Journal of Geography, Environment and Earth Science International

19(4): 1-7, 2019; Article no.JGEESI.47433 ISSN: 2454-7352

Estimation of the Temporal Change in Carbon Stock of Muthupet in Using Remote Sensing Techniques

K. Narmada1* and K. Annaidasan2

1Centre for Water Resources Management, University of Madras, , . 2Department of Geography, Central University of Tamil Nadu, , India.

Authors’ contributions

This work was carried out in collaboration between the two authors. Author KN designed the study, performed the statistical analysis, wrote the protocol and wrote the first draft of the manuscript. Author KA prepared the maps and managed the literature searches. Both the authors read and approved the final manuscript.

Article Information

DOI: 10.9734/JGEESI/2019/v19i430096 Editor(s): (1) Dr. Ojeh Vincent Nduka, Department of Geography, Taraba State University, Jalingo, Nigeria. (2) Dr. Jude Ndzifon Kimengsi, Department of Geography and Environmental Studies, Catholic University of Cameroon (CATUC), P.O. Box 782, Bamenda, Cameroon. Reviewers: (1) Paul Kweku Tandoh, KNUST- Kumasi, Ghana. (2) Bharat Raj Singh, Dr. APJ Abdul Kalam Technical University, India. Complete Peer review History: http://www.sdiarticle3.com/review-history/47433

Received 02 December 2018 Accepted 16 February 2019 Original Research Article Published 16 March 2019

ABSTRACT

Aim: To study the carbon storage potential of Muthupet mangroves in Tamil Nadu using Remote sensing techniques. Place and Duration: The study is carried out in Muthupet Mangroves for the years 2000, 2010 and 2017. Methodology: In this study the remote sensing images were processed using the ERDAS and ArcGIS software and the NDVI (Normalized Difference Vegetation Index) has also been applied to estimate the quantity of carbon sequestration capability for the Avicennia marina growing in the Muthupet region for the period 2000-2017. The formula proposed by Lai [10] was used to calculate the carbon stock using geospatial techniques. Results: The results show that the mangroves in Muthupet region has NDVI values between -0.671 and 0.398 in 2000, -0.93 and 0.621 in 2010 and -0.66 and 0.398 in 2017. The observation indicates ______

*Corresponding author: E-mail: [email protected];

Narmada and Annaidasan; JGEESI, 19(4): 1-7, 2019; Article no.JGEESI.47433

the reliability and validity of the aviation remote sensing with high resolution and with near red spectrum experimented in this research for estimating the the Avicennia marina (Forsk.) mangrove growing in this region. The estimated quantity of carbon di oxide sequestrated by the mangrove was about 1475.642 Mg/Ha in 2000, 3646.312 Mg/Ha in 2010 and 1677.72 Mg/Ha in 2017. Conclusion: The capacity of the Avicennia marina growing in Muthupet region to sequestrate carbon show that it has a great potential for development and implementation. The results obtained in this research can be used as a basis for policy makers, conservationists, regional planners, and researchers to deal with future development of cities and their surroundings in regions of highly ecological and environmental sensitivity. Thus the finding shows that wetlands are an important ecological boon as it helps to control the impact of climate change in many different ways.

Keywords: Mangroves; Avicennia marina; remote sensing; carbon sequestration; wetlands.

1. INTRODUCTION during 1980~1999 as published in the 2007 UN IPCC climate change report indicates that global An ever-increasing body of scientific evidence warming has caused the atmospheric suggests that the anthropogenic release of temperature to increase by 0.74℃ for the last carbon dioxide (CO2) has led to a rise in global 100 years (1906-2005) to cause obviously temperatures over the past several hundred increasing frequency and extend of years [1]. If this theory holds true, unabated meteorological disasters. In recent years, the release of greenhouse gases will result in global improvement of various remote sensing warming that may lead to significant and technologies and awaking environmental potential catastrophic alteration of climate, protection cognizance lead to the tendency of natural hydrological and carbon cycles globally. applying remote sensing (RS) for investigating In an effort to alleviate the possible impact of and studying mangrove [6], further used the RS atmospheric CO2 on global climate, several technology to estimate the quantity of carbon strategies are under development to sequester sequestered by mangrove. AVHRR data was the CO2 released from stationary and mobile used for monitoring of Savanna primary sources [2]. These carbon management production in Senegal [7]. An empirical strategies include: (1) increasing the efficiency of relationship between forest structural attributes energy conversion; (2) using low-carbon or and ASTER data was analysed. IKONOS image carbon-free energy sources; and (3) capturing alone was used for mapping large-scale and sequestering anthropogenic CO2 emissions vegetation communities in the urban forest [8]. from large point sources such as coal-fired The relationship between forest variables and thermoelectric generation facilities. The third vegetation indices in a Nothofagus pumilio forest strategy, termed ‘‘CO2 sequestration”, has was also investigated [9]. Landsat TM images gained increased attention in recent years, and were used to develop a regional level biomass would promote continued use of fossil fuels for maps for the Madhav National Park. In their the generation of electric power by reducing its studies they used two different approaches viz. CO2 footprint [3]. Statistical sampling technique and spectral response modelling. In this research, the multiple The mangrove plants fresh, which is mostly spectrum remote sensing technology is used for located at estuaries, is more difficult than land investigating the NDVI (Normalized Difference based forest to be investigated and monitored. Vegetation Index) of mangrove in Muthupet Being one of the earliest indicators for climate region (Tamil Nadu, India) in order to estimate its changes, the mangrove ecological system is carbon sequestration capacity [10]. easily affected by the changing environmental factors such as increasing atmospheric CO2, 2. MATERIALS AND METHODOLOGY average temperature, mean sea level elevation, and salinity. Hence, the influence of climate 2.1 Study Area changes on global distribution of mangrove and regional ecology is becoming an important topic Muthupet mangrove wetland of for the various government agencies and area is located in the southern most end of the environmental protection groups [4,5]. The Cauvery delta in the districts of , prediction of global climate toward the end of the Thrivarur and (Fig. 1). The total area 21st century based on the average data collected of the lagoon as estimated from the satellite data

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Narmada and Annaidasan; JGEESI, 19(4): 1-7, 2019; Article no.JGEESI.47433

LANDSAT ETM image, which is having a channels and small bays, bordered by thick resolution of 30 m in space for the period April mangroves; and a number of artificial canals dug 2015 is 13.32 km2 and it has a volume of 9.6 x across the mangrove wetlands, particularly in 106 m3 (as estimated for February -March 2015). their western part and fished intensively. The It is a part of a large coastal wetland called the lagoon receives inflow of freshwater during Great Vedaranyam Marsh. This area has a northeast monsoon through the above drainage gentle slope towards Palk Strait of Bay of arteries occupied by agricultural soils, mangrove Bengal. The distributaries of Cauvery viz., swamps and aquaculture ponds. From February Paminiyar, Koraiyar, Kandaparichanar, to September, freshwater discharge into the Kilaithangiyar and Marakkakoraiyar discharge mangrove wetland is insignificant. The soil in their water into the wetlands and form a large the lagoon is clayey silt and towards the lagoon before reaching the sea. Besides the landward side it is silty clay due to fresh silt lagoon, the wetland includes many tidal streams, deposits.

Fig. 1. Study area, muthupet mangroves Table 1. Data sources

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Narmada and Annaidasan; JGEESI, 19(4): 1-7, 2019; Article no.JGEESI.47433

S. no Data and sensor Spatial resolution (where: sri – standard deviation of the PIF set in 1. Landsat TM 2000 30 the reference image for band (i); ssi – standard 2. Landsat ETM 2010 30 deviation of the PIF set in the subject image for 3. Landsat 8 OLI 30 band (i); mri – the average value in the reference 2017 image for band (i); msi – the average value in the Source: USGS Earth Explorer subject image for band(i) (Table 2). From the above formulae the coefficients derived from the 2.2 Methodology imagery statistics were used in the regression equation for deriving the carbon storage of the The objective of this research is to evaluate the mangrove. Radiometric normalization using PIF feasibility of using the RS technology to evaluate was used in this study [11]. The objects that have the carbon sequestration capability of mangrove. reflectance that doesn’t change in different The satellite data has been taken up from USGS scenes from the same area. These objects don’t Earth explorer for the years 2000, 2010 and 2017 show variation in the reflectance in different (Table 1). The mangrove growth in the study seasons or biological seasons. The other areas conforms to urban forest that is defined as manmade objects that doesn’t change over a the grove growing in densely populated regions. period of time were also selected. These Hence, the tree growth is limited and controlled were taken with reference to the literatures by anthropogenic factors such as land PIF were [12,13]. selected from the imageries were distributed equally in the concrete structures and use so that PIF set = {(band 4 / band 3) < 1 and band 4 the artificially planted and natural growing trees > 180} form a closely related ecological system. The formula proposed by Lai [8] for estimating the The coefficients that were derived from the carbon sequestration is expressed as formulae were therein used to arrive at the carbon stick values for the mangroves for the Carbon = a*e (NDVIb) (1) years 2000, 2010 and 2017 by substituting the coefficient values in the formula. Where a and b are the normalization coefficients that were obtained on the basis of the PIF NDVI model was run for the 2000, 2010 and (Pseudo Invariant features) selected from the 2017 images (Fig. 2). The coefficients were subject and reference images, applying the derived from the NDVI images. From these following formula: coefficients the formula 1 was used to arrive at the carbon stock values for the Muthupet a (2) mangrove region. From the values arrived in the i = attribute table, the total carbon stock was calculated by summing the values for the period bi = mri – ai *m si (3) and similarly the total carbon stock for the rest of the years were also derived.

Table 2. The Correlation coefficients derived from Imagery statistics for three different years

Correlation coefficients Year 2000 2010 2017 Coefficient a 0.487 0.738 0.489 Coefficient b -0.0128 0.382 0.178 Mean 0.4096 -0.264 0.1866 Standard Deviation 0.415 0.274 0.202

Table 3. Carbon stock estimation of muthupet mangroves for 3 different time periods

Year AGB (Mg ha-1) BGB (Mg ha-1) TGB (Mg ha-1) CS (Mg ha-1) 2000 693.724 180.368 874.092 402.082 2010 771.942 200.705 972.648 457.145 2017 1714.192 445.689 2159.881 993.545

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Narmada and Annaidasan; JGEESI, 19(4): 1-7, 2019; Article no.JGEESI.47433

Fig. 2. Normalised difference vegetation index 2000, 2010 and 2017

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2.2.1 Carbon storage equation for muthupet Technique (PIF) using the formula 1, 2 and 3. mangroves for three different years These coefficients along with the NDVI values were used to derive the carbon stock of the area. The plot of stored Carbon (Mg /C pixel) vs scaled Using the formula mentioned in the methodology NDVI were taken for the 10 of the sample area the Carbon stored by the mangroves for three (60 plots) [9]. The final regression equation of the different years were calculated. The temporal carbon storage and the vegetation index is change of the storage is shown below.

Carbon stored (AGB) = 0.487*e (NDVI*0.-0.0128) for The carbon sequestered by the mangroves the year 2000 (4) increased to 12% from 2000 to 2010 and 54% from 2017. The carbon stored in the Muthupet Carbon stored (AGB) = 0.738 *e (NDVI*0.382) for the mangroves is found to increase gradually from year 2010 (5) 2000 to 2010. The carbon sequestered increased to 60% from 2000 to 2017 in 17 years. After the Carbon stored (AGB) = 0.489*e (NDVI* 0.178) for the disastrous effect of the tsunami in 2004, many year 2017 (6) awareness programmes have been conducted to conserve and regenerate the mangrove forests. Below Ground Biomass (BGB) = Carbon * 0.26 (7) Hence many people including government administrative bodies and non-governmental Total Ground Biomass (TGB) = AGB + BGB (8) organisations have taken immense effort to conserve the mangroves and replanted the Carbon Stock = TGB * 0.475 (9) mangroves as a conservation measure. Hence the vegetation has increased reasonably in the Where Carbon is the total Carbon Storage (Mg region. C/pixel) and NDVI is the Landsat NDVI value. (Table 3) The value was then calculated for 4. CONCLUSION hectare. The Above ground biomass was found out using ERDAS Imagine 2014. The Below  The aforementioned discussion reveals Ground Biomass was 26% of the Above Ground that if an adequate resolution is selected, Biomass(AGB). From the AGB the Below Ground and complete visible and near infrared light Biomass (BGB) was calculated. From these the spectra are covered, the RS method is Total Ground Biomass (TGB) was calculated by feasible to obtain reliable results on adding the AGB and BGB. The biomass value evaluating the mangrove species in was converted into carbon stock (CS) by using Muthupet region. Avicennia marina is the the conversion factor with the equation (Leith, dominant species in the region. 1963). Biomass values were multiplied by 0.475  For addressing the unique characteristics to get carbon storage values of the urban trees. of the Avicennia marina mangrove growing [9]. in the Muthupet region such as bushy growth in a long and narrow clustering 3. RESULTS AND DISCUSSION area, RS images with high resolution, e.g. those obtained using aviation RS or the 3.1 Assessment of the Mangrove ISONOS or QUICKBIRD satellites Vegetation Using NDVI launched after September 1999 will be appropriate. NDVI (Normalised Difference Vegetation Index)  Additionally, the Remote Sensing should model was used to understand the health of the cover visible light (especially red and green vegetation of the study area. The values derived light spectra) and near infrared spectra. from the model were ranging from 0.789 to -  This Remote Sensing research was carried 0.674. The values above 0 show that there is out for 17-year period from 2000 till 2017. vegetation and the highest positive value shows Hence, the estimated quantity of carbon the dense vegetation in the area. The values less sequestration based on RS information than 0 shows that there is no vegetation. The collected during the wet season is lower area without vegetation was comparatively very than the actual carbon sequestration minimal around 23%. The extent of vegetation capacity of the mangrove in the study can be related to the extent of carbon stored by areas. the vegetation cover. The correlation coefficients  Although the Avicennia marina mangrove derived using Psuedo Invariant Feature trees are often cut down, they grow

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