Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2019, Vol. 13 Issue (3) : 495-509     https://doi.org/10.1007/s11707-018-0744-6
RESEARCH ARTICLE
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
Download: PDF(26188 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

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.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-018-0744-6
http://journal.hep.com.cn/fesci/EN/Y2019/V13/I3/495
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
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
Detected
changed
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).
1 B Aiazzi, F Bovolo, L Bruzzone, A Garzelli, D Pirrone, C Zoppetti (2018). Change detection in multitemporal images through single- and multi-scale approaches. In: Moser G, Zerubia J, eds. Mathematical Models for Remote Sensing Image Processing. Signals and Communication Technology. Cham: Springer
2 A M Atto, E Trouve, Y Berthoumieu, G Mercier (2013). Multidate divergence matrices for the analysis of SAR image time series. IEEE Trans Geosci Remote Sens, 51(4): 1922–1938
https://doi.org/10.1109/TGRS.2012.2210228
3 Y Ban, A Jacob, P Gamba (2015). Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS J Photogramm Remote Sens, 103: 28–37
https://doi.org/10.1016/j.isprsjprs.2014.08.004
4 Y Ban, O Yousif (2016). Change detection techniques: a review. In: Ban Y, eds. Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Cham: Springer
5 Y Ban, O A Yousif (2012). Multitemporal spaceborne SAR data for urban change detection in China. IEEE J Sel Top Appl Earth Obs Remote Sens, 5(4): 1087–1094
https://doi.org/10.1109/JSTARS.2012.2201135
6 K Conradsen, A A Nielsen, H Skriver (2016). Determining the points of change in time series of polarimetric SAR data. IEEE Trans Geosci Remote Sens, 54(5): 3007–3024
https://doi.org/10.1109/TGRS.2015.2510160
7 T Esch, M Thiel, A Schenk, A Roth, A D S Muller, S Dech (2010). Delineation of urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information. IEEE Trans Geosci Remote Sens, 48(2): 905–916
https://doi.org/10.1109/TGRS.2009.2037144
8 Europe-Space-Agency (2013). Sentinel-1 User Handbook.
9 G M Foody (2010). Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens Environ, 114(10): 2271–2285
https://doi.org/10.1016/j.rse.2010.05.003
10 D F Frate, F Pacifici, D Solimini (2008). Monitoring urban land cover in Rome, Italy, and its changes by single-polarization multitemporal SAR images. IEEE J Sel Top Appl Earth Obs Remote Sens, 1(2): 87–97
https://doi.org/10.1109/JSTARS.2008.2002221
11 D Fugate, E Tarnavsky, D Stow (2010). A survey of the evolution of remote sensing imaging systems and urban remote sensing applications. In: Rashed T, Jürgens C. Remote Sensing of Urban and Suburban Areas. Springer Science
12 P Gamba, F Dell’Acqua (2016). Change detection in urban areas: spatial and temporal scales. In: Ban Y, ed. Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Cham: Springer
13 M Gong, J Zhao, J Liu, Q Miao, L Jiao (2016). Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans Neural Netw Learn Syst, 27(1): 125–138
https://doi.org/10.1109/TNNLS.2015.2435783
14 W M F Grey, A J Luckman, D Holland (2003). Mapping urban change in the UK using satellite radar interferometry. Remote Sens Environ, 87(1): 16–22
https://doi.org/10.1016/S0034-4257(03)00142-1
15 J Gruninger, A J Ratkowski, M L Hoke (2004). The Sequential Maximum Angle Convex Cone (SMACC) Endmember Model. Paper presented at the Proceedings SPIE, Algorithms for Multispectral and Hyper-spectral and Ultraspectral Imagery, Orlando FL
16 A Hecheltjen, F Thonfeld, G Menz (2014). Recent advances in remote sensing change detection – A review. In: Manakos I, Braun M. Land Use and Land Cover Mapping in Europe: Practices and Trends. Springer Science, 145–178
17 M Hussain, D Chen, A Cheng, H Wei, D Stanley (2013). Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens, 80: 91–106
https://doi.org/10.1016/j.isprsjprs.2013.03.006
18 W Kleynhans, B P Salmon, J C Olivier (2015). Detecting settlement expansion in South Africa using a hyper-temporal SAR change detection approach. Int J Appl Earth Obs Geoinf, 42: 142–149
https://doi.org/10.1016/j.jag.2015.06.004
19 F A Kruse, A B Lefkoff, J B Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, A F H Goetz (1993). The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ, 44(2‒3): 145–163
https://doi.org/10.1016/0034-4257(93)90013-N
20 A Lopes, R Touzi, E Nezry (1990). Adaptive speckle filters and scene heterogeneity. IEEE Trans Geosci Remote Sens, 28(6): 992–1000
https://doi.org/10.1109/36.62623
21 B Markus, T Antje, S Karsten, H Stefan (2017). Change classification in SAR time series: a functional approach. Proceeding of the SPIE 10428, Remote Sensing, Earth Resources and Environmental Remote Sensing/GIS Applications, Otc 2017. Warsaw, Poland
22 P Milillo, B Riel, B Minchew, S H Yun, M Simons, P Lundgren (2016). On the synergistic use of SAR constellations’ data exploitation for earth science and natural hazard response. IEEE J Sel Top Appl Earth Obs Remote Sens, 9(3): 1095–1100
https://doi.org/10.1109/JSTARS.2015.2465166
23 J Muro, M Canty, K Conradsen, C Hüttich, A Nielsen, H Skriver, F Remy, A Strauch, F Thonfeld, G Menz (2016). Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series. Remote Sens, 8(10): 795
https://doi.org/10.3390/rs8100795
24 T Nagler, H Rott, M Hetzenecker, J Wuite, P Potin (2015). The Sentinel-1 mission: new opportunities for ice sheet observations. Remote Sens, 7(7): 9371–9389
https://doi.org/10.3390/rs70709371
25 Z Pan, J Huang, F Wang (2013). Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area. Int J Appl Earth Obs Geoinf, 25: 21–29
https://doi.org/10.1016/j.jag.2013.03.002
26 J E Patino, J C Duque (2013). A review of regional science applications of satellite remote sensing in urban settings. Comput Environ Urban Syst, 37: 1–17
https://doi.org/10.1016/j.compenvurbsys.2012.06.003
27 T Perrou, A Garioud, I Parcharidis (2018). Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin. Front Earth Sci, 12(3): 506–520
https://doi.org/10.1007/s11707-018-0711-2
28 A Plaza, G Martín, J Plaza, M Zortea, S Sánchez (2011). Recent developments in endmember extraction and spectral unmixing. Optical Remote Sensing, 3: 235–267
https://doi.org/10.1007/978-3-642-14212-3_12
29 Z Qi, A G Yeh, X Li, S Xian, X Zhang (2015). Monthly short-term detection of land development using RADARSAT-2 polarimetric SAR imagery. Remote Sens Environ, 164: 179–196
https://doi.org/10.1016/j.rse.2015.04.018
30 M Quartulli, M Datcu (2004). Stochastic geometrical modeling for built-up area understanding from a single SAR intensity image with meter resolution. IEEE Trans Geosci Remote Sens, 42(9): 1996–2003
https://doi.org/10.1109/TGRS.2004.833391
31 A Savitzky, M J E Golay (1964). Smoothing and differentiation of data by simplifiedleast squares procedures. Anal Chem, 36(8): 1627–1639
https://doi.org/10.1021/ac60214a047
32 C Small (2012). Spatiotemporal dimensionality and time-space characterization of multitemporal imagery. Remote Sens Environ, 124: 793–809
https://doi.org/10.1016/j.rse.2012.05.031
33 R Torres, P Snoeij, D Geudtner, D Bibby, M Davidson, E Attema, P Potin (2012). GMES Sentinel-1 mission. Remote Sens Environ, 120: 9–24
https://doi.org/10.1016/j.rse.2011.05.028
34 G Wang, Q Weng (2013). Remote Sensing of Natural Resources. CRC Press, 1–580
35 M Watanabe, R B Thapa, T Ohsumi, H Fujiwara, C Yonezawa, N Tomii, S Suzuki (2016). Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2. Earth Planets Space, 68(1): 131–142
https://doi.org/10.1186/s40623-016-0513-2
36 S H Yun, K Hudnut, S Owen, F Webb, M Simons, P Sacco, E Gurrola, G Manipon, C Liang, E Fielding, P Milillo, H Hua, A Coletta (2015). Rapid damage mapping for the 2015 Mw 7.8 Gorkha earthquake using synthetic aperture radar data from COSMO–SkyMed and ALOS-2 satellites. Seismol Res Lett, 86(6): 1549–1556
https://doi.org/10.1785/0220150152
37 H Zhang, J Li, T Wang, H Lin, Z Zheng, Y Li, Y Lu (2018b). A manifold learning approach to urban land cover classification with optical and radar data. Landsc Urban Plan, 172: 11–24
https://doi.org/10.1016/j.landurbplan.2017.12.009
38 H Zhang, H Lin, Y Li, Y Zhang, C Fang (2016). Mapping urban impervious surface with dual-polarimetric SAR data: an improved method. Landsc Urban Plan, 151: 55–63
https://doi.org/10.1016/j.landurbplan.2016.03.009
39 H Zhang, H Lin, Y Wang (2018a). A new scheme for urban impervious surface classification from SAR images. ISPRS J Photogramm Remote Sens, 139: 103–118
https://doi.org/10.1016/j.isprsjprs.2018.03.007
40 H Zhang, R Xu (2018). Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta. International Journal of Applied Earth Observation and Geoinformation, 64: 87‒95
https://doi.org/10.1016/j.jag.2017.08.013
41 Y Zhang, H Zhang, H Lin (2014). Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sens Environ, 141: 155–167
https://doi.org/10.1016/j.rse.2013.10.028
42 Z Zhou (2014). Towards collaborative approach? Investigating the regeneration of urban village in Guangzhou, China. Habitat Int, 44: 297–305
https://doi.org/10.1016/j.habitatint.2014.07.011
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed