Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico

Applied Geography

Volumn 34, Issue , 2012, Pages 29-37

  Aguirre Gutiérrez, Jesus ^a   Seijmonsbergen, Arie C ^a   Duivenvoorden, Joost F ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Change detection; Landsat; Object based; Pixel based; Post classification; Segmentation

Indexed keywords

ACCURACY ASSESSMENT; DETECTION METHOD; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LAND CLASSIFICATION; LAND COVER; LANDSAT THEMATIC MAPPER; MOUNTAIN REGION; OPTIMIZATION; PIXEL; REMOTE SENSING; SATELLITE IMAGERY;

MEXICO [NORTH AMERICA];

EID: 80155214454     PISSN: 01436228     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apgeog.2011.10.010     Document Type: Article

References (71)

1. Addink, E. A., de Jong, S. M., & Pebesma, E. J. (2007). The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogrammetric Engineering & Remote Sensing, 73(8), 905-912.
2. Arriaga, L., Espinoza, J. M., Aguilar, C., Martínez, E., Gómez, L., Loa, E., et al. (2000). Regiones terrestres prioritarias de México. México: Comisión Nacional para el Conocimiento y Uso de la Biodiversidad.
3. Baatz, M., Schäpe, A. (2000). Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg: Vol. 200 (pp. 12-23).
4. Barsi, A. (2000). The impact of data compression and neighborhood information on the classification accuracy of artificial neural networks. International Archives of Photogrammetry and Remote Sensing, 33(7), 140-147.
5. Bayarsaikhan, U., Boldgiv, B., Kim, K. R., Park, K., & Lee, D. (2009). Change detection and classification of land cover at Hustai National Park in Mongolia. International Journal of Applied Earth Observation and Geoinformation, 11(4), 273-280.
6. Berberoglu, S., & Akin, A. (2009). Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation, 11(1), 46-53.
7. Bhaskaran, S., Paramananda, S., & Ramnarayan, M. (2010). Per-pixel and objectoriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography, 30(4), 650-665.
8. Blaschke, T., Lang, S., & Hay, G. J. (August 27, 2008). Object-based image analysis. Berlin, Germany: Springer.
9. Bontemps, S., Bogaert, P., Titeux, N., & Defourny, P. (2008). An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sensing of Environment, 112(6), 3181-3191.
10. Boyd, D. S. (2009). Remote sensing in physical geography: a twenty-first century perspective. Progress in Physical Geography, 33, 451-456.
11. Boyd, D. S., & Foody, G. M. (2011). An overview of remote sensing and GIS based research in ecological informatics. Ecological Informatics, 6, 25-36.
12. Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3), 233-249.
13. Çakir, G., Sivrikaya, F., & Keleş, S. (2008). Forest cover change and fragmentation using Landsat data in Maçka State Forest enterprise in Turkey. Environmental Monitoring and Assessment, 137(1), 51-66.
14. Campbell, J. B. (2002). Introduction to remote sensing. London, UK: Taylor & Francis.
15. CIF. (2011). Conservation international foundation. www.biodiversityhotspots.org/ xp/hotspots/pine_oak/Pages/default.aspx Accessed 06.06.11.
16. Cleve, C., Kelly, M., Kearns, F. R., & Moritz, M. (2008). Classification of the wildland-urban interface: a comparison of pixel-and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32(4), 317-326.
17. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(4), 37-46.
18. CONAFOR. (2010). Comision Nacional Forestal. http://conafor.gob.mx/index.php? option&=com_content&task&=view&id&=56&Itemid &=122 Accessed 21.05.10.
19. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.
20. Corcoran, P., & Winstanley, A. (2008). Using texture to tackle the problem of scale in land-cover classification. In T. Blaschke, S. Lang, & G. Hay (Eds.), Object-based image analysis (pp. 113-132). Berlin: Springer.
21. De Bano, L. F. (1994). Biodiversity and the management of the Madrean Archipelago: The sky islands of southwestern United States and Northwestern Mexico. Darby, PA: Diane Publishing.
22. Definiens, A. G. (2010). Definiens developer v7. http://www.definiens.com
23. Desclée, B., Bogaert, P., & Defourny, P. (2006). Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1), 1-11.
24. Dragut, L. D., & Blaschke, T. (2008). Terrain segmentation and classification using SRTM data. In Q. Zhou, B. Lees, & G. Tang (Eds.), Advances in digital terrain analysis (pp. 141-158). Berlin: Springer.
25. ERDAS Imagine. (2010). http://www.erdas.com.
26. ESRI. (2009). Redlands. CA, USA: ESRI Inc.. http://www.esri.com
27. Fan, F., Weng, Q., & Wang, Y. (2007). Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on landsat TM/ETM{thorn} imagery. Sensors, 7(7), 1323-1342.
28. FAO. (2005). Global forest resources assessment: Progress towards sustainable forest management. Rome, Italy: Food and Agriculture Organization of the United Nations.
29. Flanders, D., Hall-Beyer, M., & Pereverzoff, J. (2003). Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29(4), 441-452.
30. Fuller, R. M., Smith, G. M., & Devereux, B. J. (2003). The characterisation and measurement of land cover change through remote sensing: problems in operational applications? International Journal of Applied Earth Observations and Geoinformation, 4(3), 243-253.
31. Gao, Y., Mas, J. F., & Navarrete, A. (2009). The improvement of an object-oriented classification using multi-temporal MODIS - EVI - satellite data. International Journal of Digital Earth, 2(3), 219-236.
32. Garmin. (2010). Etrex, HC. Olathe Kansas, USA: Garmin International Inc.
33. Geneletti, D., & Gorte, B. G. H. (2003). A method for object-oriented land cover classification combining Landsat TM data and aerial photographs. International Journal of Remote Sensing, 24(6), 1273-1286.
34. Gingrich, R. (2005). Building effective international, multicultural alliances for restoration of Ejido forests in the Sierra Madre occidental. In Gottfried G. J., et al., Connecting mountain islands and desert seas: biodiversity and management of the Madrean archipelago II and 5th conference on research and resource management in the Southwestern deserts: May 11-15, 2004, Tucson, Arizona.
35. Global Land Cover Facility, GLCF. (2010). http://www.landcover.org
36. Google Earth v.5. (2010). http://earth.google.com URL
37. Guerrero, M. T., De Villa, F., Kelly, M., Reed C., Vegter, B. (2001). The forestry industry in the state of Chihuahua: economic, ecological and social impacts post-NAFTA. In The environmental effects of free trade: Papers presented at the North American symposium on assessing the linkages between trade and the environment: October 11th, 2000. Washington, D.C.
38. Hanski, I. (2011). Habitat loss, the dynamics of biodiversity, and a perspective on conservation. Ambio, 40, 248-255.
39. Hájek, F. (2008). Process-based approach to automated classification of forest structures using medium format digital aerial photos and ancillary GIS information. European Journal of Forest Research, 127(2), 115-124.
40. Harris, L. D. (1984). The fragmented forest: Island biogeography theory and the preservation of biotic diversity. Chicago: University of Chicago Press.
41. Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, 29, 399-423.
42. INEGI. (2009). Fuente: Sitio del INEGI en Internet. www.inegi.org.mx
43. Kilic, S., Evrendilek, F., Berberoglu, S., & Demirkesen, A. (2006). Environmental monitoring of land-use and land-cover changes in a Mediterranean region of Turkey. Environmental Monitoring and Assessment, 114(1), 157-168.
44. Kozak, J., Estreguil, C., & Ostapowicz, K. (2008). European forest cover mapping with high resolution satellite data: the Carpathians case study. International Journal of Applied Earth Observation and Geoinformation, 10(2), 44-55.
45. Krauss, J., Bommarco, R., Guardiola, M., Heikkinen, R. K., Helm, A., Kuussaari, M., et al. (2010). Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecology Letters, 13(5), 597-605.
46. Liu, D., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), 187-194.
47. Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.
48. Lunetta, R. S., & Elvidge, C. (1999). Remote sensing change detection: Environmental monitoring methods and applications. UK: Taylor & Francis.
49. Matinfar, H. R., Sarmadian, F., Panah, A. S. K., & Heck, R. J. (2007). Comparisons of object-oriented and pixel-based classification of land use/land cover types based on Landsat7, Etm{thorn} spectral bands (case study: arid region of Iran). American-Eurasian Journal of Agricultural & Environmental Sciences, 2(4), 448-456.
50. Monserud, R. A., & Leemans, R. (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62, 275-293.
51. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, O. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145-1161.
52. Norris, K., Asaseb, A., Collenc, B., Gockowksid, J., Masone, J., Phalanf, B., et al. (2010). Biodiversity in a forest-agriculture mosaic - the changing face of West African rainforests. Biological Conservation, 143(10), 2341-2350.
53. Polasky, S., Nelson, E., Pennington, D., & Johnson, K. A. (2011). The impact of landuse change on ecosystem services, biodiversity and returns to landowners: a case study in the state of Minnesota. Environmental and Resource Economics, 48(2), 219-242.
54. Rao, K. S., & Pant, R. (2001). Land use dynamics and landscape change pattern in a typical micro watershed in the mid elevation zone of central Himalaya, India. Agriculture, Ecosystems & Environment, 86(2), 113-124.
55. Robertson, L. D., & King, D. J. (2011). Comparison of pixel- and object-based classification in land cover change mapping. International Journal of Remote Sensing, 32(6), 1505-1529.
56. Rozenstein, O., & Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography, 31(2), 533-544.
57. Schulz, J. J., Cayuela, L., Echeverria, C., Salas, J., & Reyes, B. J. M. (2010). Monitoring land cover change of the dryland forest landscape of Central Chile (1975-2008). Applied Geography, 30(3), 436-447.
58. Shalaby, A., & Ryutaro, T. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28-41.
59. Smith, G. M. (2008). The development of integrated object-based analysis of EO data within UK national land cover products. In T. Blaschke, S. Lang, & G. Hay (Eds.), Object-based image analysis (pp. 513-528). Berlin: Springer.
60. Turner, B. L., Meyer, W. B., & Skole, D. L. (1994). Global land-use/land-cover change: towards an integrated study. Ambio, 23(1), 91-95.
61. Vinciková1, H., Hais, M., Brom, J., Procházka, J., & Pecharová1, E. (2010). Use of remote sensing methods in studying agricultural landscapes - a review. Journal of Landscape Studies, 3, 53-63.
62. Vitousek, P. M. (1994). Beyond global warming: ecology and global change. Ecology, 75(7), 1861-1876.
63. Wakild, E. (2011). Environment and environmentalism. In W. H. Beezley (Ed.), A companion to Mexican history and culture (pp. 518-537). Oxford, UK: Wiley-Blackwell.
64. Walter, V. (2004). Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 225-238.
65. Wang, L., Sousa, W. P., & Gong, P. (2004). Integration of object-based and pixelbased classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing, 25(24), 5655-5668.
66. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884-893.
67. Yan, G., Mas, J. F., Maathuis, B. H. P., Xiangmin, Z., & van Dijk, P. M. (2006). Comparison of pixel-based and object-oriented image classification approachesda case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039-4055.
68. Yang, X. (2002). Satellite monitoring of urban spatial growth in the Atlanta metropolitan area. Photogrammetric Engineering and Remote Sensing, 68(7), 725-734.
69. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Objectbased detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72(7), 799-811.
70. Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2), 317-328.
71. Zhou, W., Troy, A., & Grove, M. (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8(3), 1613-1636.