Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

Central European Journal of Geosciences

Volumn 5, Issue 2, 2013, Pages 208-222

  Dimitrov, Petar K ^a   Roumenina, Eugenia K ^a  

^a Faculty of Physics Sofia University St Kliment Ohridski   (Bulgaria)

Author keywords

coniferous forest; forest inventory; multispectral imagery; regression; Rila Mountain

Indexed keywords

CONIFEROUS FORESTS; FOREST INVENTORY; MULTI-SPECTRAL IMAGERY; REGRESSION; RILA MOUNTAIN;

FORESTRY; REGRESSION ANALYSIS;

DATA VISUALIZATION;

BIOMASS; CONIFEROUS FOREST; CORRELATION; DATA SET; ESTIMATION METHOD; FOREST COVER; MOUNTAIN; NATIONAL PLANNING; PARAMETERIZATION; REGRESSION ANALYSIS; SATELLITE IMAGERY; SPECTRAL RESOLUTION; VEGETATION INDEX;

FORESTRY; INVENTORY CONTROL; REGRESSION ANALYSIS;

EID: 84881351583     PISSN: 20819900     EISSN: 18961517     Source Type: Journal    
DOI: 10.2478/s13533-012-0124-9     Document Type: Article

References (40)

1. Dimitrov E., Modelirane na biomasata na estestvenia bial bor [Modeling of the biomass of the natural Scots pine], Pensoft, Sofia, 2001 (in Bulgarian)
2. Berterretche M., Hudak A. T., Cohen W. B., Maiersperger T. K., Gower S. T., Dungan J., Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest, Remote Sens. Environ., 2005, 96, 49-61
3. Cohen W. B., Maiersperger T. K., Gower S. T., Turner D. P., An improved strategy for regression of biophysical variables and Landsat ETM+ data, Remote Sens. Environ., 2003, 84, 561-571
4. Muukkonen P., Heiskanen J., Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data, Remote Sens. Environ., 2005, 99, 434-447
5. Reese H., Nilsson M., Pahlén T. G., Hagner O., Joyce S., Tingelöf U., Egberth M., Olsson H., Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory., Ambio, 2003, 32, 542-548
6. Ripple W., Wang S., Isaacson D., Paine D., A preliminary comparison of Landsat Thematic Mapper and SPOT-l HRV multispectral data for estimating coniferous forest volume, Int. J. Remote Sens., 1991, 12, 1971-1977
7. Hyyppä J., Hyyppä H., Inkinen M., Engdahl M., Linko S., Zhu Y., Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes, Forest Ecol. Manag., 2000, 128, 109-120
8. Mäkelä H., Pekkarinen A., Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data, Forest Ecol. Manag., 2004, 196, 245-255
9. Lefsky M. A., Cohen W. B., Spies T. A., An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon, Can. J. Forest Res., 2001, 31, 78-87
10. Wallerman J., Holmgren J., Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data, Remote Sens. Environ., 2007, 110, 501-508
11. Leboeuf A., Beaudoin A., Fournier R. A., Guindon L., Luther J. E., Lambert M. C., A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery, Remote Sens. Environ., 2007, 110, 488-500
12. Meyer P., Itten K., Kellenberger T., Sandmeier S., Sandmeier R., Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment, ISPRS J. Photogramm., 1993, 48, 17-28
13. Colby J., Keating P., Land cover classification using Landsat TM imagery in the tropical highlands: the influence of anisotropic reflectance, Int. J. Remote Sens., 1998, 19, 1479-1500
14. Vicente-Serrano S., Pérez-Cabello F., Lasanta T., Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images, Remote Sens. Environ., 2008, 112, 3916-3934
15. Dorren L., Maier B., Seijmonsbergen A., Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification, Forest Ecol. Manag., 2003, 183, 31-46
16. Petkov P., Penev N., Marinov M., Nedjalkov S., Naoumov Z., Garelkov D., Antonov G., Tipove gora I organizacia na gorskoto stopanstvo v tehnicheskia uchastak Govedartsi na Samokovskoto gorsko stopanstvo [Forest types and organization of forestry in Govedartsi technical area of the Samokov Forestry Enterprise], Forest Science, 1966, 3, 87-109 (in Bulgarian)
17. Beruchashvili N., Zhuchkova V., Methods for complex physical-geographical investigations: The manual. Moscow University Press, Moscow, 1997 (in Russian)
18. Dimitrov E., Gorskopromishlena taksatsia i lesoustroistvo [Forest mensuration and management]. University of Forestry Publishing House, Sofia, 2000 (in Bulgarian)
19. Zamolodchikov D. G., Utkin A. I., Korovin G. N., Conversion coefficients phytomass/reserves related to dendrometric parameters and stand composition, Forestry, 2005, 6, 73-81 (in Russian with English summary)
20. Land processes distributed active archive center (LP DAAC). Global Data Explorer. USGS, NASA. 2012. URL: http://gdex.cr.usgs.gov/gdex (last accessed 2012.10.24)
21. Dimap dictionary-SPOT Scene profile Version 1.1.2. SPOT Image. 2006. URL: http://www.spotimage.com/dimap/spec/dictionary/dictionary.htm (last accessed 2012.01.22)
22. Moran M. S., Jackson R. D., Slater P. N., Teillet P. M.,. Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output, Remote Sens. Environ., 1992, 41, 169-184
23. Kane V. R., Gillespie A. R., McGaughey R., Lutz J. A., Ceder K., Franklin J. F., Interpretation and topographic compensation of conifer canopy selfshadowing, Remote Sens. Environ., 2008, 112, 3820-3832
24. Teillet P. M., Guindon B., Goodenough D. G., On the slope-aspect correction of multispectral scanner data, Can. J. Remote Sens., 1982, 8, 84-106
25. Gu D., Gillespie A., Topographic normalization of Landsat TM images of forest based on subpixel Sun-canopy-sensor geometry, Remote Sens. Environ., 1998, 64, 166-175
26. Soenen S. A., Peddle D. R., Coburn C. A., SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain, IEEE T. Geosci. Remote, 2005, 43, 2148-2159
27. Rouse J. W., Haas R. H., Schell J. A., Deering D. W., Monitoring vegetation systems in the Great Plains with ERTS. In: Freden S. C., Mercanti E. P., Becker M. A. (Eds.), Proceedings of the 3rd ERTS-1 Symposium, Vol.1, Sect. A, NASA, Washington, D.C., 1974, 309-317
28. Nemani R., Pierce L., Running S., Band L., 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed Leaf Area Index estimates, Int. J. Remote Sens., 1993, 14, 2519-2534
29. Zheng D., Rademacher J., Chen J., Crow T., Bresee M., Moine J. Le, Ryu S., Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA, Remote Sens. Environ., 2004, 93, 402-411
30. Brown L., Chen J. M., Leblanc S.G., Cihlar J., A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis, Remote Sens. Environ., 2000, 71, 16-25
31. Hardisky M. A., Lemas V., Smart R. M., The influence of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alternifolia canopies, Photogramm. Eng. Rem. S., 1983, 49, 77-83
32. Gerylo G. R, Hall R.J, Franklin S. E, Gooderham S., Gallagher L. 2000. Modeling forest stand parameters from Landsat Thematic Mapper (TM) data. In: Proceedings of the 22nd Annual Canadian Remote Sensing Symposium, Victoria, British Columbia. 2000, 405-413
33. Newman M. C., Regression analysis of logtransformed data: statistical bias and its correction, Environ. Toxicol. Chem., 1993, 12, 1129-1133
34. Spies T. A., Forest Structure: A Key to the Ecosystem, Northwest Sci., 1998, 72, 34-39
35. Gemmell F. M., Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a Rocky Mountain Site, Remote Sens. Environ., 1995, 51, 291-305
36. Turner D. P., Cohen W. B., Kennedy R. E., Fassnacht K. S., Briggs J.M., Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites, Remote Sens. Environ., 1999, 70, 52-68
37. Ozdemir I., Karnieli A., Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel, Int. J. Appl. Earth Obs., 2011, 13, 701-710
38. Hall R. J., Skakun R. S., Arsenault E. J., Case B. S., Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume, Forest Ecol. Manag., 2006, 225, 378-390
39. Fazakas Z., Nilsson M., Olsson H., Regional forest biomass and wood volume estimation using satellite data and ancillary data, Agr. Forest Meteorol., 1999, 98-99, 417-425
40. Franklin S. E., Remote sensing for sustainable forest management, Lewis Publishers, Boca Raton-London-New York-Washington, 2001