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

doi:10.1007/s11707-012-0325-z

Front Earth Sci

2012, Vol. 6

Issue (2) : 188-195 https://doi.org/10.1007/s11707-012-0325-z

RESEARCH ARTICLE

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

Svetlana M. KOCHUBEY¹, Taras A. KAZANTSEV^1,2(

)

1. Institute of Plant Physiology and Genetics, National Academy of Sciences of Ukraine, Kiev, Vasylkivska 31/17, 03022, Ukraine; 2. Estonian University of Life Sciences, Tartu, Fr.R. Kreutzwaldi 5, EE-51014, Estonia

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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

Corresponding Author(s): KAZANTSEV Taras A.,Email:antarsih@ukr.net

Issue Date: 05 June 2012

Cite this article:

Svetlana M. KOCHUBEY,Taras A. KAZANTSEV. Derivative vegetation indices as a new approach in remote sensing of vegetation[J]. Front Earth Sci, 2012, 6(2): 188-195.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0325-z
https://academic.hep.com.cn/fesci/EN/Y2012/V6/I2/188

Fig.1 Reflectance spectra of model plant–soil system consisting of winter wheat leaves with different chlorophyll contents and soil of two types: A – low chlorophyll content, sand; B – high chlorophyll content, sand; C – low chlorophyll content, dark soil; D – high chlorophyll content, dark soil. 1, 2, and 3 correspond to 100%, 50% and 25% projective covering, respectively; 4 – background only

Tab.1 Influence of incomplete projective soil covering on chlorophyll estimation with / index and NDVI

Fig.2 The 1st derivative plots of reflectance spectral curves of wheat leaf corresponding to different spectral resolution: A-1 nm; B-5 nm; C-10 nm

Tab.2 Changes in magnitude of / index computed with reflectance spectral curves of different resolution (winter wheat leaves)

Tab.3 Changes in magnitude of / index computed with reflectance spectral curves of different resolution (winter wheat crops)

Tab.4 Changes in magnitude of / index at 10 nm spectral resolution in reflectance spectra of plant–soil systems, simulating incomplete projective covering

Fig.3 Heterogeneity of chlorophyll content in different areas of one of the tested crops of winter wheat. A – Photo of the crop; B – distribution of chlorophyll content, dark color corresponds to high chlorophyll content

Fig.4 Reflectance spectra of vine leaves containing chlorophyll (1) or antocyanins only (2), or a combination of both pigments (3)

Fig.5 Changes in derivative vegetation indices estimated for maize plants grown under oil contamination

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 doi: 10.1016/0034-4257(87)90089-7
2	Chupakhina G N, Maslennikov P V (2004). Plant adaptation to oil stress. Russ J Ecol , 35(5): 290–295 doi: 10.1023/B:RUSE.0000040681.75339.59
3	Collins W (1978). Remote sensing of crop type and maturity. Photogramm Eng Remote Sensing , 44(1): 43–55
4	Ery?lmaz 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 doi: 10.1104/pp.010063 pmid:11598230
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 doi: 10.1080/01431169408954177
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 doi: 10.2135/cropsci1995.0011183X003500050023x
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 doi: 10.1016/S0176-1617(11)81633-0
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 doi: 10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2 pmid:11460535
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 doi: 10.1016/j.rse.2007.06.018
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 doi: 10.1006/pmpp.1996.0052
13	Horler D N H, Dockrey M, Barber J (1983). The red edge of plant reflectance. Int J Remote Sens , 4(2): 273–288 doi: 10.1080/01431168308948546
14	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 doi: 10.1016/0034-4257(74)90046-7
15	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
16	Kochubey S M, Bidyuk P I (2003). Novel approach to remote sensing of vegetation. Proc SPIE , 5093: 181–188
17	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 doi: 10.1016/j.jplph.2006.11.007 pmid:17292510
18	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
19	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
20	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)
21	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
22	Lorenzen B, Jensen A (1989). Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sens Environ , 27(2): 201–209 doi: 10.1016/0034-4257(89)90018-7
23	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 doi: 10.1016/j.rse.2003.10.020
24	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 doi: 10.1016/S0176-1617(11)81488-4
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 doi: 10.1080/01431169008955128
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 doi: 10.1093/treephys/20.16.1113 pmid:11269963
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 doi: 10.1046/j.1365-3040.2002.00837.x
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 doi: 10.1016/0034-4257(88)90008-9
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 doi: 10.1023/B:BIBU.0000043769.80086.02
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 doi: 10.1016/j.rse.2004.06.002
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 doi: 10.1016/S0034-4257(97)00132-6
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 doi: 10.1016/j.fcr.2004.04.004
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 doi: 10.1016/S0176-1617(11)81192-2
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 doi: 10.2134/jeq2002.1433 pmid:12371159
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 doi: 10.1016/j.rse.2004.01.017
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 doi: 10.1016/S0034-4257(02)00113-X

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