A fuzzy-based method for the regional validation of global maps: The case of MODIS-derived phenological classes in a mega-diverse zone

International Journal of Remote Sensing

Volumn 31, Issue 22, 2010, Pages 5797-5811

  Couturier, Stephane ^a,b  

^a Centro de Investigacion en Geografia y Geomatica Ing Jorge L Tamayo AC   (Mexico)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

REMOTE SENSING; SENSORS;

EVERGREEN FORESTS; GLOBAL PRODUCTS; HIGH RESOLUTION; INTEGRATION SCHEME; REGIONAL VALIDATION; RESOLUTION IMAGERY; TROPICAL DRY FOREST; VALIDATION RESULTS;

FORESTRY;

EVERGREEN FOREST; FUZZY MATHEMATICS; IMAGE RESOLUTION; METHODOLOGY; MODIS; PHENOLOGY; SATELLITE IMAGERY; TROPICAL FOREST;

FORESTRY; FUZZY LOGIC; GRASS LANDS; LAND; MEXICO; QUERCUS; RESOLUTION; SITES;

EID: 78649818623     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.512305     Document Type: Article

References (21)

1. Cochran, W.G. (Ed.), 1977, Sampling Techniques (New York: Wiley and Sons).
2. Cohen, W.B., Maiersperger, T.K., Yang, Z., Gower, S.T., Turner, D.P., Ritts, W.D., Berterretche, M. and Running, S.W., 2003, Comparisons of land cover and LAI estimates derived from ETM{thorn} and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment, 88, pp. 233-255.
3. CONAFOR, 2003, Technical specifications for vegetation cover monitoring based on MODIS satellite imagery [in Spanish]. Available online at: http://148.223.105.188:2222/snif_portal/ (accessed April 2008).
4. Couturier, S., Mas, J.-F., Cuevas, G., Benitez, J., Vega, A. and Tapia, V., 2009, An accuracy index with positional and thematic fuzzy bounds for land-use/land-cover maps. Photogrammetric Engineering and Remote Sensing, 75, pp. 789-805.
5. Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D. and Strahler, A.H., 2002, Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83, pp. 287-302.
6. Hagen, A., 2003, Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17, pp. 235-249.
7. Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A. and Schmullius, C., 2008, Challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1km datasets. Remote Sensing of Environment, 112, pp. 2538-2556.
8. INEGI, 2007, National inventoryof forests and soils [in Spanish]. Available online at: www.inegi.gob.mx/rne/docs/PDFs/Mesa3/20/RodolfoOrozco.pdf (accessed June 2008).
9. Jung, M., Henkel, K., Herold, M. and Churkina, G., 2006, Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, pp. 534-553.
10. Kalacska, M., Sanchez-Azofeifa, G.A., Rivard, B., Calvo-Alvarado, J.C. and Quesada, M., 2008, Baseline assessment for environmental services payments from satellite imagery: a case study from Costa Rica and Mexico. Journal of nvironmental Management, 88, pp. 348-359.
11. Laba, M., Gregory, S.K., Braden, J., Ogurcak, D., Hill, E., Fegraus, E., Fiore, J. and Degloria, S.D., 2002, Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map. Remote Sensing of Environment, 81, pp. 443-455.
12. Mayaux, P., Eva, H., Gallego, J., Strahler, A., Herold, M., Agrawal, S., Naumov, S., Miranda, E.E.D., Bella, C.D., Ordoyne, C., Kopin, Y. and Roy, P.S., 2006, Validation of the Global Land Cover 2000 map. IEEE Transaction on Geoscience and Remote Sensing, 44, pp. 1728-1739.
13. MODIS, 2001, USGS Distributed Active Archive Center. Available online at: http://lpdaac.usgs.gov (accessed April 2008).
14. MODIS, 2003, Validation of the Consistent Year V003 MODIS Land Cover Product. Available online at: http://geography.bu.edu/landcover/userguidelc/consistent.htm (accessed May 2008).
15. Powell, R.L., Matzke, N., Souza, C.D., Clark, M., Numata, I., Hess, L.L. and Roberts, D.A., 2004, Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sensing of Environment, 90, pp. 221-234.
16. Remmel, T.K., Csillag, F., Mitchell, S. and Wulder, M., 2005, Integration of forest inventory and satellite imagery: a Canadian status assessment and research issues. Forest Ecology and Management, 207, pp. 405-428.
17. Rzedowski, J. (Ed.), 1978, Vegetación de México (Mexico City: Limusa).
18. Stehman, S.V. and Czaplewski, R.L., 1998, Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64, pp. 331-344.
19. Stehman, S.V., Wickham, J.D., Smith, J.H. and Yang, L., 2003, Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: statistical methodology and regional results. Remote Sensing of Environment, 86, pp. 500-516.
20. Stibig, H.J., Belward, A.S., Roy, P.S. and Rosalina-Wasrin, U., 2007, A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data. Journal of Biogeography, 34, pp. 625-637.
21. Waser, L.T. and Schwarz, M., 2006, Comparison of large-area land cover products with national forest inventories and CORINE land cover in the European Alps. International Journal of Applied Earth Observation and Geoinformation, 8, pp. 196-207.