Derivative vegetation indices as a new approach in remote sensing of vegetation

Abstract

This paper focuses on the advantages of derivative vegetation indices over simple reflectance-based indices that are traditionally used for remote sensing of vegetation. The idea of using reflectance derivatives instead of simple reflectance spectra was proposed several decades ago. Despite this, it has not been widely used in monitoring systems because the derivatives lack reliable parameters. In addition, most satellite monitoring systems are not equipped with hyperspectral sensors, which are considered necessary for operating with the reflectance derivatives. Here, we present original data indicating that the chlorophyll-related derivative index D₇₂₅/D₇₀₂ we derived can be accurately estimated from a reflectance spectrum of 10 nm resolution that would be suitable for most satellite-based sensors. Furthermore, the index is not sensitive to soil reflectance and can therefore be used for testing of open crops. Presence of blanc reflectance is also unnecessary. Preliminary results of index testing are presented. Perspectives on using this and other derivative indices are discussed.

Keywords

derivative vegetation indices / remote sensing / vegetation status

References

[1] Baret F, Champion I, Giot G, Podaire A (1987). Monitoring wheat canopies with a high spectral resolution radiometer. Remote Sens Environ, 22(3): 367–378

[2] Chupakhina G N, Maslennikov P V (2004). Plant adaptation to oil stress. Russ J Ecol, 35(5): 290–295

[3] Collins W (1978). Remote sensing of crop type and maturity. Photogramm Eng Remote Sensing, 44(1): 43–55

[4] Eryılmaz F (2006). The relationships between salt stress and anthocyanin content in higher plants. Biotechnol, Biotechnol Equip, 20: 47–52

[5] Feild T S, Lee D W, Holbrook N M (2001). Why leaves turn red in autumn. The role of anthocyanins in senescing leaves of red-osier dogwood. Plant Physiol, 127(2): 566–574

[6] Ferns D C, Zara S J, Barber J (1984). Application pf of high spectral resolution spectroradiometry to vegetation. Photogramm Eng Remote Sensing, 50: 1725–1735

[7] Filella I, Penuelas J (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hidric status. Int J Remote Sens, 15(7): 1459–1470

[8] Filella I, Serrano I, Serra J, Penuelas J (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci, 35(5): 1400–1405

[9] Gitelson A A, Merzlyak M N (1994). Spectral reflectance changes associate with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol, 143(3): 286–292

[10] Gitelson A A, Merzlyak M N, Chivkunova O B (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol, 74(1): 38–45

[11] González-Sanpedro M C, Le Toan T, Moreno J, Kergoat L, Rubio E (2008). Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data. Remote Sens Environ, 112(3): 810–824

[12] Hipskind J, Wood K, Nicholson R L (1996). Localised stimulation of anthocyanin accumulation and delineation of pathogen ingress in maize genetically resistant to Biplaris maydis race O. Physiol Mol Plant Pathol, 49(4): 247–256

[14] Horler D N H, Dockrey M, Barber J (1983). The red edge of plant reflectance. Int J Remote Sens, 4(2): 273–288

[15] Kanemasu E T, Niblett C L, Manges H, Lenhert D, Newman M A (1974). Wheat: its growth and disease severity as deduced from ERTS-1. Remote Sens Environ, 3(4): 255–260

[16] Kazantsev T A (2007). Spectral properties and contactless determination of anthocyanins content in leaves of Parthenocissus quinquefolia (L.) Planch. Physiology and Biochemisty of Cultivated Plants (Russian).39: 382–390

[17] Kochubey S M, Bidyuk P I (2003). Novel approach to remote sensing of vegetation. Proc SPIE, 5093: 181–188

[18] Kochubey S M, Kazantsev T A (2007). Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. J Plant Physiol, 164(12): 1648–1655

[19] Kochubey S M, Kobets N I, Shadchina T M (1987). The shape of the reflectance spectra of leaves as an information basis for remote diagnosing of crop state. Physiology and Biochemistry of Cultivated Plants (Russian), 19: 539–545

[20] Kochubey S M, Kobets N I, Shadchina T M (1988). The quantitative analysis of the shape of reflectance spectra of leaves of plants as a method for testing their state. Physiology and Biochemistry of Cultivated Plants (Russian), 20: 535–539

[21] Kochubey S M, Kobets N I, Shadchyna T M (1990). Spectral properties of leaves as a basis for remote diagnostics. Naukova Dumka, Kyiv (in Russian)

[22] Krupa Z, Baranowska M, Orzol D (1996). Can anthocyanins be considered as heavy metal stress indicator in higher plants? Acta Physiol Plant, 18(2): 147–151

[23] Lorenzen B, Jensen A (1989). Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sens Environ, 27(2): 201–209

[24] Maselli F (2004). Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data. Remote Sens Environ, 89(4): 423–433

[13] Mark Hodges D, Nozzolillo C (1995). Anthocyanin and anthocyanoplast content of cruciferous seedlings subjected to mineral nutrient deficiencies. J Plant Physiol, 147(6): 749–754

[25] Miller J R, Hare E W, Wu J (1990). Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model. Int J Remote Sens, 11(10): 1755–1773

[26] Milton N M, Monat D A (1989). Remote sensing of vegetation responses to natural and cultural environment condition. Photogramm Eng Remote Sensing, 55(8): 1167–1173

[27] Moran J A, Mitchell A K, Goodmanson G, Stockburger K A (2000). Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiol, 20(16): 1113–1120

[28] Neill S O, Gould K S, Kilmartin P A, Mitchell K A, Markham K R (2002). Antioxidant activities of red versus green leaves in Elatostema rugosum. Plant Cell Environ, 25(4): 539–547

[29] Rock B N, Hoshizaki T, Miller J R (1988). Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sens Environ, 24(1): 109–127

[30] Sid'ko A F (2004). Remote assay for chlorophyll photosynthetic potential of crops on the example of wheat. Biol Bull Russ Acad Sci, 31(5): 450–456

[31] Smith K L, Steven M D, Colls J J (2004). Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sens Environ, 92(2): 207–217

[32] Unganai L S, Kogan F N (1998). Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sens Environ, 63(3): 219–232

[33] Wang Z J, Wang J H, Liu L Y, Huang W J, Zhao C J, Wang C Z (2004). Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Res, 90(2–3): 311–321

[34] Wellburn A R (1994). The spectral determination of chlorophylls a and b as well, as the total carotenoids using various solvents with spectrophotometers of different resolution. J Plant Physiol, 144(3): 307–313

[35] Yatsenko V A, Kochubey S M, Donets V V, Kazantsev T A (2005). Hardware-software complex for chlorophyll estimation in phytocenoses under field conditions. Proc SPIE, 5964: 1–6

[36] Zarco-Tejada P J, Miller J R, Mohammed G H, Noland T L, Sampson P H (2002). Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. J Environ Qual, 31(5): 1433–1441

[37] Zarco-Tejada P J, Miller J R, Morales A, Berjón A, Agüera J (2004). Hyperspectral indicies and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens Environ, 90(4): 463–476

[38] Zarco-Tejada P J, Pushnik J C, Dobrowski S, Ustin S L (2003). Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sens Environ, 84(2): 283–294

Acknowledgements

We would like to thank Professor Viktor V. Shvartau, corresponding member of NASU, for providing wheat crops for field measurements.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg