Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve

Transcription

1 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 doi: /ijrsa Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve Emmanuel Donkor 1, Edward Matthew Osei Jnr 2, Benjamin E. K. Prah 2, Adwoa Sarpong Amoah 3, Yakubu Mohammed 1 1 Resource Management Support Centre of Forestry Commission, Kumasi, Ghana, 2 Kwame Nkrumah University of Science and technology, Kumasi, Ghana, 3 Kumasi Polytechnic, Ghana 1 dadad105@yahoo.com, 2 chief_osei@yahoo.com, 2 benprah@yahoo.com, 3 lawrena80@yahoo.com, 1 myakubu89@hotmail.com Abstract Biomass estimation has become a critical element in global environmental studies, because the change in biomass is deemed as an important component of climate change. The aim of this research is to estimate and map carbon stocks in Bosomkese forest reserve using remote sensing, GIS applications and field measurement method. Out of the six carbon pools of terrestrial ecosystem involving biomass (aboveground biomass, belowground biomass, deadwood, non tree, litter and soil organic matter), carbon sequestration of three (aboveground, belowground and deadwood) were assessed. Advanced Land Observing Satellite (ALOS) image acquired in 2010 was classified using Erdas Imagine. Total of five land use/cover classes were identified; Closed canopy natural forest, open canopy natural forest, plantation, farmland and fallow land. Diameter at breast height and total height of standing trees as well as the end diameters and the length of downed deadwood were measured in fifty sample plots in the five land use classes. These measurements were converted into aboveground carbon (AGC), belowground carbon (BGC) and deadwood carbon (DWC) using allometric equations developed in 2012 by Forest Research Institute of Ghana (FORIG). Total carbon for each plot was the summation of AGC, BGC and DWC. This research showed that closed canopy natural forest ( ton/ha) contained more carbon than the rest of the land use/cover classes. This was followed by open canopy natural forest ( ton/ha), plantation (775 ton/ha), fallow land ( ton/ha) and farmland (45.13 ton/ha) in descending order of total carbon stocks. The carbon/carbon dioxide equivalent values together with the plots coordinates were used to generate carbon stock and carbon dioxide equivalent map using Geostatistics tool of ArcGIS The total carbon stock for the whole Bosomkese forest is in the range of 2,236, ,865, tons and carbon dioxide equivalent in the range of 8,534, ,507, tons. Keywords Carbon Stocks; Bosomkese Forest Reserve; Biomass Estimation; Climate Change; Above Ground Carbon; Below Ground Carbon; Deadwood Carbon Introduction Biomass estimation for tropical forest has received much attention in recent years because the change in biomass is considered as a vital component of climate change (Richardson & Oosterom, 2013). Biomass determines potential carbon emission due to deforestation, forest degradation and conversion of natural forest lands. Therefore, accurate biomass estimation is necessary for better understanding of deforestation and forest degradation impacts on global warming and environmental degradation (Richardson & Oosterom, 2013). Natural forests accumulate a large quantity of carbon and when these forests are cleared the carbon is converted to carbon dioxide into the atmosphere (Chave et al., 2004). Forest covers nearly one-third of the Earth s land surface and accounts for half of terrestrial carbon pool (CPFC, 2008). The important role played by forest in global carbon cycle has been discussed in some papers of Kyoto protocol (Brown, 2002). Deforestation and forest degradation of the tropical forest account for about 15-25% annual global green house gas emission (Gibbs et al., 2007). Reducing carbon emissions from deforestation and forest degradation (REDD) as major effort to combat climate change has been taken up by United Nations Framework Convention on Climate Change (Gibbs et al., 2007). Global warming statistics collected by National Aeronautics and Space Administration's Goddard Institute for Space Studies have revealed that the average global temperatures have been increased by 0.8 C over the last century alone. Temperatures are increasing at startling rate of C per decade (Butler, 2007). This rise in 41

2 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 temperature is termed as global warming; this is caused by increase in some atmospheric gases, especially CO2, methane, fluorinated gases, chlorofluorocarbons and nitrous oxide (Cline, 2007). The mass of living organisms in a forest is called forest biomass. It is simply the weight of all organisms living in a precisely delineated area and it could be determined accurately by capturing every living organism and putting it on a scale (Hogan, 2014). But this would involve destroying the forest and moreover it would be extremely timeconsuming. A far more efficient method, with well-established accuracy, is to measure the size of organisms and from their dimensions estimate the weight. Estimates of carbon stocks are usually focused on living trees, fallen branches and dead standing trees (Chave et al., 2003). Soil also contains substantial amount of carbon (Lal, 2005; Kumar et al., 2006). Forest inventories have often been used as starting points for the estimation of biomass and carbon storage in natural forests (Talbot et al., 2014). Often, biomass equations have been developed on the basis of forest inventory data (Segura & Kanninen, 2005). Forest inventories usually include counting and measuring the tree diameter at breast height and total height. Many researchers have developed generalised biomass prediction equations for different types of forest and tree species (Basuki et al., 2009; Vashum & Jayakumar, 2012). Generally, the carbon concentration of the different parts of a tree is assumed to be 50% or 45% of the dry biomass (Malik et al., 2014). However, (Losi et al., 2003) in their study estimated the carbon concentration of dry bole sample to be approximately 48% of the dry bole biomass. Precise information about global biomass is limited and therefore there is the need for accurate estimation of biomass and carbon stocks in tropical forests for the understanding of global carbon cycle, formulation and evaluation of initiatives to reduce global warming and management of ecosystems carbon sequestration (Sierra et al., 2007). In Ghana, detailed information about carbon stock for the entire country is scanty and therefore there is to need to have comprehensive data on carbon stocks to help our decision makers in their planning on climate change. Accurate estimation of forest biomass is vital for many purposes including national development planning, scientific studies of ecosystem productivity and to evaluate the role of forest lands in carbon cycle (Basuki et al., 2009). The estimation of the above-ground biomass to assess the amount of Carbon stored in the forest is becoming progressively more important (Ketterings et al., 2001; Chave et al., 2005). This project is focused on estimation and mapping of Carbon stocks using remote sensing, GIS applications and field measurement (forest inventory) method. Bosomkese Forest reserve in Bechem forest district is used as case study in this project because of its variability in land use/cover as well as various forest development initiatives in the area. Methods Study Area Bosomkese forest reserve is within latitude 6 59 N to 7 10 N and longitude 2 11W to 2 19 W. It falls within Tano South and Asutifi political districts of Brong Ahafo region. Bosumkese forest reserve was demarcated in 1936 and constituted in It has a total area of square kilometers (13,841 hectares) and managed by Bechem forest district of forestry commission of Ghana. Figure 1 below shows the map of the study area. Methodology 1) Pre-Processing and Image Classification The pre-processing aims to correct distorted or degraded image data to create more accurate representation of the original scene. This typically involves the initial processing of raw data to correct for geometric distortion, to calibrate the data radiometrically and to eliminate noise present in the data (Lillesand & Kiefer, 2008). Supervised classification was used and the ALOS image was classified into five main land use/cover classes; closed canopy natural forest, open canopy natural forest, plantation, farmland and fallow land. Garmin 62Sc hand held GPS was used to pick 150 points and 100 were used as training points representing the various land 42

3 International Journal of Remote Sensing Applications (IJRSA) Volume 6, cover classes and the remaining 50 were used for validation. The Maximum Likelihood algorithm which classifies image according to the variance and covariance of the spectral response patterns of a pixel was used. In Erdas maximum likelihood algorithm gives the best classification results. 2) Accuracy Assessment FIG. 1 MAP OF STUDY AREA Accuracy Assessment is a process of testing the classification result from satellite images against any reference data or ground truth data (Foody, 2002). In the light of this it is important to test the result before using the outcome of the classification from satellite images for any land cover work. In this project all the reference data were obtained from the field work to perform accuracy assessment which yielded overall accuracy of 76% using the Classifier toolbar of Erdas Imagine. In all 50 reference points were used for the accuracy assessment. This project assessed the accuracy of the classified image from the confusion matrix generated. Area of each land cover class in hectares was generated by the classifier tool. 3) Selection of Enumeration Plots A land cover map was prepared and sample intensity of 0.015% was adopted which yielded 50 enumeration plots. The distribution of the plots within the entire study area are as follows; closed forest 10 plots, open forest 20 plots, plantation 15 plots, farmland 3 plots and fallow land 2 plots. The plots allotment was based on the area of each land use/cover class. The coordinates for each plot were extracted using data management tools of ArcGIS ) Field Work (Plot Enumeration) The purpose of the field work was to measure the diameter and the height of the trees to calculate their biomass using allometric equations. For each plot, the extracted coordinates were entered into the Garmin 62Sc hand held GPS which were used to navigate to the plot centre. 20m x 20m plot with 10m x 10m and 5m x 5m subplots within it were laid as shown in Figure 2 below. 43

4 International Journal of Remote Sensing Applications (IJRSA) Volume 6, m m 20m 5m FIG. 2 PLOT LAYOUT DESIGN The enumerations were carried out for live standing trees and dead wood. The live standing trees consist of tally trees, juvenile trees and seedlings. The dead woods were made up of dead standing trees and downed deadwood. Table 1 gives information about the various trees category. TABLE 1 TREE CATEGORY AND LOCATION Tree Diameter class(cm) Location within plot Stand area (Ha) Tallyt Tally 10 and above 20m x 20m 0.04 Juvenile m x 10m 0.01 Seedling m x 5m Dead standing 10 and above 20m x 20m 0.04 Downed deadwood 1 and above 10m x 10m 0.01 For the live and dead standing trees, their diameters at breast height (DBH- 1.3m from ground) and total tree height were measured. For downed deadwoods, their diameters at the two ends and lengths were measured as well as the decomposition states were recorded. 5) Aboveground Biomass and Carbon Stock Determination for Living Trees The above ground biomass (AGB) for tally trees, juvenile trees and seedlings were calculated using allometric equations developed in 2012 by Forest Research Institute of Ghana (FORIG) for the moist semi deciduous ecological zone which the study area falls within. The following allometric equation was used to calculate AGB per tree. Where Y: Above ground biomass per tree. dbh: Measured tree diameter at breast height ( cm ) Y (Kg / tree) = x ρ x (dbh 2 ) (1) ρ : Wood density (kg / m 3 ). Biomass for other living trees like palm trees, pawpaw trees, etc found within the plots were calculated using the allometric equation; Y (Kg / tree) = x (dbh 2 ) (2) The AGB per tree was then summed up for all trees to obtain a stand-level AGB estimate. Each stand-level has different area (Ha) as shown in Table 1. 44

5 International Journal of Remote Sensing Applications (IJRSA) Volume 6, The total stand-level AGB was converted to tons by dividing it by 1000 and further expressed it as ton / Ha by dividing the tons by its stand area. AGB values for stand-level were transformed to AGC using standard carbon fraction value of 0.48 as; AGC (Ton / Ha) = 0.48 x AGB (3) Total AGC per plot was obtained by summing AGC (ton / Ha) for tally trees, juvenile trees, seedlings and others. 6) Below Ground Biomass and Carbon Stock Determination The below ground biomass (BGB) for tally trees, juvenile trees, seedlings and others were calculated using the allometric equation; Where AGB: The above ground biomass of the tree. Y (Kg / tree) = x (AGB ) (4) The BGB per tree was then summed up for all trees to obtain a stand-level BGB estimate. The total stand-level BGB was converted to tons and further expressed it as ton / Ha by dividing the tons by its stand area. BGB values for stand-level were converted to BGC using the equation; BGC (Ton/Ha) = 0.48 x BGB (5) Total BGC per plot was obtained by summing BGC (ton/ha) for tally trees, juvenile trees, seedlings and others. 7) Determination of Carbon Stock for Dead Standing Trees Standing dead trees are classified into 4 different classes based on the trees decomposition level. The different levels are; (i) Tree with branches and twigs and resembles a live tree (except for leaves). (ii) Tree with no twig, but with persistent small and large branches. (iii) Tree with large branches only. (iv) Bole (trunk) only, no branches. The model for calculating the carbon of standing dead tree is where Z: AGC for dead standing tree DC: Decomposition coefficient Z (Kg / tree) = 0.48 x DC x AGB (6) 0.48: Carbon fraction The AGB is calculated using the living tree above ground biomass equation (1). Näslund s equations were used to calculate decomposition coefficients as follows; For levels (i) and (ii) 2 (dbh) DC= ( x dbh) 2 (7) For levels (iii) and (iv) 2 (dbh) DC= ( x dbh) 2 (8) The AGC per dead standing tree was then summed up for all dead standing trees to obtain the total AGC estimate. The total AGC (Kg) was converted to tons and further expressed it as ton/ha by dividing the tons by its stand area (0.04). 45

6 International Journal of Remote Sensing Applications (IJRSA) Volume 6, ) Determination of Carbon Stock for Downed Deadwood The volume of downed deadwood (particle) is calculated using the frusto-conical formula Volume = π x LP X (r 2 1+ r r1 r2) / 3 Using wood density, decomposition level and carbon fraction, the volume is transformed into carbon as; Z (gr / particle) = DL x CF x WD x π x LP x 100 x (r 2 1+ r r1 r2) / 3 (9) Where Z: carbon per particle (downed deadwood) in grammes. DL: Decomposition level CF: Carbon fraction = 0.48 WD: Wood density LP : Length of particle (m) r1 : Radius at the base (cm) r2 : Radius at the tip (cm) π = There are three decomposition levels for downed deadwood: Sound (blade does not sink or is bounced off) and has DL value of 1.0. Medium (blade partly sinks into the piece of wood or there has been some wood loss) and has DL value of Rotten (blade sinks well into the piece, there is extensive wood loss and the piece is crumbly) and has DL value of The carbon per particle was summed up for all the particles to obtain the total carbon (gr). The total carbon (gr) was converted to tons by dividing it by 1,000,000 and further expressed as ton/ha by dividing the tons by its stand area (0.01). Grand total carbon per plot (ton / Ha) was obtained by summing up the total carbon (ton/ Ha) for AGC (tally trees, juvenile trees, seedlings and others), BGC (tally trees, juvenile trees, seedlings and others) and deadwood carbon (dead standing trees and downed deadwood). 9) Conversion of Carbon Stock to Carbon Dioxide Equivalent Aboveground, belowground and deadwood carbon in tonnes were converted to carbon dioxide equivalent (t CO2e) as; Atomic mass of Carbon (C) = 12 Molecular mass of Carbon Dioxide (CO2) = X 2 = 44 Tonnes of Carbon dioxide equivalent (tco2e) ton / Ha per plot were obtained from the equation 10) Carbon Stock and Carbon Dioxide Equivalent Mapping tco2e (ton/ha) / plot = carbon stock x (44/12) (10) Geostatistical tool of ArcGIS 10.0 using ordinary Kriging technique was used to generate the carbon and the carbon dioxide equivalent maps of the study area. This technique was used because it provided more accurate and visually appealing map output. With the plot numbers, the coordinates of each plot in UTM coordinate system and the carbon or carbon dioxide values were entered in an excel sheet. The excel file was imported into ArcGIS 10.0 and geostatistics tool was used to generate carbon or carbon dioxide map. 46

7 International Journal of Remote Sensing Applications (IJRSA) Volume 6, Results Classification Supervised classification was used and the ALOS image was classified into five major land use/cover classes; closed canopy natural forest, open canopy natural forest, plantation, farmland and fallow land as shown in Figure 3. FIG. 3 LAND USE/COVER MAP OF THE STUDY AREA From the classification results, the area of each land use/cover and the total area are presented in Table 2. TABLE 2 AREA OF EACH LAND USE / COVER CLASS Land use / cover Area (Ha) Area (%) Closed canopy natural forest Open canopy natural forest Plantation Farmland Fallow land Total 13, The classification results were validated using 50 sample points; 6 in closed forest, 14 in open forest, 17 in plantation, 4 in farmland and 9 in fallow land. Erdas imagine was used to generate the producer accuracy, user accuracy and overall accuracy of the classification. Field Sample Plots Enumeration Total of 123 different tree species were enumerated in the 50 sample plots. About 90% of these species were found within closed and open canopy natural forest. Carbon Stocks in the Three Carbon Pools Carbon stocks were estimated for three pools; aboveground carbon (AGC), belowground carbon (BGC) and deadwood carbon (DWC) and summary are shown in Table 3. 47

8 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 TABLE 3 TOTAL CARBON STOCK FOR EACH CARBON POOL Carbon Pool Total Carbon (Ton/Ha) AGC BGC DWC Grand Total Total Carbon Stocks in the Land Use/Cover Classes Total carbon stock is the sum of all the three carbon pools. Table 4 below presents summary of total carbon stock for each land use / cover class. LAND USE/ COVER CLASS TABLE 4 TOTAL CARBON STOCK FOR EACH LAND USE / COVER CLASS AGC (Ton/ Ha) BGC (Ton/ Ha) DWC (Ton/ Ha) TOTAL CARBON (Ton/ Ha) Closed Forest Open Forest Plantation Farmland Fallow land TOTAL Mapping of Carbon Stocks and Carbon Dioxide Equivalent Total carbon stocks, carbon dioxide equivalent (CO2e) and UTM coordinates for each plot in the study area were used to generate carbon and carbon dioxide equivalent maps as shown in Figure 4 and Figure 5 respectively. FIG. 4 CARBON MAP OF THE STUDY AREA FIG. 5 CARBON DIOXIDE EQUIVALENT MAP OF THE STUDY AREA Total Carbon Stocks and Carbon Dioxide Equivalent for the Entire Study Area Tables 5 and 6 show the total carbon stocks and carbon dioxide equivalent respectively for the whole Bosomkese forest reserve. 48

9 International Journal of Remote Sensing Applications (IJRSA) Volume 6, CLASS (TON / Ha) TABLE 5 TOTAL CARBON STOCKS FOR BOSOMKESE FOREST RESERVE CLASS AREA (Ha) LOWER LIMIT CARBON (TON) UPPER LIMIT CARBON (TON) , , , , , , , , , , , , , , , , , , , TOTAL ,326, ,865, CLASS (TON / Ha) TABLE 6 TOTAL CARBON DIOXIDE EQUIVALENT FOR BOSOMKESE FOREST RESERVE CLASS AREA (Ha) LOWER LIMIT CO2 (TON) UPPER LIMIT CO2(TON) , , , , , , , ,143, ,202, ,495, ,276, ,527, ,266, ,474, ,665, ,899, ,313, ,476, , , TOTAL ,534, ,507, Discussions Image Classification From the classification results, open canopy natural forest had the largest land use/cover of Ha, followed by plantation of Ha, next to it is closed canopy natural forest of Ha, with farmland of Ha and fallow land with least area of Ha. These results show that reasonable amount of closed canopy natural forest has been converted to open canopy natural forest and this indicates forest degradation. With large plantation area within the forest reserve means the reserve is suitable for plantation development which serve as alternative to natural forest. Based on the area of each land use class, the highest number of sampling plots was allotted to open forest (20), next in plantation (15), closed forest (10), farmland (3) and fallow land (2) in descending order. Overall classification accuracy obtained was 76.0% with overall kappa statistics of Kappa statistic is a measure of agreement between the reference data and the classified image (Lillesand and Kiefer, 2008). Biomass and Carbon Stocks Biomass of a given tree species is a function of DBH, tree height and wood density which depends on tree age, sivilculture management practices, environmental and genetic factors (Kasischke and Christenen, 1990). 49

10 International Journal of Remote Sensing Applications (IJRSA) Volume 6, ) Carbon Stocks in the Three Carbon Pools IPCC has identified six carbon pools of terrestrial ecosystem, namely the AGC, BGC, deadwood, non tree carbon, litter and soil organic matter (Eggleston et al., 2006). This research was able to identify 3 carbon pools; AGC, BGC and deadwood. Aboveground carbon pool had the highest carbon of ton/ha, followed by below ground with ton / Ha and deadwood with least carbon of ton/ha. This shows that above ground carbon pool sequesters large amount of CO2 and the least by deadwood. In this study above ground carbon pool sequesters 79.16%, 14.76% by below ground and 6.08% of carbon by deadwood. 2) Total Carbon Stocks in the Land Use/Cover Classes In this research, the total carbon stock is the sum of all the three carbon pools (AGC, BGC and deadwood), was the highest in closed canopy natural forest with total carbon of ton/ha, followed by open canopy natural forest with total carbon of ton/ha. The next is plantation with summed carbon of of ton/ha, followed by fallow land with carbon of ton/ha and the least in farmland with total carbon of ton/ha. The high total carbon stock of natural forest is an indication that natural forest sequesters the greatest amount of CO2. Plantation also sequester reasonable amount of CO2 and therefore serve as an alternative to natural forest in terms of terrestrial carbon sink. Conclusions and Recommendations Conclusions The research showed that closed canopy natural forest contained more carbon than the rest of the land use/cover classes ( ton/ha with CO2e of ton/ha). This is followed by open canopy natural forest ( ton /Ha with CO2e of ton/ha), plantation (775 ton/ha with CO2e of ton/ha), fallow land ( ton/ha with CO2e of ton/ha) and farmland (45.13 ton/ha with CO2e of ton/ha) in descending order of total carbon stocks and carbon dioxide equivalent respectively. Also aboveground had more carbon ( ton/ha) in the terrestrial carbon pools, followed by belowground ( ton/ha) and the least in deadwood ( ton/ha). Terrestrial carbon and its corresponding carbon dioxide equivalent were successfully estimated from forest inventory data using allometric equations and mapped using Geostatistical tool of ArcGIS. The total carbon stock for the whole Bosomkese forest is in the range of 2,236, ,865, tons and carbon dioxide equivalent in the range of 8,534, ,507, tons. Recommendations It is recommended that: (1) More field sample plots are to be selected in future because the geostatistical tool of ArcGIS 10.0 uses interpolation method to determine the carbon values for unsampled points. The closer the sample plots, the better the values for the unsampled points. (2) Using lidar method for the biomass assessment. Lidar offers tremendous potential for monitoring forest biomass with major advantage as the acquisition of three dimensional data of forest structure and canopy cover. (3) Below ground biomass values in this project depend on the aboveground ones. Below ground allometric equation which is not function of above ground should be used in calculating BGB in future and subsequently determine the BGC. The reason is that if a mistake is made in calculating the AGB, it is going to affect the BGB. 50

11 International Journal of Remote Sensing Applications (IJRSA) Volume 6, REFERENCES [1] Basuki TM, Van Laake PE, Skidmore AK, Hussin YA (2009).Allometric equations for estimating the above-ground biomass in tropical lowland dipterocarp forests. Forest Ecology and Management 257: [2] Brown, S (2002).Measuring Carbon in Forests: Current Status and future challenges. Environmental pollution 116(3): [3] Butler, R. A. (2007). Amazon rainforest locks up 11 years of CO2 emissions mongabay.com. [4] Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Folster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riera, B and Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Ecosystem Ecology Oecologia (2005) 145: DOI /s x. [5] Chave, J., Condit, R., Aguilar, S., Hernandez, A., Lao, S., Perez, R Error propogation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London, Series B359: [as of July 2008, download from [6] Chave, J., Condit, R., Lao, S., Caspersen, J.P., Foster, R.B., Hubbell, S.P Spatial and temporal variation of biomass in tropical forest: results from a large census plot in Panama. [7] Cline, W.R. (2007).Global warming and Agriculture [8] CPFC (2008).Strategic framework Forests and Climate change- a proposal by Collaborative Partnership on Forests for a Coordinated Forest- sector response to Climate change. [9] Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (2006). IPCC Guidelines for National Greenhouse Gas Inventories Volume - IV Agriculture, Forestry and other land-use. Institute of Global Environmental Strategies (IGES), Hayama, Japan. [10] Foody,G.(2002).Status of land cover classification accuracy assessment. Remote sensing of Environment. Vol.80,NO 1.pp [11] Gibbs, H.K., Brown, S., Forley, J.A. (2007). Monitoring and Estimating Forest carbon stocks: Making REDD a reality. Environmental Research letters 2(4): [12] Hogan,C.M.(2014).Biomass-Encyclopaedia of Earth (Available at ) [13] Kasischke,E.S. and Christenen, N.L.J.(1990). Connecting forest ecosystem and microwave backscatter models. International Journal of Remote Sensing 11(7): [14] Ketterings, Q. M., Coe, R., van Noordwijk, M., Ambagau, Y., and Palm, C. A. (2001). Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For. Ecol. Manage. 146: [15] Kumar R, Pandey S, Pandey A (2006). Plant roots and carbon sequestration. Current Science 9: [16] Lal R (2005). Forests soil and carbon sequestration, 220: [17] Lillesand, T. and R. Kiefer (2008). Remote sensing and image interpretation (6th Edition). JohnWiley & Sons Inc. New York. [18] Losi CJ, Siccama TG, Condit R, Morales JE (2003). Analysis of alternative methods for estimating carbon stock in young tropical plantations. Forest Ecology and Management 184: [19] Malik,K.I., Hassain, A., Negi,A.(2014).An Overview of Biomass Estimation Methods.The International Journal Research Publication s.vol 4,NO 6 (2014). [20] Richardson,D.E.and Oosterom,P.V.(2013).Advances in spatial data handling: 10 th International symposium on spatial data handling. [21] Segura,M & Kanninen,M.(2005).Allometric models for tree volume and total aboveground Biomass in Tropical Humid Forest in Costa Rica.BIOTROPICA 37(1): [22] Sierra,C.A.,Del valle,j.i., Moreno F.H.,Harmon,M.E., Zapata,M.(2007).Tropical carbon stocks in a tropical forest landscape of the Porce region, Colombia. Forest Ecology and management 243(2-3):

12 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 [23] Talbot,J.,Lewis,S.L.,Gonzalez,G.L., Erwin, T.(2014).Forest Ecology and Management.Methods of estimate aboveground wood productivity from long term forest inventory plots. [24] Vashum,K.T. & Jaayakumar,S.(2012).Methods to Estimate Above ground Biomass and Carbon Stock in Natural forests A review. Department of Ecology and Environmental Sciences,Pondicherry university, India. 52