-
18078
-
2765
-
2091
-
2050
-
1558
A Review of Landsat TM/ETM based Vegetation Indices as Applied to Wetland Ecosystems
DOI:
https://doi.org/10.30564/jgr.v2i1.499Abstract
A review of vegetation indices as applied to Landsat-TM and ETM+ multispectral data is presented. The review focuses on indices that have been developed to produce biophysical information about vegetation biomass/greenness, moisture and pigments.In addition, a set of biomass/greenness and moisture content indices are tested in a Mediterranean semiarid wetland environment to determine their appropriateness and potential for carrying redundant information.The results indicate that most vegetation indices used for biomass/greenness mapping produce similar information and are statistically well correlated.
Keywords:
Greenness determination; Mediterranean wetland areas; Moisture estimation; Remote sensing; Vegetation spectral indices; Thematic Mapper sensorReferences
[1] Steven, M.D., Malthus, T.J., Baret, F., Xu, H., Chopping, M.J. (2003). Intercalibration of vegetation indices from different sensor systems [C]. Remote Sensing of Environment, 88: 412-422. (DOI: https://doi.org/10.1016/j.rse.2003.08.010)
[2] Richardson, A.J., Everitt, J.H. (1992). Using Spectral Vegetation Indices to Estimate Rangeland Productivity [C]. Geocarto International, 1: 63-77. (DOI: https://doi.org/10.1080/10106049209354353)
[3] Lyon, J.G., Yuan, D., Lunetta, R.S., Elvidge, C.D. (1998) A Change Detection Experiment Using Vegetation indices [C]. Photogrammetric Engineering & Remote Sensing, 64, 2: 143-150.
[4] Jensen, J.R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective [M]. Upper Saddle River (NJ), USA: Prentice Hall.
[5] Jensen, J.R. (2004). Introductory Digital Image Processing. A Remote Sensing Perspective. Third edition. [M] Upper Saddle River (NJ), USA: Prentice Hall.
[6] Chuvieco, E., Riaño, D., Aguado, I., Cocero, D. (2002). Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment [C]. International Journal of Remote Sensing, 23, 11: 2145-2162. (DOI: https://doi.org/10.1080/01431160110069818)
[7] Xue, J., Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications [C]. Journal of Sensors, 2017, Article ID 1353691. (DOI: https://doi.org/10.1155/2017/1353691)
[8] Cetin, M., Sevik, H. (2016). Evaluating the recreation potential of Ilgaz Mountain National Park in Turkey [C]. Environmental Monitoring and Assessment, 188, 52. (DOI: https://doi.org/10.1007/s10661-015-5064-7)
[9] Potapov, P., Yaroshenko, A., Turubanova, S., Dubinin, M., Laestadius, L., Thies, C., Aksenov, D., Egorov, A., Yesipova, Y., Glushkov, I., Karpachevskiy, M., Kostikova, A., Manisha, A., Tsybikova, E., Zhuravleva, I. (2008). Mapping the World’s Intact Forest Landsacapes by Remote Sensing [C]. Ecology and Society, 13, 2: 51. (http://www.ecologyandsociety.org/vol13/iss2/art51/)
[10] Curran, P. (1980). Multiespectral remote sensing of vegetation amount [C]. Progress in Physical Geography: Earth and Environment, 4, 3: 315-341. (DOI: https://doi.org/10.1177/030913338000400301)
[11] Running, S.W., Loveland, T.R, Pierce, L.L., Nemani, R.R., Hunt, E.R. Jr. (1995). A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis [C]. Remote Sensing of Environment, 51: 39-48. (DOI: https://doi.org/10.1016/0034-4257(94)00063-S)
[12] Estes, J.E., Jensen, J.R., Simonett, D.S. (1980). Impacts of remote sensing on the U.S. Geography [C]. Remote Sensing of Environment, 10: 43-80. (DOI: https://doi.org/10.1016/0034-4257(80)90098-X)
[13] Houborga, R., Soegaard, H., Boeghb, E. (2007). Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data [C]. Remote Sensing of Environment, 106, 1: 39-58. (DOI: https://doi.org/10.1016/j.rse.2006.07.016)
[14] Jensen, J.R. (1983). Biophysical Remote Sensing. Review Article [C]. Annals of the Association of American Geographers, 73, 1: 111-132. (DOI: https://doi.org/10.1111/j.1467-8306.1983.tb01399.x)
[15] Jensen, J.R., Coombs, C., Porter, D., Jones, B., Schill, S., White, D. (1998). Extraction of Smoth Cordgrass (Sparthina alterniflora) Biomass and Leaf Area Index Parameters from High Resolution Imagery [C]. Geocarto International, 13, 4:25-46. (DOI: https://doi.org/10.1080/10106049809354661)
[16] Hanna, M.M., Steyn-Ross, D.A., Steyn-Ross, M. (1999). Estimating Biomass for New Zealand Pasture Using Optical Remote Sensing Techniques [C]. Geocarto International, 14, 3: 89-94. (DOI: https://doi.org/10.1080/10106049908542121)
[17] Haboudane, D., Miller, J.R., Pattey, E., Zarco-tejada, P., Strachan, I.B. (2004). Hiperespectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modelling and validation in the context of precision agriculture [C]. Remote Sensing of Environment, 90: 337-352. (DOI: https://doi.org/10.1016/j.rse.2003.12.013)
[18] Muukkonen, P. & Heiskanen, J. (2005). Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data [C]. Remote Sensing of Environment, 99, 4: 434-447. (DOI: https://doi.org/10.1016/j.rse.2005.09.011)
[19] Gitelson, A.A. (2004) Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation [C]. Journal of Plant Physiology. 161, 2: 165-173. (DOI: https://doi.org/10.1078/0176-1617-01176)
[20] Jackson, R.D. (1982). Canopy temperature and crop water stress [C]. Advances in Irrigation Research, 1: 45-85. (DOI: https://doi.org/10.1016/B978-0-12-024301-3.50009-5)
[21] Hunt, E.R., Rock, B.N., Nobel, P.S. (1987). Measurement of Leaf Relative Water Content by Infrared Reflectance [C]. Remote Sensing of Environment, 22: 429-435. (DOI: https://doi.org/10.1016/0034-4257(87)90094-0)
[22] Hunt, E.R., Rock, B.N. (1989). Detection of Changes in Leaf Water Content Using Near- and Middle-Infrared Reflectances [C]. Remote Sensing of Environment, 30: 43-54. (DOI: https://doi.org/10.1016/0034-4257(89)90046-1)
[23] Gao, B.C. (1996). NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space [C]. Remote Sensing of Environment, 58: 257-266. (DOI: https://doi.org/10.1016/S0034-4257(96)00067-3)
[24] Gamon, J.A., Surfus, J.S. (1999). Assessing leaf pigment content and activity with a reflectometer [C]. New Phytologist, 143: 105-117. (DOI: https://doi.org/10.1046/j.1469-8137.1999.00424.x)
[25] Gamon, J.A., Serrano, L., Surfus, J.S. (1997). The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, function types, and nutrient levels [C]. Acta Oecologica, 112: 492-501. (DOI: https://doi.org/10.1007/s004420050337)
[26] Sims, D.A., Gamon, J. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range os species, leaf structures and developmental stages [C]. Remote Sensing of Environment, 81, 2-3: 337-354. (DOI: https://doi.org/10.1016/S0034-4257(02)00010-X)
[27] Schultz, M., Clevers, J.G.P.W., Carter, S., Verbesselt, J., Avitabile, V., Quang, H.V., Herold, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring [C]. International Journal of Applied Earth Observation and Geoinformation, 52: 318-327. (DOI: https://doi.org/10.1016/j.jag.2016.06.020)
[28] Huete, A., Justice, C. (1999). MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document. Version 3 [S]. Greenbelt (MD), USA: NASA Goddard Space Flight Center.
[29] Kokaly, R.F., Clark, R.N. (1999). Spectroscopic Determination of Leaf Biochemistry using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression [C]. Remote Sensing of Environment, 67: 267-287. (DOI: https://doi.org/10.1016/S0034-4257(98)00084-4)
[30] Jackson, R.D., Huete, A.R. (1991). Interpreting vegetation indices [C]. Preventive Veterinary Medicine, 11: 185-200. (DOI: https://doi.org/10.1016/S0167-5877(05)80004-2)
[31] Eastman, J.R. (2003). IDRISI Kilimanjaro. Guide to GIS and Image Processing [S]. Worcester (MA), USA: Clark University.
[32] Fox, G.A., Sabbagh, G.J. (2002). Estimation of Soil Organic Matter from Red and Near-Infrared Remotely Sensed Data Using a Soil Line Euclidean Distance Technique [C]. Soil Science Society of America Journal, 66, 6: 1922-1929. (DOI: https://doi.org/10.2136/sssaj2002.1922)
[33] Clark, R.N., Roush, T.L. (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications [C]. Journal of Geophysical Research, 89: 6329-6340. (DOI: https://doi.org/10.1029/JB089iB07p06329)
[34] Clark, R.N. (1999). Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In: Rencz, A.N. (ed.) [M]. Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sciences. New York (USA): John Wiley & Sons, Ltd.: 3-58. (ISBN: 0471-29405-5)
[35] Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS [S]. Proceeding, Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351. Goddard Space Flight Center, Greenbelt (MD), USA: 309-317.
[36] Birth, G.S., McVey, G. (1968). Measuring the Color of Growing Turf with a Reflectance Spectrophotometer [C]. Agronomy Journal, 60, 6: 640-643. (DOI: https://doi.org/10.2134/agronj1968.00021962006000060016x)
[37] Deering, D.W., Rouse, J.W., Haas, R.H., Schell, J.A. (1975). Measuring Forage Production of Grazing Units from Landsat MSS data [S]. Proceedings of the 10th International Symposium on Remote Sensing of Environment, ERIM 2. Ann Arrbor, USA: 1169-1178.
[38] Chuvieco, E. (2002). Teledetección ambiental. La observación de la Tierra desde el espacio [M]. Barcelona (Spain): Ariel Ciencia. (ISBN: 8434480727)
[39] Rondeaux, G., Steven, M., Baret, F. (1996) Optimization of Soil-Adjusted Vegetation Indices [C]. Remote Sensing of Environment, 55: 95-107. (DOI: https://doi.org/10.1016/0034-4257(95)00186-7)
[40] Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modelling: the SAIL model [C]. Remote Sensing of Environment, 16, 2: 125-141. (DOI: https://doi.org/10.1016/0034-4257(84)90057-9)
[41] Kuusk, A. (1991). The hot-spot effect in plant caopy reflectance [M]. In R.B. Myneni and J. Ross Eds.), Photon-Vegetation interactions, Application in Optical Remote Sensing and Plant Ecology. New York: Springer Verlag.: 139-159. (DOI: https://doi.org/10.1007/978-3-642-75389-3_5)
[42] Huete, A.R. (1988) A Soil Adjusted Vegetation Index (SAVI) [C]. Remote Sensing of Environment, 25, 3: 295-309. (DOI: https://doi.org/10.1016/0034-4257(88)90106-X)
[43] Huete, A.R., Hua, G., Qi, J., Chehbouni, A., Van Leeuwem, W.J. (1992). Normalization of Multidirectional Red and Near-Infrared Reflectances with the SAVI [C]. Remote Sensing of Environment, 41, 2-3: 143-154. (DOI: https://doi.org/10.1016/0034-4257(92)90074-T)
[44] Steven, M.D. (1998). The sensitivity of the OSAVI vegetation index to observational parameters [C]. Remote Sensing of Environment, 63, 1: 49-60. (DOI: https://doi.org/10.1016/S0034-4257(97)00114-4)
[45] Kaufman, Y.J., Tanre, D. (1992). Atmospherically Resistant Vegetation index (ARVI) for EOS-MODIS [C]. IEEE Transactions on Geosciences and Remote Sensing, 30, 2: 261-270. (DOI: https://doi.org/10.1109/36.134076)
[46] Hardisky, M.A., Klemas, V., Smart, M. (1983). The Influence of Soil Salinity, Growth From, and Leaf Moisture on the Spectral Radiance of Spartina alternifolia Canopies [C]. Photogrammetric Engineering and Remote Sensing, 49, 1: 77-83. (DOI: https://doi.org/0099-1112183/4901-77$02.25/0)
[47] Carter, G. (1991). Primary and Secondary Effects of Water Content on the Spectral Reflectance of Leaves [C]. American Journal of Botany, 78, 7: 916-924. (DOI: https://doi.org/10.1002/j.1537-2197.1991.tb14495.x)
[48] Ceccato, P., Flasse, S., Tarantola, S., Jacquemound, S., Grégoire, J.M. (2001). Detecting vegetation leaf water content using reflectance in the optical domain [C]. Remote Sensing of Environment, 77, 1: 22-33. (DOI: https://doi.org/10.1016/S0034-4257(01)00191-2)
[49] Gould, K.S., Kuhn, D.N., Lee, D.W., Oberbauer, S.F. (1995). Why leaves are sometimes red [S]. Nature, 378, 6554: 241-242. (DOI: https://doi.org/10.1038/378241b0)
[50] Coley, P.D., Aide, T.M., (1989). Red coloration of tropical young leaves: a possible anti-fungal defence? [C]. Journal of Tropical Ecology, 5, 03: 293-300. (DOI: https://doi.org/10.1017/S0266467400003667)
[51] Coley, P.D., Barone, J.A. (1996). Herbivory and plant defenses in tropical forest [C]. Annual Review of Ecology and Systematics, 27: 305-335. (DOI: https://doi.org/10.1146/annurev.ecolsys.27.1.305)
[52] Qi, J., Chehbouni, Al, Huete, A.R., Kerr, Y.H., Sorooshian, S. (1994). A modified soil adjusted vegetation index (MSAVI) [C]. Remote Sensing of Environment, 48, 2: 119-126. (DOI: https://doi.org/10.1016/0034-4257(94)90134-1)
[53] Richardson, A.J., Wiegand, C.L. (1977). Distinguishing vegetation from soil background information [C]. Photogrammetric Engineering & Remote Sensing, 43, 12: 1541-1552. (ISSN: 0099-1112)
[54] Fox, G.A., Sabbagh, G.J., Searcy, S.W., Yang, C. (2004). An Automated Soil Line Identification Routine for Remotely Sensed Images [C]. Soil Science Society of America Journal, 68, 4: 1326-1331. (DOI: https://doi.org/10.2136/sssaj2004.1326)
[55] Clevers, J.G.P.W. (1988). The derivation of a simplified reflectance model for the estimation of leaf area index [C]. Remote Sensing of Environment, 25, 1: 53-69. (DOI: https://doi.org/10.1016/0034-4257(88)90041-7)
[56] Clevers, J.G.P.W., Verhoef, W. (1993). LAI estimation by means of the WDVI: A sensitivity analysis with a combined PROSPECT-SAIL model [C]. Remote Sensing Reviews, 7, 1: 43-64. (DOI: https://doi.org/10.1080/02757259309532165)
[57] Baret, F., Guyot, G.; Major, D. (1989). TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects on LAI and APAR Estimation [S]. 12th Canadian Symposium on Remote Sensing and IGARSS’90. Volume 4. Vancouver, Canada.: 10-14. (DOI: https://doi.org/10.1109/IGARSS.1989.576128)
[58] Gilabert, M.A., González-Piqueras, J., García-Haro, F.J., Meliá, J. (2002). A generalizad soil-adjusted vegetation index [C]. Remote Sensing of Environment, 82, 2-3: 303-310. (DOI: https://doi.org/10.1016/S0034-4257(02)00048-2)
[59] Huete, A.R., Liu, H.Q. (1994). An Error and Sensitivity Analysis of the Atmospheric- and Soil-Correcting Variants of the Normalized Difference Vegetation Index for the MODIS-EOS [C]. IEEE Transactions on Geosciences and Remote Sensing, 32, 4: 897-905. (DOI: https://doi.org/10.1109/36.298018)
[60] Kauth, R.J., Thomas, G.S. (1976). The Tasseled Cap: A Graphic Description of the Spectral Temporal Development of Agricultural Crops as Seen By Landsat [S]. In Proceedings, Machine Processing of Remotely Sensed Data. Laboratory for the Applications of Remote Sensing (LARS), Purdue University, West Lafayette (IN), USA: 41-51. (http://docs.lib.purdue.edu/lars_symp/159)
[61] Crist, E.P. (1985). A Thematic Mapper Tasseled Cap Equivalent for Reflectance Factor Data [C]. Remote Sensing of Environment, 17, 3: 301-306. (DOI: https://doi.org/10.1016/0034-4257(85)90102-6)
[62] Crist, E.P. (1983). The TM tasselled cap: A preliminary formulation [S]. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. Laboratory for the Applications of Remote Sensing (LARS), Purdue University, West Lafayette (IN), USA: 357-364.
[63] Crist, E.P., Cicone, R.C. (1984a). Comparison of the dimensionality and features of simulated Landsat-4 MSS and TM data [C]. Remote Sensing of Environment, 14, 1-3: 235-246. (DOI: https://doi.org/10.1016/0034-4257(84)90018-X)
[64] Crist, E.P., Cicone, R.C. (1984b). A physically-based transformation of thematic mapper data – the TM Tasseled Cap [C]. IEEE Transactions on Geoscience and Remote Sensing, 22, 3: 256-263. (DOI: https://doi.org/10.1109/TGRS.1984.350619)
[65] Crist, E.P., Kauth, (1986). The Tasselled Cap de-mystified [C]. Photogrammetric Engineering and Remote Sensing, 52, 1: 81-86. (DOI: https://doi.org/0099-1112186/5201-0081$02.25/0)
[66] Jackson, R.D. (1983). Spectral Indices in n-Space [C]. Remote Sensing of Environment, 13, 5: 409-421. (DOI: https://doi.org/10.1016/0034-4257(83)90010-X)
[67] Clark, R.N., Roush, T.L. (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications [C]. Journal of Geophysical Research, 89, B7: 6329-6340. (DOI: https:doi.org/10.1029/JB089iB07p06329)
[68] Clark, R.N. (1999). Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy [M]. In: Rencz, A.N. (ed.). Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sciences. New York (USA): John Wiley & Sons, Ltd.: 3-58.
[69] Clark, R. N., Swayze, G. A., Livo, K. E., Kokaly, R. F., Sutley, S. J., Dalton, J. B., McDougal, R. R., Gent, C. A. (2003). Imaging Spectroscopy: Earth and Planetary Remote Sensing with the USGS Tetracorder and Expert Systems [C]. Journal of Geophysical Research, 108, E12: 5131. (DOI: https://doi.org/10.1029/2002JE001847.v)
[70] Van Niel, T.G., McVicar, T.R., Fang, H., Liang, S. (2003). Calculating environmental moisture for per-field discrimination of rice crops [C]. International Journal of Remote Sensing, 24, 4: 885-890. (DOI: https://doi.org/10.1080/0143116021000009921)
[71] Mather, P.M. (2004). Computer Processing of Remotely-Sensed Images. An Introduction [M]. Third edition. West Sussex (England), UK: John Wiley & Sons, Ltd. (ISBN: 9780470849187)
[72] Chander, G., Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges [R]. IEEE Transactions of Geosciences and Remote Sensing, 41, 11: 2674-2677. (DOI: https://doi.org/10.1109/TGRS.2003.818464)
[73] Chavez, P. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data [C]. Remote Sensing of Environment, 24, 3: 459-479. (DOI: https://doi.org/10.1016/0034-4257(88)90019-3)
[74] Koch, M. (2000). Geological controls of land degradation as detected by remote sensing: a case study in Los Monegros, north-east Spain [C]. International Journal of Remote Sensing, 21, 3: 457-473. (DOI: https://doi.org/10.1080/014311600210687)
[75] Dewa, R.P., Danoedoro, P. (2017). The effect of image radiometric correction on the accuracy of vegetation canopy density estimate using several Landsat-8 OLI’s vegetation indices: A case study of Wonosari area, Indonesia [S]. IOP Conference Series: Earth and Environmental Science, 54, 012046. (DOI: https://doi.org/10.1088/1755-1315/54/1/012046)
[76] Hoffer, R.M. (1978). Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data. In Swain, P.H. and Davis, S.M. (eds.), Remote Sensing: The Quantitative Approach, McGraw- Hill Book Company, New York: 227-289.
Downloads
How to Cite
Issue
Article Type
License
Copyright © 2019 Jose Navarro Pedreño, Gema Marco Dos Santos, Ignacio Meléndez-Pastor, Ignacio Gómez Lucas
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.