Imaging spectroscopy of foliar biochemistry in forestry environments

Southern Forests

Volumn 70, Issue 3, 2008, Pages 275-285

  Majeke, B ^a   Van Aardt, J A N ^b   Cho, M A ^a  

^a DSI CSIR Nanotechnology Innovation Centre   (South Africa)

^b ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Artificial neural networks; Foliar chemistry; Hyperspectral; Partial least squares; Remote sensing; Stepwise multiple linear regression

Indexed keywords

EUCALYPTUS;

EID: 58149475512     PISSN: 20702620     EISSN: 20702639     Source Type: Journal    
DOI: 10.2989/SF.2008.70.3.11.672     Document Type: Review

References (99)

1. Aber JD and Federer CA (1992) A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92: 463-474
2. American Botanical Council (2007) Herbal medicine. Available at http://cms.herbalgram.org/herbalmedicine/index.html [accessed 28 January 2008]
3. Asner GP, Hicke JA and Lobell DB (2003) Per-pixel analysis of forest structure: vegetation indices, spectral mixture analysis and canopy reflectance modeling. In: Wulder MA and Franklin SE (eds) Remote sensing of forest environments: concepts and case studies. Kluwer, Boston. pp 209-254
4. Asner GP, Wessman CA, Bateson CA and Privette JL (2000) Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of arid ecosystems. Remote Sensing of Environment 74: 69-84
5. Atkinson PM and Tatnall ARL (1997) Neural networks in remote sensing. International Journal of Remote Sensing 18: 699-709
6. Bacour C, Jacquemoud S, Tourbier Y, Dechambre M and Frangi JP (2002) Design and analysis of numerical experiments to compare four canopy reflectance models. Remote Sensing of Environment 79: 72-83
7. Barton CVM and North PRJ (2001) Remote sensing of canopy light use efficiency using the photochemical reflectance index: model and sensitivity analysis. Remote Sensing of Environment 78: 264-273
8. Blackburn GA (1998) Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment 66: 273-285
9. Bolster KL, Martin ME and Aber JD (1996) Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods. Canadian Journal of Forest Research 26: 590-600
10. Bortolot ZJ and Wynne RH (2003) A method for predicting fresh green leaf nitrogen concentrations from shortwave infrared reflectance spectra acquired at the canopy level that requires no in situ nitrogen data. International Journal of Remote Sensing 24: 619-624
11. Broge NH and Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76: 156-172
12. Bruzzone L, Conese C, Maselli F and Roli F (1997) Multisource classification of complex rural areas by statistical and neuralnetwork approaches. Photogrammetric Engineering and Remote Sensing 63: 523-533
13. Card DH, Peterson DL, Matson PA and Aber JD (1988) Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy. Remote Sensing of Environment 26: 123-147
14. Carter GA (1994) Ratios of leaf reflectance in narrow wavebands as indicator of plant stress. International Journal of Remote Sensing 15: 697-704
15. Chappelle EW, Kim MS and McMurtrey III JE (1992) Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and caroteniods in soybean leaves. Remote Sensing of Environment 39: 239-247
16. Cho MA (2007) Hyperspectral remote sensing of biochemical and biophysical parameters: the derivative red-edge "double-peak feature", a nuisance or an opportunity? PhD thesis, Wageningen University, Wageningen
17. Cho MA and Skidmore AK (2006) A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sensing of Environment 101: 181-193
18. Cho MA, Skidmore AK and Atzberger C (2008) 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. International Journal of Remote Sensing 29: 2241-2255
19. Clevers JGPW, de Jong SM, Epema GF, van der Meer FD, Bakker WH, Skidmore AK and Scholte KH (2002) Derivation of the red edge index using MERIS standard band setting. International Journal of Remote Sensing 23: 3169-3184
20. Collins W (1978) Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing 44: 43-55
21. Coops NC, Dury S, Smith ML, Martin M and Ollinger S (2002) Comparison of green leaf Eucalyptus spectra using spectral decomposition. Australian Journal of Botany 50: 567-576
22. Coops NC, Smith M, Martin ME and Ollinger SV (2003) Prediction of Eucalyptus foliage nitrogen content from satellite-derived hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 41: 1338-1346.
23. Coops NC, Stone C, Culvenor DS and Chisholm L (2004) Assessment of crown condition in Eucalyptus vegetation by remotely sensed optical indices. Journal of Environmental Quality 33: 956-964
24. Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sensing of Environment 30: 271-278
25. Curran PJ (1994) Imaging spectrometry. Progress in Physical Geography 18: 247-266
26. Curran PJ (2001) Imaging spectrometry for ecological applications. International Journal of Applied Earth Observation and Geoinformation 3: 305-312
27. Curran PJ and Dungan JL (1989) Estimation of signal-to-noise: a new procedure applied to AVIRIS data. IEEE Transactions on Geoscience and Remote Sensing 27: 620-628
28. Curran PJ, Dungan JL and Gholz HL (1990) Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology 7: 33-48
29. Curran PJ, Dungan JL and Peterson DL (2001) Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment 76: 349-359
30. Curran PJ, Windham WR and Gholz HL (1995) Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. Tree Physiology 15: 203-206
31. Daughtry CST, Walthall CL, Kim MS, de Colstoun EB and McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74: 229-239
32. Dawson TP, Curran PJ, North PRJ and Plummer SE (1999) The propagation of foliar biochemical absorption features in forest canopy reflectance: a theoretical analysis. Remote Sensing of Environment 67: 147-159
33. Dawson TP, Curran PJ and Plummer SE (1998) LIBERTY - modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment 65: 50-60
34. Dury SJ and Turner BJ (2001) Nutrient estimation of Eucalyptus foliage derived from hyperspectral data. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '01), 9-13 July, 2001, Sydney, vol. 2. IEEE, New York. pp 774-776
35. Fang H, Liang S and Kuusk A (2003) Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment 85: 257-270
36. Foody GM, Boyd DS and Cutler MEJ (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment 85: 463-474
37. Fourty T, Baret F, Jacquemoud S, Schmuck G and Verdebout J (1996) Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems. Remote Sensing of Environment 56: 104-117
38. 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
39. Gao B (1996) NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: 257-266
40. Geladi P and Kowalski BR (1986) Partial least-squares regression: a tutorial. Analytica Chimica Acta 185: 1-17
41. Gitelson AA and Merzlyak MN (1997) Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18: 2691-2697
42. Gitelson AA, Zur Y, Chivkunova OB and Merzlyak MN (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology 75: 272-281
43. Gobron N, Pinty B and Verstraete MM (1997) Theoretical limits to the estimation of the leaf area index on the basis of visible and near-infrared remote sensing data. IEEE Transactions on Geoscience and Remote Sensing 35: 1438-1445
44. Goel PK, Prasher SO, Landry JA, Patel RM, Bonnell RB, Viau AA and Miller JR (2003) Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture 38: 99-124
45. Gong O, Pu R and Heald RC (2002) Analysis of in situ hyperspectral data for nutrient estimating of giant sequia. International Journal of Remote Sensing 32: 1827-1850
46. Grossman YL, Ustin SL, Jacquemoud S, Sanderson EW, Schmuck G and Verdebout J (1996) Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sensing of Environment 56: 182-193
47. Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ and Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81: 416-426
48. Hansen PM and Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86: 542-553
49. Hill MJ, Held AA, Leuning R, Coops NC, Hughes D and Cleugh HA (2006) MODIS spectral signals at a flux tower site: relationships with high-resolution data, and CO2 flux and light use efficiency measurements. Remote Sensing of Environment 103: 351-368
50. Horler DNH, Dockray M and Barber J (1983) The red edge of plant leaf reflectance. International Journal of Remote Sensing 4: 273-288
51. Huang Z, Jia X, Turner B, Folley W and Dury S (2002) Use of HYMAP image data to estimate sideroxylonal-A concentration of Eucalyptus foliage. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS '02), 24-28 June, 2002, Toronto, vol. 3. IEEE, New York. pp 1652-1654
52. Huang Z, Turner BJ, Dury SJ, Wallis IR and Foley WJ (2004) Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment 93: 18-29
53. Huete AR, Hua G, Qi J, Chehbouni A and van Leeuwen WJD (1992) Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment 41: 143-154
54. Huete AR and Jackson RD (1988). Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment 25: 89-105
55. Jacquemoud S and Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34: 75-91
56. Jacquemoud S, Ustin SL, Verdebout J, Schmuck G, Andreoli G and Hosgood B (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment 56: 194-202
57. Jago RA, Cutler MEJ and Curran PJ (1999) Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment 68: 217-224
58. Johnson LF (2001) Nitrogen influence on fresh-leaf NIR spectra. Remote Sensing of Environment 78: 314-320
59. Johnson LF and Billow CR (1996) Spectrometry estimation of total nitrogen concentration in Douglas-fir foliage. International Journal of Remote Sensing 17: 489-500
60. Johnson LF, Hlavka CA and Peterson DL (1994) Multivariate analysis of AVIRIS data for canopy biochemical estimation along the oregon transect. Remote Sensing of Environment 47: 216-230
61. Kaufman YJ and Tanré D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30: 261-270
62. Kokaly RF and Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment 67: 267-287
63. Kupiec JA and Curran PJ (1995) Decoupling effects of the canopy and foliar biochemicals in AVIRIS spectra. International Journal of Remote Sensing 16: 1731-1739
64. Kuusk A (1991) The angular distribution of reflectance and vegetation indices in barley and clover canopies. Remote Sensing of Environment 37: 143-151
65. Lichtenthaler HK, Gitelson AA and Lang MJ (1996) Non-destructive determination of chlorophyll content of leaves of a green and aurea mutant tobacco by reflectance measurements. Journal of Plant Physiology 148: 483-493
66. Marschner H (1995) Mineral nutrition of higher plants. 2nd edn. Academic Press, London
67. Martin ME and Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen and ecosystem process. Ecological Applications 7: 431-443
68. Martin ME, Newman SD, Aber JD and Congalton RG (1998) Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment 65: 249-254
69. Mas JF, Puig H, Palacio JL and Sosa-Lopez A (2004) Modelling deforestation using GIS and artificial neural networks. Environmental Modelling and Software 19: 461-471
70. Matson P, Johnson L, Billow C, Miller J and Pu R (1994) Seasonal patterns and remote spectral estimation of canopy chemistry across the Oregon transect. Ecological Applications 4: 280-298
71. McNaughton SJ (1988) Mineral nutrition and spatial concentrations of African ungulates. Nature 334: 343-345
72. Miller DM, Kaminsky EJ and Rana S (1995) Neural network classification of remote-sensing data. Computers and Geosciences 21: 377-386
73. Mooney HA (1986) Photosynthesis. In: Crawley MJ (ed) Plant ecology. Blackwell Scientific Publications, Oxford. pp 345-373
74. Mutanga O and Skidmore AK (2004) Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment 90: 104-115
75. Mutanga O and Skidmore AK (2007) Red edge shift and biochemical content in grass canopies. ISPRS Journal of Photogrammetry and Remote Sensing 62: 34-42
76. Mutanga O, Skidmore AK and Prins HHT (2004) Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89: 393-408
77. Mutanga O, Skidmore AK, Kumar L and Ferwerda J (2005) Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. International Journal of Remote Sensing 26: 1093-1108
78. Ollinger SV, Smith ML, Martin ME, Hallett RA, Goodale CL and Aber JD (2002) Regional variation in foliar chemistry and N cycling among forests of diverse history and composition. Ecology 83: 339-355
79. Peñuelas J, Baret F and Filella I (1995) Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31: 221-230.
80. Peterson DL, Aber JD and Matson P (1988) Remote sensing of forest canopy and leaf biochemical contents. Remote Sensing of Environment 24: 85-108
81. Qi J, Moran MS, Cabot F and Dedieu G (1995) Normalization of sun/view angle effects using spectral albedo-based vegetation indices. Remote Sensing of Environment 52: 207-217
82. Schlerf M and Atzberger C (2006) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sensing of Environment 100: 281-294
83. Schmidt KS and Skidmore AK (2001) Exploring spectral discrimination of grass species in African rangelands. International Journal of Remote Sensing 22: 3421-3434
84. Serrano L, Penuelas J and Ustin SL (2002) Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment 81: 355-364
85. Serrano L, Ustin SL, Roberts DA, Gamon JA and Penuelas J (2000) Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment 74: 570-581
86. Shedley E, Dell B and Grove T (1995) Diagnosis of nitrogen deficiency and toxicity of Eucalyptus globulus seedlings by foliar analysis. Plant and Soil 177: 183-189
87. Skidmore AK, Brinkhof, TW and Knowles E (1997) Performance of a neural network: mapping forest using GIS and remotely sensed data. Photogrammetric Engineering and Remote Sensing 63: 501-514
88. Smith ML, Martin ME, Plourde L and Ollinger SV (2003) Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and spaceborne (Hyperion) sensor. IEEE Transactions on Geoscience and Remote Sensing 41: 1332-1337
89. Smith ML, Ollinger SV, Martin ME, Aber JD, Hallett RA and Goodale CL (2002) Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecological Applications 12: 1286-1302
90. Stimson HC, Breshears DD, Ustin SL and Kefauver SC (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment 96: 108-118
91. Vane G and Goetz AFH (1988) Terrestrial imaging spectroscopy. Remote Sensing of Environment 24: 1-29
92. Verhoef W (1984) Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment 16: 125-141
93. Vogelmann JE, Rock BN and Moss DM (1993) Red-edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563-1575
94. Wang GG and Klinka K (1997) White spruce foliar nutrient concentrations in relation to tree growth and soil nutrient amounts. Forest Ecology and Management 98: 89-99
95. Wessman CA, Aber JD and Peterson DL (1989) An evaluation of imaging spectrometry for estimating forest canopy chemistry. International Journal of Remote Sensing 10: 1293-1316
96. Yoder BJ and Pettigrew-Crosby RE (1995) Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sensing of Environment 53: 199-211
97. Zagolski F, Pinel V, Romier J, Alcayde D, Fontanari J, Gastellu-Etchegorry JP, Giordano G, Marty G, Mougin E and Joffre R (1996) Forest canopy chemistry with high spectral resolution remote sensing. International Journal of Remote Sensing 17: 1107-1128
98. Zarco-Tejada PJ, Berjon A, Lopez-Lozano R, Miller JR, Martin P, Cachorro V, Gonzalez MR and de Frutos A (2005) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row structured discontinuous canopy. Remote Sensing of Environment 99: 271-287
99. Zarco-Tejada PJ, Miller JR, Morales A, Berjon A and Aguera J (2004) Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment 90: 463-476