Accuracy of forest mapping based on Landsat TM data and a kNN-based method

Remote Sensing of Environment

Volumn 110, Issue 4, 2007, Pages 420-430

  Gjertsen, Arnt Kristian ^a  

^a NORWEGIAN INSTITUTE OF BIOECONOMY RESEARCH   (Norway)

Author keywords

Accuracy; kNN; Landsat TM; Multi source forest inventory

Indexed keywords

ERROR ANALYSIS; MAPPING; PARAMETER ESTIMATION; SATELLITE IMAGERY;

K-NEAREST NEIGHBOUR; MULTI-SOURCE FOREST INVENTORY (MSFI); REFERENCE DATASETS;

FORESTRY;

DATA SET; FOREST INVENTORY; LANDSAT THEMATIC MAPPER; MAPPING; SATELLITE IMAGERY;

ERRORS; FOREST MANAGEMENT; MAPPING; PARAMETERS; SATELLITES;

EID: 34548405471     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2006.08.018     Document Type: Article

References (17)

1. Bernstein R., Lotspiech J.B., Myers H.J., Kolsky H.G., and Lees R.D. Analysis and processing of landsat-4 sensor data using advanced image-processing techniques and technologies. IEEE Transactions on Geoscience and Remote Sensing 22 (1984) 192-221
2. Dees M., Duvenhorst J., Gross C.P., and Koch B. Combining remote sensing data sources and terrestrial sample-based inventory data for the use in forest management inventories. Proc. XIX ISPRS Congress, Commission VII, 16-22 July 2000, Amsterdam Vol. XXXIII, Suppl. B7/1 (2000) 355-362
3. Fazakas Z., Nilsson M., and Olsson H. Regional forest biomass and wood volume estimation using satellite data and ancillary data. Agricultural and Forest Meteorology 98 (1999) 417-425
4. Gjertsen A.K., and Eriksen R. Test av MSFI-metoden: Nøyaktighetstest på datasett fra Østfold og Hobøl, Norsk institutt for jord-og skogkartlegging (NIJOS), Report No. 2/04 (2004) 52 pp
5. Gjertsen A.K., Tomppo E., and Tomter S. Combined use of NFI sample plots and Landsat TM data to provide forest information on municipality level. Proc. Remote Sensing and Forest Monitoring, IUFRO, 1-3 June 1999, Rogow, Poland Vol. 1 (1999) 167-174
6. Heikkonen J., and Varjo J. Forest change detection applying Landsat Thematic Mapper difference features: A comparison of different classifiers in boreal forest conditions. Forest Science 50 (2004) 579-588
7. Jensen J.R. Introductory digital image processing: A remote sensing perspective (1996), Prentice Hall, New Jersey
8. Katila M., and Tomppo E. Selecting estimation parameters for the Finnish multisource National Forest Inventory. Remote Sensing of Environment 76 (2001) 16-32
9. Liu C.M., Zhang L.J., Davis C.J., Solomon D.S., Brann T.B., and Caldwell L.E. Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Science 49 (2003) 619-631
10. Nilsson M., Holm S., Reese H., Wallerman J., and Engberg J. Improved forest statistics from the Swedish National Inventory by combining field data and optical satellite data using post-stratification. Proc. ForestSat 2005, 31 May-3 June 2005, Borås, Sweden Vol. 8a (2005) 22-26
11. Rencher A.C. Methods of multivariate analysis (2002), Wiley, New York
12. Tokola T., Pitkanen J., Partinen S., and Muinonen E. Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. International Journal of Remote Sensing 17 (1996) 2333-2351
13. Tomppo E. The Finnish multi-source inventory. ForestSat 2005, 31 May-3 June 2005, Borås, Sweden Vol. 8a (2005) 27-34
14. Tomppo E., Goulding C., and Katila M. Adapting Finnish multi-source forest inventory techniques to the New Zealand preharvest inventory. Scandinavian Journal of Forest Research 14 (1999) 182-192
15. Tomppo E., and Halme M. Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: A genetic algorithm approach. Remote Sensing of Environment 92 (2004) 1-20
16. Tomppo E., and Katila M. Satellite image-based national forest inventory of Finland. Proc. International Geoscience and Remote Sensing Symposium 1991, Espoo, Finland Vol. 3 (1991) 1141-1144
17. Tomppo E., and Siitonen M. The national forest inventory of Finland. Paper and Timber 73 (1991) 1-8