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Frontiers of Earth Science

Front. Earth Sci.    2019, Vol. 13 Issue (3) : 495-509
Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping
Zhuokun PAN1,6, Yueming HU1,2,3,4,5(), Guangxing WANG6,1()
1. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
2. Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, Guangzhou 510642, China
3. Guangdong Provincial Key Laboratory of Land Use and Consolidation, Guangzhou 510642, China
4. Guangdong Provincial Land Information Engineering Research Center, Guangzhou 510642, China
5. College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
6. Department of Geography, Southern Illinois University at Carbondale, IL 62901, USA
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Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

Keywords Sentinel-1 SAR      time series images      urban land use change detection      temporal endmember      spectral angle mapping     
Corresponding Authors: Yueming HU,Guangxing WANG   
Just Accepted Date: 20 November 2018   Online First Date: 28 February 2019    Issue Date: 15 October 2019
 Cite this article:   
Zhuokun PAN,Yueming HU,Guangxing WANG. Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping[J]. Front. Earth Sci., 2019, 13(3): 495-509.
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Zhuokun PAN
Yueming HU
Guangxing WANG
Fig.1  (a) The study area-Tianhe District shown with an ASTER image and its location in Guangzhou City; and (b) Time series of Sentinel-1 SAR data for the study area.
Fig.2  The examples of old villages, buildings and factories within the study area (courtesy of Guangzhou Urban Renewal Bureau).
No. Image product Acquisition date
1 S1A_IW_GRDH_1SDV_20150615T103312_20150615T103341_006383_0086AA_07EC.SAFE 20150615
2 S1A_IW_GRDH_1SDV_20150627T103313_20150627T103342_006558_008B9C_100F.SAFE 20150627
3 S1A_IW_GRDH_1SDV_20150709T103312_20150709T103341_006733_009049_CF8D.SAFE 20150709
4 S1A_IW_GRDH_1SDV_20150721T103313_20150721T103342_006908_009560_6200.SAFE 20150721
5 S1A_IW_GRDH_1SDV_20150802T103314_20150802T103343_007083_009A41_715F.SAFE 20150802
6 S1A_IW_GRDH_1SDV_20150814T103315_20150814T103344_007258_009F0C_B933.SAFE 20150814
7 S1A_IW_GRDH_1SDV_20150907T103316_20150907T103351_007608_00A898_4C6E.SAFE 20150907
8 S1A_IW_GRDH_1SDV_20150919T103316_20150919T103345_007783_00AD3B_5EEA.SAFE 20150919
9 S1A_IW_GRDH_1SDV_20151001T103316_20151001T103351_007958_00B1F9_7B09.SAFE 20151001
10 S1A_IW_GRDH_1SDV_20151013T103316_20151013T103345_008133_00B69F_093F.SAFE 20151013
11 S1A_IW_GRDH_1SDV_20151212T103310_20151212T103339_009008_00CEA9_F2C9.SAFE 20151212
12 S1A_IW_GRDH_1SDV_20151224T103309_20151224T103338_009183_00D39A_BF49.SAFE 20151224
13 S1A_IW_GRDH_1SDV_20160105T103309_20160105T103344_009358_00D892_2822.SAFE 20160105
14 S1A_IW_GRDH_1SDV_20160117T103308_20160117T103337_009533_00DD94_83AB.SAFE 20160117
15 S1A_IW_GRDH_1SDV_20160129T103308_20160129T103343_009708_00E2BE_6878.SAFE 20160129
16 S1A_IW_GRDH_1SDV_20160210T103308_20160210T103337_009883_00E7C2_99AA.SAFE 20160210
17 S1A_IW_GRDH_1SDV_20160305T103308_20160305T103337_010233_00F1DB_C69B.SAFE 20160305
18 S1A_IW_GRDH_1SDV_20160329T103308_20160329T103338_010583_00FBDA_D2EC.SAFE 20160329
19 S1A_IW_GRDH_1SDV_20160422T103309_20160422T103338_010933_010659_9465.SAFE 20160422
20 S1A_IW_GRDH_1SDV_20160504T103310_20160504T103339_011108_010BD3_47E3.SAFE 20160504
21 S1A_IW_GRDH_1SDV_20160516T103313_20160516T103342_011283_011177_7199.SAFE 20160516
22 S1A_IW_GRDH_1SDV_20160528T103314_20160528T103343_011458_011737_2431.SAFE 20160528
Tab.1  Image product checklist in the time series dataset
Fig.3  The flowchart of image preprocessing. (a) Radiometric calibration; and (b) spatial co-registration, image enhancement and time domain filting.
Fig.4  Methodological framework of time series-based change detection. (a) Linear transformation of SAR time series data; (b) spectral unmixing analysis to obtain temporal endmember; and (c) spectral angle mapping for change detection.
Fig.5  Change detection results by combining the time series of SAR data (2015?2016) and spectral angle mapping.
Fig.6  The locations of the selected six sites on the land use and land cover change (LULC) detection image (upper left) with the details of the changes for the selected (a) site 1; (b) site 2; (c) site 3 and (d) site 4 (right); and (e) site 5 and (f) site 6 (lower left) validated by the Google Earth images.
Fig.7  Comparison of the results from two change detection methods. (a) ENVI change detection method; (b) time series-based SAM change detection method; and right: ENVI change detection method versus time series-based method.
Result Actual changed
Increase Decrease Unchanged Total
Increase 24 10 2 36
Decrease 6 25 4 35
Unchanged 0 0 29 29
Total 30 35 35 100
Tab.2  Confusion matrix of change detection results
Fig.8  The spatial distributions (upper) of 100 sites randomly selected for overall accuracy of change detection results with 13 specific validation areas (lower).
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