wither in growing or mature phases. Both of them have similar NDVI values that enable distinguishing paddy for building, paved for building, paved road, and fish pond.
On October 3. the crop was vegetative. Highest NDVI values allow one best rule to find the rice paddies. If only one images can be used, image acquiesced from reproducing stage may be the best choice. On December, the crops have been harvested. However, some paddies did not return status of bare soil but was covered by inter-cropping vegetables. It confuses classification and reduce accuracy.
Table 2 shows the error matrix estimated from results on 1997/10/03 (Reproducing stage). Classes of rice and Other Crops are inside the cadastral parcel and OTHERS (most for non-agricultural use) is outside of cadastral parcel. Data show that a lot of rice class misplaced to other-crop class.
Table 2. Error Matrix for Reproducing stage (1997/10/03) data
| |
Rice |
Other Crops |
Others |
Total |
Producer's Accuracy |
| Rice |
107,049 |
17,901 |
7 |
124,957 |
85.67% |
| Other Crops |
4,636 |
51,782 |
0 |
56,418 |
91.78% |
| Others |
1,397 |
5,932 |
83,024 |
90,353 |
91.89% |
| Total |
113,082 |
75,615 |
83,031 |
0271,728 |
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| User's Accuracy |
94.66% |
68.48% |
99.99% |
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2)Difference between two images
Classifying NDVI ranges and the corresponding accuracies are shown in Table 3.
Simple rule of finding NDVI difference is subtracting a NDVI image by succeeds NDVI image. An additional couple between reproducing and transplanting stages which yields largest difference and best accuracy. Difference between Growing-Transplanting stages ranks secondary in the difference value and accuracy. It might imply that bigger the change, better the detection. Cases of reproducing-growing and mellowing- reproducing show similar NDVI ranges and accuracies. It suggests the difference calculated before or after the reproducing stages lead similar result. Error matrix (Table 4) shows the primary modification of difference
approach. 17,901 other-crop pixels were misplaced to rice in single-image case but the number is 7,747 in two-image approach. Consequently, correctly placed non-crop pixels numbered 64,484 compared with 51,782 in single image approach.
Table 3. NDVI ranges and accuracies of using difference between images
| Stages |
NDVI Range |
Overall Accuracy |
| Growing-Transplanting |
0.19<NDVI<0.50 |
91.42% |
| Reproducing-Growing |
0.04<NDVI<0.30 |
83.73% |
| Reproducing-Transplanting |
0.24<NDVI<0.60 |
92.24% |
| Mellowing-Reproducing |
-0.20<NDVI<0.02 |
82.67% |
| Harvesting-Mellowing |
-0.33<NDVI<0.05 |
76.01% |
Table 4. Error Matrix for Reproducing-Transplanting Data
| |
Rice |
Other Crops |
Others |
Total |
Producer's Accuracy |
| Rice |
103,140 |
7,747 |
7 |
110,894 |
93.01% |
| Other Crops |
8,011 |
64,484 |
0 |
72,495 |
88.95% |
| Others |
1,931 |
3,384 |
83,024 |
88,339 |
93.98% |
| Total |
113,082 |
76,615 |
83,031 |
271,728 |
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| User's Accuracy |
91.21% |
85.28% |
99.99% |
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3) Adding a constraint difference approach.
Constraint means screening the non-rice paddy data before applying classify rule.
However, the overall accuracy has no significant improvement (Table 5). Error matrix (Table 6) show that the constraint cannot efficiently screen non-rice parcel. The OTHERS class was pre-screened but it is misplaced to non-crop class.
Table 5. NDVI ranges and accuracies of using difference between images with constrain
| Stages |
NDVI Range |
Overall Accuracy |
| Growing-Transplanting |
-0.14<NDVI<0.05 |
92.33% |
| Reproducing-Transplanting-Transplanting |
0.24<NDVI<0.60 |
92.92% |
Table 6. Error Matrix for Reproducing-Transplanting Data (with constrain)
| |
Rice |
Other Crops |
Others |
Total |
Producer's Accuracy |
| Rice |
103,245 |
8,076 |
7 |
111,328 |
92.74% |
| Other Crops |
8,414 |
66,341 |
0 |
74,755 |
88.74% |
| Others |
1,423 |
1,198 |
83,024 |
85,645 |
96.94% |
| Total |
113,082 |
75,615 |
83,031 |
271,728 |
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| User's Accuracy |
91.30% |
87.74% |
99.99% |
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The study has illustrated integration of multi-temporal SPOT data and cadastral GIS data can effectively increasing the classification accuracy. Parcel-based classification yields producer accuracy up to 89% in single image case and 92% in two-image case. For operational propose, a rule base combining NDVI, GREENNESS, BRIGHTNESS, and their stage-difference is under constructed. Users can select of combined rules depend on the available remotely sensed data.
Acknowledgements
This study was funded by the Agricultural Council of the Republic of China under the grants of 87-RS-3-(3). During the course of this study, considerable support of cadastral and census data were provided from the Crop Bureau of Taiwan and the Department of Agricultural Engineering. National Taiwan University. We also acknowledge the advice from Professor Yi-Hsing Tseng, National Cheng Kung University.
References
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