Thematic land-cover map assimilation and synthesis: The case of locating potential bioenergy feedstock in eastern Ontario, Canada

International Journal of Geographical Information Science

Volumn 28, Issue 2, 2014, Pages 274-295

  Calvert, Kirby ^a   Luciani, Peter ^b   Mabee, Warren ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b QUEEN S UNIVERSITY   (Canada)

Author keywords

accuracy assessment; bioenergy; data quality; error magnitude; land cover map

Indexed keywords

ACCURACY ASSESSMENT; BIOENERGY; DATA ASSIMILATION; DATA QUALITY; LAND COVER; THEMATIC MAPPING;

CANADA; ONTARIO [CANADA];

EID: 84888063723     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2013.833619     Document Type: Article

References (53)

1. Ahamed, T., et al., 2011. Review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35, 2455-2469.
2. Anderson, J.R., 1976. A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
3. Boots, B. and Csillag, F., 2006. Categorical maps, comparison, and confidence. Journal of Geographical Systems, 8, 109-118.
4. Calvert, K., 2011. Geomatics and bioenergy feasibility assessments: taking stock and looking forward. Renewable and Sustainable Energy Reviews, 15, 1117-1124.
5. Calvert, K., Pearce, J., and Mabee, W.E., 2013. Toward renewable energy geo-information infrastructures: applications of GIS and remote sensing that build institutional capacity. Renewable and Sustainable Energy Reviews, 18, 416-429.
6. Congalton, R.G. and Green, K., 1999. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton, FL: Lewis Publishers.
7. Deloitte & Touche LLP, 2008. Wood pellet plant cost study for the forests of North Eastern Ontario. Available from: http://www.canadiancleanpowercoalition.com/index.php/clean-power-projects-and-technology/reports/industry-reports/ [Accessed 10 October 2010].
8. Dominguez, J., Casals, X.G., and Pascua, I.P., 2007. GIS approach to the definition of capacity and generation ceilings of renewable energy technologies. Energy Policy, 35, 4879-4892.
9. Durbha, S.S., et al., 2009. A framework for semantic reconciliation of disparate earth observation thematic data. Computers and Geosciences, 35, 761-773.
10. Finn, J.T., 1993. Use of the average mutual information index in evaluating classification error and consistency. International Journal of Geographical Information Systems, 7, 349-366.
11. Foody, G.M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185-201.
12. Foody, G.M., 2007. Map comparison in GIS. Progress in Physical Geography, 31, 439-446.
13. Foody, G.M., 2008. Harshness in image classification accuracy assessment. International Journal of Remote Sensing, 29, 3137-3158.
14. Franklin, S.E. and Wulder, M.A., 2002. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, 173-205.
15. Goodchild, M.F., 1992. Geographical data modeling. Computers and Geosciences, 18, 401-408.
16. Horner, M.W., Zhao, T., and Chapin, T.S., 2011. Toward an integrated GIScience and energy research agenda. Annals of the Association of American Geographers, 101, 764-774.
17. Inc, S., 2004. Introduction to the Ontario land cover data base: outline of production methodology and description of 27 land cover classes. 2nd ed. Report prepared for the Ontario Ministry of Natural Resources.
18. Jensen, J.R., 2005. Introductory digital image processing: a remote sensing perspective. Upper Saddle River, NJ: Prentice Hall.
19. Jung, M., et al., 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, 534-553.
20. Layzell, D.B., Stephen, J., and Wood, S., 2006. Exploring the potential for biomass power in Ontario: a response to the OPA Supply Mix Advice Report. BIOCAP Foundation. Available from: http://www.biocap.ca/files/Ont_bioenergy_OPA_Feb23_final.pdf [Accessed 5 May 2012].
21. Lee, H., et al., 1998. Ecological land classification for southern Ontario: first approximation and its application. Ontario Ministry of Natural Resources, Southcentral Science Section, Science Development and Transfer Branch. SCSS Field Guide FG-02.
22. Lillesand, T.M., Kiefer, R.W., and Chipman, J.W., 2004. Remote sensing and image interpretation. 5th ed. New York: John Wiley and Sons.
23. Liu, C., Frazier, P., and Kumar, L., 2007. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107, 606-616.
24. Lo, C.P. and Yeung, A.K.W., 2007. Concepts and techniques in geographic information systems. Upper Saddle River, NJ: Prentice Hall.
25. Lu, D. and Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823-870.
26. Luciani, P.D., Li, J.Y., and Banting, D., 2011. Distributed urban stormwater modeling within GIS integrating analytical probabilistic hydrologic models and remote sensing digital image analysis. Water Quality Research Journal of Canada, 46, 183-199.
27. Lunetta, R.S., et al., 1991. Remote sensing and geographic information system data integration: error sources and research issues. Photogrammetric Engineering and Remote Sensing, 57, 677-687.
28. Mabee, W. and Mirck, J., 2011. A regional evaluation of potential bioenergy production pathways in Eastern Ontario, Canada. Annals of the Association of American Geographers, 101, 897-906.
29. Maxie, A.J., et al., 2010. A comparison of forest resource inventory, provincial land cover maps and field surveys for wildlife habitat analysis in the Great Lakes - St. Lawrence forest. The Forestry Chronicle, 86, 77-86.
30. McCallum, I., et al., 2006. A spatial comparison of four satellite derived 1 km global land cover datasets. International Journal of Applied Earth Observation and Geoinformation, 8, 246-255.
31. McKendry, P., 2002. Energy production from biomass (part 1): overview of biomass. Bioresource Technology, 83, 37-46.
32. Olofsson, P., et al., 2013. Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122-131.
33. Ontario Ministry of Agriculture, Food and Rural Affairs, 2012. Area of Census Farms (Acres) by County, 1991, 1996, 2001, 2006 and 2011. Available from: http://www.omafra.gov.on.ca/english/stats/census/cty30a.htm [Accessed 16 May 2013].
34. Ontario Ministry of Natural Resources, 2005. Ontario Land Cover Database Metadata Detail Page. Available from: http://lioapp.lrc.gov.on.ca/edwin/EDWINCGI.exe?IHID=4711&AgencyID=1&Theme=All_Themes [Accessed 4 July 2011].
35. Ontario Ministry of Natural Resources, 2007. Ecological land classification: a primer. Available from: http://www.mnr.gov.on.ca/stdprodconsume/groups/lr/@mnr/@lueps/documents/document/264777.pdf [Accessed 25 September 2012].
36. Ontario Ministry of Natural Resources, 2008a. SOLRIS accuracy assessment report 2: SOLRIS V. 1.2 (2008 release). ISBN 978-01-4249-8448-0 TN001.
37. Ontario Ministry of Natural Resources, 2008b. SOLRIS Metadata Detail Page. Available from: http://lioapp.lrc.gov.on.ca/edwin/EDWINCGI.exe?IHID=4933&AgencyID=1&Theme=All_Themes [Accessed 4 July 2011].
38. Petit, C.C. and Lambin, E.F., 2001. Integration of multi-source remote sensing data for land cover change detection. International Journal of Geographical Information Science, 15, 785-803.
39. Pohl, C. and Van Genderen, J.L., 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823-854.
40. Sims, R.E.H., et al., 2010. An overview of second generation biofuel technologies. Bioresource Technology, 101, 1570-1580.
41. Smyth, I., 2008. SOLRIS Phase 2-Data specifications version 1.2. Ontario Ministry of Natural Resources.
42. Stehman, S.V., 1999. Comparing thematic maps based on map value. International Journal of Remote Sensing, 20, 2347-2366.
43. Stehman, S.V., 2006. Design, analysis, and inference for studies comparing thematic accuracy of classified remotely sensed data: a special case of map comparison. Journal of Geographic Systems, 8, 209-226.
44. Stehman, S.V., 2013. Estimating area from an accuracy assessment error matrix. Remote Sensing of Environment, 132, 202-211.
45. Stehman, S.V. and Czaplewski, R.L., 1998. Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64, 331-344.
46. Tchuenté, A.T.K., Roujean, J.L., and De Jong, S.M., 2011. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 13, 207-219.
47. Trietz, P. and Howard, P., 2000. Integrating spectral, spatial, and terrain variables for forest ecosystem classification. Photogrammetric Engineering and Remote Sensing, 66, 305-317.
48. Voivontas, D., Assimacopoulos, D., and Mourelatos, A., 1998. Evaluation of renewable energy potential using a GIS decision support system. Renewable Energy, 13, 333-344.
49. Wang, S., Leduc, S., Obersteiner, M., Schill, C., and Koch, B., 2009. A new thinking for renewable energy model: Remote sensing-based renewable energy model. International Journal of Energy Research 33, 778-786.
50. Wormstead, S. and Zuccarino-Crowe, C., 2009. Forest Lands - Maintenance of Productive Capacity of Forest Ecosystems. State of the Great Lakes Forest Report, Indicator # 8501 396-400.
51. Wulder, M.A., et al., 2006. An accuracy assessment framework for large-area land cover classification products derived from medium-resolution satellite data. International Journal of Remote Sensing, 27, 663-683.
52. Yang, L., et al., 2001. Thematic accuracy of MRLC land cover for the eastern United States. Remote Sensing of the Environment, 76, 418-422.
53. Yang, X. and Lant, C., 2011. A geographical and temporal analysis of US thermoelectric withdrawals in comparison to renewable water supplies. Applied Geography, 31, 1154-1165.