Estimating regional forest cover in East Texas using Enhanced Thematic Mapper (ETM+) data

Forest Ecology and Management

Volumn 218, Issue 1-3, 2005, Pages 342-352

  Sivanpillai, Ramesh ^a,b   Smith, Charles T ^a   Srinivasan, R ^a   Messina, Michael G ^a   Wu, X Ben ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

^b UNIVERSITY OF WYOMING   (United States)

Author keywords

East Texas; FIA; Forest inventory; LANDSAT; USDA Forest Service

Indexed keywords

AERIAL PHOTOGRAPHY; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; ESTIMATION; IMAGE PROCESSING; INVENTORY CONTROL; REMOTE SENSING; SATELLITES;

EAST TEXAS; FIA; FOREST INVENTORIES; LANDSAT; USDA FOREST SERVICES;

DEFORESTATION;

AERIAL PHOTOGRAPH; FOREST INVENTORY; LANDSAT; SPATIAL DISTRIBUTION; VEGETATION COVER;

AIR CRAFT; DEFORESTATION; IMAGE ANALYSIS; PHOTOGRAPHY; REMOTE SENSING; SATELLITES;

NORTH AMERICA; TEXAS; UNITED STATES; WESTERN HEMISPHERE; WORLD;

EID: 26444561807     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2005.08.033     Document Type: Article

References (45)

1. M.E. Bauer, T.E. Burk, A.R. Ek, P.R. Coppin, S.D. Lime, T.A. Walsh, D.K. Walters, W. Befort, and D.F. Heinzen Satellite inventory of Minnesota forest resources Photogramm. Eng. Remote Sens. 60 1994 287 298
2. J. Beaubien Landsat TM satellite images of forests: from enhancement to classification Can. J. Remote Sens. 20 1994 17 29
3. D.H. Card Using known map category marginal frequencies to improve estimates of thematic map accuracy Photogramm. Eng. Remote Sens. 48 1982 431 439
4. R.G. Congalton, R.G. Oderwald, and R.A. Mead Assessing landsat classification accuracy using discrete multivariate analysis statistical techniques Photogramm. Eng. Remote Sens. 49 1983 1671 1678
5. R.G. Congalton, and R.A. Mead A quantitative method to test for consistency and correctness in photo interpretation Photogramm. Eng. Remote Sens. 49 1983 69 74
6. R.G. Congalton A review of assessing the accuracy of classifications of remotely sensed data Remote Sens. Environ. 37 1991 35 46
7. Cooke, W.H., 1999. Forest/non-forest stratification in Georgia with LANDSAT TM data. In: Proceedings of the First Annual Forest Inventory and Analysis Symposium. General Technical Report No. NC-213. USDA Forest Service, North Central Research Station, St. Paul, MN, pp. 28-30.
8. R.L. Czaplewski Multistage remote sensing: toward an annual national inventory J. For. 97 1999 44 48
9. A.G. Dodge, and E.S. Bryant Forest type mapping with satellite data J. For. 74 1976 526 531
10. C.C. Dymond, D.J. Mlandenoff, and V.J. Radeloff Phenological differences in tasseled cap indices improve deciduous forest classification Remote Sens. Environ. 80 2002 460 472
11. ERDAS Field Guide 1996 ERDAS Inc. Atlanta, GA
12. K. Fitzpatrick-Lins Comparison of sampling procedures and data analysis for a land-use and land-cover map Photogramm. Eng. Remote Sens. 47 1981 343 351
13. L. Fox, K.E. Mayer, and A.R. Forbes Classification of forest resources with LANDSAT data J. For. 81 1983 283 286
14. H. Franco-Lopez, A.R. Ek, and M.E. Bauer Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbor method Remote Sens. Environ. 77 2001 251 274
15. W.E. Frayer, and G.M. Furnival Forest survey sampling designs: a history J. For. 97 1999 4 10
16. Hansen, M.H., Wendt, D.G., 1999. Using classified LANDSAT thematic mapper data for stratification in a statewide forest inventory. In: Proceedings of the First Annual Forest Inventory and Analysis Symposium. General Technical Report No. NC-213. USDA Forest Service, North Central Research Station, St. Paul, MN, pp. 20-27.
17. P. Holmgren, and T. Thuresson Satellite remote sensing for forestry planning - a review Scand. J. For. Res. 13 1998 90 110
18. W.D. Hudson, and C.W. Ramm Correct formulation of the kappa coefficient of agreement Photogramm. Eng. Remote Sens. 53 1987 421 422
19. L.R. Iverson, E.A. Cook, and R.L. Graham A technique for extrapolating and validating forest cover across large regions - calibrating AVHRR data with TM data Int. J. Remote Sens. 10 1989 1805 1812
20. J.R. Jensen Introductory Digital Image Processing: A Remote Sensing Perspective 2000 Prentice-Hall Englewood Cliffs, NJ
21. Kelly, J.K., Miller, P.E., Hartsell, A.J., 1992. Forest Statistics for Northeast Texas Counties. Resource Bulletin No. SO-171. USDA Forest Service, Southern Forest Experiment Station, New Orleans, LA.
22. Lannom, K.B., Evans, D.L., Zhu, Z., 1995. Comparison of AVHRR Classification and Aerial Photography Interpretation for Estimation of Forest Area. Research Paper No. SO-292. USDA Forest Service, Southern Forest Experiment Station, New Orleans, LA.
23. T. Lillesand, and R.W. Kiefer Remote Sensing and Image Interpretation 2000 John Wiley and Sons New York
24. M. Liu, and A. Samal A fuzzy clustering approach to delineate agroecozones Ecol. Model. 149 2002 215 228
25. Lund, H.G., Thomas, C.E., 1989. A primer on stand and forest inventory designs. General Technical Report No. WO-54. USDA Forest Service, Washington, DC.
26. McWilliams, W.H., Bertelson, D.F., 1986. Forest Statistics for Southeast Texas Counties - 1986. Resourc. Bull. No. SO-114. US Department of Agriculture, Forest Service, Southern Forest Research Station, New Orleans, LA.
27. R.E. McRoberts, D.G. Wendt, M.D. Nelson, and M.H. Hansen Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates Remote Sens. Environ. 81 2002 36 44
28. M.M. Moore, and M.E. Bauer Classification of forest vegetation in north-central Minnesota using LANDSAT multispectral scanner and thematic mapper data Forest Sci. 36 1990 330 342
29. Murphy, P.A., 1976. East Texas Forests: Status and Trends. Resourc. Bull. No. SO-61. US Department of Agriculture, Forest Service, New Orleans, LA.
30. R. Nelson Regression and ratio estimators in integrated AVHRR and MSS data Remote Sens. Environ. 30 1989 201 206
31. Peterson, D.J., Resetar, S., Brower, J., Diver, R., 1999. Forest monitoring and remote sensing - A survey of accomplishments and opportunities for the future. Report No. MR-1111.0-OSTP, RAND Corporation: Science and Technology Policy Institute, Washington, DC.
32. G.A. Reams, and P.C. van Deusen The southern annual forest inventory system J. Agric. Biol. Environ. Stat. 4 1999 346 360
33. G.H. Rosenfield, and K. Fitzpatrick-Lins A coefficient of agreement as a measure of thematic classification accuracy Photogramm. Eng. Remote Sens. 52 1986 223 227
34. R. Sivanpillai Mapping and Monitoring Forest Cover in East Texas using Multi-resolution Satellite Imagery. Doctoral Dissertation 2002 Department of Forest Science, Texas A&M University College Station, TX, USA
35. J.M. Stenback, and R.G. Congalton Using thematic mapper imagery to examine forest understory Photogramm. Eng. Remote Sens. 56 1990 1285 1290
36. M. Story, and R.G. Congalton Accuracy assessment: a user's perspective Photogramm. Eng. Remote Sens. 52 1986 397 399
37. K.B. Teuber Use of AVHRR imagery for large-scale forest inventories Forest Ecol. Manage. 33/34 1990 621 631
38. P.S. Thenkabail, C. Nolte, and J.G. Lyon Remote sensing and GIS modeling for selection of a benchmark research area in the inland valley agroecosy stems of West and Central Africa Photogramm. Eng. Remote Sens. 66 2000 755 768
39. USFS, 1992. Forest Service Resource Inventory and Analysis. Washington, DC.
40. USFS, 2004. Forest Inventory and Analysis 2003 Annual Business Report (FS-806). Washington, DC.
41. J.P. Wayman, R.H. Wynne, J.A. Scrivani, and G.R. Reams Landsat TM-based forest area estimation using iterative guided spectral class rejection Photogramm. Eng. Remote Sens. 67 2001 1155 1165
42. E.H. Wilson, and S.A. Sader Detection of forest harvest type using multiple dates of Landsat TM imagery Remote Sens. Environ. 80 2002 385 396
43. R.H. Wynne, R.G. Oderwald, G.R. Reams, and J.A. Scrivani Optical remote sensing for forest area estimation J. For. 98 2000 31 36
44. Z. Zhu, and D.L. Evans Mapping midsouth forest distributions J. For. 90 1992 27 30
45. Z. Zhu, and D.L. Evans US forest types and predicted percent forest cover from AVHRR data Photogramm. Eng. Remote Sens. 60 1994 525 531