Mapping paddy rice agriculture using multi-temporal. FORMOSAT-2 images

Transcription

1 Mapping paddy rice agriculture using multi-temporal FORMOSAT-2 images Y. S. SHIU, S. H.CHIANG, T. H. CHU and K. T. CHANG Department of Geography, National Taiwan University, Taipei, Taiwan Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. Both factors make it difficult to map paddy rice fields precisely. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, thus ideal for mapping paddy rice fields in East Asia. This paper first determined transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. Unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels data in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index (NDVI) and principal component data to cultivation parcels data to improve classification accuracy. This paper demonstrates more precise and automatic methods for mapping paddy rice fields. Keywords: Paddy rice fields; Multi-temporal; FORMOSAT-2 images; Normalized difference vegetation index; Principal component 1 Introduction Paddy rice fields account for over 11% of global cropland area (Maclean et al. 2002; Xiao et al. 2006). Over half of the world s population live in major countries of Asia, which produce rice, and rice represents over 35% of their daily caloric intake. 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). Paddy rice needs flooded soils during the growing period, so water resource management is a major concern. Irrigation for agriculture accounts for over 80% of the fresh water withdrawals in Corresponding author. ktchang@ntu.edu.tw

2 South-East Asia and South Asia, with several of these countries reporting over 95% of fresh water used for irrigation (FAOSTAT 2001). The Agriculture and Food Agency in Taiwan investigates the circumstances of cultivated paddy fields by interpreting aerial photographs manually. Although considered to be more precise, this manual method has some disadvantages. First, the cultivation parcels in Taiwan are usually small; therefore interpreting the paddy fields in aerial photographs manually is both time and energy consuming. Second, cloudy climate in Taiwan hinders the photography work because getting proper-time aerial photographs is difficult. Third, although aerial photographs have good resolution, only 10 km 2 areas or less can be covered during every shot. As satellite technology has improved in recent years, satellite images are good alternatives to aerial photographs because satellite images cover wide areas and even have higher temporal resolution. The potential of computer-aided classification of multi-temporal imagery data to map traditionally-managed rice fields is evaluated using SPOT-XS, SPOT-4 VEGETATION (VGT), MODIS, Landsat, and so on. Supervised and unsupervised classifications are commonly used for interpreting land features. Images derived data, such as normalized difference vegetation index (NDVI) and principal component reduction are used to improve classification. But supervised and unsupervised classification of a database formed by principal component reduction proved unsuccessful as a result of the high spectral heterogeneity of ploughed and unploughed surfaces. An alternative classification approach has been developed which utilizes a series of steps (unsupervised classification, stratification, and supervised classification) that progressively reduce image heterogeneity in such a way to better aid interpretation and comparison with training data (Turner and Congalton 1998). A VGT-derived normalized difference water index (NDWIVGT = (B3-MIR) / (B3+MIR)) for describing temporal and spatial dynamics of surface moisture using SPOT-4 VEGETATION (VGT) sensor, and their results indicate that normalized difference water index (NDWI) and NDVI temporal anomalies may provide a simple and effective tool for detection of flooding and rice transplanting across the landscape (Xiao et al. 2002). Land surface water index (LSWI), enhanced vegetation index (EVI) and NDVI derived from MODIS images can also be use to identify the changes in the mixture of surface water and green vegetation in paddy rice fields over time requires spectral bands or vegetation indices that are sensitive to both water and vegetation (Xiao et al. 2006). NDVI can also derive from FORMOSAT-2 images, hence this study used NDVI to improve paddy rice fields classification in our study area. Using artificial neural network (ANN) to classify remotely sensed images has been performed for many years. Besides constructing non-linear models, ANN also does

3 not require assumptions, which are needed for building multiple regression and autoregressive integrated moving average (ARIMA) models (Rumelhart et al. 1986). Based on these benefits, Shao and Shih (2000) used ANN to interpret paddy fields with multi-spectral satellite images and compared error back-propagation (BP) and learning vector quantization (LVQ) neural network algorithms with Gaussian maximum likelihood algorithm. The results showed that BP performed the best, and Gaussian maximum likelihood algorithm performed the worst. These results prove that ANN can improve the classification accuracy. Shupe and Marsh (2004) focused on the difficulty of images classification caused by the sparse vegetation in arid areas and solved the difficulty by combining optical Landsat TM images, ERS-1 C band radar images, and elevation data. They also compared BP with Gaussian maximum likelihood but found Gaussian maximum likelihood to be better than BP. In addition, they found changing activation function in BP from sigmoid transfer to hyperbolic tangent transfer could significantly improve the accuracy. The degree of improvement is close to Gaussian maximum likelihood, but still not better than Gaussian maximum likelihood. From the results above, it shows that ANN does not necessarily improve the classification. Traditionally, choosing training areas of supervised classification is a manual job. In other words, we have to be sure where the paddy fields are before we process the supervised classification, and then choose the real paddy fields as the training sites. We can accomplish this selection process by interpreting satellite images with naked eyes or investigating in the field, but both are difficult. This paper used geographic information system (GIS) overlay mapping with the cultivation data to pick up the training areas. These training areas were used for the process of supervised classification to interpret paddy fields and non-paddy fields. And we generated differential normalized difference vegetation index (NDVI) and principal components data from primary images for improving classification accuracy. 2. Brief description of the study area This paper classified an area of 30 km2 in Yunlin County for the first rice crops in The range is from E to E and N to N (Figure 1). Paddy rice fields account for half of this study area, hence the paddy rice plays an important role for inhabitants livelihood. In Taiwan, there are two cultivating periods: first rice crops and second rice crops. Like most regions of East Asia, paddy rice goes through flooding, transplanting, tillering, flowering, and harvesting stages in each period. These specific stages bring special spectral characteristics in remotely sensed imagery different from other land features.

4 Yunlin Study Area County Chayi Yunlin City Figure 1. Study area in Yunlin County, Taiwan. 3. Data used and methodology 3.1 Cultivation parcels data 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) (Figure 2). Based on the digitized boundaries of cultivation parcels, these data record every parcel if it s paddy fields or not. This paper acquired cultivation parcels data of Yunlin County from 1996 to 2006 from the Agriculture and Food Agency. The accuracy of these data are claimed to be 96% or above. 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. (a) (b) Figure 2. Cultivation parcels data interpreted manually by personnel of CSPRS with aerial photographs. Figure 2. (a) shows the spatial relationship of land parcels. Area compassed by lines means one parcel. The number within one parcel means the kind of crops. Number 1 means paddy fields, and number 0 means not paddy fields. Figure 2. (b) is the attribute table of the cultivation data.

5 3.2 Formosat-2 satellite images processing As a result of inferior spatial resolution of the Formosat-2 multi-spectral satellite images, the texture of the crops is hard to be recognized. Fortunately, paddy crops have special growing characteristics that are different from most other crops. The most different spectrum characteristics during the paddy growing stages are transplanting stage and the most active tillering stage. Hence this paper acquired Formosat-2 satellite images for two periods: March 10, 2006 representing the transplanting stage, and April 7, 2006 representing the most active tillering stage. Images of two periods were stacked into one image, which contained 8-layer spectral information for the supervised classification. NDVI has been used to classify paddy rice in many researches (Turner and Congalton 1998; Xiao et al. 2002; Xiao et al. 2006). NDVI difference between transplanting and the most active tillering stages must be evident. Accordingly, four combinations can be acquired for classification: (1) original images of two stages and NDVI image of the first stage, including 9-layer spectral information; (2) original images of two stages and NDVI image of the second stage, including 9-layer spectral information; (3) original and NDVI images of the two stages, including 10-layer spectral information; (4) original images of two stages and differential NDVI image of two stages. Principal component analysis can generate new variables, which are not relative to each other, but these new variables can retain information of original variables. Principal component analysis also performs data reduction when there are too many variables in original data, such as hyper-spectral remotely sensed images. This paper acquired reduction data to that described by Turner and Congalton (1998). Rather than taking the common strategy of reducing multiple-date single-band data sets, the approach taken was to reduce single-date multi-spectral data sets using principal component analysis. This was done to facilitate the interpretation of multi-temporal signatures since ploughing can occur during any of the two growing stages. As seen in Table 1, the first two components of two images both represent approximately 97 percent of all the variation. A new image was formed by combining the first two principal components from the March 10 and April 7 scenes (Table 1).

6 Table 1. Results of principal component analyses performed separately on Formosat-2 data acquired on two separate dates. Shaded components are those combined to form the multi-temporal database used in classification. Eigenvector Date Component Eigenvalue Percentage B1 B2 B3 B March 10, April 7, Training sites extraction with GIS overlay mapping According to the experience of the Agriculture and Food Agency, farmers usually don t easily change their cultivating crops in the same periods for the next year. That is to say, if farmers cultivate rice this year, they will have high potential to cultivate rice in the same periods for the next year. The longer the farmers have cultivated rice, the higher potential the farmers will cultivate rice in the following year. The same is true for farmers cultivating non-paddy crops. According to this principle and our cultivation data from 1996 to 2005, this paper made the following two assumptions: (1) fields which had been paddy fields for past eleven years will still be paddy fields for this year; (2) fields which had been non-paddy fields for past eleven years will still not be paddy fields for this year. Consequently, we followed the steps outlined by the flowchart in Figure 3 to produce training areas. Here we take the paddy fields as an example: (1) intersect cultivation data from 1996 to 2005; (2) dissolve the intersected results to eliminate the common separating line between two adjacent polygons; (3) produce inside buffer zones of the dissolved results to pick the areas where the mixels may exist; (4) erase the areas where buffer zones cover in dissolved results, and finally we get training areas for The purpose of step 2 is used to reduce the regions of mixels (i.e. mixed pixels) as explained in step 3. In other words, we assume that if the adjacent surface features are both paddy fields, there will be no mixels. So we dissolve adjacent polygons which are all paddy fields in order to avoid too many areas being rejected.

7 Cultivation Data from 1996 to 2005 Rice Paddy Fields Non-rice Fields Intersect Intersect Inside 16-meter Buffer Dissolve Adjacent Paddy Fields Dissolve Adjacent Non- Rice Fields Inside 16-meter Buffer Erase Buffercovered Areas in Dissolved Results Erase Buffercovered Areas in Dissolved Results Rice Paddy Training Sites for 2006 Non-rice Paddy Training Sites for 2006 Figure 3. The process to extract training sites 3.4 Image Classification The stacked images mentioned in subsection 3.2 can be masked according to the extracted rice and non-rice sites and then the spectral information of these images can be acquired in these areas (Figure 4). Then, we used unsupervised iterative self-organizing data analysis technique algorithm (ISODATA) to classify the spectral information into twenty-category rice paddy and twenty-category non-rice individually. By this process, we got forty-category signature information which contained statistics and covariance for all categories. The process of ISODATA and acquisition of signature information was accomplished by classifier in ERDAS IMAGINE. The statistics and covariance information were then used for supervised classification (Figure 5). The reason why we classified the rice information into twenty-category was because if we regarded all rice information as one category, the total rice spectral information might easily mix with total non-rice spectral information, and the same would happen if we regarded all non-rice information as one category. The forty-category signature information can be used to classify the stacked satellite images with maximum likelihood classification. After classification, we got the outcome with forty categories. The first twenty categories represented rice paddy classes, and the remaining twenty categories represented non-rice paddy

8 classes. Finally, we reclassified the forty-category outcome into two main groups of rice paddy class and non-rice class. The classified results are shown in Figure 6. Stacked satellite image Mask Paddy training sites with spectral information Paddy training sites Figure 4. Mask the stacked images with the training sites extracted with GIS analysis. The training sites shown here are paddy training sites.

9 Unsupervisedly classify into 20 categories and produce signature information Paddy training sites with spectral information Non-rice training sites with spectral information Unsupervisedly classify into 20 categories and produce signature information 40-category signature information Figure 5. Unsupervisedly classifying the spectral information into twenty categories of paddy and twenty categories of non-rice fields. By this process, we got totally forty-category signature information which contains statistics and covariance for all categories. 40-category signature Reclassify 40 categories Maximum likelihood 8-layer 2 categories Figure 6. Process to obtain two-category classified outcome. The green areas in final two-category map are the portion classified as rice, and the pink areas are the portion classified as non-rice.

10 3.5 Accuracy assessment This paper used the overall accuracy and kappa value to assess accuracy. The ground truth data used for accuracy assessment are cultivation data for the first rice crops in The file format of cultivation data is ArcInfo coverage, which is vector data, but the file format of classified outcome is raster file. Different file formats between classified data and ground truth data made it difficult to do accuracy assessment. Furthermore, mixels in classified results also influence the accuracy. In order to make accuracy assessment more convenient, we regulated the ground truth data with the some steps Eliminate mixels areas. First we dissolved the same land features in cultivation data to eliminate the common separating line between two adjacent polygons and then processed inside four-meter buffer and erasing buffer-covered areas, just like step 2 to step 4 in extracting training sites. Because we knew that the pixels located on the boundaries between rice and non-rice land features will be the mixels whose spectral information is mixed, the classification results of these mixels must be wrong. Obviously, the occurrence of mixels is one kind of data restriction, so we didn t deal with this restriction and excluded these pixels from accuracy assessment Rasterize ground truth data. Convert ground truth data from ArcInfo coverage to grid file format with 8m 8m cell size (i.e. the same cell size as the spatial resolution of multi-spectral formosat-2 images). 4 Results and discussion Table 2 shows the error matrix of classification results. According to the results, the overall accuracy almost ranges from 82% to 85%, except for the result of the addition of two-stage NDVI data. Kappa value is almost from 0.60 to In Figure 7, we can see the areas where error pixels occur are almost all in the northwest quadrant of the study area. Based on the accuracy assessment results, we induced four factors which caused errors.

11 (a) Table 2. Error matrix of all combinations. (a) Original 8-layer data; (b) combinations of original 8-layer data and first-stage NDVI data; (c) combinations of original 8-layer data and second-stage NDVI data; (d) combinations of original 8-layer data and two-stage NDVI data; (e) combinations of original 8-layer data and two-stage differential NDVI data; (f) combinations of the first two PC from each image. (b) Classified result Ground truth data (Reference data) Rice Non-rice total Rice % Non-rice % total % 77.04% 83.77% Kappa Classified result Ground truth data (Reference data) Rice Non-rice total Rice % Non-rice % total % 78.62% 84.53% Kappa (c) (d) Classified result (e) Ground truth data (Reference data) Rice Non-rice total Rice % Non-rice % total % 79.59% 84.38% Kappa Ground truth data (Reference data) Rice Non-rice total Classified Rice % result Non-rice % total % 87.75% 42.70% Kappa (f) Classified result Ground truth data (Reference data) Rice Non-rice total Rice % Non-rice % total % 77.99% 84.34% Kappa Ground truth data (Reference data) Rice Non-rice total Classified Rice % result Non-rice % total % 80.41% 82.93% Kappa

12 Figure 7. Classification result of all combinations. (a) Original 8-layer data; (b) combinations of original 8-layer data and first-stage NDVI data; (c) combinations of original 8-layer data and second-stage NDVI data; (d) combinations of original 8-layer data and two-stage NDVI data; (e) combinations of original 8-layer data and two-stage differential NDVI data; (f) combinations of the first two PC from each image. (a) (b) (c) (d) (e) (f)

13 4.1 Homogeneity of land features Table 2 shows that both user s accuracy and producer s accuracy of rice are higher than those of non-rice. This may be because the total area of rice fields is greater than non-rice fields in this study area. That is to say, many and successive rice paddy fields makes paddy areas homogeneous and easy to classify in satellite images. Figure 7 shows that land features in northwest quadrant where error pixels locate are more complex and minute than other quadrants. In other words, this condition also suggests that regional homogeneity plays an important role in classification. 4.2 Discrepancy of cultivation habits Some farmers planted rice earlier or later than other farmers. That is to say, we cannot observe growing characteristics of rice in images of early March and late April. This condition may result in omission errors just like the upper row in Figure 8 shows. 4.3 Similarity of growing pattern between different crops Some crops have the same growing pattern as rice paddy, such as calla. Consequently, commission errors may happen just like the lower row in Figure 8 shows. 4.4 Error of ground truth data As mentioned in the data sets section, the accuracy of cultivation data is about 96%. This means some errors of our classified results may not be wrong, but unfortunately it is difficult to be confirmed.

14 Commission error Ground truth Image in Image in for rice paddy indicates non-rice planting stage tillering stage Omission error Ground truth Image in Image in for rice paddy indicates rice planting stage tillering stage Figure 8 Commission and omission error for rice paddy. The upper row shows that, in most situations, commission errors happen because the image shows the growing characteristics of rice but the ground truth data indicates non-rice. The lower row shows that, in most situations, omission errors happen because the image shows the growing characteristics of non-rice but the ground truth data indicates rice. 5 Conclusions With overlay analysis, we got training sites more automatically and the results of maximum likelihood classification also be satisfied. Some commission and omission errors occur because of farmers cultivation habits and crops which possess the same growing pattern as rice paddy. These errors could be improved by adding more images of different periods. Raising temporal resolution can increase the precision to trace growing pattern of rice and other crops. The results show that addition NDVI or differential NDVI in original images doesn t improve the classification accuracy very much. Low accuracy will be expected if we use differential NDVI as additional information for wider areas because the cultivation time series are different in wider areas. The combination of the first two PC from each image even gets lower accuracy than original 8-layer data, and this result may be caused by the loss of information when we only get the first two PC from each image. Besides spectral information, we can also add more information, such as texture, into training sites. Increases in temporal resolution and other information both enlarge the data dimensions. This kind of reasonable expansion of data dimensions will make the classified outcome better.

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