A review of Landsat TM/ETM based vegetation indices as applied to wetland ecosystems.

Jose Navarro Pedreño, Gema Marco Dos Santos, Ignacio Meléndez-Pastor, Ignacio Gómez Lucas

Abstract


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 sensor

Full Text:

PDF

References


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. (https://doi.org/10.1016/j.rse.2003.08.010)

Richardson, A.J., Everitt, J.H. (1992). Using Spectral Vegetation Indices to Estimate Rangeland Productivity [C]. Geocarto International, 1, 63-77. (DOI: 10.1080/10106049209354353)

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.

Jensen, J.R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective [M]. Upper Saddle River (NJ), USA: Prentice Hall.

Jensen, J.R. (2004). Introductory Digital Image Processing. A Remote Sensing Perspective. Third edition. [M] Upper Saddle River (NJ), USA: Prentice Hall.

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. (https://doi.org/10.1080/01431160110069818)

Xue, J., Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications [C]. Journal of Sensors, 2017, Article ID 1353691. (https://doi.org/10.1155/2017/1353691)

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. (https://doi.org/10.1016/0034-4257(80)90098-X)

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. (https://doi.org/10.1016/j.rse.2006.07.016)

Jensen, J.R. (1983). Biophysical Remote Sensing. Review Article [C]. Annals of the Association of American Geographers, 73(1), 111-132. (https://doi.org/10.1111/j.1467-8306.1983.tb01399.x)

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. (https://doi.org/10.1080/10106049809354661)

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. (https://doi.org/10.1080/10106049908542121)

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: 10.1016/j.rse.2003.12.013)

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: 10.1016/j.rse.2005.09.011)

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. (https://doi.org/10.1078/0176-1617-01176)

Jackson, R.D. (1982). Canopy temperature and crop water stress [C]. Advances in Irrigation Research, 1, 45-85. (https://doi.org/10.1016/B978-0-12-024301-3.50009-5)

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. (https://doi.org/10.1016/0034-4257(87)90094-0)

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. (https://doi.org/10.1016/0034-4257(89)90046-1)

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. (https://doi.org/10.1016/S0034-4257(96)00067-3)

Gamon, J.A., Surfus, J.S. (1999). Assessing leaf pigment content and activity with a reflectometer [C]. New Phytologist, 143, 105-117. (https://doi.org/10.1046/j.1469-8137.1999.00424.x)

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]. Oecologica, 112, 492-501. (https://doi.org/10.1007/s004420050337)

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.

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.

(https://doi.org/10.1016/S0034-4257(98)00084-4)

Jackson, R.D., Huete, A.R. (1991). Interpreting vegetation indices [C]. Preventive Veterinary Medicine, 11:185-200. (https://doi.org/10.1016/S0167-5877(05)80004-2)

Eastman, J.R. (2003). IDRISI Kilimanjaro. Guide to GIS and Image Processing [S]. Worcester (MA), USA: Clark University.

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:10.2136/sssaj2002.1922)

Clark, R.N., Roush, T.L. (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications [C]. Journal of Geophysical Research, 89, 6329-6340. (https://doi.org/10.1029/JB089iB07p06329)

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. (pp. 3-58). New York (USA): John Wiley & Sons, Ltd. (ISBN: 0471-29405-5)

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 (pp. 309-317).Goddard Space Flight Center, Greenbelt (MD), USA.

Birth, G.S., McVey, G. (1968). Measuring the Color of Growing Turf with a Reflectance Spectrophotometer [C]. Agronomy Journal, 60(6), 640-643. (DOI: 10.2134/agronj1968.00021962006000060016x)

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 (pp. 1169-1178). Ann Arrbor, USA.

Chuvieco, E. (2002). Teledetección ambiental. La observación de la Tierra desde el espacio [M]. Barcelona (Spain): Ariel Ciencia. (ISBN: 8434480727)

Rondeaux, G., Steven, M., Baret, F. (1996) Optimization of Soil-Adjusted Vegetation Indices [C]. Remote Sensing of Environment, 55, 95-107. (https://doi.org/10.1016/0034-4257(95)00186-7)

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. (https://doi.org/10.1016/0034-4257(84)90057-9)

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 (pp. 139-159). New York: Springer Verlag. (DOI: 10.1007/978-3-642-75389-3_5)

Huete, A.R. (1988) A Soil Adjusted Vegetation Index (SAVI) [C]. Remote Sensing of Environment, 25(3), 295-309. (https://doi.org/10.1016/0034-4257(88)90106-X)

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. (https://doi.org/10.1016/0034-4257(92)90074-T)

Steven, M.D. (1998). The sensitivity of the OSAVI vegetation index to observational parameters [C]. Remote Sensing of Environment, 63(1), 49-60. (https://doi.org/10.1016/S0034-4257(97)00114-4)

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: 10.1109/36.134076)

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 & Remote Sensing, 49(1), 77-83.

Carter, G. (1991). Primary and Secondary Effects of Water Content on the Spectral Reflectance of Leaves [C]. American Journal of Botany, 78(7). (https://doi.org/10.1002/j.1537-2197.1991.tb14495.x)

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. (https://doi.org/10.1016/S0034-4257(01)00191-2)

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: 10.1038/378241b0)

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: 10.1017/S0266467400003667)

Coley, P.D., Barone, J.A. (1996). Herbivory and plant defenses in tropical forest [C]. Annual Review of Ecology and Systematics, 27, 305-335. (https://doi.org/10.1146/annurev.ecolsys.27.1.305)

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: 10.1016/0034-4257(94)90134-1)

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. (https://doi.org/10.1016/0034-4257(88)90041-7)

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: 10.1080/02757259309532165)

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 (pp. 10-14). Vancouver, Canada.

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. (https://doi.org/10.1016/S0034-4257(02)00048-2)

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: 10.1109/36.298018)

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)

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: 10.1109/TGRS.2003.818464)

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. (https://doi.org/10.1016/0034-4257(88)90019-3)

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: 10.1080/014311600210687)

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: 10.1088/1755-1315/54/1/012046)

Hoffer, R. (1978). Biological and Physical Considerations in applying computer-aided analysis techniques to remote sensing [S]. Remote Sensing: The Quantitative Approach, 241-251.




DOI: https://doi.org/10.30564/jgr.v2i1.499

Refbacks

  • There are currently no refbacks.
Copyright © 2019 Jose Navarro Pedreño, Gema Marco Dos Santos, Ignacio Meléndez-Pastor, Ignacio Gómez Lucas Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.