Land cover change detection by integrating object-based data blending model of Landsat and MODIS

Remote Sensing of Environment

Volumn 184, Issue , 2016, Pages 374-386

  Lu, Miao ^a   Chen, Jun ^b   Tang, Huajun ^a   Rao, Yuhan ^c   Yang, Peng ^a   Wu, Wenbin ^a  

^a INSTITUTE OF PLANT PROTECTION   (China)

^b NATIONAL GEOMATICS CENTER OF CHINA   (China)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

Change detection; Data blending; NDVI gradient difference; NDVI time series; Object based

Indexed keywords

DATA INTEGRATION; IMAGE ACQUISITION; IMAGE SEGMENTATION; MAPPING; MIXING; PHYSICAL ADDRESSES; RADIOMETERS; SIGNAL DETECTION;

CHANGE DETECTION; ECOSYSTEM MANAGEMENT; LAND COVER MAPPING; LAND-COVER CHANGE; NDVI GRADIENT DIFFERENCE; NDVI TIME SERIES; OBJECT BASED; TEMPORAL ADAPTIVE;

OBJECT DETECTION;

DATA PROCESSING; DETECTION METHOD; GLOBAL CHANGE; LAND COVER; LANDSAT; MAPPING METHOD; MODIS; NDVI; SATELLITE DATA; SPATIAL ANALYSIS; TIME SERIES;

EID: 84979496989     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.07.028     Document Type: Article

References (51)

1. Ban, Y., Jacob, A., Object-based blending of multitemporal multi-angle ENVISAT ASAR and HJ-1 multispectral data for urban land-cover mapping. IEEE Trans. Geosci. Remote Sens. 51:4 (2013), 1998–2006.
2. Ban, Y., Gong, P., Giri, C., Global land cover mapping using Earth observation satellite data: recent progresses and challenges. ISPRS J. Photogramm. Remote Sens. 103 (2015), 1–6.
3. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58 (2004), 239–258.
4. Blaschke, T., Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2010), 2–16.
5. Bodart, C., Eva, H., Beuchle, R., Raši, R., Simonetti, D., Stibig, H.J., et al. Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS J. Photogramm. Remote Sens. 66 (2011), 555–563.
6. Bontemps, S., Bogaert, P., Titeux, N., Defourny, P., An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sens. Environ. 112 (2008), 3181–3191.
7. Bontemps, S., Herold, M., Kooistra, L., van Groenestijn, A., Hartley, A., Arino, O., et al. Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 9 (2012), 2145–2157.
8. Bontemps, S., Langner, A., Defourny, P., Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. Int. J. Remote Sens. 33:15 (2012), 4673–4699.
9. Boschetti, L., Roy, D.P., Justice, C.O., Humber, M.L., MODIS-Landsat blending for large area 30 m burned area mapping. Remote Sens. Environ. 161 (2015), 27–42.
10. Busetto, L., Meroni, M., Colombo, R., Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sens. Environ. 112 (2008), 118–131.
11. Chen, J., Jönsson, P., Tamura, M., Gua, Z., Matsushitab, B., Eklundhd, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91 (2004), 332–344.
12. Chen, G., Hay, G.J., Carvalho, L.M.T., Wulder, M.A., Object-based change detection. Int. J. Remote Sens. 33:14 (2012), 4434–4457.
13. Chen, J., Lu, M., Chen, X., Chen, J., Chen, L., A spectral gradient difference based approach for land cover change detection. ISPRS J. Photogramm. Remote Sens. 85 (2013), 1–12.
14. Chen, G., Zhao, K., Powers, R., Assessment of the image misregistration effects on object-based change detection. ISPRS J. Photogramm. Remote Sens. 87:1 (2014), 19–27.
15. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103 (2015), 7–27.
16. Chen, X., Yang, D., Chen, J., Cao, X., An improved automated land cover updating approach by integrating with downscaled NDVI time series data. Remote Sens. Lett. 6:1 (2015), 29–38.
17. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., Review Article Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25:9 (2004), 1565–1596.
18. Desclée, B., Bogaert, P., Defourny, P., Forest change detection by statistical object-based method. Remote Sens. Environ. 102 (2006), 1–11.
19. Drǎguţ, L., Tiede, D., Levick, S.R., ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 24:6 (2010), 859–871.
20. Gao, F., Masek, J., Schwaller, M., Hall, F., On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44:8 (2006), 2207–2218.
21. Gevaert, C.M., García-Haro, F.J., A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data blending. Remote Sens. Environ. 156 (2015), 34–44.
22. Gong, J., Sui, H., Sun, K., Ma, G., Liu, J., Object-level change detection based on full-scale image segmentation and its application to Wenchuan earthquake. Sci. China, Ser. E: Technol. Sci. 51:2 (2008), 110–122.
23. Hansen, M.C., Loveland, T.R., A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 122 (2012), 66–74.
24. Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., et al. A new data blending model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 113 (2009), 1613–1627.
25. Hu, Q., Wu, W., Xia, T., Yu, Q., Yang, P., Li, Z., et al. Exploring the use of google earth imagery and object-based methods in land use/cover mapping. Remote Sens. 5 (2013), 6026–6042.
26. Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D., Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80 (2013), 91–106.
27. Im, J., Jensen, J.R., Tullis, J.A., Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 29:2 (2008), 399–423.
28. Jia, K., Liang, S., Zhang, N., Wei, X., Gu, X., Zhao, X., et al. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS J. Photogramm. Remote Sens. 93 (2014), 49–55.
29. Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., Xian, G., A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sens. Environ. 132 (2013), 159–175.
30. Kovalskyy, V., Roy, D.P., The global availability of Landsat 5 TM and Landsat 7 ETM + land surface observations and implications for global 30 m Landsat data product generation. Remote Sens. Environ. 130 (2013), 280–293.
31. Lhermitte, S., Verbesselt, J., Jonckheere, I., Nackaerts, K., Aardt, J., Verstraeten, W.W., et al. Hierarchical image segmentation based on similarity of NDVI time series. Remote Sens. Environ. 112 (2008), 506–521.
32. Lu, M., Wu, W., Zhang, L., Liao, A., Peng, S., Tang, H., A comparative analysis of five global cropland datasets in China. Sci. China Earth Sci. 59 (2016), 1–12.
33. Maselli, F., Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses. Remote Sens. Environ. 75 (2001), 29–38.
34. Mora, B., Tsendbazar, N.E., Herold, M., Arino, O., Global land cover mapping: Current status and future trends. Manakos, I., Braun, M., (eds.) Land Use and Land Cover Mapping in Europe: Practices and Trends, 2014, Springer, Netherlands, 11–30.
35. Morisette, J.T., Khorram, S., Accuracy assessment curves for satellite-based change detection. Photogramm. Eng. Remote. Sens. 66 (2000), 875–880.
36. Niemeyer, I., Marpu, P.R., Nussbaum, S., Change detection using the object features. IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007, 2374–2377.
37. Pohl, C., Van Genderen, J.L., Multisensor image blending in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19:5 (1998), 823–854.
38. Powers, R.P., Hay, G.J., Chen, G., How wetland type and area differ through scale: a GEOBIA case study in Alberta's Boreal Plains. Remote Sens. Environ. 117 (2012), 135–145.
39. Rao, Y., Zhu, X., Chen, J., Wang, J., An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM + images. Remote Sens. 7 (2015), 7865–7891.
40. Raši, R., Bodart, C., Stibig, H.J., Eva, H., Beuchle, R., Carboni, S., et al. An automated approach for segmenting and classifying a large sample of multi-date Landsat imagery for pan-tropical forest monitoring. Remote Sens. Environ. 115 (2011), 3659–3669.
41. Raši, R., Beuchle, R., Bodart, C., Vollmar, M., Seliger, R., Achard, F., Automatic updating of an object-based tropical forest cover classification and change assessment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6:1 (2013), 66–73.
42. Reed, B.C., Brown, J.F., Vanderzee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., Measuring phenological variability from satellite imagery. J. Veg. Sci. 5:5 (1994), 703–714.
43. Sun, J., Wu, W., Tang, H., Liu, J., Spatiotemporal patterns of non-genetically modified crops in the era of expansion of genetically modified food. Sci. Rep., 2015, 5.
44. Tewkesbury, A.P., Comber, A.J., Tate, N.J., Lamb, A., Fisher, P.F., A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens. Environ. 160 (2015), 1–14.
45. Wu, W., Verburg, P.H., Tang, H., Climate change and the food production system: impacts and adaptation in China. Reg. Environ. Chang. 14 (2014), 1–5.
46. Xian, G., Homer, C., Fry, J., Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens. Environ. 113 (2009), 1133–1147.
47. Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., et al. The role of satellite remote sensing in climate change studies. Nat. Clim. Chang. 3:10 (2013), 875–883.
48. Yu, Q., Wu, W., Verburg, P.H., Vliet, J.V., Yang, P., Zhou, Q., et al. A survey-based exploration of land-system dynamics in an agricultural region of northeast China. Agric. Syst. 121 (2013), 106–116.
49. Yue, A., Yang, J., Zhang, C., Su, W., Yun, W., Zhu, D., et al. The optimal segmentation scale identification using multispectral Worldview-2 images. Sens. Lett. 10 (2012), 285–291.
50. Zhang, J., Multi-source remote sensing data fusion: status and trends. International Journal of Image & Data Fusion 1:1 (2010), 5–24.
51. Zhu, X., Chen, J., Gao, F., Chen, X., Masek, J.G., An enhanced spatial and temporal adaptive reflectance blending model for complex heterogeneous regions. Remote Sens. Environ. 114:11 (2010), 2610–2623.