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Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats, India

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Abstract

Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = −5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels.

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(Adapted from Proisy et al. 2007)

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References

  • Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., & Houghton, R. A. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2(3), 182–185.

  • Barbier, N., Couteron, P., Proisy, C., Malhi, Y., & Gastellu-Etchegorry, J.-P. (2010). The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests. Global Ecology and Biogeography, 19, 72–84.

    Article  Google Scholar 

  • Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., & Papale, D. (2010). Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science (New York, NY), 329, 834–838.

    Article  Google Scholar 

  • Bruniquel-Pinel, V., & Gastellu-Etchegorry, J. P. (1998). Sensitivity of texture of high resolution images of forest to biophysical and acquisition parameters. Remote Sensing of Environment, 65, 61–85.

    Article  Google Scholar 

  • Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145, 87–99.

    Article  Google Scholar 

  • Couteron, P. (2002). Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs. International Journal of Remote Sensing, 23, 3407–3425.

    Article  Google Scholar 

  • Couteron, P., Barbier, N., & Gautier, D. (2006). Textural ordination based on Fourier spectral decomposition: A method to analyze and compare landscape patterns. Landscape Ecology, 21, 555–567.

    Article  Google Scholar 

  • Couteron, P., Barbier, N., Proisy, C., Pélissier, R., & Vincent, G. (2012). Linking remote-sensing information to tropical forest structure: The crucial role of modelling. Earthzine, 4, 1–4.

    Google Scholar 

  • Couteron, P., Pelissier, R., Nicolini, E. A., & Paget, D. (2005). Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. Journal of Applied Ecology, 42, 1121–1128.

    Article  Google Scholar 

  • De Boer, Y. (2008). Kyoto protocol reference manual on accounting of emissions and assigned amount. United Nations Framework Convention on Climate Change, pp 1–130.

  • DeFries, R. S., Houghton, R. A., Hansen, M. C., Field, C. B., Skole, D., & Townshend, J. (2002). Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proceedings of the National Academy of Sciences of the United States of America, 99, 14256–14261.

    Article  Google Scholar 

  • Devagiri, G. M., Money, S., Singh, S., Dadhawal, V. K., Patil, P., Khaple, A., Devakumar, A. S., & Hubballi, S. (2013). Assessment of above ground biomass and carbon pool in different vegetation types of south western part of Karnataka, India using spectral modeling. Tropical Ecology, 54, 149–165.

    Google Scholar 

  • Dixon, R. K., Brown, S., Houghton, R. A., Solomon, A. M., Trexler, M. C., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science (Washington), 263, 185–189.

    Article  Google Scholar 

  • Frazer, G. W., Wulder, M. A., & Niemann, K. O. (2005). Simulation and quantification of the fine-scale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights. Forest Ecology and Management, 214, 65–90.

    Article  Google Scholar 

  • Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters, 2, 45023.

    Article  Google Scholar 

  • Grainger, A. (2010). Uncertainty in the construction of global knowledge of tropical forests. Progress in Physical Geography, 34, 811–844.

    Article  Google Scholar 

  • Houghton, R. A. (2005). Aboveground forest biomass and the global carbon balance. Global Change Biology, 11, 945–958.

    Article  Google Scholar 

  • Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27, 1297–1328.

    Article  Google Scholar 

  • Madugundu, R., Nizalapur, V., & Jha, C. S. (2008). Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India. International Journal of Applied Earth Observation and Geoinformation, 10, 211–219.

    Article  Google Scholar 

  • Malhi, Y., & Román-Cuesta, R. M. (2008). Analysis of lacunarity and scales of spatial homogeneity in IKONOS images of Amazonian tropical forest canopies. Remote Sensing of Environment, 112, 2074–2087.

    Article  Google Scholar 

  • Mitchard, E. T. A., Saatchi, S. S., White, L. J. T., Abernethy, K. A., Jeffery, K. J., Lewis, S. L., Collins, M., Lefsky, M. A., Leal, M. E., Woodhouse, I. H., & Meir, P. (2012). Mapping tropical forest biomass with radar and spaceborne LiDAR in Lop{é} National Park, Gabon: Overcoming problems of high biomass and persistent cloud. Biogeosciences, 9, 179–191.

    Article  Google Scholar 

  • Neigh, C. S. R., Masek, J. G., Bourget, P., Cook, B., Huang, C., Rishmawi, K., & Zhao, F. (2014). Deciphering the precision of stereo IKONOS canopy height models for US forests with G-LiHT airborne lidar. Remote Sensing, 6, 1762–1782.

    Article  Google Scholar 

  • Nizalapur, V., Jha, C. S., & Madugundu, R. (2010). Estimation of above ground biomass in Indian tropical forested area using multifrequency DLRESAR data. International Journal of Geomatics and Geosciences, 1, 167–178.

    Google Scholar 

  • NRSA. (2006). CARTOSAT-1. Data user’s handbook. Scientific Note: CARTOSAT-1/NRSA/NDC/HB-09/06 Sept 2006. National Remote Sensing Agency: Hyderabad.

  • Pascal, J.-P. (1988). Wet evergreen forests of the Western Ghats of India. Puducherry: Institut francais de Pondichery.

    Google Scholar 

  • Pascal, J.-P., & Pelissier, R. (1996). Structure and floristic composition of a tropical evergreen forest in south-west India. Journal of Tropical Ecology, 12, 191–214.

    Article  Google Scholar 

  • Pélissier, R., Pascal, J.-P., Ayyappan, N., Ramesh, B. R., Aravajy, S., & Ramalingam, S. R. (2011). Tree demography in an undisturbed Dipterocarp permanent sample plot at Uppangala, Western Ghats of India: Ecological Archives E092-115. Ecology, 92, 1376.

    Article  Google Scholar 

  • Pelissier, R., Pascal, J.-P., Houllier, F., & Laborde, H. (1998). Impact of selective logging on the dynamics of a low elevation dense moist evergreen forest in the Western Ghats (South India). Forest Ecology and Management, 105, 107–119.

    Article  Google Scholar 

  • Ploton, P., Pélissier, R., Proisy, C., Flavenot, T., Barbier, N., Rai, S. N., & Couteron, P. (2012). Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications, 22, 993–1003.

    Article  Google Scholar 

  • Proisy, C., Couteron, P., & Fromard, F. (2007). Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sensing of Environment, 109, 379–392.

    Article  Google Scholar 

  • Rai, S. N. (1981). Productivity of tropical rain forests of Karnataka. Bombay: University of Bombay.

    Google Scholar 

  • Rai, S. N., & Proctor, J. (1986). Ecological studies on four rainforests in Karnataka, India: I. Environment, structure, floristics and biomass. The Journal of Ecology, 2, 439–454.

  • Singh, M., Malhi, Y., & Bhagwat, S. (2014). Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery. International Journal of Remote Sensing, 35, 3331–3349.

    Article  Google Scholar 

  • Straub, C., Tian, J., Seitz, R., & Reinartz, P. (2013). Assessment of Cartosat-1 and WorldView-2 stereo imagery in combination with a LiDAR-DTM for timber volume estimation in a highly structured forest in Germany. Forestry, 86, 463–473.

    Article  Google Scholar 

  • Swamy, S. L., Dutt, C. B. S., Murthy, M. S. R., Mishra, A., & Bargali, S. S. (2010). Floristics and dry matter dynamics of tropical wet evergreen forests of Western Ghats, India. Current Science, 99, 353–364.

    Google Scholar 

  • Thumaty, K. C., Fararoda, R., Middinti, S., Gopalakrishnan, R., Jha, C. S., & Dadhwal, V. K. (2015). Estimation of above ground biomass for central Indian deciduous forests using ALOS PALSAR L-band data. Journal of the Indian Society of Remote Sensing, 44(1), 31–39.

  • Tuominen, S., Eerikäinen, K., Schibalski, A., Haakana, M., & Lehtonen, A. (2010). Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica, 44, 109–119.

    Article  Google Scholar 

  • Van der Werf, G. R., Morton, D. C., DeFries, R. S., Giglio, L., Randerson, J. T., Collatz, G. J., & Kasibhatla, P. S. (2009). Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling. Biogeosciences, 6, 235–249.

  • Véga, C., Vepakomma, U., Morel, J., Bader, J.-L., Rajashekar, G., Jha, C. S., Ferêt, J., Proisy, C., Pélissier, R., & Dadhwal, V. K. (2015). Aboveground-biomass estimation of a complex tropical forest in India using lidar. Remote Sensing, 7, 10607–10625.

    Article  Google Scholar 

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Acknowledgments

We duly acknowledge the funding by Indo-French Centre for the Promotion of Advanced Research (CEFIPRA) and Indian Space Research Organisation-Geosphere Biosphere Program (ISRO-GBP) for the current study.

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Authors and Affiliations

  1. National Remote Sensing Centre (ISRO), Balanagar, Hyderabad, 500037, Telangana, India

    R. Suraj Reddy, G. Rajashekar, C. S. Jha & V. K. Dadhwal

  2. Institut de Recherche pour le De´veloppement, UMR-AMAP, Montpellier, France

    Raphel Pelissier & Pierre Couteron

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  1. R. Suraj Reddy
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  2. G. Rajashekar
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  3. C. S. Jha
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  4. V. K. Dadhwal
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  5. Raphel Pelissier
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  6. Pierre Couteron
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Correspondence to R. Suraj Reddy.

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Suraj Reddy, R., Rajashekar, G., Jha, C.S. et al. Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats, India. J Indian Soc Remote Sens 45, 657–665 (2017). https://doi.org/10.1007/s12524-016-0630-1

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