Mapping land cover using multi-Temporal sentinel-1A data: A case study in Hanoi

Vietnam Journal of Earth Sciences, 39(4), 345-359, DOI: 10.15625/0866-7187/39/4/10730 345 (VAST) Vietnam Academy of Science and Technology Vietnam Journal of Earth Sciences Mapping land cover using multi-temporal sentinel-1A data: A case study in Hanoi Le Minh Hang*1, Vu Van Truong1, Nguyen Dinh Duong2, Tran Anh Tuan3 1Military Technical Academy, 236 Hoang Quoc Viet street, Cau Giay, Hanoi, Vietnam 2Institute of Geography (VAST), Hanoi, Vietnam 3Institute of Ecology and Biological Resources (VAST), Hanoi, Vietnam Received 22 November 2016. Accepted 01 September 2017 ABSTRACT Land cover mapping is one of the most important applications of both optical and microwave remote sensing. The optical remote sensing recognizes land cover objects using spectral reflectance of the material constituting the land cover. The microwave remote sensing recognizes ground objects using backscatter, of which the intensity depends on the roughness of the ground’s surface. Therefore, the multi temporal SAR images owning a lot of phenology infor- mation of land cover are the potential ideal data source for land cover mapping, in particular in the urban area. In this article, the authors present a new approach to the classification of land cover by using multi-temporal Sentinel-1A data. The experience data are single-pole (VV) in Interferometric Wide Swath mode (IW) collected from December 2014 to October 2015 along descending orbit over Hanoi, Vietnam. Decision tree method is applied base on analyz- ing threshold of standard deviation, mean backscatter value of land cover patterns, and combining double-crop rice classification image. The double-crop rice image is classified by rice phenology using multi-temporal Sentinel-1A images. The threshold in decision tree method is analyzed by field surveying data. The resulting classified image has been assessed using the test points in high-resolution images of Google Earth and field data. The accuracy of pro- posed method achieved 84.7%. Keywords: Multi-temporal SAR images; Land cover; Sentinel-1A; Decision tree classification. ©2017 Vietnam Academy of Science and Technology 1. Introduction1 According to FAO (Food and Agriculture Organization of the United Nations), land cover is the observed as biophysical cover on the Earth's surface. Land cover, conventional- ly, is mapped by using satellite imagery, aerial photo, field survey, or the combination of these data. *Corresponding author, Email: leminhhang81@gmail.com Optical satellite imagery plays an im- portant part in mapping land cover. Recogni- tion of the land cover is based on spectral re- flectance characteristics of land cover catego- ries (Abdalla and Abdulaziz, 2012; Nguyen Dinh Duong et al., 2014; Li et al., 2004) or NDVI time series (Lambin et al., 1999; Myneni et al., 1995). However, optical image- ry has many disadvantages due to weather condition and cloudiness. This is apparent Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 346 truth for countries in the tropical region in- cluding Vietnam. The highest expectancy of cloud-free observation could be just one or two times per year only. It is difficult to ex- tract land cover objects, particularly cultivated land if we use only one or two observations. Therefore, agricultural cultivation with har- vest interval of three months cannot be detect- ed sufficiently using such infrequent optical imagery. In recent years, microwave remote sensing technology has been intensively used for mon- itoring natural resources. Many studies have indicated the correlation between radar backscatter and the different land cover types (Björn, 2009; Thiel et al., 2009) or agricultur- al land (Nguyen Ba Duy et al., 2015). For ex- ample, (Nguyen Ba Duy et al., 2015) extract- ed rice region of the Mekong Delta by using decision tree method with accuracy 92%... The broad use of the former SAR systems was, however, quite limited due to high cost and low temporal resolution. The new SAR system as Sentinel-1A provides polarimetric data in high spatial and temporal resolution for allowing land cover mapping with a quite high accuracy up to 93.28% (Abdikan, 2016; Heiko, 2015; Wagner et al., 2012). The Sentinel-1A, a European radar imag- ing satellite, was launched in 2014. The satel- lite was developed for the specific needs of the Copernicus program in collaboration with European Commission (EC) and European Space Agency (ESA). Sentinel-1A satellite provides SAR images in band C. The system operates in four observation modes offering medium and high spatial resolution data (up to 5 m) in a swath up to 400 km. Sentinel-1A da- ta has two polarizations such as VV and VH and repeated observation cycle of 12 days. The data is public free. In this paper, the authors used multi- temporal Sentinel 1A data to map land cover in Hanoi city. Using means and standard devi- ations computed from the one-year multi- temporal Sentinel 1A data helps us to find out phenology patterns for major land cover cate- gories in the study area. By combining the phenology patterns and thresholds of mean backscatter values of major land cover types and applying decision-tree method, we suc- ceed to develop automatically land cover map with detail cropland and developed land dis- tribution. 2. Study area and data used The study area is located in Hanoi city in the North Vietnam. Hanoi is situated between 20°53' to 21°23' North latitude and 105°44' to 106°02' East longitude (Figure 1). The Red River, a major river, flows through Hanoi. The main topography of Hanoi includes delta and hills. Hanoi has the humid and tropical climate. Hanoi was chosen as the study area be- cause it has enough dynamics to achieve the goals of this research. Moreover, Hanoi is a challenging area regarding land cover changes (urban sprawl). The cultivated land is being changed to developed land. Some parts of the cultivated land are not used and changed to barren land. In addition, the land cover of Ha- noi is complex, it has many types such as de- veloped land, barren land, cultivated land, forest land, water, and wetland In this study, the Sentinel-1A data in ob- servation mode Interferometric Wide Swath (IW) with single VV polarization, acquired in a period from December 2014 to October 2015 has been used. Detail characteristics of the used data are shown in Table 1. Vietnam Journal of Earth Sciences, 39(3), 345-359 347 Figure 1. Location of study area Table 1. Multi-temporal Sentinel-1A experiment data Specifications Sentinel-1A experiment data Acquisition time 13/12/2014; 06/01/2015; 23/02/2015; 19/03/2015; 12/04/2015 30/05/2015; 23/06/2015; 17/07/2015; 03/09/2015; 21/10/2015 Acquisition orbit Ascending Imaging Mode IW (Interferometry Wide Mode) Imaging frequency C-band (5.46 Hz) Polarization single VV polarization Data product Level-1 GRD (Ground Range Detected) Resolution mode 10 × 10 m Bit depth 16 bit 3. Methods 3.1. Pre-processing The SAR data are preprocessed by the open source software SNAP Toolbox which is provided by the European Space Agency. Pre- processing of Sentinle-1A images consists of radiometric calibration, geocoding. The ge- ocoding step involves a Range Doppler Ter- rain correction processing that uses the eleva- tion data from the 3 arc-second DEM products from the Shuttle Radar Topography Mission (SRTM) provided by ESA. In this process, da- ta are resampled and geocoded to a grid of 10m spacing to preserve the 20 m × 5 m spa- tial resolution according to the NYQuist sam- pling thermo (Nguyen Ba Duy et al, 2015). The pre-processing includes three main teps such as (1) Backscatter normalization to sigma-naught (°) of intensity band; (2) Resampled and geo-coded by DEM product and (3) Convert linear to/from dB (Figure 2). Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 348 False Image No1 Image No2 Image No n . Radiometric Calibration Sigma naught (°) intensity band Terrain Correction - UTM/WGS 84 - Resolution: 10 × 10 m Convert linear to dB i ≤ number of images Stop pre-processing DEM product SRTM True Save to file Figure 2. Pre-processing flowchart of multi temporal Sentinel-1A data According to the theory of SAR image processing, the backscatter signal is not only influenced by the characteristics of land cover but also incidence angle. In order to extract the changed of land cover objects by backscatter values, it is necessary to adjust the effect of incidence angle by normalizing all acquisition to a common incidence angle by which intensity value is converted to sigma naught (°). This is described in Daniel Sa- bel’s paper (Daniel et al, 2012). In this SNAP toolbox, the objective of SAR calibration out- put scaling applied by the processor must be undone and the desired scaling must be ap- plied. Level-1 products provide four calibra- tions Look Up Tables (LUTs) to produce °. The LUTs apply a range-dependent gain in- cluding the absolute calibration constant. For GRD products, a constant offset is also ap- plied. The radiometric calibration is applied by the following Eq (1): 2 0 2 i i i DN A   (1) where: DNi-depending on the selected LUT, iA - beta-naught value (i). Bi-linear interpola-tion is used for any pixels that fall between points in the LUTs. Vietnam Journal of Earth Sciences, 39(3), 345-359 349 After normalized radar cross section, the data needs to be converted to dB by SNAP Toolbox. Figure 3b shows the backscatter value in the cross section of the preprocessed image which was acquired on 13/12/2014. In Figure 3b the backscatter of the near and far range is balanced. It is proved that the backscatter value of the preprocessed image is unaffected by the incident angle. Another while, the stability backscatter of pre-processed multi-temporal images is evalu- ated by maximum, minimum, average and standard deviation values of overall the study area (Table 2). Figure 3. (a) Rotated Sentinel-1A image acquired on 13/12/2014 to azimuth; (b) Backscatter value in cross section at line 7712 of the pre-processed image Table 2. Stability of backscatter value of pre-processed Sentinel-1A images Acquisition date Minimum value Maximum value Average Value Standard deviation 13/12/2014 -31.650 34.861 -4.491 5.586 06/01/2015 -29.671 35.580 -4.787 5.958 23/02/2015 -31.517 34.747 -4.789 5.964 19/03/2015 -29.661 36.022 -3.941 4.988 12/04/2015 -31.021 35.855 -3.862 4.967 30/05/2015 -30.287 35.424 -3.873 5.015 23/06/2015 -30.851 35.844 -4.278 5.547 17/07/2015 -30.371 36.358 -3.897 4.998 03/09/2015 -30.839 35.353 -3.763 4.976 21/10/2015 -30.530 35.259 -3.814 4.970 3.2. Fieldwork In this study, the authors sampled a total of 48 field sites by using the Locus map software on March 16, 2015 and December 22, 2014 (Figure 17b). Locus map software, an Android application, is capable of locating GPS loca- tions, collecting photos and recording tracks. The types of land cover objects in the study area consist of cultivated land, evergreen for- est land, double-cropped rice land, barren land, fruit trees, developed land in urban and rural, water (rivers and lakes), wetland and trees in urban area (Figures 4-7). (a) (b) Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 350 Figure 4. (a) Wetland in the Western Hanoi (16/03/2015); (b) High building (22/12/2014) Figure 5. (a) Trees in urban area at Vietnam National Convention Center; (B) Trees on Thang Long Highway (22/12/2014) Figure 6. (a) Double-cropped rice fields in Dan Phuong, Ha Tay (22/12/2014); (B) Double-cropped rice fields in Dan Phuong, Ha Tay (16/03/2015) (a) (b) (a) (b) (a) (b) Vietnam Journal of Earth Sciences, 39(3), 345-359 351 Figure 7. (a) Barren land in the city; (b) Difference type of crop is grown in paddy field (22/12/2014) 3.3. Determining the phenology patterns 3.3.1. Analysis of SAR backscatter for land- cover types Changed/unchanged patterns: Analyzing multi-temporal SAR images (from December 2014 to October 2015), land cover patterns can be divided into two main groups: (i) Changed land-cover patterns (pat- terns have changed during the timeframe of the study) and (ii) Unchanged land-cover pat- terns (patterns have not changed or changed little during the timeframe of the study). The major changed patterns include culti- vated land (for example double-cropped rice, other croplands) and wetland in the Western Hanoi because of changing the purpose of land use. The unchanged patterns are devel- oped land, forest land, water and barren land. In which, forest pattern, in this study, is de- fined as trees in urban, evergreen forest and fruit-trees. As along river or lake, backscatter coefficient is sometimes higher than mean value because of moving of ships, boats,... Figure 8 displays the RGB composite im- age of Sentinel-1A in three observation times. The different color in the composite image represents for different types of land cover. The white or black colors in RGB image are unchanged patterns because of stability of backscatter value in time-series image (Figure 8a). On the other hand, the color shades in RGB image are changed patterns because of having difference backscatter signal of each pixel in time-series (Figure 8b). To separate changed and unchanged land cover categories, we use mean and standard deviation of tem- poral backscatter. Standard deviation value is determined by the Eq. (2):   21 1 2 1 1      n i ii xxn std        (2)  where: istd is standard deviation value of multi-temporal image; ix is backscatter value of one pixel in each time; x is mean backscat- ter value of the multi-temporal image. Averaged backscatter value of land-cover patterns: Figure 9 and Figure 10 show the variation of the backscatter value of each land cover ob- ject in the time frame of the study. In Figure 9, backscatter value of developed land in ur- ban has the highest values ranging from +15dB to +20dB, developed land in rural has the lower value than in urban from +5dB to +10dB. By contrast, backscatter of water has the lowest value, ranging from -20dB to -5dB and forest land has the value from -10dB to - 5dB. Backscatter of barren land has the same value as forest land. (a) (b) Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 352 RGB = R ( 30/01/2015): G (19/3/2015): B (23/06/2015) Figure 8. Color composite using temporal Sentinel-1 data (a) The unchanged patterns shown in black and white; (b) The changed patterns shown in different color shades Figure 9. The backscatter value of barren land, water, developed land and forest on multi-temporal Sentinel-1A images Vietnam Journal of Earth Sciences, 39(3), 345-359 353 Figure 10. Backscatter value of double-cropped rice, other croplands and wetland on multi-temporal Sentinel-1A images Figure11 shows that the standard deviation values of unchanged patterns (such as water, developed land in urban, developed land in rural, barren land, forest) are less than value +2.85dB. By contrast, the standard deviation values of changed patterns (such as double- cropped rice, wetland, the other's cropland) are greater than +2.85dB. Besides, with pat- terns having the same standard deviation val- ues (such as water and developed land) we can use the mean backscatter value to discrim- inate them. For example, mean backscatter of water is -7.586dB whereas the value for de- veloped land in urban is +16.558dB. There- fore, we can extract water and developed land by using simultaneously both thresholds of standard deviation and mean backscatter value simultaneously. As a result, by combining the standard deviation and mean backscatter val- ues of multi temporal SAR data, we can ex- tract different land cover objects. However, Figure 10 and Figure 11 show that it is diffi- cult to discriminate between rice and the oth- ers cropland by mean backscatter or standard deviation value because of having the same texture surface information. Double-cropped rice phenology by multi- temporal SAR images: Many studies have proposed methods which help to extract rice regions by using multi-temporal SAR images (Nguyen Ba Duy et al., 2015; Yuan et al., 2009; Zhiyuan et al., 2011). Figure 12 shows the morphol- ogy of water rice crop with eight main stages. Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 354 According to field survey data, there are two main crops in Hanoi. Summer-Autumn rice crop is from February to May and Winter- Spring rice crop is from August to November. Figure 11. Standard deviations and mean values of backscatter of major land cover objects in Hanoi area Figure 12. Water rice morphology Vietnam Journal of Earth Sciences, 39(3), 345-359 355 Figure 13 shows the correlation between phenology of double-cropped rice and backscat- ter values of multi-temporal Sentinel-1A data in Hanoi within 10 months. There are two periods with the lowest peaks backscatter values which correspond to follow stages. Based on the low- est peaks in January and June, we generate double-cropped rice classification image in bi- nary format in which value 1 refers to rice and the value 0 stand for the other objects. Figure 13. Phenology of double-crop rice with two low-peaks of backscatter 3.3.2. Calculating the intermediate images As the result of section 3.2 and section 3.3, we calculate standard deviation image (Figure 14a) and mean backscatter image (Figure 14b) and double-cropped rice classification image (Figure 14c). The result images were calculat- ed by MATLAB 2014a software. Figure 14 shows the difference between land cover objects in the intermediate images. For example, the unchanged patterns such as water and developed land have low standard deviation values with the dark color, and the changed pattern's values cultivated land in the Western Hanoi has a high standard deviation with bright color. On the contrary, water has low-intensity value, developed land has high- intensity value and cultivated land have the average value in the mean image. Figure14c shows the double-cropped rice classification image which double-cropped rice are shown by black color and others pattern by white color. Figure 15 shows the difference intensity of standard deviation image, mean backscatter image and double-cropped rice image at the same area, in particular, rice land. In this paper, the authors apply decision tree method to classify land cover. We pro- pose using standard deviation image, mean backscatter image and double-cropped rice classification image, which are input data of decision tree method. The flowchart of the classification process is shown in Figure 16a and the thresholds in decision tree method are shown in Figure 16b. The threshold backscat- ter values of nodes in decision tree method are chosen based on field survey and analysis phenology patterns in multi-temporal Senti- nel-1A images (Figure 11) and (Figure 14). Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 356 Figure 14. (a) Standard deviation image; (b) Mean backscatter image; (c) Double-cropped rice classification image Figure 15. Different intensity of standard deviation images, mean backscatter image and double-cropped rice image at the same area; (a) Standard deviation image; (b) Mean image; (c) Double-cropped rice classification image (a) (b) Figure 16. (a) The flowchart of proposed classification method; (b) The threshold of hierarchical class in decision tree method in which band 1 is the standard deviation image; band 2 is the mean backscatter image, and band 3 is the double-cropped rice classification image (a) (b) (c) Vietnam Journal of Earth Sciences, 39(3), 345-359 357 4. Results and Discussion The result of mapping land cover by using multi-temporal Sentinel-1A images is shown in Figure 17. To assess the accuracy of the re- sult, we use 48 field surveying points and 96 random test points in the study area (Figure 18). The test points are interpreted and checked on Google Earth’s high resolution satellites images in 2014 and in 2015. The overall accuracy achieved 84.7%. (a) (b) Figure 17. (a) Land cover mapping in Hanoi; (b) The location of field surveying points and random check points According to the land cover mapping result by using multi-temporal Sentinel-1A images, the authors have the following discussions: Firstly, interpretation and analyzing of land cover patterns in multi temporal SAR data should be combined optical satellite images and field surveying because SAR image only provides texture information surface of objects. Secondly, the standard deviation value of multi-temporal Sentinel-1A images allows evaluating the changes of land cover objects in a period of time. As a result, using standard deviation and mean backscatter value as input data of decision tree method can classify land cover objects with high accuracy. Land cover objects as developed land, riv- ers, lakes can be determined by multi- temporal Sentinel-1A images with high accu- racy (Figure 18c). The accuracy of classifica- tion developed land in this study is 78.8% in which there are 41 corrected points out of 52 test points. In particular, crop land is classi- fied by analyzing the correlation between crop phenology and backscatter signal of multi temporal SAR images (Figure 18d). The accu- racy of classification rice-land is achieved 82.6% including 19 correct out of 23 test points. Hence, the major crops such as rice, maize etc. in Hanoi can be exactly classified by multi-temporal Sentinel-1A images by us- ing cropland phenology. Le Minh Hang, et al./Vietnam Journal of Earth Sciences 39 (2017) 358 However, the proposed method has some disadvantages. Firstly, land cover objects which have the same roughness surface, (e.g. the airport and lakes) are misclassified to the same class. Secondly, terrain shadow is mis- classified to developed land because of terrain slope reflection phenomenon inside looking principle of SAR (Figure 18b). In this study, the authors do not apply speckle noise filtering for the experience data because Sentinel-1A with Level-1 GRD data have been processed by the multi-look over- lay. If we use adaptive filter method, the backscatter value in each time will be changed. As a result, it is difficult to find phe- nology patterns of land cover objects by time series SAR images. However, the final land cover classification results will be filtered by the majority to remove isolated pixels. Figure 18. Analysis of classification result (a) Area of missing image; (b) Terrain shadows; (c) Developed land in Hanoi; (d) Double-cropped rice land 5. Conclusions The proposed method in this paper allows classifying land cover maps in delta area with complex land cover types. However, the limi- tations of SAR image, including terrain shad- ows, speckle noise, and similarity in the sur- face texture of land cover objects can cause confusions in classification result. By combin- ing standard deviation, mean backscatter val- ue and phenology of rice crop in decision tree method allows generating the land cover map in Hanoi using multi-temporal Sentinel-1A images. The overall estimated accuracy can achieve up to 84.7%. The signal on SAR im- ages provides the only backscatter generated by the roughness of ground surface object, and the observation is unaffected by weather condition. Meanwhile, the signal on optical images contains spectral reflectance infor- mation from land cover objects, which is more appropriate for recognition of land cover, but this information is hard to obtain due to weather condition. Therefore, in the future, Vietnam Journal of Earth Sciences, 39(3), 345-359 359 we need to study a combination of optical im- age and SAR image to improve the accuracy of land cover classification. 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