Towards an efficacious method of using Landsat TM imagery to map forest in complex mountain terrain in Northwest Yunnan, China

Tropical Ecology

Volumn 48, Issue 2, 2007, Pages 227-239

  Yang, Xuefei ^a   Skidmore, Andrew K ^b   Melick, David ^a   Zhou, Zhekun ^a   Xu, Jianchu ^c  

^a KUNMING INSTITUTE OF BOTANY   (China)

^b International Institute of Geoinformation Science and Earth Observation   (Netherlands)

^c WORLD AGROFORESTRY CENTRE ICRAF   (Kenya)

Author keywords

Forest type mapping; GIS expert system; Matsutake mushroom habitat; Maximum likelihood classifier

Indexed keywords

CLASSIFICATION; GIS; IMAGERY; LANDSAT; MOUNTAIN ENVIRONMENT; NDVI; TERRAIN; VEGETATION MAPPING;

ASIA; CHINA; EURASIA; FAR EAST; YUNNAN;

BASIDIOMYCOTA; TRICHOLOMA MATSUTAKE;

EID: 53849111238     PISSN: 05643295     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (36)

1. Aspinall, R. 1992. An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial data. International Journal of Geographical Information Systems 6: 105-121.
2. Aspinall, R. 1993. Habitat mapping from satellite imagery and wildlife survey data suing a bayesian modeling procedure in a GIS. Photogrammetric Engineering & Remote Sensing 59: 537-543.
3. Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157: 101-118.
4. Azadi, H., M. Shahvali, J. van den Berg & N. Faghih. 2007. Sustainable rangeland management using a multi-fuzzy model: How to deal with heterogeneous experts' knowledge. Journal of Environmental Management 83: 236-249.
5. Burgess, D.W., P. Lewis & J.P.A.L. Muller. 1995. Topographic effects in AVHRR NDVI data. Remote Sensing of Environment 54: 223-232.
6. CI. 2003. Biodiversity Hotspots-Hengduan Mountains of South-Central China. Conservation International (CI). World Wide Web: http://www. biodiversity hotspots.org/xp/Hotspots/china/
7. Cingolani, A.M., D. Renison, M.R. Zak & M.R. Cabido. 2004. Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units. Remote Sensing of Environment 92: 84-97.
8. Cohen, J. 1960. A coefficient of agreement for nominal scales. Education and Psychological Measurement 20: 37-46.
9. Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37: 35-46.
10. Dorren, L.K.A., B. Maier & A.C. Seijmonsbergen. 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology & Management 183:31-46.
11. Dymond, J.R. & J.D. Shepherd. 2004. The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery. Remote Sensing of Environment 90: 116-125.
12. Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19: 474-499.
13. Gu, D. & A. Gillespie. 1998. Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry. Remote Sensing of Environment 64: 166-175.
14. Guisan, A. & N.E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186.
15. Horsch, B. 2003. Modelling the spatial distribution of montane and subalpine forests in the central Alps using digital elevation models. Ecological Modelling 168: 267-282.
16. Ji, W.Z. 1999. A Report on the World Environment and Resources Program, Eastern Himalaya and South Asia Grantees Meeting. Yunnan Science and Technology Press, Kunming, China.
17. Jiang, H.Q. 1980a. Distributional features and zonal regularity of vegetation in Yunnan - I. Acta Botanica Yunnanica 2: 22-32.
18. Jiang, H.Q. 1980b. Distributional features and zonal regularity of vegetation in Yunnan - II. Acta Botanica Yunnanica 2: 142-151.
19. Jin, Z. 1981. A mossy forest appearing in the sclerophyllous evergreen broad-leaf forests- Quercetum pannosum nimborum. Acta Botanica Yunnanica 3: 75-88.
20. Liu, X.H., A. K. Skidmore & H. van Oosten. 2002. Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing 56: 257-268.
21. Mehner, H., M. Cutler, D. Fairbairn & G. Thompson. 2004. Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors. Global Ecology & Biogeography 13: 359-369.
22. Nangendo, G., A.K. Skidmore & H. van Oosten. 2007. Mapping East African tropical forests and woodlands - A comparison of classifiers. ISPRS Journal of Photogrammetry and Remote Sensing 61: 393-404.
23. Ogawa, M. 1975. Microbial ecology of mycorrhizal fungus in pine forest - mycorrhiza formed by Tricholoma matsutake. Bulletin of Government Forest Experiment Station 272: 79-121.
24. Riano, D., E. Chuvieco, J. Salas & I. Aguado. 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE Transactions on Geoscience and Remote Sensing 41: 1056-1061.
25. Richards, J.A. 1999. Remote Sensing Digital Image Analysis. Springer-Verlag, Berlin.
26. Rogan, J., J. Franklin & DA Roberts. 2002. A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sensing of Environment 80: 143-156.
27. Shrestha, D.P. & J.A. Zinck. 2001. Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal). International Journal of Applied Earth Observation and Geoinformation 3: 78-85.
28. Skidmore, A.K 1989. An expert system classifies eucalypt forest types using thematic mapper data and a digital terrain model. Photogrammetric Engineering & Remote Sensing 55:1449-1464.
29. Skidmore, A.K., F. Watford, P. Luckananurug & P.J. Ryan. 1996. An operational GIS expert system for mapping forest soils. Photogrammetric Engineering & Remote Sensing 62: 501-511.
30. Stock, M. 1987. AI and expert systems: an overview. AI applications 1: 9-17.
31. Vaiphasa, C., A.K. Skidmore & W.F. de Boer. 2006. A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry & Remote Sensing 61: 1-10.
32. Wang, Y., I.R. Hall & L.A. Evans. 1997. Ectomycorrhizal fungi with edible fruiting bodies: Tricholoma matsutake and related fungi. Economic Botany 51: 311-327.
33. st Edition). Chinese Academy Publisher, Beijing.
34. Xu, J. & A. Wilkes. 2004. Biodiversity impact analysis in northwest Yunnan, southwest China. Biodiversity & Conservation 13: 959-983.
35. Yang, Q.Z. 1990. The characteristics and classification of oak durisilvae in the Himalayan region of China. Acta Phytoecologica Et Geobotanica Sinica 14: 197-211.
36. Yang, X., A.K. Skidmore, D.R. Melick, Z. Zhou & J. Xu. 2006. Mapping non-wood forest product (matsutake mushrooms) using logistic regression and a GIS expert system. Ecological Modelling 198: 208-218.