Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture

Applied Geography

Volumn 35, Issue 1-2, 2012, Pages 208-218

  Rodriguez Galiano, V ^a   Chica Olmo, M ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Change analysis; Digital terrain models; Land surface temperature; Land cover mapping; Texture

Indexed keywords

ALGORITHM; DIGITAL TERRAIN MODEL; LAND COVER; LAND SURFACE; LANDSAT; MAXIMUM LIKELIHOOD ANALYSIS; SATELLITE IMAGERY; SPATIAL VARIATION; SURFACE TEMPERATURE; VEGETATION MAPPING;

MEDITERRANEAN REGION; SPAIN;

EID: 84864326878     PISSN: 01436228     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apgeog.2012.06.014     Document Type: Article

References (75)

1. Abd El-Kawy O.R., Rød J.K., Ismail H.A., Suliman A.S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography 2011, 31(2):483-494.
2. Abreu L.W., Anderson G.P. The MODTRAN 2/3 report and LOWTRAN 7 MODEL 1996, Phillips Laboratory, Geophysics Directorate, PL/GPOS.
3. Agüera F., Aguilar F.J., Aguilar M.A. Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS Journal of Photogrammetry and Remote Sensing 2008, 63(6):635-646.
4. Aguirre-Gutiérrez J., Seijmonsbergen A.C., Duivenvoorden J.F. Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography 2012, 34(0):29-37.
5. Asner G.P., Keller M., Pereira R., Zweede J.C. Remote sensing of selective logging in Amazonia: assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sensing of Environment 2002, 80(3):483-496.
6. Bakr N., Weindorf D.C., Bahnassy M.H., Marei S.M., El-Badawi M.M. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography 2010, 30(4):592-605.
7. Bellman R. Dynamic programming 2003, Dover Publications, Mineola, NY. 2nd ed.
8. Berberoglu S., Curran P.J., Lloyd C.D., Atkinson P.M. Texture classification of Mediterranean land cover. International Journal of Applied Earth Observation and Geoinformation 2007, 9(3):322-334.
9. Berberoglu S., Lloyd C.D., Atkinson P.M., Curran P.J. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Computers & Geosciences 2000, 26(4):385-396.
10. Berk, A., & Adler-Golden, S. M. (2002). Exploiting MODTRAN radiation transport for atmospheric correction: the FLAASH algorithm. In: Fifth international conference on information fusion, (pp. 798-803), Annapolis.
11. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
12. Brewster C.C., Allen J.C., Kropp D.D. IPM from space: using satellite imagery to construct regional crop maps for studying crop insect interaction. American Entomologist 1999, 45(2):105-117.
13. Brisco B., Brown R.J. Multidate SAR/TM synergism for crop classification in western Canada. Photogrammetric Engineering & Remote Sensing 1995, 61(8):1009-1014.
14. Collins J.B., Woodcock C.E. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data. Remote Sensing of Environment 1996, 56(1):66-77.
15. Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E. Review ArticleDigital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 2004, 25(9):1565-1596.
16. Crabtree R., Potter C., Mullen R., Sheldon J., Huang S., Harmsen J., et al. A modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator. Remote Sensing of Environment 2009, 113(7):1486-1496.
17. Crapper P.F., Hynson K.C. Change detection using Landsat photographic imagery. Remote Sensing of Environment 1983, 13(4):291-300.
18. Cutler D.R., Edwards T.C., Beard K.H., Cutler A., Hess K.T. Random forests for classification in ecology. Ecology 2007, 88:2783-2792.
19. Chan J.C.-W., Paelinckx D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment 2008, 112(6):2999-3011.
20. Chan J.C.W., Laporte N., Defries R.S. Texture classification of logged forests in tropical Africa using machine-learning algorithms. International Journal of Remote Sensing 2003, 24(6):1401-1407.
21. Chica-Olmo M., Abarca-Hernández F. Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences 2000, 26(4):373-383.
22. Dewan A.M., Yamaguchi Y. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Applied Geography 2009, 29(3):390-401.
23. Dixon B., Candade N. Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing 2008, 29(4):1185-1206.
24. Fearnside P.M. Global warming and tropical land-use change: greenhouse gas emissions from biomass burning, decomposition and soils in forest conversion, shifting cultivation and secondary vegetation. Climatic Change 2000, 46(1):115-158.
25. Felicísimo A.M. Modelos Digitales del Terreno: Introducción y aplicaciones en las ciencias ambientales 1994, Oviedo.
26. Foley J.A., DeFries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., et al. Global consequences of land use. Science 2005, 309(5734):570-574.
27. Franklin J. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 1995, 19(4):474-499.
28. Franklin J. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal Of Vegetation Science 1998, 9(5):733-748.
29. Franklin S.E., Hall R.J., Moskal L.M., Maudie A.J., Lavigne M.B. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing 2000, 21(1):61-79.
30. Friedl M.A., Brodley C.E., Strahler A.H. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. Geoscience and Remote Sensing, IEEE Transactions on 1999, 37(2):969-977.
31. Ghimire B., Rogan J., Miller J. Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sensing Letters 2010, 1:45-54.
32. Gislason P.O., Benediktsson J.A., Sveinsson J.R. Random forests for land cover classification. Pattern Recognition Letters 2006, 27(4):294-300.
33. Guo L., Chehata N., Mallet C., Boukir S. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing 2011, 66(1):56-66.
34. Hansen L.K., Salamon P. Neural network ensembles. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1990, 12(10):993-1001.
35. Hepcan C.C., Turan I.A., Ozkan M.B. Monitoring land use change in the Cesme coastal zone, Turkey using aerial photographs and satellite imaging. Land Degradation & Development 2011, 22(3):326-333.
36. Howarth P.J., Boasson E. Landsat digital enhancements for chage detection in urban environments. Remote Sensing of Environment 1983, 13(2):149-160.
37. IPCCC Land use, land-use change, and forestry 2000, Cambridge, pp. 184.
38. Johansen K., Coops N.C., Gergel S.E., Stange Y. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment 2007, 110(1):29-44.
39. Kennedy R.E., Townsend P.A., Gross J.E., Cohen W.B., Bolstad P., Wang Y.Q., et al. Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment 2009, 113(7):1382-1396.
40. Kotsiantis S., Pintelas P. Combining bagging and boosting. International Journal of Computational Intelligence 2004, 1(4):324-333.
41. Krogh A., Vedelsby J. Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 1995, 231-238.
42. Lu D., Mausel P., Brondízio E., Moran E. Change detection techniques. International Journal of Remote Sensing 2004, 25(12):2365-2401.
43. Lunetta R.S., Balogh M.E. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering & Remote Sensing 1999, 65:1303-1310.
44. Malila, W. A. (1980). Change vector analysis: an approach for detecting forest changes with Landsat. In: Proceedings of the 6th annual symposium on machine processing of remotely sensed data, pp. 326-335.
45. Mas J.F. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing 1999, 20:139-152.
46. Menor-Toribio J. Transformaciones recientes en la organización territorial de la Vega de Granada: del espacio agrario territorial a la aglomeración urbana actual. Estudios regionales 1997, 48:189-214.
47. Moreira-Madueño J.M. El sistema de información geográfica-ambiental de Andalucía. Del SINAMBA a la Red de Información Ambiental de Andalucía. Geofocus 2006, 6:4-10.
48. Mota J.F., Pérez-García F.J., Jiménez M.L., Amate J.J., Peñas J. Phytogeographical relationships among high mountain areas in the Baetic Ranges (South Spain). Global Ecology and Biogeography 2002, 11(6):497-504.
49. Mountrakis G., Watts R., Luo L., Wang J. Developing collaborative classifiers using an expert-based model. Photogrammetric Engineering and Remote Sensing 2009, 75(7):831-844.
50. Nagler P.L., Glenn E.P., Hinojosa-Huerta O. Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico. Remote Sensing of Environment 2009, 113(7):1473-1485.
51. Nemani R., Hashimoto H., Votava P., Melton F., Wang W., Michaelis A., et al. Monitoring and forecasting ecosystem dynamics using the terrestrial observation and prediction system (TOPS). Remote Sensing of Environment 2009, 113(7):1497-1509.
52. Oetter D.R., Cohen W.B., Berterretche M., Maiersperger T.K., Kennedy R.E. Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Remote Sensing of Environment 2001, 76(2):139-155.
53. Pax-Lenney M., Woodcock C.E. Monitoring agricultural lands in Egypt with multitemporal Landsat TM imagery: how many images are needed?. Remote Sensing of Environment 1997, 59(3):522-529.
54. Pax-Lenney M., Woodcock C.E., Collins J.B., Hamdi H. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from landsat TM. Remote Sensing of Environment 1996, 56(1):8-20.
55. Potapov P., Turubanova S., Hansen M.C. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment 2011, 115(2):548-561.
56. Rodriguez-Galiano V.F., Chica-Olmo M., Abarca-Hernandez F., Atkinson P.M., Jeganathan C. Random forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sensing of Environment 2012, 121:93-107.
57. Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sánchez J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing 2012, 67:93-104.
58. Rodriguez-Galiano V.F., Pardo-Iguzquiza E., Sanchez-Castillo M., Chica-Olmo M., Chica-Rivas M. Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images. International Journal of Applied Earth Observation and Geoinformation 2012, 18:515-527.
59. Rogan J., Chen D. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning 2004, 61(4):301-325.
60. Rogan J., Franklin J., Stow D., Miller J., Woodcock C., Roberts D. Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sensing of Environment 2008, 112(5):2272-2283.
61. Schriever J.R., Congalton R.G. Evaluating seasonal variability as an aid to cover-type mapping from Landsat thematic mapper data in the Northeast. Photogrammetric Engeenering and Remote Sensing 1995, 61(3):321-327.
62. Sesnie S., Gessler P., Finegan B., Thessler S. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sensing of Environment 2008, 112(5):2145-2159.
63. Shalaby A., Tateishi R. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography 2007, 27(1):28-41.
64. Singh A. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 1989, 10:989-1003.
65. Steele B.M. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment 2000, 74(3):545-556.
66. Svancara L.K., Scott J.M., Loveland T.R., Pidgorna A.B. Assessing the landscape context and conversion risk of protected areas using satellite data products. Remote Sensing of Environment 2009, 113(7):1357-1369.
67. Townsend P.A., Lookingbill T.R., Kingdon C.C., Gardner R.H. Spatial pattern analysis for monitoring protected areas. Remote Sensing of Environment 2009, 113(7):1410-1420.
68. Wang Y., Mitchell B.R., Nugranad-Marzilli J., Bonynge G., Zhou Y., Shriver G. Remote sensing of land-cover change and landscape context of the National Parks: a case study of the northeast temperate network. Remote Sensing of Environment 2009, 113(7):1453-1461.
69. Watanachaturaporn P., Arora M.K., Varshney P.K. Multisource classification using support vector machines: an empirical comparison with decision tree and neural network classifiers. Photogrammetric Engeenering and Remote Sensing 2008, 74(2):239-246.
70. Wickware G.M., Howarth P.J. Change detection in the Peace-Athabasca delta using digital Landsat data. Remote Sensing of Environment 1981, 11(1):9-25.
71. Wolter P.T., Mladenoff D.J., Host G.E., G.E., T.R. Improved forest classification in the northern lake states using multi-temporal Landsat imagery. Photogrammetric Engineering & Remote sensing 1995, 61(9):1129-1143.
72. Wright C., Gallant A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment 2007, 107(4):582-605.
73. Yuan D., Elvidge C. NALC land cover change detection pilot study: Washington D.C. area experiments. Remote Sensing of Environment 1998, 66(2):166-178.
74. Yuan F., Bauer M.E., Heinert N.J., Holden G. Multi-level land cover mapping of the Twin Cities (Minnesota) metropolitan area with multi-seasonal Landsat TM/ETM + data. Geocarto International 2005, 20(2):5-14.
75. Yuan F., Sawaya K.E., Loeffelholz B.C., Bauer M.E. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment 2005, 98(2-3):317-328.