Assessment of tropical forest degradation with canopy fractional cover from Landsat ETM+ and IKONOS imagery

Earth Interactions

Volumn 9, Issue 22, 2005, Pages 1-18

  Wang, Cuizhen ^a   Qi, Jiaguo ^b   Cochrane, Mark ^b  

^a UNIVERSITY OF MISSOURI   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Forest degradation; Remote sensing; Selective logging

Indexed keywords

DEFORESTATION; IMAGE ENHANCEMENT; REMOTE SENSING; TROPICS; VEGETATION;

DEGRADED FORESTS; FOREST DEGRADATION; LANDSAT ENHANCED THEMATIC MAPPER PLUS (ETM+) IMAGE; LINEAR MIXTURE MODELING; SELECTIVE LOGGING; SQUARED CORRELATION COEFFICIENTS; UNDISTURBED FORESTS; VEGETATION INDEX;

SATELLITE IMAGERY;

EID: 77955937578     PISSN: 10873562     EISSN: 10873562     Source Type: Journal    
DOI: 10.1175/EI133.1     Document Type: Article

References (29)

1. Asner, G. P., M. Keller, R. Pereira, and J. C. Zweede, 2002: Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sens. Environ., 80, 483-496.
2. -. and J. N. M. Silva, 2004: Spatial and temporal dynamics of forest canopy gaps following selective logging in the eastern Amazon. Global Change Biol, 10, 765-783.
3. Cochrane, M. A., A. Alencar, M. D. Schulze, C. M. Souza Jr., D. C. Nepstad, P. Lefebvre, and E. A. Davidon, 1999: Positive feedbacks in the fire dynamic of closed canopy tropical forests. Science, 284, 1832-1835.
4. DeFries, R. S., M. C. Hansen, and J. R. G. Townshend, 2000: Global continuous fields of vegetation characteristics: A linear mixture model applied to multi-year 8km AVHRR data. Int. J. Remote Sens., 21, 1389-1414.
5. Ellera, P., A. Fernandez, and J. A. Delgado, 1996: Temporal evolution of the NDVI as an indicator of forest fire danger. Int. J. Remote Sens., 17, 1093-1105.
6. Gobron, N., B. Pinty, M. Verstraete, and Y. Govaerts, 1999: The MERIS Global Vegetation Index (MGVI): Description and preliminary application. Int. J. Remote Sens., 20, 1917-1927.
7. Gutman, G., and A. Ignatov, 1998: The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19, 1533-1543.
8. Hanan, N. P., and S. D. Prince, 1991: Spectral modeling of multicomponent landscapes in the Sahel. Int. J. Remote Sens., 12, 1242-1258.
9. Houghton, R. A., D. L. Skole, C. A. Nobre, J. L. Hackler, K. T. Lawrence, and W. H. Chomentowski, 2000: Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature, 403, 301-304.
10. Huete, A. R., 1988: A soil-adjusted vegetation index (SAVI). Remote Sens. Environ, 25, 295-309.
11. -. C. Justice, and W. Van Leeuwen, 1999: MODIS vegetation index, MODIS algorithm theoretical basis document. NASA Goddard Space Flight Center, 120 pp.
12. Jasinski, M. F., 1990: Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale. Remote Sens. Environ, 32, 169-187.
13. -. and P. S. Eagleson, 1990: Estimation of subpixel vegetation cover using red-infrared scattergrams. IEEE Trans. Geosci. Remote Sens., 28, 253-267.
14. Laurance, W. F., M. A. Cochrane, S. Bergen, P. M. Fearnside, P. Delamonica, C. Barber, S. D'Angelo, and T. Fernandes, 2001: The future of the Brazilian Amazon. Science, 291, 438-439.
15. Lu, D., E. Moran, and M. Batistell, 2003: Linear mixture model applied to Amazonian vegetation classification. Remote Sens. Environ, 87, 456-469.
16. Lyon, J. G., D. Yuan, R. S. Lunetta, and C. D. Elvidge, 1998: A change detection experiment using vegetation indices. Photogramm. Eng. Remote Sens., 64, 143-150.
17. Maas, S. J., 2000: Linear mixture modeling approach for estimating cotton canopy ground cover using satellite multispectral imagery. Remote Sens. Environ, 72, 304-308.
18. Nepstad, D. C, and Coauthors, 1999: Large-scale impoverishment of Amazonian forests by logging and fire. Nature, 398, 505-508.
19. Pereira, R., Jr., J. Zweede, G. P. Asner, and M. Keller, 2002: Forest canopy damage and recovery in reduced-impact and conventional selective logging in eastern Para, Brazil. For. Ecol. Manage., 168, 77-89.
20. Pinty, B., and M. M. Verstraete, 1992: GEMI: A non-linear index to monitor global vegetation from satellites. Vegetatio, 101, 15-20.
21. Qi, J., A. Chehbouni, A. R. Huete, and Y. Kerr, 1994: A modified soil adjusted vegetation index (MSAVI). Remote Sens. Environ., 48, 119-126.
22. -. and Coauthors, 2000: Spatial and temporal dynamics of vegetation in the San Pedro River basin area. Agric. For. Meteor., 105, 55-68.
23. Skole, D. L., C. O. Justice, J. R. G. Townshend, and A. C. Janetos, 1997: A land cover change monitoring program: Strategy for an international effort. Mitigation Adapt. Strategies Global Change, 2, 157-175.
24. Townshend, J. R. G., 1999: MODIS enhanced land cover and land cover change product: Algorithm theoretical basis documents (ATBD), version 2.0. 92 pp.
25. Tucker, J. J., 1979: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ., 8, 127-150.
26. Uhl, C, A. Veríssimo, M. M. Mattos, Z. Brandino, and I. C. G. Vieira, 1991: Social, economic, and ecological consequences of selective logging in an Amazon frontier: The case of Tailândia. For. Ecol. Manage., 46, 243-273.
27. Verhoef, W., 1984: Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sens. Environ., 16, 125-141.
28. Young, S. S., and C. Y. Wang, 2001: Land-cover change analysis of China using global-scale pathfinder AVHRR Landcover (PAL) data, 1982-1992. Int. J. Remote Sens., 22, 1457-1477.
29. Zeng, X., R. E. Dickinson, A. Walker, and M. Shaikh, 2000: Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteor., 39, 826-839.