Change detection, accuracy, and bias in a sequential analysis of Landsat imagery in the Pearl River Delta, China: Econometric techniques

Agriculture, Ecosystems and Environment

Volumn 85, Issue 1-3, 2001, Pages 95-105

  Kaufmann, Robert K ^a   Seto, Karen C ^a,b  

^a Department of Geography   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

Accuracy; Bias; Land cover change detection; Landsat TM; Time series techniques

Indexed keywords

LAND COVER; LAND USE CHANGE; LANDSAT THEMATIC MAPPER; REMOTE SENSING;

CHINA;

EID: 0035009124     PISSN: 01678809     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8809(01)00190-6     Document Type: Article

References (25)

1. Barbosa P.M., Gregoire J.-M., Pereira J.M.C. An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale. Rem. Sen. Environ. 69:1999;253-263.
2. Collins J.B., Woodcock C.E. Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality. Rem. Sen. Environ. 50:1994;267-279.
3. Collins J.B., Woodcock C.E. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Rem. Sen. Environ. 56:1996;66-77.
4. Crist E.P., Cicone R.C. A physically-based transformation of Thematic Mapper data - the TM Tasseled Cap. IEEE Trans. Geosci. Rem. Sen. 22:1984;256-263.
5. Dai X.L., Khorram S. Remotely sensed change detection based on artificial neural networks. Photogramm. Eng. Rem. Sen. 65:1999;1187-1194.
6. Diebold F.X., Mariano R.S. Comparing Predictive Accuracy. J. Bus. Econ. Statist. 13:1995;253-263.
7. Eastman R.J., Fulk M. Long sequence time series evaluation using standardized principal components. Photogramm. Eng. Rem. Sen. 59:1993;1307-1312.
8. Fung T. An assessment of TM imagery for land-cover change detection. IEEE Trans. Geosci. Rem. Sen. 28:1990;681-684.
9. Gopal S., Woodcock C.E. Remote sensing of forest change using artificial neural networks. IEEE Trans. Geosci. Rem. Sen. 34:1996;398-404.
10. Green K., Kempka D., Lackey L. Using remote sensing to detect and monitor land-cover and land-use change. Photogramm. Eng. Rem. Sen. 60:1994;331-337.
11. Hall F.G., Strebel D.E., Nickeson J.E., Goetz S.J. Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Rem. Sen. Environ. 35:1991;11-27.
12. Howarth P.J., Boasson E. Landsat digital enhancements for change detection in urban environments. Rem. Sen. Environ. 13:1983;149-160.
13. Hsaio, C., 1986. Analysis of Panel Data. Cambridge University Press, Cambridge.
14. Jensen J.R., Rutchey K., Koch M.S., Narumalani S. Inland wetland change detection in the everglades water conservation area 2A using a time series of normalized remotely sensed data. Photogramm. Eng. Rem. Sen. 51:1995;199-209.
15. Kwarteng A.Y., Chavez P.S. Jr. Change detection study of Kuwait City and environs using multi-temporal Landsat Thematic Mapper data. Int. J. Rem. Sen. 19:1998;1651-1662.
16. Lambin E.F. Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogramm. Eng. Rem. Sen. 62:1996;931-938.
17. Lambin E.F., Strahler A.H. Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Rem. Sen. Environ. 48:1994;231-244.
18. Mas J.-F. Monitoring land-cover changes: a comparison of change detection techniques. Int. J. Rem. Sen. 20:1999;139-152.
19. Pax Lenney M., Woodcock C.E., Collins J.B. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Rem. Sen. Environ. 56:1996;8-20.
20. Schott J.R., Salvaggio C., Volchok W.J. Radiometric scene normalization using psuedoinvariant features. Rem. Sen. Environ. 26:1998;1-16.
21. Seto, K.C., Woodcock, C.E., Song, C., Huang, X., Lu., J., Kaufmann, R.K., in press. Monitoring land-use change in the Pearl River Delta using Landsat TM. Int. J. Rem. Sen.
22. Singh A. Digital change detection techniques using remotely-sensed data. Int. J. Rem. Sen. 10:1989;989-1003.
23. Song C., Woodcock C.E., Seto K.C., Pax Lenney M., Macomber S.A. Classification and change detection using Landsat TM data: when and how to correct the atmospheric effects? Rem. Sen. Environ. 75:2001;230-244.
24. Stow, D.A., Tinney, L.R., Estes, J.E., 1980. Deriving land use/land cover change statistics from Landsat: a study of prime agricultural land. In: Proceedings of the 14th International Symposium on Remote Sensing of the Environment held in Ann Arbor in 1980, Ann Arbor, Environmental Research Institute of Michigan, Michigan, pp. 1227-1237.
25. Zellner A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J. Amer. Statist. Assoc. 57:1962;348-368.