XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/7: Forestry]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: INVESTIGATING THE CAPABILITY OF IRS-P6-LISS IV SATELLITE IMAGE FOR PISTACHIO FORESTS DENSITY MAPPING (CASE STUDY: NORTHEAST OF IRAN) F. Hoseini, A. A. Darvishsefat, N. Zargham*

Document type:: Multivolume work

Structure type:: Chapter

XIX-B8, 2012 avipanah, 2003]. gression analysis es existed in the " image analysis. ed. ig bhattacharrya ased on training etic bands with to mean (MD) primary results density classes, sifications were viating from the 35mx35m) was assessment of the use of error ccuracy and an )]. ayers of digital mage indicated classes (0-596, jared for about asses showed ent of 50% and :ctral similarity forest density th 3 classes (0- ppa coefficient sure 4 presents TRO ters ses resulted 84) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia Table 1. Error matrix of the best classification with three density classes = > a Dn S an 8 + eiu g ST | IT) ES | 5 = 2 = g Dnm = 2° > E co o oo Ô SE D = D S 9 c = e = È 9 Be os 2 2 = > SI s ON 5s oo — — o c o 5 en co Mo c = 5 c e e e 8 S M = No] © uv pr 3 «&| uni SS o © + | N| ES e EM — U- = xt e e © ee = en -— x S NISI C — s = mn | 2 |5 & £z eu = oo > en = v — oo g © ON en oo e — <t uv e = v — © — CQ en 8 — = s c © = > " un c3 w— = 5. CONCLUSION Based on this investigation, fuzzy and maximum likelihood classifiers indicated almost the same and the highest overall accuracy and kappa coefficient. According to the best band sets, the distance-based vegetation indices could improve the classification results slightly. Considering the low kappa coefficient (0.44), even if reaching to pretty good overall accuracy (7096), the result of classification was not desirable because forest canopy is a continuous variable and the decision boundaries do overlap [Skidmore ef al., 1988]. Remotely sensed data and classification methods applied in this research could not well classify forest density as a continuous variable in this low density forested area. Similar research confirms this too [Joshi et al., 2006]. Overall, it could be concluded that this approach is not appropriate for operational mapping of vast Pistachio forests. Higher spectral and spatial satellite data or multispectral digital aerial photos such as UltraCam images and object-based classification should be investigated as an alternative approach. In this case, the tree crowns could be detected and sum of areas per ha can be used for density estimation. 6. REFERENCES Alavipanah, S. K., 2003, Remote sensing application in the earth science. University of Tehran Publication, 478 pp. Congalton, K.G. and Green, K., 1999. Assessing the accuracy of remotely sensed data: Principles and practices. New York, NY: Lewis Publishers, 137 pp. Dorren, L.K., Maier, A.B. and Seijmonsbergen, A.C., 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183, 31-46 (2003). Eastman, J. R., 2006. IDRISI Andes Guide to GIS and Image Processing. CLARK University, Version 15.00, 327 pp. Joshi, C., Leeuw, J. D., Skidmore, A. K., Duren, I. C. V. and Oosten, H. V., 2006. Remotely sensed estimation of forest canopy density: a comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation, 8, 84-95. Revised Plan of Pistachio Forest of Khajehkalat Studies, 2009. Natural Resources General Office of Razavi Khorasan Province, 139 pp. Skidmore, A. K., Forbes, G.W. and Carpender, D.J., 1988. Non- parametric test of overlap in multispectral classification. International Journal of Remote sensing, 9, 777-785.

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