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Dominant Expression of SAR Backscatter in Predicting Aboveground Biomass: Integrating Multi-Sensor Data and Machine Learning in Sikkim Himalaya

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Abstract

Accurate assessment of aboveground biomass (AGB) is crucial for understanding carbon budgets, climate change impacts, and evaluating forest responses to environmental shifts. In this study, AGB was estimated in Sikkim State of India by leveraging the capabilities of machine learning (ML) and integrating multi-sensor satellite data. Specifically, the random forest (RF) and categorical boosting algorithm (CatBoost) models were utilised. Field estimated AGB ranges from 1.99 to 530.02 Mg/ha with an average of 252.58 Mg/ha, utilised for model prediction and validation. The RF model slightly outperformed the CatBoost model, with a coefficient of determination (R2) of 0.71 and root mean square error (RMSE) of 72.98 Mg/ha, compared to the CatBoost model’s R2 of 0.67 and RMSE of 80.69 Mg/ha, The former showed a greater capacity to combat overfitting. Synthetic aperture radar variables have emerged as significant predictors because of their contribution to the structural properties of plants. This study acknowledges the limitations and challenges due to data availability, especially for ground truth measurements, which pose constraints on the accuracy and representativeness of AGB estimates. Uncertainties associated with AGB estimation, such as variations in vegetation structure and species composition, also affected model performance. Despite these limitations, this study emphasises the significance of multi-sensor data integration and ML models in AGB estimation and highlights their potential applications in forest management and climate change mitigation efforts in the Himalayan mountainous region.

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References

  • Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., et al. (2016). An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology, 22(4), 1406–1420. https://doi.org/10.1111/gcb.13139

    Article  Google Scholar 

  • Baccini, A., Walker, W., Carvalho, L., Farina, M., & Houghton, R. A. (2019). Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 363(6423), 230–234. https://doi.org/10.1126/science.aat1205

    Article  CAS  Google Scholar 

  • Behera, M. D., Tripathi, P., Mishra, B., Kumar, S., Chitale, V. S., & Behera, S. K. (2016). Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data. Advances in Space Research, 57(2), 552–561. https://doi.org/10.1016/j.asr.2015.11.010

    Article  CAS  Google Scholar 

  • Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

    Article  Google Scholar 

  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Dang, A. T. N., Nandy, S., Srinet, R., Luong, N. V., Ghosh, S., & Senthil Kumar, A. (2019). Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park. Vietnam. Ecological Informatics, 50, 24–32. https://doi.org/10.1016/j.ecoinf.2018.12.010

    Article  Google Scholar 

  • David, R. M., Rosser, N. J., & Donoghue, D. N. M. (2022). Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery. Remote Sensing of Environment, 282, 113232. https://doi.org/10.1016/j.rse.2022.113232

    Article  Google Scholar 

  • Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363

  • Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., et al. (2016). Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186, 64–87. https://doi.org/10.1016/j.rse.2016.08.013

    Article  Google Scholar 

  • Forkuor, G., Benewinde Zoungrana, J.-B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020). Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study. Remote Sensing of Environment, 236, 111496. https://doi.org/10.1016/j.rse.2019.111496

    Article  Google Scholar 

  • FSI.(1996). Volume equations for forests of India, Nepal and Bhutan. Forest Survey of India, Ministry of Environment and Forests, Govt. of India, Dehradun

  • FSI. (2019). India state of forest report. Forest Survey of India

  • FSI. (2021). India State of Forest Report. Dehradun: Forest Survey of India, Ministry of Environment Forest and Climate Change

  • Ghosh, S. M., & Behera, M. D. (2018). Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 96, 29–40. https://doi.org/10.1016/j.apgeog.2018.05.011

    Article  Google Scholar 

  • Ghosh, S. M., Behera, M. D., Jagadish, B., Das, A. K., & Mishra, D. R. (2021). A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India. Journal of Environmental Management, 292, 112816. https://doi.org/10.1016/j.jenvman.2021.112816

    Article  CAS  Google Scholar 

  • Ghosh, S. M., Behera, M. D., Kumar, S., Das, P., Prakash, A. J., Bhaskaran, P. K., et al. (2022a). Predicting the forest canopy height from lidar and multi-sensor data using machine learning over India. Remote Sensing. https://doi.org/10.3390/rs14235968

    Article  Google Scholar 

  • Ghosh, S. M., Behera, M. D., Kumar, S., Das, P., Prakash, A. J., Bhaskaran, P. K., & Behera, S. K. (2022b). Predicting the forest canopy height from LiDAR and multi-sensor data using machine learning over India. Remote Sensing, 14(23), 5968.

    Article  Google Scholar 

  • Guerra-Hernández, J., Narine, L. L., Pascual, A., Gonzalez-Ferreiro, E., Botequim, B., Malambo, L., et al. (2022). Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests. Giscience & Remote Sensing, 59(1), 1509–1533. https://doi.org/10.1080/15481603.2022.2115599

    Article  Google Scholar 

  • Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: An interdisciplinary review. Journal of Big Data, 7(1), 94. https://doi.org/10.1186/s40537-020-00369-8

    Article  Google Scholar 

  • Jha, N., Tripathi, N. K., Barbier, N., Virdis, S. G. P., Chanthorn, W., Viennois, G., et al. (2021). The real potential of current passive satellite data to map aboveground biomass in tropical forests. Remote Sensing in Ecology and Conservation, 7(3), 504–520. https://doi.org/10.1002/rse2.203

    Article  Google Scholar 

  • Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  • Li, Y., Li, M., Li, C., & Liu, Z. (2020). Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific Reports, 10(1), 9952. https://doi.org/10.1038/s41598-020-67024-3

    Article  CAS  Google Scholar 

  • Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63–105. https://doi.org/10.1080/17538947.2014.990526

    Article  Google Scholar 

  • Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., & Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests. https://doi.org/10.3390/f12020216

    Article  Google Scholar 

  • Malhi, R. K. M., Anand, A., Srivastava, P. K., Chaudhary, S. K., Pandey, M. K., Behera, M. D., et al. (2021). Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India. Advances in Space Research. https://doi.org/10.1016/j.asr.2021.03.035

    Article  Google Scholar 

  • Mutanga, O., Masenyama, A., & Sibanda, M. (2023). Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 297–309. https://doi.org/10.1016/j.isprsjprs.2023.03.010

    Article  Google Scholar 

  • Nandy, S., Srinet, R., & Padalia, H. (2021). Mapping forest height and aboveground biomass by integrating ICESat-2, sentinel-1 and sentinel-2 data using random forest algorithm in northwest himalayan foothills of India. Geophysical Research Letters. https://doi.org/10.1029/2021GL093799

    Article  Google Scholar 

  • Prakash, A. J., Behera, M. D., Ghosh, S. M., Das, A., & Mishra, D. R. (2022). A new synergistic approach for Sentinel-1 and PALSAR-2 in a machine learning framework to predict aboveground biomass of a dense mangrove forest. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2022.101900

    Article  Google Scholar 

  • Rosenqvist, A., Shimada, M., Suzuki, S., Ohgushi, F., Tadono, T., Watanabe, M., et al. (2014). Operational performance of the ALOS global systematic acquisition strategy and observation plans for ALOS-2 PALSAR-2. Remote Sensing of Environment, 155, 3–12.

    Article  Google Scholar 

  • Sharma, N., Behera, M. D., Das, A. P., & Panda, R. M. (2019). Plant richness pattern in an elevation gradient in the Eastern Himalaya. Biodiversity and Conservation, 28(8), 2085–2104. https://doi.org/10.1007/s10531-019-01699-7

    Article  Google Scholar 

  • Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., & Lucas, R. (2014). New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sensing of Environment, 155, 13–31.

    Article  Google Scholar 

  • Singh, C., Karan, S. K., Sardar, P., & Samadder, S. R. (2022). Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308, 114639. https://doi.org/10.1016/j.jenvman.2022.114639

    Article  Google Scholar 

  • Singh, R. K., Biradar, C. M., Behera, M. D., Prakash, A. J., Das, P., Mohanta, M. R., & Rizvi, J. (2023). Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning. Ecological Informatics, 79, 102408.

    Article  Google Scholar 

  • Nations, U. (2016). The Sustainable Development Goals 2016. eSocialSciences

  • Vaglio Laurin, G., Chen, Q., Lindsell, J. A., Coomes, D. A., Frate, F. D., Guerriero, L., et al. (2014a). Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing, 89, 49–58. https://doi.org/10.1016/j.isprsjprs.2014.01.001

    Article  Google Scholar 

  • Vaglio Laurin, G., Chen, Q., Lindsell, J. A., Coomes, D. A., Frate, F. D., Guerriero, L., et al. (2014b). Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing, 89, 49–58. https://doi.org/10.1016/j.isprsjprs.2014.01.001

    Article  Google Scholar 

  • Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, R. B., & Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 189–201. https://doi.org/10.1016/j.isprsjprs.2019.06.007

    Article  Google Scholar 

  • Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., et al. (2019). Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 233, 111383. https://doi.org/10.1016/j.rse.2019.111383

    Article  Google Scholar 

  • Yu, Y., & Saatchi, S. (2016). Sensitivity of L-band SAR backscatter to aboveground biomass of global forests. Remote Sensing. https://doi.org/10.3390/rs8060522

    Article  Google Scholar 

  • Zhang, Y., Liang, S., & Sun, G. (2014). Forest biomass mapping of northeastern china using GLAS and MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 140–152. https://doi.org/10.1109/JSTARS.2013.2256883

    Article  Google Scholar 

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Acknowledgements

AJP and SP thank the Ministry of Education, Government of India, for the grant of PhD Research Fellowships. SS thanks Ministry of Education, Government of India, New Delhi, for providing fellowship for M.Tech study. All Authors acknowledge the authorities of IIT Kharagpur for instrumental support and facilities provided; the Sikkim state forest and wildlife department is thanked for support in conducting fieldwork.

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No external funding was received for this research.

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Authors and Affiliations

  1. Spatial Analysis and Modelling Lab, Centre for Ocean, River, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    A. Jaya Prakash, Sujoy Mudi, Somnath Paramanik, Mukunda Dev Behera & Shanu Shekhar

  2. Sikkim State Council of Science and Technology, Vigyan Bhawan, Department of Science and Technology, Gangtok, 737102, Sikkim, India

    Narpati Sharma

  3. Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi, 835222, India

    Bikash Ranjan Parida

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  1. A. Jaya Prakash
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  4. Mukunda Dev Behera
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Contributions

AJP and MDB contributed to the conceptualization, data curation, formal analysis, methodology, supervision, and writing—original draft. SP and SM were involved in the investigation, methodology, resources, supervision, and writing—review and editing. BRP and SS assisted in the visualization and writing—review and editing. NS contributed to writing—review and editing.

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Correspondence to Mukunda Dev Behera.

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Prakash, A.J., Mudi, S., Paramanik, S. et al. Dominant Expression of SAR Backscatter in Predicting Aboveground Biomass: Integrating Multi-Sensor Data and Machine Learning in Sikkim Himalaya. J Indian Soc Remote Sens 52, 871–883 (2024). https://doi.org/10.1007/s12524-024-01812-6

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