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7 - Remote Sensing of Landscape Biophysical Properties

from Section Two - Remote Sensing

Published online by Cambridge University Press:  05 June 2016

Derek Eamus
Affiliation:
University of Technology, Sydney
Alfredo Huete
Affiliation:
University of Technology, Sydney
Qiang Yu
Affiliation:
University of Technology, Sydney
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Summary

Introduction

The goal of this chapter is to discuss the basic optical properties of common landscape surface features, including plants, soils, water and detrital material. Variations of these optical properties over space and time provide the basis for remote sensing advancements on the detection and characterization of physical, chemical and biologic landscape properties across global terrestrial ecosystems. The remotely-sensed signal is a combination of the intrinsic optical properties of landscape components interacting with spectral, angular (sun–view geometries) and polarization radiation aspects of the incoming solar radiation. Information collected in spectral, spatial, angular and temporal optical domains are utilized in the development of methods for retrievals of ecological, hydrological and biogeochemical variables.

Spectral Signatures

Remotely-sensed information about landscape constituents is most often obtained through analyses of spectral reflectance signatures acquired across the reflected solar spectrum (400–2500 nm). Landscape spectral signatures are a function of the optical properties of the biogeochemical constituents, moisture condition and the size, shape and geometry of leaf elements or litter and soil particles (Fig. 7.1). There is considerable knowledge about vegetation, soil, litter and water optical signatures obtained through extensive field and laboratory spectroscopic analyses (Ben-Dor et al. 2008, Ustin et al. 2004).

Vegetation Optics

The absorption spectrum of a green leaf is related to its chemical (pigments, lignins and cellulose), moisture and physical properties (Fig. 7.2). Leaf optical properties in the visible wavelengths (400–700 nm) are controlled by the presence of biologically active pigments which generally result in low reflectance due to strong absorptions by chlorophylls, carotenoids, xanthophylls and anthocyanins (Fig. 7.3; Chapter 2; Carter and Knapp 2001, Blackburn 2007). Strong chlorophyll absorption features in the blue (450 nm) and red (680 nm) result in a ‘green’ appearance to healthy leaves as there is less-absorption and more reflection in the green.

Leaf pigment contents provide valuable information on the physiological performance of leaves. Plant disease and stress will alter pigment contents and hence the visible spectrum (Knipling 1970). Mineral deficiencies affect chlorophyll content to varying extents with chlorosis directly affecting the visible spectra. Nitrogen deficiencies cause visible reflectance to increase and NIR reflectance to decrease by altering the number of cell layers and leaf necrosis results in spectral signatures comparable to that of senescent leaves (Gates et al. 1965).

Type
Chapter
Information
Vegetation Dynamics
A Synthesis of Plant Ecophysiology, Remote Sensing and Modelling
, pp. 206 - 236
Publisher: Cambridge University Press
Print publication year: 2016

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References

Asner, GP, (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 64, 234–253.Google Scholar
Asner, GP, Martin, RE, Carlson, KM, Rascher, U and Vitousek, PM, (2006). Vegetation climate interaction among native and invasive species in Hawaiian rainforest. Ecosystems 9, 1106–1117.Google Scholar
Asner, GP, Scurlock, JMO and Hicke, JA, (2003). Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Global Ecology and Biogeography 12, 191–205.Google Scholar
Asrar, G, Fuchs, M, Kanemaus, ET and Hatfield, JL, (1984). Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal 76, 300–306.Google Scholar
Baret, F and Guyot, G (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173.Google Scholar
Bates, LM and Hall, AE, (1981). Stomatal closure with soil water depletion not associated with changes in bulk leaf water status. Oecologia 50, 62–65.Google Scholar
Baumgardner, MF, Silva, LF, Biehl, LL and Stoner, ER, (1985). Reflectance properties of soils. Advances in Agronomy 38, 1–44.Google Scholar
Ben-Dor, E and Bannin, A, (1994). Visible and near infrared (0.4–1.1 µm) analysis of arid and semi-arid soils. Remote Sensing of Environment 48, 261–274.Google Scholar
Ben-Dor, E, Taylor, RG, Hill, J, Dematte, JAM, Whiting, ML, Chabrillat, S and Sommer, S, (2008). Imaging spectrometry for soil applications. Advances in Agronomy 97, 321–392.Google Scholar
Blackburn, GA, (2007). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany 58, 855–867. doi:10.1093/jxb/erl123.Google Scholar
Caccamo, G, Chisholm, LA, Bradstock, RA and Puotinen, ML, (2011). Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment 115, 2626–2639. Elsevier Inc. doi:10.1016/j.rse.2011.05.018Google Scholar
Carter, GA and Knapp, AK, (2001). Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88, 67–84.Google Scholar
Chen, X, Vierling, L, Deering, D and Conley, A, (2005). Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. International Journal of Remote Sensing 26(24): 5433–5451.
Clark, RN, (1999). Chapter 1: Spectroscopy of Rocks and Minerals and Principles of Spectroscopy, In: Manual of Remote Sensing, (Rencz, AN, Ed.) John Wiley and Sons, New York, pp. 3–58.
Crist, EP and Cicone, RC, (1984). A physically based transformation of Thematic Mapper data–the TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing GE-22, 256–263.Google Scholar
Donohue, RJ, Roderick, ML and McVicar, TR, (2008). Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework. Remote Sensing of Environment 112, 2938–2949.Google Scholar
Fassnacht, KS, Gower, ST, MacKenzie, MD, Nordheim, EV and Lillesand, TM, (1997). Estimating the leaf area index of North Central Wisconsin forests using the Landsat Thematic Mapper. Remote Sensing of Environment 61(2):229–245.
Fensholt, R and Sandholt, I, (2003). Derivation of a shortwave infrared water stress index from MODIS near–and shortwave infrared data in a semiarid environment. Remote Sensing of Environment 87, 111–121.Google Scholar
Fensholt, R, Sandholt, I and Rasmussen, MS, (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment 91, 490–507.Google Scholar
Field, CB, Randerson, JT and Malmstrom, CM (1995). Global net primary production: Combining ecology and remote sensing. Remote Sensing of Environment 51, 74–88.Google Scholar
Franklin, J, Duncan, J and Turner, D, (1993). Reflectance of vegetation and soil in Chihuahuan desert plant communities from ground radiometry using SPOT wavebands. Remote Sensing of Environment 46, 291–304.CrossRefGoogle Scholar
Gao, BC, (1996). NDWI–A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58, 257–266.Google Scholar
Gamon, JA, Penuelas, J and Field, CB, (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41, 35–44.Google Scholar
Garrigues, S, Lacaze, R, Baret, F, Morisette, JT, Weiss, M, Nickeson, JE, Fernandes, R, Plummer, S, Shabonov, NV, Myneni, RB, Knyazikhin, Y and Yang, W, (2008). Validation and inter-comparison of global leaf area index products derived from remote sensing data. Journal of Geophysical Research, 113:doi:10.1029/2007JG000635.Google Scholar
Gates, DM, Keegen, HJ, Schleter, JC and Weidner, VR, (1965). Spectral properties of plants. Applied Optics 4, 11–20.Google Scholar
Gitelson, AA, Gritz, Y and Merzlyak, MN. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160, 271–82.Google Scholar
Gitelson, AA, Chivkunova, OB and Merzlyak, MN, (2009). Non-destructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. American Journal of Botany 96, 1861–1868.
Gitelson, AA, Peng, Y, Arkebauer, TJ and Schepers, J, (2014). Relationships between gross primary production, green LAI and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environment 144, 65–72.CrossRefGoogle Scholar
Glenn, EP, Huete, AR, Nagler, PL and Nelson, SG, (2008). Relationship between remotely sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160.Google Scholar
Gobron, N, Pinty, B, Verstraete, MM and Widlowski, JL, (2000). Advanced vegetation indices optimized for upcoming sensors: Design, performance and applications. IEEE Transactions of Geosciences and Remote Sensing 38, 2489–2505.Google Scholar
Goward, SN, Tucker, CJ and Dye, DG. (1985). North American vegetation patterns observed with the NOAA-7 Advanced Very High Resolution Radiometer. Vegetatio 64, 3–14.Google Scholar
Graetz, RD, (1990). Remote sensing of terrestrial ecosystem structure: an ecologist's pragmatic view, In: Hobbs, RJ and Mooney, HA, Eds., Remote Sensing of Biosphere Functioning. Springer-Verlag, New York, pp. 5–30.
Hardisky, MA, Smart, RM and Klemas, V, (1983). Seasonal spectral characteristics and above ground biomass of the tidal marsh plantSpartina alterniflora. Photogrametric Engineering and Remote Sensing 49, 85–92.Google Scholar
Huemmrich, KF, Privette, JL, Mukelabai, M, Myneni, RB and Knyazikhin, Y, (2005). Time series validation of MODIS land biophysical products in a Kalahari woodland, Africa. International Journal of Remote Sensing 26, 4381–4398.Google Scholar
Huete, AR, (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.Google Scholar
Huete, AR, Didan, K, Miura, T, Rodriguez, EP, Gao, X and Ferreira, LG, (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.Google Scholar
Huete, AR and Glenn, EP, (2011). Recent advances in remote sensing of ecosystem structure and function, In: Advances in Environmental Remote Sensing: Sensors, Algorithms and Applications (Weng Q, , Ed.), CRC Press, Taylor and Francis Group, pp. 291–319.
Hunt, ER and Rock, BN, (1989). Detection of changes in leaf water content using near–and middle–infrared reflectances. Remote Sensing of Environment 30, 43–54.Google Scholar
Irons, JR, Campbell, G, Norman, JM, Graham, DW and Kovalick, WM, (1992). Prediction and measurement of soil bidirectional reflectance. IEEE Transactions on Geoscience and Remote Sensing GE-30, 249–260.Google Scholar
Jacquemoud, S, (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34, 75–91.Google Scholar
Jiang, Z, Huete, AR, Li, J and Chen, Y, (2006). An analysis of angle-based with ratio-based vegetation indices. IEEE Transactions on Geoscience and Remote Sensing, 44, 2506–2513.Google Scholar
Karnieli, A, Kidron, GJ, Glaesser, C and Ben-Dor, E, 1999. Spectral characteristics of cyanobacteria soil crust in semiarid environments. Remote Sensing of Environment 69, 67–75.Google Scholar
Kaufman, Y and Tanré, D, (1992). Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30, 261–270.Google Scholar
Kauth, RJ and Thomas, GS, (1976). The tasseled cap–a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Indiana USA, pp. 41–51.
Kerr, JT and Ostrovsky, M, (2003). From space to species: Ecological applications for remote sensing. Trends in Ecology and Evolution 18, 299–305.Google Scholar
Knipling, EB, (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1, 155–159.CrossRefGoogle Scholar
Miura, T, Huete, AR, Yoshioka, H and Holben, BN, (2001). An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sensing of Environment 78, 284–298.Google Scholar
Myneni, RB, Hoffman, S, Knyazikhin, Y, Privette, JL, Glassy, J, Tian, Y and Wang, Y, 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83, 214–231.Google Scholar
Myneni, RB, Ross, J and Asrar, G, (1989). A review on the theory of photon transport in leaf canopies. Agricultural and Forest Meteorology 45, 1–153.Google Scholar
Myneni, RB and Williams, DL, (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment 49, 200–211.Google Scholar
Nagler, PL, Inoue, Y, Glenn, EP, Russ, AL and Daughtry, CST, (2003). Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing of Environment 87, 310–325.Google Scholar
Okin, GS, Roberts, D, Murray, D, B and Okin, WJ, (2001). Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment 77, 212–225.Google Scholar
Ollinger, SV, (2010). Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist doi: 10.1111/j.1469-8137.2010.03536.x, pp. 1–20.Google Scholar
Pettorelli, N, Vik, JO, Mysterus, A, Gaillard, J-M, Tucker, CJ and Stenseth, NC, (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution 20, 503–510.Google Scholar
Ponce-Campos, GE, Moran, MS, Huete, AR, Zhang, Y, Bresloff, C, Huxman, TE, Eamus, D, Bosch, DD, Buda, AR, Gunter, SA, Scalley, TH, Kitchen, SG, McClaran, MP, McNab, WH, Montoya, DS, Morgan, JA, Peters, DPC, Sadle, EJ, Seyfrie, MS and Starks, PJ, (2013). Ecosystem resilience under large-scale altered hydroclimatic condition. Nature 494, 349–352.Google Scholar
Richardson, AJ and Wiegand, CL, (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43, 1541–1552.Google Scholar
Ripullone, F, Rivelli, AR, Baraldi, R, Guarini, R, Guerrieri, R, Magnani, F and Raddi, S, (2011). Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses. Functional Plant Biology 38, 177–186.CrossRefGoogle Scholar
Running, SW, Nemani, RS, Heinsch, FAZhao, M, Reeves, M and Hashimoto, H, (2004). A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547–560.Google Scholar
Sims, DA, Rahman, AF, Cordova, VD, El-Masri, BZ, Baldocchi, DD, Flanagan, LB, Goldstein, AH, Hollinger, DY, Misson, L, Monson, RK, Oechel, WC, Schmid, HP, Wofsy, SC and Xu, L, (2006). On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research 111, G04015, doi:04010.01029/02006JG000162.Google Scholar
Sims, DE and Gamon, JA, (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stage. Remote Sensing of Environment 81, 337–354.Google Scholar
Smith, AMS., Falkowski, MJ, Hudak, AT, Evans, JS, Robinson, AP and Steele, CM, (2009). A cross-comparison of field, spectral and lidar estimates of forest canopy cover. Canadian Journal of Remote Sensing 35, 447–459.Google Scholar
Smith, WK., Vogelmann, TC, Delucia, EH, Bell, DT, and Shepherd, KA, (1997). Leaf form and photosynthesis: do leaf structure and orientation interact to regulate internal light and carbon dioxide?BioScience 47, 785–793.Google Scholar
Tucker, CJ, (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127–150.Google Scholar
Tucker, CJ and Sellers, PJ (1986). Satellite remote sensing of primary productivity, International Journal of Remote Sensing 7, 1395–1416.Google Scholar
Ustin, SL, Roberts, DA, Gamon, JA, Asner, GP and Green, RO, (2004). Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54, 523–534.Google Scholar
Verstraete, MM and Pinty, B, (1996). Designing optimal spectral indexes for remote sensing applications. IEEE Transactions of Geosciences and Remote Sensing 34, 1254–1265.Google Scholar
Vuolo, F, Dash, J, Curran, PJ, Lajas, D and Kwiatkowska, E, (2012). Methodologies and uncertainties in the use of the terrestrial chlorophyll index for the sentinel-3 mission. Remote Sensing 4, 1112–1133.Google Scholar
Wang, Q, Adiku, S, Tenhunen, J and Granier, A, (2005). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 94, 244–255.Google Scholar
Xiao, X, Braswell, B, Zhang, Q, Boles, S, Frolking, S and Moore, I, (2003). Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sensing of Environment 84, 385–392.Google Scholar
Xiao, X, Boles, SS, Frolking, C, Li, JY, Babu, W and Moore, B III, (2006). Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment 100, 95–113.Google Scholar
Zarco-Tejada, PJ, Rueda, CA and Ustin, SL, (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment 85, 109–124.CrossRefGoogle Scholar
Zhang, QY, Xiao, XM, Braswell, B, Linder, E, Baret, F and Moore, B, (2005). Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sensing of Environment 99, 357–371.Google Scholar
Zhang, Y, Moran, MS, Nearing, MA, Ponce, G, Huete, AR and Kitchen, HSG, (2013). Extreme precipitation patterns reduced terrestrial ecosystem production across biomes. Journal of Geophysical Research, Biogeosciences 118, doi: 10.1029/2012JG002136.Google Scholar

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