Leaf Area Index retrieval using Hyperion EO-1 data based vegetation indices in Himalayan forest system Dharmendra Singh1a, Sarnam Singh b a
SpaceApplication Centre, ISRO Ahmedabad; bIndian Institute of Remote Sensing, ISRO, 4th Kalidas Road, Dehradun
ABSTRACT Present Study is being taken to retrieve Leaf Area Indexn(LAI) in Himalayan forest system using vegetation indices developed from Hyperion EO-1 hyperspectral data. Hemispherical photograph were captured in the month of March and April, 2012 at 40 locations, covering moist tropical Sal forest, subtropical Bauhinia and pine forest and temperate Oak forest and analysed using an open source GLA software. LAI in the study region was ranging in between 0.076 m2/m2 to 6.00 m2/m2. These LAI values were used to develop spectral models with the FLAASH corrected Hyperion measurements.Normalized difference vegetation index (NDVI) was used taking spectral reflectance values of all the possible combinations of 170 atmospherically corrected channels. The R2 was ranging from lowest 0.0 to highest 0.837 for the band combinations of spectral region 640 nm and 670 nm. The spectral model obtained was, spectral reflectance (y) = 0.02x LAI(x) - 0.0407. Key words: Hyperspectral, LAI, Hyperion EO-1, Himalayan forest system, GLA(gap light analyzer)
1 INTRODUCTION Leaf Area Index (LAI) is defined as the sum of one-sided area of leaves per unit ground area[1,2,3]. It can be measured by three methods- (a) Direct through the harvesting, (b) Indirect using Instruments like Li-COR 2000, Ceptometer or Hemiview photographs[4] and (c) Integrated method, including merits of Direct and Indirect method with the integration of satellite remote sensing data[5,6,7].Direct method of LAI estimation is based on leaf area measurement using measuring tape/scale or counting/ weighing. Indirect method is the way to measure other parameter (generally light dependent, such as PAR or gap fraction) related to the leaf area and can be converted in to LAI using some coefficients such as extinction coefficient ‘k’ as defined by[3](eq. 1) on the basis of Beer-Lambert law (equation,
=1−
×
, where c is canopy
1
Correcponding author: Dharmendra Singh, phone: 07926914333, Email ID:
[email protected] Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, edited by Allen M. Larar, Prakash Chauhan, Makoto Suzuki, Jianyu Wang, Proc. of SPIE Vol. 9880, 98800U © 2016 SPIE · CCC code: 0277-786X/16/$18 · doi: 10.1117/12.2228151 Proc. of SPIE Vol. 9880 98800U-1
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cover fraction (1-gap fraction), K is extinction coefficient and indicating exponential decay of light during its pass through multiple leaf layers from top to bottom in a canopy). (1)
=
Integrated method also known as remote sensing based method, includes coupling of instrument/ground based in situ measurements with that of satellite remote sensing-based measurement
[8,9,10,11,12]
. Here in situ measurement are first
correlated with spectral measurement obtained either from ground, air or space platform and a regression model developed, which may be linear
[13,9,14]
.Exponential
[15]
, Logarithmic[16] polynomial
[16]
and/or power function type[17].
These regression models are then applied on the images and geospatial map of LAI is prepared for the landscape. Currently integrated method is most popular because of its efficiency, reliability, landscape level coverage and economy.Developments of high computational ability and statistical techniques along with landscape level coverage of satellite measurements from multispectral to hyperspectral level have increased the popularity of this method[1]. Based on the studies done specially using hyperspectral measurements it has been concluded that these measurements are more suitable for LAI mapping as compared to the multispectral measurements
[18]
. The improved results of LAI estimation
using hyperspectral measurements is due to narrow and contiguous weve-bands able to capture finer details of structural and chemical changes in the leaf or at canopy level. Narrow bands of hyperspectral measurements have increased the opportunity to calculate thousands of narrow band vegetation indices for LAI estimation or the weve-form analysis of the spectral response obtained from contiguous measurements. Several studies have reported the estimation of LAI using hyperspectral measurements (from hand held instruments, airborn platform, space basedplatform) such as by [6,9,13,15,18,19, 20,21, 22].They all have used different techniques such as weveform analysis, absorption feature analysis, narrow band vegetation indices, radiative transfer models, etc.Vegetation index based studies have found more consideration because of their easy inversion on the satellite data. Simple ratio and normalized difference ratio have been found more suitable for LAI estimation[6,15,]. Simple ratio (SR) based vegetation indices uses the two spectral bands representing the most absorptive (red spectral region) and reflective (NIR spectral region) features of the canopy. This means if more leaves are present in unit area (high LAI) the spectral measurement of this area will show high absorption in red spectral region (due to chlorophyll absorption) and high reflectance in the NIR spectral region (due to structural components of the leaves). Thus high values of SR will represent the high LAI condition. However in some cases especially in shadow regions of hill slopes, the SR may mislead the actual ground condition due to its similar values for low LAI in light regions. This problem has been overcome by the Normalized difference vegetation index (NDVI) first reported by[23]. NDVI (eq. 2) uses the normalized ratio of the two spectral bands for highlighting vegetation and its related biophysical characteristics. NDVI =
(2)
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whereρNIR is the reflectance of NIR region and ρR is the reflectance of red region. This ratio represents the actual condition of the vegetation and its structure, even in shadow regions. Suppose an area with high LAI situated in the shadow regions of the hill slopes. Both the NIR and red reflectance will be less but in any case their normalized ratio will be high. The condition is same for light regions also and the normalized ratio for both the shadow and light regions will be similar for similar vegetation condition. Thus NDVI have been used extensively for the LAI estimation since its development along with several modifications[1,15] and use of different satellite data such as Landsat, MERIS, MODIS, AWiFS, LISS-III, CHERIS-PROBAand Hyperion EO-1. Among all the hyperspectral data Hyperion EO-1 have been used exhaustively for the LAI estimation
[13,18,22]
due to its
high spectral (357 nm to 2576 nm, with 10nm band width)and spatial coverage (7.5 km×42 km).Hyperion isaboard the EO-1 satellite experimental technology having 242 continuous spectral channels with 256 samples ×3407 lines in each band. The data from Hyperion sensor is distributed by the USGSavailable freely on www.earthexplorer.usgs.gov. The spatial resolution of this sensor is 30 m. Using Hyperion data for LAI estimation in Indian forests is still having potential because very few studies are reported such as by[22]in tropical forest system of Central India. Case of Himalayan forest system is still data deficient. Thus present study is being done to estimate LAI in Himalayan forest system using Hyperion data and Up-word looking hemispherical photographs. FLASH corrected reflectance of the Hyperion data were used to calculate Narrow band Normalized reflectance vegetation indices using the combination of different spectral bands (
(
)
) where n is the total number of spectral bands. Up-word looking hemispherical photographswereanalysed
using GLA open source software for computing ground level LAI. These LAI values were than correlated with the NDVI obtained using
(
)
band combination. Most appropriate band combination showing high coefficient of
determination (R2) with LAI was used to map the LAI of the study region.
2 MATERIALS AND METHODS 2.1 Study area The study area (Figure 1) is situated at Shiwalik foothills and Mussoorie hills in Dehradun District (part of lesser Himalaya) of Uttarakhand, India. The area is hilly with minimum and maximum elevation 600 – 2277 m. The soil of the area is developed under udic moisture regime and messic temperature regime. The climate conditions of the area vary from tropical to subtropical to temperate depending on altitude with main seasons of monsoon, winter and summer
[24]
.
The average annual rainfall of the area is 2073 mm in a year with 60 percent received during monsoon followed by 20 percent during post monsoon, 10 percent during winter months and 10 percent during pre-monsoon months [25]. The main forest vegetation types include tropical moist deciduous Sal forest, subtropical Pine and Bauhinia forest, subtropical mixed forest of Pine, temperate oak forest and temperate coniferous forest [24]. The study area is rich in biodiversity and a total of 486 species are reported from the study area by state forest department including Tree (116), Shrubs (83), Herbs (143), Grass/Bamboo (86), Climbers (46) and Epiphytes (4) [25].
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HYPERION STRIP ON STUDYAItEA
DEHRADLI DICTRICT LTZ AFLUCH_A.D Figure 1: Study area
2.2 Field data collection Field survey of the study area was done in the month of March and April 2012 just near to the image acquisition date (10 March 2012).Stratified random sampling method was adopted for selecting locations to collect Hemispherical photograph. Area of the strata (species distribution map by[24]) was used to allocate the sample points. The Hemispherical photographs using Hemi-view were collected at 61 sites and 5 photographs at each site, covering different forest types and dominant species in moist tropical-subtropical-temperate ecosystem in clear sky condition generally in the early morning and late evening time so that error due to sun illumination condition can be avoided.
2.3 Hemispherical photograph Analysis Hemispherical photographs were analysed separately for each community and/or species using an open source gap light analyser (GLA) software. This software provided information about the gap fraction which can further be converted in to LAI using Beers-Lambert equation as suggested by [13].
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2.4 Atmospheric and Geometric Correction of Hyperion Data Hyperion EO-1 data of March 10, 2012 were used to develop spectral models for LAI estimation because this image was nearly in sync the time of field data collection. The data downloaded from http://www.earthexplorar.com and saved in the ENVI standard format. Hyperion-EO1 data is having high spectral resolution (10nm) and thus very sensitive to atmospheric attenuation[26], thus atmospheric correction of this data is the pre-requisite for any qualitative or quantitative analysis of earth system including water, soil, air and forest systems. Atmospheric correction improves the data quality of the earth surface feature by removing the moisture absorption effect, atmospheric particle absorption effect and haze effect and increases the signal-to-noise ratio which is very less in the space born Hyperion Hyperspectral data. Several atmospheric correction models such as QUick Atmospheric Correction (QUAC), Atmospheric/topographic correction (ATCOR), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) etc. are available for satellite images. FLAASH model has been found suitable for the Hyperion hyperspectral data [27] therefore, we applied this model inthe present study. The FLAASH algorithm support data from Non-Nadir looking sensors also. It is suitable for water vapour (the major problem of the study area) and aerosol retrieval and adjacency effect correction which is nothing but the spatial mixing of radiance among nearby pixels[28]. Parameters used for atmospheric correction using FLAASH algorithm are given in table 1. Table 1: FLAASH Model Parameter for Atmospheric correction of Hyperspectral data
S. No. 1
PARAMETER Scene Centre Location
2 3 4
Sensor Type Flight Date Flight Time GMT(hh:mm:ss) Sensor Elevation(Km) Ground altitude (Km) Pixel Size(m) Atmospheric model Aerosol Model Spectral Polishing Water Retrieval Aerosol Retrieval Water absorption feature Initial Visibility(Km) Wavelength Recalibration CO2 mixing ratio Modtran Resolution Zenith Angle/Azimuth Angle(non-nadir looking sensors)
5 6 7 8 9 10 11 12 13 14 15 16 17 18
Hyperion data 2012 30.332753, 78.035219 Hyperion 10th Mar.2012 05:02:20 705 Km 0.7 Km 30 m Tropical Rural Yes Yes Yes 1135 Km 15 Km Yes 390 5 cm-1 180/0.0
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Geometric correction of the Hyperion data was done using the Landsat image of the area as base image in ENVI software environment. Permanent ground features such as road junctions, buildings etc. were selected as ground control points, well distributed over the image.Clouds were removed by manual digitization and masking. After atmospheric and geometric correction 170 spectral bands were obtained for further analysis out of 242 spectral bands.
2.5 Correlation between ground based LAI and atmospherically corrected Hyperion reflectance Out of these 61 sites only 40 sites covering different dominant forest types were selected for developing spectral models using NDVI outputs obtained from different band combinations. Linear spectral modeling approach was used for the fitting regression line between grounds based LAI (obtained from Up-word looking hemispherical photographs) and FLAASH corrected Hyperion reflectance (obtained directly and after inversion of minimum noise fraction, MNF components). The best band combination was selected based on R2 values. Regression equation was developed using two best bands and this was used for final estimation of the LAI.
3 RESULTS AND DISCUSSION Results obtained in present study have shown potential of Hyperion data in LAI estimation in Himalayan terraain. Both the visible and SWIR regions are showing high R2 for the LAI.Spectral reflectance obtained after Inversion of MNF components (Hyperion spectral reflectance hereafter) have increased the model accuracy (R2 ranges between 0 to 0.84) as compared to the direct used of FLAASH corrected Hyperion reflectance (R2 ranges between 0 to 0.62). However the pattern of the R2 matrix remains same for both the cases.
3.1 LAI estimates from field The field based LAI was ranging from 0.79 to 5.13 m2/m2. LAI for Salforest (Shorea robusta Gaertn. f.)was varying between 1.76 m2/m2to 3.89 m2/m2, for Bauhinia (Bauhinia Mixed forest)between 2.22 m2/m2to 3.68 m2/m2, for Oakforest (Quercus leucotrichophora A.) between 1.06 m2/m2 to 5.13 m2/m2, for Deodarforest (Cedrus deodara (Roxb. ex))between 1.62 m2/m2to 3.77 m2/m2, for ThujaForest (Thuja orientalisL.)between 2.01 m2/m2to 5.02 m2/m2, for the PineForest (Pinus roxburghiiRoxb. ex Sarg.))between 2.53 m2/m2 to 3.86 m2/m2. Eucalyptus dominated mixed plot were sampled only at one location and its LAI was 3.23 m2/m2. The lowest LAI value (0.79 m2/m2) was found in sparse mixed vegetation and highest (5.13 m2/m2) in Oak dominated forest ecosystem. The LAI for mixed type vegetationwas varying between 0.79 m2/m2 to 4.11 m2/m2.
3.2 Spectral modeling of field based LAI with Hyperion Spectral Reflectance based NDVI It is observed that the NDVI developed from FLAASH corrected Hyperion spectral reflectance isable to explain 84 per cent variability of LAI (Figure 2).NDVI obtained using the band combinations of NIR and SWIR spectral regions are showing high R2. However, the NDVI developed from visible bands 640 nm and 670 nm have shown highest R2. Both
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these bands are from red spectral region, where the 640nm band is showing less absorption and considered as reflectance band and 670 nm band is showing high absorption and considered as absorption band. The 670 nm band is the absorption region of the chlorophyll and thus it showed less reflectance in case of high LAI due to high chlorophyll concentration dependent absorption.Similarly the 640 nm band is influenced by chlorophyll reflectance region i.e. green spectral region and high reflectance will be obtained in case of high chlorophyll and thus high LAI. Due to this region the NDVI values will be high in case of high LAI and positive linear relationship was found as expected. Since spectral reflectance of both these bands are characterized by the leaves and leaves are the representatives of LAI, the NDVI developed from these two bands was showing high R2 value. Similarly NDVI developed from spectral region 1538 nm and 1548 nm of SWIR region is showing High R2 value (0.801) for LAI. Improvement in the prediction of LAI, when inverse MNF based reflectancewere used, is due to the normalization of sudden spectral peaks due to atmospheric anomalies. NDVI obtained with the combination of bands from NIR region have shown very less R2 with LAI.
R2
Figure 2: R2 matrix for NDVI developed by Hyperion reflectance and LAI
Spectral model (eq. 3) obtained from the relationship between field level LAI and NDVI obtained from the spectral reflectances of the spectral band 640 nm and 670 nm was used to map the LAI (figure 3) at landscape level.
= 41.836 ×
+ 2.1665 (3)
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z
b Ell CV
o n
Figure 3: FCC (left) and LAI (right) map of the study area
4 CONCLUSIONS Based on above analysis following conclusions could be made: •
General range of the LAI in the study region 3 to 4 as obtained from both the field based measurement varied from 0.076 m2/m2 to 6.00 m2/m2 however, Hyperion data based spectral modelling could find highest LAI 8.4 m2/m2areas.
•
Coefficient of determination (R2) between NDVI (developed from all the possible combination for spectral bands) and field based LAI (obtained from the analysis of up-word looking hemispherical photograph) is ranging from 0.00 to 0.84.
•
NDVI developed from the Combination of spectral bands from SWIR and NIR region are showing high coefficient of determination (R2).
•
NDVI developed from spectral bands 640 nm and 670 nm have shown highest R2 (0.84).
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•
More vegetation indices such as simple ratio, soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI) etc. can be analysed in the same manner to obtain improved spectral models. Further validation of the results in terms of the RMSE is required for the improvements in the results after more sampling in high LAI areas.
5 ACKNOWLEDGEMENTS Authors are thankful the Mr.Amit Kumar Dubay Scientist Space Applications Centre, Ahmedabad and Mr. Vishal Pathak, Research Scholar, Department of Physics, Sardar Patel University, Gujarat for their technical support during the research work.
6 REFERENCES [1]
Gong, P., Pu, R., Biging, G. S. andLarrieu, M. R. “Estimation of Forest Leaf Area Index Using Vegetation Indices Derived From Hyperion Hyperspectral Data”,IEEE T Geosci Remote. 41(6), 1355-1362(2003).
[2]
Lee, K. S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K. and Gower, S. T. “Hyperspectral versus multispectral data for estimating leaf area index in four different biomes”,Remote Sens. Environ., 91, 508520(2004).
[3]
Pierce, L.L., and Running, S.W. “Rapid Estimation of Coniferous Forest Leaf Area Index Using a Portable Integrating Radiometer.” Ecology 69 (6), 1762–1767(1988).doi:10.2307/1941154.
[4]
Gonsamo, A. and estimation
using
Pellikka, P. “Methodology comparison for slope correction in canopy leaf area index hemispherical
photography”,
Forest
EcolManag.
256,749–759(2008)..
doi:10.1016/j.foreco.2008.05.032. [5]
Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsi, F. and Cho M. “LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements”, ISPRS J Photogramm.63, 409– 426(2008), available online www.elsevier.com/locate/isprsjprs.
[6]
Delalieux, S., Somers, B., Hereijgers, S., Verstraeten, W.W., Keulemans, W. and Coppin, P. “A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards”, Remote Sens. Environ.,112(10), 3762-3772 (2008). doi:10.1016/j.rse.2008.05.003.
[7]
Le Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J., Bréda, N., Genet, H., Davi, H., Dufrêne, E.,“Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,”Remote Sens. Environ. 112, 3846– 3864(2008). doi:10.1016/j.rse.2008.06.005.
[8]
Cho, M.A., Skidmore, A.K. and Sobhan, I.,” Mapping beech (Fagussylvatica L.) forest structure with airborne hyperspectral imagery,”Int J Appl Earth Obs.,11, 201–211 (2009). doi:10.1016/j.jag.2009.01.006.
[9]
Delegido, J., Verrelst, J., Rivera, J. P., Ruiz-Verdú, A. and Moreno, J.,“Brown and green LAI mapping through spectral indices,” Int J Appl Earth Obs, 35, 350–358(2015). http://dx.doi.org/10.1016/j.jag.2014.10.001.
Proc. of SPIE Vol. 9880 98800U-9 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/17/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
[10]
Heiskanen, J., Rautiainen, M., Korhonen, L., Mõttus, M. and Stenberg, P.,“Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions,”Int J Appl Earth Obs, 13, 595–606(2011). doi:10.1016/j.jag.2011.03.005.
[11]
Kale, M., Singh, S. and Roy, P. S.,“Estimation of Leaf Area Index in dry deciduous forests from IRS-WiFS in central India,”Int J Remote Sens. 26(21), 4855–4867(2005).doi: 10.1080/01431160500181309.
[12]
Qi, Y., Li, F., Liu, Z., Jin, G.,“Impact of understorey on overstorey leaf area index estimation fromoptical remote sensing
in
five
forest
types
in
northeastern
China,”AgricForMeteorol.
198–199,
72–80(2014).
http://dx.doi.org/10.1016/j.agrformet.2014.08.001. [13]
Bulcock, H. H. and Jewitt, G. P. W.,“Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception,”Hydrol Earth Syst Sc. 14, 383– 392(2010).
[14]
Lausch, A., Pause, M., Merbach, I., Zacharias, S., Doktor, D., Volk, M., and Seppelt, R., “A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape,”Environ Monit Assess. 185, 1215–1235(2013).doi: 10.1007/s10661-012-2627-8.
[15]
Canisius, F. and
Fernandes, R.,“Evaluation of the information content of Medium Resolution Imaging
Spectrometer (MERIS) data for regional leaf area index assessment,”Remote Sens. Environ. 119, 301– 314(2012). doi:10.1016/j.rse.2011.10.013. [16]
Potithep, S., Nagai, S., Nasahara, K. N., Muraoka, H. and Suzuki, R.,“Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest,”Agric For Meteorol. 169, 148– 155(2013). http://dx.doi.org/10.1016/j.agrformet.2012.09 .003.
[17]
Liang, L., Di, L., Zhang, L., Deng, M., Qin, Z., Zhao, S., Lin, H., “Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method,” Remote Sens. Environ. 165, 123–134 (2015.) http://dx.doi.org/10.1016/j.rse.2015.04. 032.
[18]
Pu, R., Yu, Q., Gong, P. and Biging, G. S.,“ EO-1 Hyperion, ALI and Landsat 7 ETM+ data comparison for estimating forest crown closure and leaf area index,”Int J Remote Sens.26(3), 457–474(2005).
[19]
Li, X.; Zhang, Y.; Bao, Y.; Luo, J.; Jin, X.; Xu, X.; Song, X.; Yang, G.,“Exploring the best hyperspectral features
for
LAI
estimation
using
partial
least
squares
regression,” Remote
Sens. 6,
6221–
6241(2014).doi:10.3390/rs6076221. [20]
Sykioti, O., Paronis, D., Stagakis, S. and Kyparissis, A., “Band depth analysis of CHRIS/PROBA data for the study of a Mediterranean natural ecosystem. Correlations with leaf optical properties and ecophysiological parameters,”Remote Sens. Environ. 115(2), 752–766 (2011).
[21]
Thenkabail, P. S., Enclona, E. A., Ashton, M. S., Legg, C., & De Dieu, M. J.,“Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests,”Remote Sens. Environ. 90(1), 23−43(2004).
[22]
Vyas, D., Christian, B. and Krishnayya, N.S.R.,“Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data,”Int J Remote Sens. 34(5), 1676-1690(2012).doi: 10.1080/01431161.2012.725484.
Proc. of SPIE Vol. 9880 98800U-10 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/17/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
[23]
Rouse, J. W., Haas, R. H. and Schell, J. A. Monitoring the vernal advancement of retrogradiation of natural vegetation. (NASA/GSFC, type III, final report Greenbelt, MD: USA) 371 pp(1974).
[24]
Singh , D., Singh, S., Krishna Murthy, Y.V.N.,“Assessment of Foliar Biochemicals in Tropical, Subtropical and Temperate Ecosystems of Lesser Himalayas”Int J Ecol. Environ. Sci.40 (4), 205-218(2014).
[25]
Shah, R.K. and Subudhi, S.P.,“Forest working plan of Dehradun Forest Division”, Vl-I 2009-10 to 2018-19. pp.,1-157(2009). Available on , accessed on date 28.01.2016.
[26]
Van der Meer, F. D. and de Jong, S. M. [Analytical techniques in spectrometry. Imaging spectrometry basic principles and perspective applications]. Kluwer Academic Publishers, Netherland,Page 45-46(2003).
[27]
Yuan J. G., Niu Z. and Wang X. P.,“Atmospheric Correction of Hyperion Hyperspectral Image Based on FLAASH,”SpectroscSpect Anal.29(5), 1181-1185(2009).
[28]
Zhang, Z., He, G., Dai, Q., Jiang, H., “Leaf Area Index Estimation Using MESMA Based on EO-1 Hyperion Satellite Imagery”International Journal of Information and Electronic Engineering, 14(1), 11-15(2014).
Proc. of SPIE Vol. 9880 98800U-11 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/17/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
