multifrequency DLRESAR data - Integrated Publish › jggsvol1no12010 › EIJGGS1017.pdfThe use of L band multi polarization SAR data improved the AGB estimation . Castel et al ., (2002)

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 2, 2010

© Copyright 2010 All rights reserved Integrated Publishing services Research Article ISSN 0976 – 4380

167

Estimation of above ground biomass in Indian tropical forested area using multifrequency DLRESAR data

Vyjayanthi Nizalapur, Chandra Sekhar Jha and Rangaswamy Madugundu

Forestry and Ecology Division, Land Resource Group National Remote Sensing Centre, Balanagar, Hyderabad 500625, A.P.

ABSTRACT

This paper presents experimental results obtained with DLRESAR airborne data in C, L and P bands over the parts of Gujarat, India. The aim is to estimate the above ground biomass and quantify various levels of above ground forest biomass with the help of empirical relationship of the backscattering coefficients and ground inventory. Extensive field data were collected to characterize forest vegetation parameters in the plots. Ground inventory data viz., GBH, tree height was collected. Plot level above ground tree biomass (AGB) is estimated from the collected ground data using the existing allometric equations at stand level in the study area. Regression analysis is carried out between above ground biomass and backscatter coefficient of C, L and P bands in VV polarization. The obtained regression equation was used to estimate the above ground biomass from ESAR data in C, L and P bands. Results showed that with increase in the biomass levels, backscattering coefficient also increases and Cband ESAR data is capable of predicting biomass up to 70 Mg/ha, Lband upto 150 Mg/ha and Pband upto 200 Mg/ha. Comparison of the estimated above ground biomass of airborne microwave DLRESAR data in C, L and Pbands was also carried out.

Keywords: GBH, Polarization, Bands, ESAR data, Ground inventory.

1. Introduction

Microwave remote sensing has emerged out to be a very useful tool over the past two decades with the availability of radar imaging systems for a wide range of resource management applications. Its potential use has been demonstrated in various sectors viz., forestry, agriculture, land use land cover, geology and hydrology with specific applications in each field like discrimination of crop types, crop condition monitoring, soil moisture retrieval, delineation of forest openings, estimation of forest above ground biomass, forest mapping; forest structure and fire scar mapping, vegetation mapping (Kasischke et al., 1997).

Optical remote sensing is being used very successfully in various applications related to earth resources studies and monitoring of the environment. However, optical remote sensing is not suitable for all atmospheric conditions. It cannot penetrate through clouds and haze. In many parts of the world, frequent cloud conditions often restrain the utility of optical sensors for natural resource management, limiting the acquisition at the time of need. In such cases, radar data is the only feasible way to acquire remotely sensed data for a specified period of time as



168

radar sensors are capable of collecting information on the earth features in all weather conditions. One of these unique features of radar data makes it a better choice for a variety of applications including forestcover identification and mapping, discrimination of gross forest types, estimation of forest stand parameters and monitoring of forests during cloudinfested ground conditions (Lu, 2006).

Many airborne and spaceborne SAR systems have been used to carry out several experiments for investigating the forest ecosystems. The airborne systems, NASA/JPL AIRSAR system, DLR ESAR operating at P, L and C bands and spaceborne systems, Seasat SAR, SIRB, SIRC/X SAR and ERS1, ASAR, PALSAR were used for investigations of boreal, temperature and tropical forests (Ford et al., 1988). These experiments and studies have shown that radar is sensitive to forest structural parameters including aboveground biomass (Dobson et al., 1992; Pulliainen et al., 1994; Skriver et al., 1994; Ferrazzoli et al., 1995; Ranson et al., 1996).

Studies have shown the potential of radar data in estimating AGB (Hussin et al., 1991; Ranson and Sun, 1994; Dobson et al., 1995; Rignot et al., 1995; Saatchi and Moghaddam, 1995; Foody et al., 1997; Harrell et al., 1997; Ranson et al., 1997; Luckman et al., 1997; Pairman et al., 1999; Imhoff et al., 2000; Kuplich et al., 2000; Castel et al., 2002; Santos et al., 2003). Kasischke et al., (1995, 1997) reviewed SAR data for AGB estimation. In particular, SAR Lband data have proven to be valuable for AGB estimation (Luckman et al., 1997; Kurvonen et al., 1999; Lu, 2006). The estimation of above ground biomass of regenerating forests was done by Kuplich et al., (2000) and concluded that Lband JERS data is capable of estimating AGB for young, regenerating forests. The use of Lband multipolarization SAR data improved the AGB estimation. Castel et al., (2002) identified the significant relationships between the backscatter coefficient of Lband JERS SAR data and the stand biomass of a pine plantation. Santos et al., (2003) used JERSSAR data to analyse the relationships between backscatter signals and biomass of forest.

The wavelength of the radar system has a significant effect on the depth of penetration of the radar signal into a forest canopy, and therefore on the resulting backscatter. The capability of radar to penetrate the forest canopy increases with the longer wavelengths and hence different wavelengths will give different interpretation results. Dobson et al., (1995) showed an increasing range of backscatter with changing biomass from C to Pband, as well as higher biomass levels at which backscatter relationships to biomass saturate. The prevalent method to estimate above ground biomass using SAR is variants of regression analysis, where a regression curve is fitted to a set between backscatter and groundmeasured biomass values. This curve is then used over other areas and forest stands to obtain biomass for given SAR backscatters. Several studies are primarily focused on developing regression type algorithms for directly estimating vegetation biomass from radar data (Dobson et al., 1995; Ranson et al., 1994, 1995; Rignot et al., 1994; Saatchi and Moghaddam, 1995; Saatchi et al., 1995).

The main objective of the paper is to analyse the relationship of the backscattering coefficients with above ground biomass using regression analysis in tropical dry deciduous forested areas of Gujarat, India and quantify the estimation of above ground biomass values in different wavelength bands.



169

1. Study area

The study area forms part of Shoolpaneshwar Wild life Sanctuary in Narmada district of Gujarat, India. It is bounded in the north by the Narmada River and the Sardar sarovar, in the west by Karjan River, Karjan reservoir and Tarav River. The central coordinates of the area are 21°45’00” N and 73°45’00” E. The study area lies between 21°42'36" N to 21°50'30" N latitude and 73°36' 2" E to 73°50'31" E longitudes (fig 1). The area has a typical monsoonal climate, with three distinct seasons: summer from March June, rainy from July October and winter from NovemberFebruary.

Figure 1: Study area – Rajpipla, Gujarat

The study area predominantly consists of Type 5A Tropical Dry Deciduous forest as classified by Champion and Seth (1969).The predominant plant species in the study area is Teak with patches of bamboo. Apart from bamboo, the study area is also dominated by other species viz., Tectona grandis L. followed by Dendrocalamus strictus Nees. Both species are homogenously distributed and occupy about 30% of the study area. Tectona grandis, Bamboosa, Terminalia paniculata and Cleistanthus, Butea monosperma are the dominant species in the study area.

2.1 Data Description

The DLRESAR data was acquired on 2 nd October 2004. The DLRESAR is a multifrequency and multipolarimetric airborne Experimental Synthetic Aperture Radar with 4.5 m spatial resolution and pixel size of 1.2 m. The backscatter intensities were obtained in X, C, L and P bands; X band data is acquired in two polarizations HH and VV. C, L and Pbands data is acquired in all the four polarizations viz., HH, HV, VH and VV. Multi look geocoded data of C, L and Pbands of HH, HH, VV and VH polarizations. The depression angle for Cband and L band is 35 0 with 8 look processing and the depression angle for Pbands is 40 0 with 4 look



170

processing. The centre frequency for C, L and Pbands are 5.3 G Hz, 1.3 G Hz and 0.35 G Hz respectively.

3.0 Methodology

The methodology section is divided into two sections: Ground inventory, preprocessing of SAR data and regression analysis between field inventoried AGB and backscattering coefficient retrieved from sample plots (fig 2).

3.1 Ground Inventory

40 sample plots (20x20m) were laid through stratified random sampling method in the various qualitatively classified biomass levels to account for the largest variability in the biomass range. Ground inventory data of tree parameters i.e., girth at breast height, crown diameter, and height of the trees are collected. Above ground biomass of a tree comprises of the biomass of stem, branches, twigs and leaves. Plot level biomass is estimated based on field based methods. Species wise regression equations available from the earlier studies in the study area/literature were used to calculate above ground biomass from parameters measured in ground viz., diameter at breast height and tree height.

3.2 Speckle suppression and calibration

Enhanced Lee filter of window size 3x3 is applied for speckle suppression. The local incidence angle and the backscatter intensity are used to calibrate the backscattering coefficient by the

Figure 2: Methodology flowchart – Above

SAR data Field inventory

• Vegetation type • Tree height • Crown diameter • GBH

• Power images generation • Radiometric calibration • Speckle suppression • Geometric correction

Ground based AGB estimation Sigma nought generation

Regression analysis between ground based biomass and SAR backscattering coefficient

Obtained regression equations

Biomass map of study areas

SAR data Field inventory

• Vegetation type • Tree height • Crown diameter • GBH

• Power images generation • Radiometric calibration • Speckle suppression • Geometric correction

Ground based AGB estimation Sigma nought generation

Regression analysis between ground based biomass and SAR backscattering coefficient

Obtained regression equations

Biomass map of study areas



171

following equation

σ0 = [10.0*alog10 (Pixel value) 2 A0]*sin θ

where,

sin θ is the local incidence angle.

A0 is the scaling factor.

The scaling factor is 60. The sigma nought values obtained are in decibels (dB).

3.3 Geocoding of ESAR data

The DLR ESAR data is georectifiedusing UTM projected LISSIV as the master image. Mosaicking of all segments was done in C, L and Pbands. Scatter plots are drawn for backscatter values of C, L, P bands and biomass values in all polarizations. The ground truth points are overlaid on the ESAR data and the backscattering coefficients of the corresponding points are calibrated in different polarizations.

3.4 Statistical analysis

Regression analysis was carried out between the backscattering values of C, L and Pbands in HH, VV, HV and VH polarizations and field inventoried above ground biomass. The backscatter coefficient (σ°), retrieved from sample plots at HH, VV, VH and HV were used to investigate relationships with aboveground biomass in forested areas. This regression equation is used to generate a biomass map of the study area in different bands of VV polarization.

4. Results and discussions

4.1 Biomass and backscattering coefficient in C, L and Pbands of VV polarization

As the field inventoried AGB increases; the backscattering coefficient also increases and attains saturation (fig 3). Backscattering coefficient of Cband is greater than L and Pbands. This is attributed to the canopy scattering which is more in Cband than L and Pbands and penetration of Land Pbands deep into the canopy which caused attenuation for the backscattered wave due to leaves. Hence, the backscattering values of the Cband are high when compared to the L and Pbands. The sensitivity of Synthetic Aperture Radar (SAR) data to forest stem volume increases significantly as the radar wavelength increases (Israelsson et al. 1997).



172

The change of backscatter is also due to leaf, branch and trunk moisture content. An increase in dielectric constant results from an increase in the moisture content of the scatterers. As the biomass increases, there is an increase in backscatter for all polarizations. VV polarization in C band has high backscattering values than HH and HV. In Lband HH has high backscatter than other polarizations. The backscatter is more from the tree branches and trunks than the crown in Pband. The variation in the allocation of the biomass to various structures such as stems, branches, leaves; their sizes, numbers and orientation could influence backscatter. Backscatter saturates at a biomass, which is related to Radar wavelength. Cband can measure forestry biomass up to ~ 50 tons/ha, Lband can measure up to 100 tons/ha and Pband can measure up to 200 tons/ha (Floyd et al, 1998).

4.2 Estimation of above ground biomass in different wavelength bands

4.2.1 Estimation of Biomass in CBand

Scatter plots are drawn between field inventoried AGB and backscattering coefficient of C band in HH and VV polarizations. The backscattering coefficient of VV is greater than HH polarization. The scattering is mainly due to leaves, twigs and small branches.

Figure 3: Variation of backscatter with field inventoried AGB in different wavelength bands of VV polarization



173

Regression analysis is carried out between AGB and backscattering coefficient in HH and VV polarizations. The correlation of biomass is significant in VV polarization (r = 0.31, P≤0.05). Estimation of biomass is done using the obtained regression equation of VV polarization (fig 3). Scatter plot is drawn between the estimated and predicted biomass values (Fig 4). The correlation is significant and the estimation of biomass can be done from low biomass ranges upto 70 Mg/ha.

4.3 Estimation of Biomass in LBand

Scatter plots are drawn between Biomass and Backscattering coefficient of Lband in HH, HV, VV and VH polarizations. The backscattering coefficient of HH is greater than any other polarization and it is followed by VV, HV and VH. As the interaction of Lband is mainly due to primary, secondary branches and trunks, greater HH than VV is from lower levels of the canopy. The scattering mechanisms at Lband in a forest canopy are mainly volume scattering in the canopy, doublebounce scattering between trunks and the ground, and surface scattering from the forest floor.

Regression analysis is carried out between AGB (ground measured) and backscattering coefficient in all the four polarizations. The correlation of biomass is significant in VV (fig 1) polarization (r = 0.58, P≤0.05) when compared to HH, HV and VH polarizations. Estimation of biomass is done using the regression equation of VV polarization. Scatter plot is drawn between the estimated and predicted biomass values (fig 5). The correlation is significant and the estimation of biomass can be done from low biomass ranges to 160 Mg/ha (Fig 4).

Figure 4: Scatter plots of estimated and predicted biomass in C band



174

4.4 Estimation of Biomass in PBand

Scatter plots are drawn between Biomass and Pband in HH, HV, VV and VH polarizations. In Pband VV has high backscatter than other polarizations. It is followed by HH, HV and VH. The interaction of the Pband is with main branches, trunks and ground. As the trunks are vertical, greater VV is observed in Pband than HH. Vertical branches have higher VV than HH or other cross polarizations. The differences between HH and VV are also due to the incident angle, which will change with canopy architecture.

Figure 5: Scatter plots of estimated and predicted biomass in L band



175

Regression analysis is done between AGB (ground measured) and backscattering coefficient in all the four polarizations. The correlation of biomass is significant in VV polarization (r=0.349, P≤0.05) than any other polarization. Hence, the estimation of biomass is done using the regression equation of VV polarization. Scatter plot is also drawn between the estimated and predicted biomass values. At lower biomass ranges, the predicted biomass is very high. These values are encircled and shown in fig 6. This is due to soil backscatter as there are many gaps in the study area. Pband is capable of predicting the biomass ranges from 80 Mg/ha to 200 Mg/ha (fig 7).

Biomass maps are generated for C, L and Pbands using the obtained regression equations in VV polarization. In the present study, Lband predicted the biomass levels significantly as Cband and Pband are not capable of predicting higher and lower biomass levels respectively. At low frequency, the branches and the trunks are dominant scatterers, and as frequency increases, the effect of the leaves dominate the trends of backscatter. The influence of the wavelength on the radar backscatter is related to the penetration capability of the radar energy into forest canopy.

Figure 6: Scatter plots of estimated and predicted biomass in P band

Encircled values are the highly predicted AGB values at lower ranges



176

5. Conclusions

In the present study, the above ground biomass is estimated in different wavelength bands. Influence of the wavelength on the radar backscatter is related to the penetration capability of the radar energy into forest canopy. The limitation of Cband is that it cannot penetrate more into the canopy and also it saturates around 60 70 tons/ha. This limitation can be overcome using longer wavelength bands such as Lband (23.5cm), which has higher ability to penetrate forest canopy. Lband is capable of estimating the biomass from lower frequencies to 160 Mg/ha where as P band is capable of estimating biomass from 100 Mg/ha to 200 Mg/ha. Advanced techniques like polarimetry and interferometry in SAR and multiple frequencies need to be developed to obtain much higher correlations with biophysical parameters.

Acknowledgements

The authors thank Director, Dr. P.S.Roy (Dean, IIRS), Associate Director (NRSC), Deputy Director (RS & GIS–AA), Group Director (LRG), NRSC, Head FED and forestry and ecology division colleagues, NRSC for providing facilities. This study is part of RISAT JEP project of Indian Space Research Organisation. Dr. M. Chakraborti, PD from SAC, Ahmedabad and Dr. Sesha Sai, DPD, RISATJEP, NRSC is acknowledged for technical suggestions.

6. References

1. Kasischke, E.S., Melack, J.M., Dobson, M.C., 1997. The use of imaging radars for ecological applicationsa review. Remote Sensing of Environment. 59, pp. 141–156.

Figure 7: Scatter plots of estimated and predicted biomass in P band



177

2. Lu, D., 2006. The potential and challenge of remote sensingbased biomass estimation. International Journal of Remote Sensing. 27, pp.1297–1328.

3. Ford, J. P. and Casey, D. J., 1988. Shuttle radar mapping with diverse incidence angles in the rainforest of Borneo. International Journal of Remote Sensing. 9(5), pp. 927943.

4. Dobson, C, L. Pierce, Sarabandi, K., Ulaby, F.T., Sharik, T., 1992. Preliminary analysis of ERS1 SAR for forest ecosystem studies, IEEE Transactions on Geosciences and Remote Sensing. 30 (2), pp. 203211.

5. Pulliainen K. H., Rauste, Y., Hame, J., Hallikainen, M., 1994. Radarbased forest biomass estimation. International Journal of Remote Sensing, 15 (14), pp. 27972808.

6. Skriver, H, Mortensen, B.B., Grudmandsen, P., 1994. Calibration and modeling of MAESTRO 1 polarimetric SAR data of a forest area in Les landes, France. International Journal of Remote Sensing. 15 (14), pp 27372754,

7. Ferrazzoli, P., and Guerriero, L., 1995. Radar sensitivity to tree geometry and woody volume: a model analysis. IEEE Transactions on Geosciences and Remote Sensing, 33 (2), pp. 360371.

8. Ranson, J.G., Sun, J.F., Weishampei, and Knox, R.G., 1996. Forest biomass from combined ecosystem and radar backscatter modeling. Remote Sensing Environment. 59, pp. 118133.

9. Hussin, Y.A., Reich, R.M., Hoffer, R.M., 1991. Estimating slash pine biomass using radar backscatter. IEEE Transactions on Geoscience and Remote Sensing, 29, 427–431.

10. Ranson, K.J. and G. Sun, 1994. Northern forest classification using temporal multi frequency and multipolarimetric SAR images. Remote Sensing of Environment. 47 (2), pp: 142153,

11. Dobson, M.C., Ulaby, F.T., Pierce, L.E., Sharik, T.L., Bergen, K.M., Kellndorfer, J., Kendra, J.R., Li, E., Lin, Y.C., Nashashibi, A., Sarabandi, K.L., Siqueira, P., 1995. Estimation of Forest Biomass Characteristics in Northern Michigan with SIRCIXSAR Data, IEEE Transactions on Geoscience and Remote Sensing. 33, pp. 877894.

12. Rignot, E., Tesek, K.C., Sohn, H.G., 1995. Ice flow dynamics of the Greenland ice sheet from SAR interferometry. Geo physics Research letters. 22 (5), pp. 575578,

13. Saatchi, S.S. and Moghaddam, M., 1995. Biomass distribution in boreal forest using SAR imagery. SPIE. 2314, pp. 437–448.

14. Foody, G.M., Green, R.M., Lucas, R.M., Curran, P.J., Honzak, M. and Do Amaral, I., 1997. Observations on the relationship between SIRC radar backscatter and biomass of regenerating tropical forests. International Journal of Remote Sensing. 18, pp. 687–694.

15. Harrell, P.A., Kasischke, E.S., BourgeauChavez, L.L., Haney, E.M. Christensen, N.L., JR, 1997. Evaluation of approaches to estimating above ground biomass in southern pine forests using SIRC data. Remote Sensing of Environment, 59, pp. 223–233.

16. Luckman, A., Baker, J.R., Kuplich, T.M., Yanasse, C.C.F. and Frery, A.C., 1997. A study of the relationship between radar backscatter and regenerating forest biomass for space borne SAR instrument. Remote Sensing of Environment. 60, pp. 1–13.

17. Pairman, D., Mc Neill, S., Scott, N., Belliss, S., 1999. Vegetation identification and biomass estimation using AIRSAR data. Geocarto International, 14, pp. 69–77.

18. Imhoff, M.L., Johnson, P., Holford, W., Hyer, J., May, L., Lawrence, W., Harcombe, P., 2000. BioSar (TM): an inexpensive airborne VHF multiband SAR system for vegetation biomass measurement. IEEE Transactions on Geoscience and Remote Sensing. 38, pp. 1458–1462.



178

19. Kuplich, T.M., Salvatori, V., Curran, J.P., 2000. JERS1/SAR backscatter and its relationship with biomass of regenerating forests. International Journal of Remote Sensing. 21, pp. 2513–2518.

20. Castel, T., Guerra, F., Caraglio, Y., Houllier, F., 2002. Retrieval biomass of a large Venezuelan pines plantation using JERS1 SAR data: analysis of forest structure impact on radar signature. Remote Sensing of Environment, 79, pp. 30–41.

21. Santos, J.R., Freitas, C.C., Araujo, L.S., Dutra, L.V., Mura, J.C., Gama, F.F., Soler, L.S. and Sant’ Anna, S.J.S., 2003. Airborne Pband SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. Remote Sensing of Environment. 87, pp. 482–493.

22. Kasischke, E. S., Christensen, N. L., BougeauChavez, L. L., 1995. Correlating radar backscatter with components of biomass in loblolly pine forests. IEEE Transactions on Geoscience and Remote Sensing. 33, pp. 643659.

23. Kasischke, E.S., Melack, J.M., Dobson, M.C., 1997. The use of imaging radars for ecological applications—a review. Remote Sensing of Environment. 59, pp. 141–156.

24. Kurvonen, L., Pulliainen, J., Hallikainen, M., 1999. Retrieval of biomass in boreal forests from multitemporal ERS1 and JERS1 SAR data. IEEE Transactions on Geoscience and Remote Sensing. 37, pp. 198–205.

25. Champion, H. G., and Seth, S. K., 1968. A Revised Survey of Forest Types of India (New Delhi: Government of India Publication)

Documents

multifrequency DLRESAR data - Integrated Publish › jggsvol1no12010 › EIJGGS1017.pdfThe use of L band multi polarization SAR data improved the AGB estimation . Castel et al ., (2002)