Remote sensing vegetation dynamics analytical methods: a review of vegetation indices techniques

Ayanlade, A.

doi:10.4467/21995923GP.17.001.7188

Artykuł - szczegóły

Tytuł artykułu

Remote sensing vegetation dynamics analytical methods: a review of vegetation indices techniques

Autorzy

Ayanlade A.

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.4467/21995923GP.17.001.7188

Warianty tytułu

Przegląd analitycznych metod teledetekcyjnych w badaniu dynamiki zmian wegetacji: techniki oparte na wskaźnikach wegetacji

Języki publikacji

Abstrakty

Scientists have made great eﬀ orts in developing techniques to assess and monitor the rate of change in vegetation on global, regional and local scales. Vegetation indices are remote sensing measurements used to quantify vegetation cover, vigor or biomass for each pixel in an image. Besides the fact that no single method can be applied to all cases and regions, there are some factors that determine the remote sensing methods to be used in environmental change studies. Such factors include the spatial, temporal, spectral and radiometric resolutions of satellite image and environmental factors. The major question usually comes to mind of environmental researchers in any remote sensing research project is: What remote sensing method should be used to solve the research problem? Therefore, this paper evaluates methods used in the literature to assess, monitor and model environmental change, considering factors that determine the selection of those methods. The review shows over forty vegetation indices, out of which only three (Ratio Vegetation Index, Transformed Vegetation Index and Normalized Diﬀ erence Vegetation Index) are commonly applied to vegetation assessment. The study show that out of all the vegetation indices, NDVI is the most widely applied to monitor vegetation change on regional and local scales.

Naukowcy podjęli znaczny wysiłek, mający na celu rozwój technik oceny i monitoringu tempa zmian wegetacji w skali globalnej, regionalnej oraz lokalnej. Wskaźniki wegetacji stanowią pomiary teledetekcyjne, używane do ilościowej oceny pokrycia wegetacją, wigoru wegetacji lub biomasy, dla każdego piksela w zobrazowaniu. Oprócz tego, że nie ma jednej metody, która może być zastosowana we wszystkich przypadkach i regionach, istnieje szereg czynników, które determinują wybór metod teledetekcyjnych do zastosowania w badaniach nad zmianami zachodzącymi w środowisku. Należą do nich uwarunkowania przestrzenne, czasowe, rozdzielczość spektralna i radiometryczna zobrazowań satelitarnych oraz czynniki środowiskowe. Podstawowe pytanie, które przychodzi na myśl badaczom środowiska w dowolnym przedsięwzięciu związanym z teledetekcją to: Która metoda teledetekcyjna powinna zostać użyta do rozwiązania problemu badawczego? Tak więc, artykuł ten stanowi przegląd metod używanych w literaturze do oceny, monitoringu i modelowania zmian środowiskowych, które wyznaczają wybór poszczególnych metod. Przegląd pokazuje ponad czterdzieści wskaźników wegetacji, spośród których tylko trzy (proporcjonalny wskaźnik wegetacji – RVI, transformowany wskaźnik wegetacji – TVI i znormalizowany różnicowy wskaźnik wegetacji – NDVI) są powszechnie używane do oceny wegetacji. Badania pokazują, że spośród wszystkich wskaźników wegetacji, w monitoringu zmian wegetacji w skali regionalnej i lokalnej, najczęściej stosuje się NDVI.

Słowa kluczowe

remote sensing vegetation assessment change detections

teledetekcja ocena wegetacji wykrywanie zmian

Wydawca

Polska Akademia Umiejętności

Czasopismo

Geoinformatica Polonica

Rocznik

2017

Tom

T. 16

Strony

7--17

Opis fizyczny

Bibliogr. 108 poz., tab.

Twórcy

autor

Ayanlade A.

sinaayanlade@yahoo.co.uk

Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria

Bibliografia

1. Ahmad, F. (2012) A review of remote sensing data change detection: Comparison of Faisalabad and Multan Districts, Punjab Province, Pakistan. Journal of Geography and Regional Planning, 5(9), 236–251.
2. Akgn A., Eronat A. H., and Trk N. (2004) Comparing Different Satellite Image Classification Methods: An Application in Ayvalik District, Western Turkey. In ISPRS Congress Istanbul 2004, Proceedings of Commission IV. 1091–1097.
3. Almutairi, A. and Warner, T.A. (2010) Change Detection Accuracy and Image Properties: A Study Using Simulated Data. Remote Sensing. 2010, 2, 1508–1529.
4. Aplin, P (2005) Using remote sensing data. In Clifford and valentine (edu): Key methods in geography. Reprint edition, Sage publication, London. 230–308.
5. Avitabile, V., Baccini, A., Friedl, M.A and Schmullius, C. (2012) Capabilities and limitations of Landsat and land cover data for above ground woody biomass estimation of Uganda. Remote Sensing of Environment, 117, 366–380.
6. Bannari, A., Morin, D., Bonn, F. and Huete, A. R. (1995) A review of vegetation indices. Remote Sensing Reviews, 13: 1, 95–120.
7. Balzter, H. (2000) Markov chain models for vegetation dynamics. Ecological Modelling, 126, 139–154.
8. Bhatta, B. (2010) Analysis of Urban Growth and Sprawl from Remote Sensing Data, 65 Advances in Geographic Information Science, 65–83.
9. Boakye, E. Odai, S. N. Adjei, K. A. and Annor, F. O. (2008) Landsat Images for Assessment of the Impact of Land Use and Land Cover Changes on the Barekese Catchment in Ghana. European Journal of Scientifi c Research, 22 (2) 269–278.
10. Brown, D. G., Pijanowski, B. C. and Duh, J. D. (2000). Modelling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59, 247–263.
11. Briassoulis, H. (2000) Analysis of Land Use Change: Theoretical and Modelling Approaches. Regional Research Institute. [Online]. Available from: http://www.rri.wvu.edu /WebBook/Briassoulis/contents. htm [Accessed 08 March, 2010].
12. Camplbell, J. B. (2002) Introduction to remote sensing. Third edition, Taylor and Francis, London and New York. Carvalho, L. M. T., Fonseca, L. M. G., Murtagh, F. and Cleves, J. G. P. W., (2001) Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing, 22, 3871–3876.
13. Chavez,P.S.JR, and Mackinnon, D. J. (1994) Automatic detection of vegetation changes in the south-western United States using remotely sensed images. Photogrammetric Engineering and Remote Sensing, 60, 571–583.
14. Chen, X.W. (2002) Using remote sensing and GIS to analyze land cover change and its impacts on regional sustainable development. International Journal of Remote Sensing, 23, 107–124.
15. Chen, S., Chen, L., Liu, Q., Li, X. and Tan Q. (2005) Remote sensing and GIS-based integrated analysis of coastal changes and their environmental impacts in Ling ding Bay, Pearl River Estuary, South China. Ocean and Coastal Management 48 (2005), 65–83.
16. Chen, N.W., Li, H.C. and Wang, L.H., (2009). A GIS-based approach for mapping direct use value of ecosystem services at a county scale: management implications. Ecological Economics 68 (11), 2768–2776.
17. Collins, J. B. and Woodcock, C. E. (1996) An assessment of several linear change detection techniques for mapping forest mortality using multi-temporal Landsat TM data. Remote Sensing of Environment, 56, 66–77.
18. Coppin, P. R. and Bauer, M. E. (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13, 207–234.
19. Coppin, P.R., Jonckheere, I., Nackaerts, K., Muys, B. and Lambin, E. (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25 (9) 1565–1596.
20. DeFries, R. and Belward, A. S. (2000) Global and regional land cover characterization from satellite data; an introduction to the Special Issue. International Journal of Remote Sensing, 21, (6and 7), 1083–1092.
21. Ellis, E.A and Porter-Bolland, L. (2008) Is community-based forest management more eff ective than protected areas? A comparison of land use/land cover change in two neighbouring study areas of the Central Yucatan Peninsula, Mexico. Forest Ecology and Management 256, 1971–1983.
22. Estes, A.B., Kuemmerle, T., Kushnir, H., Radeloff V.C., and Shugart, H.H (2012) Land-cover change and human population trends in the greater Serengeti ecosystem from 1984–2003. Biological Conservation 147, 255–263.
23. Foody, G. M. (2003): Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, 24(23):4035–4046.
24. Foody, G.M. and Mathur, A., (2004), A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1336–1343.
25. Fung, T. (1990) An assessment of TM imagery for land-cover change detection. IEEE Transactions on Geosciences and Remote Sensing, 28, 681–684.
26. Gao, J. and Liu, Y. (2010) Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. International Journal of Applied Earth Observation and Geoinformation 12, 9–16.
27. Geist, H.J and Lambin, E.F (2001) What Drives Tropical Deforestation?: A meta-analysis of proximate and underlying causes of deforestation based on sub national case study evidence. LUCC Report Series No. 4.
28. Gong, P., (1993) Change detection using principal component analysis and fuzzy set theory. Canadian Journal of Remote Sensing, 19, 22–29.
29. Gordon,H.R.(1978),Removal of atmospheric effects from satellite imagery of the ocean. Appl. Opt. 17, 1631–1636.
30. Guo, Q., Kelly, M., Gong, P. and Liu, D. (2007) An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience and RemoteSensing, 44, pp. 24–47.
31. Hall, F. G., Strebel, D. E., Nickeson, J. E., and Goets, S. J. (1991a) Radiometric rectification: toward a common radiometric response among multidate, multisensory images. Remote Sensing of Environment, 35, 11–27.
32. Hall, F. G., Botin, D. B., Strebel, D. E., Woods, K. D., and Goets, S. J. (1991b) Large-scale patterns of forest succession as determined by remote sensing. Ecology, 72, 628–640.
33. Healey, S.P., Yang, Z., Cohen, W.B. and Pierce, D.J. (2006) Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Land sat data. Remote Sensing of Environment, 101, 115–126.
34. Heo, J., and Fitzhugh, T. W. (2000) A standardized radiometric normalization method for change detection using remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 66, 173–182.
35. Huber, W. (2001). Estimating Markov transitions. Journal of Environmental Managements, 61, 381–385.
36. Hay, G.J. and Castilla, G. (2008) Geographic object-based image analysis (GEOBIA): a new name for a new discipline. In Object-Based Image Analysis-Spatial Concepts forKnowledge-Driven Remote Sensing Applications, T. Blaschke, S. Lang and G. Hay (Eds.), 75–89.
37. Ji, L., and A.J. Peters, 2007, Performance evaluation of spectral vegetation indices using a statistical sensitivity function. Remote Sensing of Environment, vol. 106,(1), 59–65.
38. Jeyanthi1, P. and Kumar, V.J.S. (2010) Image Classification by K-means Clustering. Advances in Computational Sciences and Technology, 3(1), 1–8.
39. Jensen, J. R. (1996) Introductory Digital Image Processing: a Remote Sensing Perspective. Englewood Cliff s, NJ: Prentice-Hall, 56–78.
40. Kaufman, Y. J., and Sendra, C. (1988), Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. International Journal of Remote Sensing 9, 1357–1381.
41. Kovalskyy, V. and Roy D.P. (2013) The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30 m Landsat data product generation Remote Sensing of Environment 130, 280–293.
42. Lambin, E.F (1996) Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogrammetric Engineering and Remote Sensing. 62, 931–938.
43. Lambin E.F. and Ehrlich, D (1995) Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales. International Journal of Remote Sensing. 16, 573–579.
44. Lambin E.F and Ehrlich, D (1996) The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing. 17, 463–487.
45. Lambin E.F. and Ehrlich D. (1997) Land-cover changes in sub-Saharan Africa (1982–1991): Application of a change index based on remotely-sensed surface temperature and vegetation indices at a continental scale, Remote Sensing of Environment, 61, (2) 181–200.
46. Lambin E.F., Geist H. and Lepers E. (2003) Dynamics of land use and cover change in tropical regions. Annual Review of Environment and Resources, (28) 205–241.
47. Lambin E.F and Strahler, A.H (1994a) Indicators of landcover change for change-vector analvsis in multi-temporal space at coarse spatial scales. International Journal of Remote Sensing 15, 2099–2119.
48. Lambin E.F and Strahler, A.H (1994b) Change-vector analysis: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment. 48, 231–244.
49. Lambin E.F., Turner II B.L., Geist H., Agbola S., Angelsen A., Bruce J.W., Coomes O., Dirzo R., Fischer G., Folke C., George P.S., Homewood K., Imbernon J., Leemans R., Li X., Moran E.F. Mortimore M., Ramakrishnan P.S., Richards J.F., Skånes H., Steff en W., Stone G.D., Svedin U., Veldkamp T., Vogel C., Xu J. (2001) The Causes of Land-Use and – Cover Change: Moving beyond the Myths. Global Environmental Change. 11, 261–269.
50. Li, X., and Yeh, A. G. O., (1998) Principal component analysis of stacked multitemporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing, 19, 1501–1518.
51. Lillesand, T.M and Kiefer, R.W. (2000) Remote sensing and image interpretation. Fourth edition, John Wiley and Son, Inc. 470–605.
52. Liu, D. and Xia, F. (2010) Assessing object-based classification: advantages and limitations. Remote Sensing Letters Vol. 1, (4), 187–194.
53. Logofet, D. O., and Lesnaya, E. V. (2000). The mathematics of Markov models: What Markov chains can really predict in forest successions. Ecological Modelling, 126, 285–298.
54. Lu, D. S., Mausel, P., Brondizio, E. S., and Moran, E. (2002a) Change detection of successional and mature forests based on forest stand characteristics using multitemporal TM data in the Altamira, Brazil. XXII FIG International Congress, ACSM–ASPRS Annual Conference Proceedings, Washington, DC, USA, 19–26.
55. Lu, D., Mausel, P., Brondízio, E. and Moran, E (2002b) Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, vol. 23 (13) 2651–2671.
56. Lu, D., Mausel, P., Brondízio, E. and Moran, E.(2004) Change detection techniques. International Journal of Remote Sensing, 25 (12), 2365–2407.
57. Lu, D., Mausel, P., Brondízio, E. and Moran, E.(2005) Land-cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study. International Journal of Remote Sensing, 26 (1), 101–114.
58. Lu, D. and Weng Q. (2007) A survey of image classifi cation methods and techniques for improving classifi cation performance. International Journal of Remote Sensing, 28 (5), 823–870.
59. Mantero, P., Moser, G. and Serpico, S.B. (2005) Partially supervised classification of remote sensing images through SVMbased probability density estimation. IEEE Transactions on Geoscience and Remote Sensing 43 (3), 559–570.
60. Matricardi, E.A.T, Skole, D.L., Pedlowski, M.A., Chomentowski, W. and Fernandes, L.C (2010) Assessment of tropical forest degradation by selective logging and fi re using Land sat imagery. Remote Sensing of Environment, 114, 1117–1129.
61. McGraw H. (2009) Digital analysis of remote sensing image. The Mcgraw Hill Companies, Inc. USA. 645–649.
62. Mertens B. and Lambin E.F. (2000) Land-cover change trajectories in southern Cameroon. Annals of the Association of American Geographers, September issue, 90 (3), 467–494.
63. Meyfroidt P. and Lambin E.F., (2008) Causes of the reforestation in Vietnam Land Use Policy, (25), 182–197.
64. Mountrakis, G., Im, J. and Ogole, C. (2011) Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetric and Remote Sensing 66, 247–259.
65. NASA, (1979) Landsat data user handbook, 1979, 7–8.
66. NASA, (2000) Instructor’s Guide for computer resources: global land vegetation module. Online]. Available from: http://www.ccpo.odu.edu/~lizsmith/SEES/pdf_files/vg_ig.pdf [Accessed 2 July 2010].
67. NASA, (2010) sTechnical detail about the Thematic Mapper. [Online]. Available from: http://landsat.gsfc.nasa.gov/about/tm.html [Accessed 12 June 2010].
68. NRC (national Resource Centre) (2005), Glossary of remote sensing terms. Canada Centre for Remote Sensing. [Online]. Available from: http://www.ccrs.nrcan.gc.ca/glossary/index-_e.php?id=2965 [Accessed 28 March, 2010].
69. Ouyang, W., Hao, F.H., Zhao, C. and Lin, C., (2010) Vegetation response to 30 years hydropower cascade exploitation in upper stream of Yellow River. Commun Nonlinear Sci Numer Simulat 15, 1928–194.
70. Otukei, J. R. and Blaschke T. (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation 12S, S27–S31.
71. Pandy, A.C and M. S. Nathawat, M.S. (2006) Land Use Land Cover Mapping Through Digital Image Processing of Satellite: A case study from Panchkula, Ambala and Yamunanaga Districts, Haryana State, India.
72. Patarasuk, R. and Binford, M.W. (2012) Longitudinal analysis of the road network development and land-cover change in Lop Buri province, Thailand, 1989–2006. Applied Geography 32, 228–239.
73. Pax-Lenney, M., Woodcock, C. E., Collins, J. B., and Hamdi, H. (1996), The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment, 56, 8–20.
74. Perumal, K. and Bhaskaran, R. (2010) Supervised classification performance of multispectral images. Journal of computing, vol. 2(2), 124–129.
75. Petit, C., Scudder, T., and Lambin, E. (2001) Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover change in south eastern Zambia. International Journal of Remote Sensing, 22, 3435–3456.
76. Powell, C.B. (1995) Wildlife Study 1. Report submitted to the Environmental Aff airs Department, Shell Petroleum Development Company of Nigeria, Ltd.
77. Prakash, A., and Gupta, R. P. (1998) Land-use mapping and change detection in a coal mining area – a case study in the Jharia coalfield, India. International Journal of Remote Sensing, 19, 391–410.
78. Reddy, C.S., Rao, K. R. M., Pattanaik, C. And Joshi. P. K. (2009) Assessment of large-scale deforestation of Nawarangpur district, Orissa, India: a remote sensing based study. Environ Monit Assess. 154. 325–335.
79. Ringrose, S, Vanderpost, C., and Matbeson, W. (1996) The use of integrated remotely sensed and GIS data to determine causes of vegetation cover change in southern Botswana. Applied Geography, 16 (3), 225–242.
80. Rogan, J., and Yool, S. R. (2001) Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico. International Journal of Remote Sensing, 22, 3101–3121.
81. Salami, A.T., Akinyede, J., and de Gier, A., (2010) A preliminary assessment of NigeriaSat-1 for sustainable mangrove forest monitoring. International Journal of Applied Earth Observation and Geoinformation 12. S18–S22.
82. Schowengerdt, R.A. (2007) Remote sensing models and methods for image processing. US Academic press.
83. Serneels S. and Lambin E.F. (2001) Proximate causes of land-use changes in Narok District, Kenya. Agriculture, Ecosystems and Environment, 85, (1–3), 65–82.
84. Seto, K. C., Woodcock, C. E, Song, C. Huang, X., L U, J and Kaufmann, R.K (2002) Monitoring g land-use change in the Pear l River Delta usin g Landsat TM. International Journal of Remote Sensing 23, (10), 1985–2004.
85. Singh,A.(1989) Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10, 989–1003.
86. Silapaswan, C. S., Verbyla, D. L., and Mcguire, A. D. (2001)
87. Land cover change on the Seward Peninsula: the use of remote sensing to evaluate the potential influences of climate warming on historical vegetation dynamics. Canadian Journal of Remote Sensing, 27, 542–554.
88. Song, C. Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A. (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230–244.
89. Soudani, K., Francxois C, and Maire G. (2006) Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment 102: 161–75.
90. Stow, D. A., and Chen, D. M. (2002) Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover change and misregistration. Remote Sensing of Environment, 80, 297–307.
91. Stehman, S.V. (2012) Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change. Remote Sensing Letters 3(2), 111–120.
92. Sunar, F. (1998) An analysis of changes in a multi-date data set: a case study in the Ikitelli area, Istanbul, Turkey. International Journal of Remote Sensing, 19, 225–235.
93. Tuia, D., Pacifici, F., Kanevski, M. and Emery, W.J. (2009) Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Transactions on Geoscience and Remote Sensing 47 (11), 3866–3879.
94. Tuia, D., Muñoz-Marí, J., Kanevski, M and Camps-Valls, G. (2011) Structured Output SVM for Remote Sensing Image Classification Journal of Signal Processing Systems, 65(3), 301–310.
95. Van Leeuwen, W., B. Orr, S. Marsh, and S. Herrmann (2006), Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications, Remote sensing of environment, 100(1), 67–81.
96. Verbyla, D. L. and Boles, S. H. (2000) Bias in land cover change estimates due to misregistration. International Journal of Remote Sensing, 21, 3553–3560.
97. Yang, X. and Liu, Z. (2005) Using satellite and GIS for land-use and land-cover change mapping in an estuarine watershed. International Journal of Remote Sensing 26(23): 5275–5296.
98. Yang, X., and Lo, C. P. (2000) Relative radiometric normalization performance for change detection from multi-date satellite images. Photogrammetric Engineering and Remote Sensing, 66, 967–980.
99. Yang, X., and LO, C. P., (2002) Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23, 1775–1798.
100. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M. and Schirokauer, D. (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72, 799–811.
101. Wang, S.Y., Liu, J.S. and Ma, T.B (2010) Dynamics and changes in spatial patterns of land use in Yellow River Basin, China. Land Use Policy 27, 313–323.
102. Wang, Y., Mitchell, B.R., Nugranad-Marzilli, J., Bonynge, G., Zhou, Y., and Shriver, G. (2009) 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, 113, 1453–1461.
103. Weber, K. T. (2001) A method to incorporate phenology into land cover change analysis. Journal of Range Management, 54, 1–7.
104. Wen, G., Tsay, S., Cahalan, R. F., and Oreopoulos, L. (1999),Path radiance technique for retrieving aerosol optical thick- ness over land. J. Geophys. Res. 104(24), 321–332.
105. Weng, Q. (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management, 64, 273–284.
106. Xie, Z., Xu, X. and Yan, L. (2010) Analyzing qualitative and quantitative changes in coastal wetland associated to the effects of natural and anthropogenic factors in a part of Tianjin, China. Estuarine, Coastal and Shelf Science 86, 379–386.
107. Xiang, M., Hung, C. Pham, M.. Kuo, B. and Coleman, T. (2005) A Parallelepiped Multispectral Image Classifier Using Genetic Algorithms. Geoscience and Remote Sensing Syposium, 2005 proceeding, IEEE International. 482–485.
108. Xiang, S., Nie, F. And Zhang, C. (2008) Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition Vol. 41, 3600–3612.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-40f3ae49-760a-48d1-98d2-d84fda5b3ce7