Estimation of Vegetation Indices and Biomass by the Fusion of … · 2020. 7. 28. · Estimation of...

Estimation of Vegetation Indices and Biomass by the Fusion of LiDAR Data and Landsat Images in Mudumalai Forests of the Western Ghats, India Indu Indirabai1*, M.V. Harindranathan Nair1 School of Environmental Studies, Cochin University of Science & Technology, Kochi, Kerala, India.

Jaishankar. R. Nair2

C.V Raman Laboratory of Ecological Informatics, IIITM-Kerala, Technopark Campus, Thiruvananthapuram, Kerala, India.

. Rama Rao Nidamanuri3 Department of Earth & Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, Kerala, India.

Abstract

Accurate estimation of biomass and vegetation indices of forests play a significant role in monitoring

ecological processes, global distribution of forests as well as the forest ecosystem productivity. Biomass and the vegetation indices in forest are highly correlated and their accurate estimation is required for carbon budget studies. Both passive and active remote sensing systems have immense potential to estimate vegetation parameters with varying accuracies and cost. Major challenge for these technologies is the estimation of vegetation indices along with the structural parameters in a highly complex biodiversity forest ecosystem such as Western Ghats. The objective of the present study is the estimation of vegetation indices and biomass in Mudumalai forest of Western Ghats regions of India by the fusion of space borne LiDAR (ICESat/GLAS) data with the Landsat Imagery (ETM+/8OLI). The structural parameters from the LiDAR data and the spectral parameters from Landsat imagery are integrated based on the pixel based image registration techniques. The vegetation indices extracted in this study are Normalized Difference Vegetation Indices(NDVI), Difference Vegetation Indices (DVI), Enhanced Vegetation Indices(EVI), Green Difference Vegetation Indices (GDVI), Green Ratio Vegetation Indices (GRVI), Leaf Area Index(LAI), Green Normalised Difference Vegetation Indices (GNDVI), Moisture Stress Index(MSI), Normalised Difference Water Index(NDWI), Ratio Vegetation Index(RVI), Soil Adjusted Vegetation Index(SAVI). The results indicate that biomass estimated have strong correlation(r=0.98) with in situ measurements. The approach revealed that the integration method used in the study leads to meaningful extraction of the vegetation indices and biomass. The study represents an important step towards future tasks of biomass and carbon budget estimation of heterogeneous forests in India. The study provides contributory benchmarks for monitoring the forest health conditions, moisture content, chlorophyll content and forest growth rate which also can possibly help to model global carbon cycle, deforestation and forest degradation.

Keywords--- Vegetation Indices; Biomass; Full waveform; ICESat; GLAS; Landsat; LiDAR; Support Vector Machine.

I. INTRODUCTION

Forest ecosystems of Western Ghats

region of India are characterised by large number

of woody and non woody tree species along with

shrubs, moss covers and thick understorey

vegetation. Monitoring the forest health conditions

and measurement of the biophysical parameters is a

challenge in these heterogeneous forests.

Vegetation indices as well as biomass are

the simple and effective forest parameters which

are generally used to indicate canopy density, forest

health, moisture content, chlorophyll content, forest

growth rate. In several studies, biomass and

vegetation indices are strongly correlated [1], [2].

Normalised Difference Vegetation Index (NDVI) is

a common and most widely used index in the case

of global environmental vegetation monitoring and

climate change [3],[4],[5]. The other common

vegetation indices include Difference Vegetation

Indices(DVI)[6],[7], Enhanced Vegetation Indices

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 3 (2018) Spl. © Research India Publications. https://dx.doi.org/10.37622/IJAER/13.3.2018.56-62

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(EVI) [8],[9],Green Difference Vegetation Indices

(GDVI)[10],Green Ratio Vegetation Indices

(GRVI)[11],Green Vegetation Indices(GVI) [12],

[13], Leaf Area Index(LAI) [14], [15], Green

Normalised Difference Vegetation Indices

(GNDVI) [16], [17], Moisture Stress Index (MSI)

[18],[19], Normalised Difference Water

Index(NDWI) [20],[21], Ratio Vegetation

Index(RVI) [22], [23],Soil Adjusted Vegetation

Index(SAVI)[24]. Several studies were done based

on these indices.

Traditional methods for vegetation

parameter estimation are found to be time

consuming, expensive and have limited spatial

coverage. Remote Sensing technologies including

active and passive methods can estimate the

vegetation parameters to a great extent [25],[26].

There are so many remote sensing technologies for

the estimation of vegetation indices by the

appropriate selection or combination of

wavelengths. Multispectral remote sensing

technologies like Landsat series are found to be

useful for estimating biomass and vegetation

indices in several studies [27],[28],[29]. However

the use of optical remote sensing imagery can only

provide horizontal structure of forest but are

limited in the case of estimating vertical structural

details [30],[31]. The evolution of Light Detection and

Ranging (LiDAR) is relatively successful and

promising for the estimation of structural

parameters. The introduction of this technology

provides three dimensional structure of forest

including vertical and horizontal structures. LiDAR

is found to be useful for estimating the canopy

heights, canopy density, vegetation cover etc and in

forest measurements in several case studies

[32],[33],[34],[35]. The extensive literature review

shows that airborne LiDAR have tremendous

applications in the estimation of forest density,

biomass and the vegetation parameters [36], [37],

[38], [39], [40], [41] ,[42]. But it is evident from

these studies that the airborne discrete return

LiDAR have limitations including high cost and

limited spatial coverage.

The introduction of the space borne full

waveform LiDAR, Geoscience Laser altimetry

system onboard Ice Cloud and land Elevation

satellite (ICESat) launched in January 13, 2003,

which can provide full wave form LiDAR data with

global coverage and large footprint having diameter

70m with 172m spacing. GLAS ICESat data have

found applications in measuring the 3D information

of forest in global scale [43], [44], [45], [46], [47],

[48],[49],[50]. Several studies are done to integrate

GLAS data with optical imagery for the extraction

of forest attributes [51],[52]. But it is found from

the literatures that most of these studies are done in

homogeneous forests which are of pine deciduous

types. But the forest ecosystem of Western Ghats is

highly complex and heterogeneous which consists

of large number of woody, non woody tree species

and thick understory cover. The methodology of

integration of LiDAR data with Landsat imagery

has not been yet done in India for the estimation of

vegetation indices and the biomass, which signifies

the novelty of this approach.

The objective of the given study is to

extract the vegetation indices and biomass in

Mudumalai region of Western Ghats of India by the

combination of GLAS ICESat LiDAR data and

Landsat Imagery. The results obtained indicated

strong correlation of 97.58% with the measured

values which shows the relevance of the study. The

methodology applied in this study can be used to

understand the forest health conditions, degradation

as well as forest monitoring, management and

practices.

II. MATERIALS AND METHODS

A. Study Area The location of the study area is depicted

in the Fig 1. The study area is Mudumalai forests

(411 km2) in Tamilnadu, which is one of the

protected forests in the Western Ghats region of

India. Mudumalai forest has an annual rainfall of

range 1700mm and the type of forests is of tropical

moist deciduous, dry deciduous, semi evergreen

and thorn forests. The elevation values ranges from

960m to 1266m for the forest area

Fig 1: Study area Mudumalai Forest (Source: Esri Arcgis Earth)

B. Data Sets Used Space Borne LiDAR Data

Multispectral satellite imagery

Ground validation data


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1. Space Borne LiDAR Data The GLAS data used in the given study was

collected through the ICESat/GLAS NSIDC

website. The waveform data used in the study

contains the land elevation and the elevation

distribution corrected for atmospheric and geodetic

effects. About 15 passes were procured for the

study site. The data collected are pre-processed by

ICESat /GLAS tools for the conversions needed for

the extraction of the data and waveform

visualisation. The data sets were filtered by means

of the available quality flags including i_satCorrflg,

i_gvl_rcv for saturation, presence of cloud and

validity of elevation. The GLAS data distribution

along with the elevation values obtained from the

LiDAR data for Mudumalai region is shown in the

Fig 2.

Fig 2: GLAS data distribution over the study area.

2. Multispectral Satellite Imagery: Landsat products are used for the vegetation

indices extraction in the given study. The Landsat

data were downloaded through USGS earth

explorer (https://earthexplorer.usgs.gov/). The

Landsat imagery were pre-processed to reduce the

atmospheric effects and clouds by atmospheric

calibration techniques. The reflectance values were

computed for the Landsat imagery used in the

study.

C. Methodology The methodology depicting the estimation of

vegetation indices and biomass is shown in the

Fig.3. The GLAS data after extraction were

processed to obtain the structural parameters which

were further used for the fusion with the Landsat

imagery.

Fig 3: Flow chart depicting the methodology.

I. Extraction of Vegetation Indices The calibrated Landsat imagery were

integrated with the GLAS derived elevation models

by appropriate registration techniques which

resulted in an enhanced single image with

additional information. From the GLAS data,

structural parameters in the given area were

estimated from the digital surface models and the

elevation models generated. The following

vegetation indices were extracted from the

integrated data set.

a. Normalized Difference vegetation Indices (NDVI)

Normalized Difference vegetation Indices

(NDVI) is calculated using (1) and the values

ranges from 0 to 1.

NDVI=REDNIR

REDNIR

(1)

The values of NDVI range from 0 to 1 and

have sensitive response for even low vegetation

and calculated through normalisation procedure.

NDVI is related to canopy structure and canopy

photosynthesis but sensitive to effects of soil

brightness, soil colour atmosphere, clouds and leaf

canopy shadow. NDVI is calculated for the

integrated data sets. NDVI is found to be sensitive

to the effects of soil brightness, soil colour

atmosphere etc. NDVI specifies the structure of

canopies in the study area and the rate of

photosynthesis of canopies in the forest.

b. Difference Vegetation Indices (DVI) Difference Vegetation Indices (DVI), also

called Environmental vegetation index is applied to

vegetation ecological monitoring of the study area.

The DVI is calculated using (2).

DVI=NIR –RED (2)

Using both the Landsat images, DVI is obtained.

DVI index accounts for the difference between soil

and vegetation


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https://earthexplorer.usgs.gov/

c. Enhanced Vegetation Indices (EVI) Enhanced Vegetation Indices (EVI) corrects

the soil and atmospheric effects and is useful in

high leaf area index where NDVI may saturate.

Blue reflectance region is used in the index in order

to correct for soil back ground regions and reducing

atmospheric influences. The EVI for the study site

was calculated as in (3).

EVI=2.51BLUE*7.5RED*6NIR

REDNIR

(3)

d. Green Difference Vegetation Indices (GDVI)

Green difference vegetation indices (GDVI) are calculated for the study area and it is related to

nitrogen content. The GDVI for the given study

was computed using (4).

GDVI = NIR - GREEN (4)

e. Green Ratio Vegetation Indices (GRVI) Green Ratio Vegetation Indices (GRVI) is

simple to interpret and less sensitive to cover

variations with high vegetation cover areas. The

index is very sensitive to photosynthetic rates in

forest canopies. The GRVI for the study area was

calculated using (5).

GRVI=GREEN

NIR

(5)

f. Leaf Area index (LAI) Leaf Area index (LAI) is defined as the total

one sided green leaf per area. It is considered as a

climate variable as well as a very important canopy

characteristics. All vegetation indices have an

interrelationship with LAI. It is used to calculate

the foliage cover and is an indication of vegetation

dynamics, water and carbon cycling. For the given

study, the LAI is calculated as in (6).

LAI=3.618×EVI-0.118 (6)

g. Green Normalised Difference Vegetation Indices (GNDVI)

Green Normalised Difference Vegetation

Indices (GNDVI), is sensitive to chlorophyll

concentration measuring green spectrum from

540m to 570 m. GNDVI is calculated as in (7).

GNDVI=GREENNIR

GREENNIR

(7)

h. Moisture Stress Index (MSI) Moisture Stress Index is found to be very

sensitive to increasing canopy water content and

can be used in canopy stress analysis, productivity

prediction and modelling ecosystem physiology.

The higher values indicate great water stress and

less water content. MSI is calculated using (8).

Moisture Stress Index=NIR

SWIR

(8)

i. Normalised Difference Water Index (NDWI)

Normalised Difference Water Index can

distinguish water and vegetation extracting water

bodies and it is a ratio combining NIR and Green

which can enhance water spectral signals. It has

wide applications in forest canopy stress analysis,

leaf area index studies, fire hazard studies and plant

productivity analysis. The index can be calculated

using (9).

NDWI=SWIRNIR

SWIRNIR

(9)

j. Ratio Vegetation Index (RVI) Ratio Vegetation Index (RVI) is used for green

biomass estimation and monitoring at high density

vegetation cover because it has high correlation

with biomass. RVI index is given by (10).

RVI= RED

NIR

(10)

k. Soil Adjusted Vegetation Index (SAVI) Soil Adjusted Vegetation Index (SAVI)

suppresses the effect of soil pixels and it contain a

canopy background adjustment factor with value

0.5 which is a function of vegetation density and

can be used in areas with relatively sparse

vegetation. SAVI is given by (11).

SAVI=

0.5REDNIR

REDNIR1.5

(11)

II. Estimation of biomass Biomass is estimated for the study area by

making use of the supervised learning non

parametric regression algorithm. Support vector

machine learning algorithm is used in the given

study, which mainly establishes a relationship

between tree biomass and vegetation indices. The

integrated data which is the combination of LiDAR

point cloud and Landsat Imagery is processed by

means of the support vector machine algorithm for

regression and classification. The biomass

estimated for the study area is depicted in the Fig 7.

III. RESULTS AND DISCUSSION

For Mudumalai region, about 11 vegetation

indices are estimated. The mean and standard

deviation of the vegetation indices were also noted.


59

The vegetation indices extracted are depicted in the

Fig 4, Fig 5 and Fig 6. Biomass values were

compared with the measured values and the

correlation between biomass and NDVI is also

noted. The NDVI values of the region and the

biomass are also related such that for higher NDVI

values, the biomass is high.

Fig 4: NDVI, DVI, EVI and GDVI for Mudumalai on

integrating LiDAR point cloud with Landsat Image

Fig 5: GRVI, LAI, MSI and NDWI for Mudumalai on integrating LiDAR point cloud with Landsat Imagery

Fig 6: GNDVI, RVI and SAVI for Mudumalai on integrating

LiDAR point cloud with Landsat Imagery

Fig 7: Estimated Biomass of Mudumalai forest by Support

Vector Machine algorithm

Fig 8: Scatter plot showing the variation of estimated biomass

and measured values

The biomass of Mudumalai region

obtained from image estimates varies at a range of

130.5 t/ha to 385.4 t/ha. The estimated biomass and

the measured biomass are compared by means of

the scatter plot and the results indicated a strong

correlation of 97.58%. Fig 8 shows the variation of

estimated verses measured biomass.

The results indicated that the integrated

product of LiDAR GLAS data with Landsat images

can extract the vegetation indices in an effective

manner. Biomass are also computed which can be

an indirect indicator of the carbon content in the

forest area. A significant correlation is seen

between the biomass and the vegetation indices.

The paper presented the potential of the

combination of GLAS data and Landsat images for

the extraction of vegetation indices along with the

structural parameters in a cost effective and

accurate way. Vegetation indices were calculated

based on the spectral parameters thus ensuring the

quality of the results as well as the ability to

identify the forest conditions and the canopy

changes.

IV. CONCLUSION

The major conclusions of the study are the

following. The study estimated the vegetation

indices and the biomass by the integration of space

borne LiDAR and Landsat images in the

Mudumalai forest of Western Ghats. The

vegetation indices estimated are NDVI, DVI, EVI,

GDVI, GRVI, LAI, GNDVI, MSI, NDWI, RVI and

SAVI. Biomass for the study area is also estimated

with 97% accuracy. The study focussed on a cost

effective and accurate estimation of the vegetation

indices along with the structural parameters by the

integration approach. The results have good

correlation with the in situ measurements.

The significance of this study is that

structural parameters can be estimated along with

the vegetation parameters by the integration

technology which is not possible in the case of

other methods. The approach used here is not yet

used in the heterogeneous complex forests of

Western Ghats, which depicts the novelty of this

method. The methodology used in this study can

overcome the limitations of traditional methods as


60

well as passive remote sensing technologies. The

vegetation indices calculated here were a good

indicator of the forest canopy cover, moisture

content, vegetation density, biomass and the

vegetation growth. The data fusion of the GLAS

and the Landsat images proved to be a promising

technology for the forest structural and vegetation

parameters in addition to the accurate estimation of

biomass. The vegetation indices extracted showed

accurate and desirable results which can play a

significant role in the national forest monitoring

and the forest management practices.

Acknowledgment

Special thanks to the National Snow and

Ice Data Centre (NSIDC), for the distribution of

ICESat/GLAS product 14 data and providing the

data processing software. Sincere thanks to Kerala

State Council for Science Technology and

Environment (KSCSTE), Thiruvananthapuram,

Kerala, India for funding of the research work.

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