We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl > 40 mg/cm 2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI.
References
[1]
Chen, J.M.; Cihlar, J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ 1996, 55, 153–162.
[2]
Gitelson, A.A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B: Biol 1994, 22, 247–252.
[3]
Skianis, G.; Vaiopoulos, D.; Nikolakopoulos, K. A Comparative Study of the Performance of the NDVI, the TVI and the SAVI Vegetation Indices over Burnt Areas, Using Probability Theory and Spatial Analysis Techniques. Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires, Thessaloniki, Greece, 27–29 September 2007.
[4]
Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ 1979, 8, 127–150.
[5]
Clevers, J.G.P.W. Application of a vegetation index in correcting the infrared reflectance for soil background. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 1988, 16, 221–226.
[6]
Gitelson, A.A. Wide Dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol 2004, 161, 165–173.
[7]
Steele, M.R.; Gitelson, A.A.; Rundquist, D.C. Nondestructive estimation of anthocyanin content in Grapevine leaves. Am. J. Enol. Vitic 2009, 60, 87–92.
[8]
Salas, E.A.L.; Henebry, G.M. Area between peaks feature in the derivative reflectance curve as a sensitive indicator of change in chlorophyll concentration. GISci. Remote Sens 2009, 46, 315–328.
[9]
Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ 2001, 76, 156–172.
[10]
Okin, G.S. The contribution of brown vegetation to vegetation dynamics. Ecology 2010, 91, 743–755.
[11]
Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.-M.; Tucker, C.J.; Stenseth, N.C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol 2005, 20, 503–510.
[12]
Townshend, J.R.G.; Justice, C.O. Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int. J. Remote Sens 1986, 7, 1435–1445.
[13]
Vi?a, A.; Henebry, G.M.; Gitelson, A.A. Satellite monitoring of vegetation dynamics: Sensitivity enhancement by the Wide Dynamic Range Vegetation Index. Geophys. Res. Lett 2004, doi:10.1029/2003GL019034.
[14]
Hurcom, S.J.; Harrison, A.R. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. Int. J. Remote Sens 1998, 19, 3109–3125.
[15]
Simms, E.L.; Ward, H. Multisensor NDVI-based monitoring of the Tundra-Taiga interface (Mealy Mountains, Labrador, Canada). Remote Sens 2013, 5, 1066–1090.
[16]
Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation System in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, MD, USA, 10–14 December 1974; pp. 309–317.
[17]
Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ 2004, 90, 337–352.
[18]
Myneni, R.B.; Hall, F.G.; Sellers, P.J.; Marshak, A.L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens 1995, 33, 481–486.
[19]
Zhao, X.; Tan, K.; Zhao, S.; Fang, J. Changing climate affects vegetation growth in the arid region of the northwestern China. J. Arid Environ 2011, 75, 946–952.
[20]
Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ 1996, 55, 95–107.
[21]
Elvidge, C.D.; Chen, Z. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens. Environ 1995, 54, 38–48.
[22]
Jordan, C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969, 50, 663–666.
[23]
Pearson, R.L.; Miller, L.D. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-Grass Prairie. Proceedings of the Eighth International Symposium on Remote Sensing of Environment, Pawnee National Grasslands, Colorado, Ann Arbor, MI, USA, 2–6 October 1972; pp. 357–1381.
[24]
Baret, F.; Guyot, G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ 1991, 35, 161–173.
[25]
Gitelson, A.A.; Vi?a, A.; Arkebauer, T.J.; Rundquist, D.C.; Keydan, G.; Leavitt, B. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett 2003, doi:10.1029/2002GL016450.
[26]
Brantley, S.T.; Zinnert, J.C.; Young, D.R. Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sens. Environ 2011, 115, 514–523.
[27]
Vi?a, A. Evaluating vegetation indices for assessing productivity along a tropical rain forest chronosequence in Western Amazonia. Isr. J. Plant Sci 2012, 60, 123–133.
[28]
Vi?a, A.; Gitelson, A.A. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys. Res. Lett 2005, doi:10.1029/2005GL023647.
[29]
Aguilar-Amuchastegui, N.; Henebry, G.M. Characterizing tropical forest spatio-temporal heterogeneity using the Wide Dynamic Range Vegetation Index (WDRVI). Int. J. Remote Sens 2008, 29, 7285–7291.
[30]
Aguilar-Amuchastegui, N.; Henebry, G.M. Monitoring sustainability in tropical forests: How changes in canopy spatial pattern can indicate forest stands for biodiversity surveys. IEEE Geosci. Remote Sens. Lett 2006, 3, 329–333.
[31]
Gitelson, A.A. Remote estimation of crop fractional vegetation cover: The use of noise equivalent as an indicator of performance of vegetation indices. Int. J. Remote Sens 2013, 34, 6054–6066.
[32]
Deering, D.W.; Rouse, J.W.; Haas, R.H.; Schell, J.A. Measuring Forage Production of Grazing Units from Landsat MSS Data. Processings of the 10th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, USA, October 1975; pp. 1169–1178.
[33]
Gilabert, M.A.; González-Piqueras, J.; Garcyìa-Haro, F.J.; Meliá, J. A generalized soil-adjusted vegetation index. Remote Sens. Environ 2002, 82, 303–310.
[34]
Baret, F.; Jacquemoud, S.; Hanocq, J.F. About the soil line concept in remote sensing. Adv. Sp. Res 1993, 13, 281–284.
[35]
Richardson, A.J.; Wiegand, C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens 1977, 43, 1541–1552.
[36]
Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ 1994, 48, 119–126.
[37]
Perry, J.C.R.; Lautenschlager, L.F. Functional equivalence of spectral vegetation indices. Remote Sens. Environ 1984, 14, 169–182.
[38]
Kauth, R.J.; Thomas, G.S. The Tasseled Cap—A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen in Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 29 June–1 July 1976; pp. 41–51.
[39]
Crist, E.P.; Cicone, R.C. A physically-based transformation of thematic mapper data: The TM Tassed Cap. IEEE. Trans. Geosci. Remote Sens 1984, 22, 256–263.
[40]
Gitelson, A.A.; Stark, R.; Grits, U.; Rundquist, D.; Kaufman, Y.; Derry, D. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. Int. J. Remote Sens 2002, 23, 2537–2562.
[41]
Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ 1988, 25, 295–309.
[42]
Baret, F.; Guyot, G.; Major, D.J. TSAVI: A Vegetation Index which Minimizes Soil Brightness Effects on LAI and APAR Estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing, Vancouver, BC, Canada, 10–14 July 1989; pp. 1355–1358.
[43]
Major, D.J.; Baret, F.; Guyot, G. A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sens 1990, 11, 727–740.
[44]
Bannari, A.; Huete, A.R.; Morin, D.; Zagolski, F. Effects of soil colour and brightness on vegetation index. Int. J. Remote Sens 1996, 17, 1885–1906.
[45]
Liu, H.Q.; Huete, A.R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens 1995, 33, 457–465.
[46]
Gitelson, A.A.; Keydan, G.P.; Merzlyak, M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett 2006, doi:10.1029/2006GL026457.
[47]
Nguy-Robertson, A.; Gitelson, A.; Peng, Y.; Vina, A.; Arkebauer, T.; Rundquist, D. Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity. Agron. J 2012, 104, 1336–1347.
[48]
Yoshioka, H.; Miura, T.; Demattê, J.A.M.; Batchily, K.; Huete, A.R. Derivation of soil line influence on two-band vegetation indices and vegetation isolines. Remote Sens 2009, 1, 842–857.
Kruse, F.A.; Taranik, J.V.; Coolbaugh, M.; Michaels, J.; Littlefield, E.F.; Calvin, W.M.; Martini, B.A. Effect of reduced spatial resolution on mineral mapping using imaging spectrometry—Examples using Hyperspectral Infrared Imager (HyspIRI)-simulated data. Remote Sens 2011, 3, 1584–1602.
[51]
Mariotto, I.; Thenkabail, P.S.; Huete, A. R.; Slonecker, E.T.; Platonov, A. Hyperspectral vs. multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ 2013, 139, 291–305.
[52]
Wang, L.; Hunt, E.R.; Qu, J.J.; Hao, X.; Daughtry, C.S.T. Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sens. Environ 2011, 115, 836–840.
[53]
Roberts, D.A.; Roth, K.L.; Perroy, R.L. Hyperspectral Vegetation Indices. In Hyperspectral Remote Sensing of Vegetation; Thenkabail, P.S., Lyon, J.G., Huete, A.R., Eds.; CRC Press: Boca Raton, FL, USA, 2012.
[54]
Cho, M.A.; Skidmore, A.K.; Atzberger, C. Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data. Int. J. Remote Sens 2008, 29, 2241–2255.
[55]
Filella, I.; Pe?uelas, J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens 1994, 15, 1459–1470.
[56]
Banskota, A.; Wynne, R.H.; Thomas, V.A.; Serbin, S.P.; Kayastha, N.; Gastellu-Etchegorry, J.P.; Townsend, P.A. Investigating the utility of wavelet transforms for inverting a 3-D radiative transfer model using hyperspectral data to retrieve forest LAI. Remote Sens 2013, 5, 2639–2659.
[57]
Gitelson, A.A.; Merzlyak, M.N. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. J. Plant Physiol 1996, 148, 494–500.
[58]
Salas, E.A.L.; Henebry, G.M. Separability of maize and soybean in the spectral regions of chlorophyll and carotenoids using the Moment Distance Index. Isr. J. Plant Sci 2012, 60, 65–76.
[59]
Horler, D.N.H.; Dockray, M.; Barber, J. The red-edge of plant leaf reflectance. Int. J. Remote Sens 1983, 4, 273–288.
[60]
Liao, Q.; Wang, J.; Yang, G.; Zhang, D.; Li, H.; Fu, Y.; Li, Z. Comparison of spectral indices and wavelet transform for estimating chlorophyll content of maize from hyperspectral reflectance. J. App. Remote Sens 2013, 7, doi:10.1117/1.JRS.7.073575.
[61]
Gitelson, A.A. Nondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, and Anthocyanins) Contents: Evaluating a Semianalytical Three-Band Model. In Hyperspectral Remote Sensing of Vegetation; Thenkabail, P.S., Lyon, J.G., Huete, A.R., Eds.; CRC Press: Boca Raton, FL, USA, 2011.
[62]
Jacquemoud, S.; Baret, F. PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ 1990, 34, 75–91.
[63]
Verhoef, W. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sens. Environ 1984, 16, 125–141.
[64]
Atzberger, C.; Darvishzadeh, R.; Schlerf, M.; Le Maire, G. Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies. Remote Sens. Lett 2013, 4, 55–64.
[65]
Uto, K.; Kosugi, Y. Leaf parameter estimation based on leaf scale hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens 2013, 6, 699–707.
[66]
Croft, H.; Chen, J.M.; Zhang, Y.; Simic, A. Modelling Leaf Chlorophyll Content in Broadleaf and Needle Leaf Canopies from Ground, CASI, Landsat TM 5 and MERIS Reflectance Data. Remote Sens. Environ 2013, 133, 128–140.
[67]
Navarro-Cerrillo, R.M.; Trujillo, J.; Sanchez de la Orden, M.; Hernandez-Clemente, R. Hyperspectral and multispectral satellite sensors for mapping chlorophyll content in a Mediterranean pinus sylvestris L. plantation. Int. J. Appl. Earth Obs. Geoinf 2014, 26, 88–96.
[68]
Feret, J.-B.; Fran?ois, C.; Asner, G.P.; Gitelson, A.A.; Martin, R.E.; Bidel, L.P.R.; Ustin, S.L.; le Maire, G.; Jacquemoud, S. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ 2008, 112, 3030–3043.
[69]
Clark, R.N.; Swayze, G.A.; Wise, R.; Livo, E.; Hoefen, T.; Kokaly, R.; Sutley, S.J. USGS Digital Spectral Library Splib06a. Available online: http://speclab.cr.usgs.gov/spectral.lib06 (accessed on 10 March 2010).
[70]
Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ 1997, 62, 241–252.
[71]
Ahern, F.J. The effects of bark beetle stress on the foliar spectral reflectance of Lodgepole Pine. Int. J. Remote Sens 1988, 9, 1451–1468.
[72]
Curran, P.J.; Dungan, J.L.; Gholz, H.L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol 1991, 7, 33–48.
[73]
Baret, F.; Vanderbilt, V.C.; Steven, M.D.; Jacquemoud, S. Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties. Remote Sens. Environ 1994, 48, 253–260.
[74]
Daughtry, C.S.T.; Gallo, K.P.; Bauer, M.E. Spectral estimates of solar radiation intercepted by corn canopies. Agron. J 1983, 75, 527–531.
[75]
Clevers, J.G.P.W.; Kooistra, L.; Salas, E.A.L. Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data. Int. J. Remote Sens 2004, 25, 3883–3895.
[76]
Gitelson, A.A.; Vina, A.; Rundquist, D.C.; Ciganda, V.; Arkebauer, T.J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett 2005, doi:10.1029/2005GL022688.
[77]
Wang, F.-M.; Huang, J.-F; Tang, Y.-L; Wang, X.-Z. New vegetation index and its application in estimating leaf area index of rice. Rice Sci 2007, 14, 195–203.
[78]
Demetriades-Shah, T.H.; Steven, M.D.; Clark, J.A. High resolution derivative spectra in remote sensing. Remote Sens. Environ 1990, 33, 55–64.
[79]
Baret, F.; Jacquemoud, S.; Guyot, G.; Leprieur, C. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens. Environ 1992, 41, 133–142.
[80]
Wu, C.; Niu, Z.; Tang, Q.; Huang, W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol 2008, 148, 1230–1241.
[81]
Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ 2012, 122, 11–21.
[82]
Mishra, D.R.; Narumalani, S.; Rundquist, D.; Lawson, M.; Perk, R. Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach. J. Geophys. Res 2007, 112, doi:10.1029/2006JC003892.
[83]
Hook, S.J.; Myers, J.E.J.; Thome, K.J.; Fitzgerald, M.; Kahle, A.B. The MODIS/ASTER airborne simulator (MASTER)—A new instrument for earth science studies. Remote Sens. Environ 2001, 76, 93–102.
[84]
Miller, J.R.; Freemantle, J.R.; Belanger, M.J.; Elvidge, C.D.; Boyer, M.G. Potential for Determination of Leaf Chlorophyll Content Using AVIRIS. Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, Pasadena, CA, USA, 4–5 June 1990; pp. 72–77.
[85]
Green, R.O.; Eastwood, M.L.; Sarture, C.M.; Chrien, T.G.; Aronsson, M.; Chippendale, B.J.; Faust, J.A.; Pavri, B.E.; Chovit, C.J.; Solis, M.; et al. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ 1998, 65, 227–248.
[86]
Liao, L.; Jarecke, P.; Gleichauf, D.; Hedman, T. Performance characterization of the Hyperion imaging spectrometer instrument. Proc. SPIE 2000, 4135, doi:10.1117/12.494253.