Integration of ICESat/GLAS data and random forest to estimate canopy height and biomass in Central Indian Forest
Article Main
Abstract
Satellite lidar systems, such as ICESat, GEDI, and ICESat-2, have revolutionized global above-ground biomass (AGB) estimation by providing precise forest height data. These missions highlight the transformative potential of spaceborne lidar in advancing biomass assessment and forest monitoring. The present research effectively utilized spaceborne ICESat -1 LiDAR to measure forest canopy height and estimate above-ground biomass (AGB) in Madhya Pradesh's Central Indian Forest. Data from LiDAR, radar, optical, and digital elevation models were integrated with ancillary climate variables and field-based forest inventory during 2009-10. Further, this approach was validated against new data obtained from ICESAT-2 (October 2018 onwards), GEDI (March 2019 onwards), and other spaceborne LiDAR sensors by numerous researchers globally. Lorey's height method established a relationship between GLAS-derived AGB and other variables, creating a spatial–height map using a K-Nearest Neighbour-based random forest approach. Estimated forest canopy height ranged from 2.16 m to 17.63 m with an RMSE of ± 2.57 m. Spatial AGB was estimated for prominent forest types, with R2 values ranging from 0.62 to 0.71 (p < 0.01). The total AGB over Madhya Pradesh's forests were estimated at 315.77 Mt with an RMSE of ±19.22 t ha^-1. Relative errors ranged from 33% to 45% over different forest types, suggesting newer missions like ICESat/GLAS-2 are needed for more precise estimates in future research.
Article Details
Article Details
Keywords
Above Ground Biomass, ICESat/GLAS, k- Nearest Neighbour, Madhya Pradesh, Random Forest, Tree height
References
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Breiman, L. (2001). Random Forests. Machine Learning, Vol. 45, 5–32. https://doi.org/10.1023/A:1010933404324
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Chaturvedi, R., Raghubanshi, A., & Singh, J. (2011). Carbon density and accumulation in woody species of tropical dry forest in India. Forest Ecology and Management, 262(8), 1576–1588. https://doi.org/10.1016/j.foreco.2011.07.006
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Crookston, N. L., & Finley, A. O. (2008). yaImpute: An R Package for kNN Imputation. Journal of Statistical Software, 23(10). https://doi.org/10.18637/jss.v023.i10
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Fararoda, R., Reddy, R. S., Rajashekar, G., Chand, T. K., Jha, C. & Dadhwal, V. (2021). Improving forest above-ground biomass estimates over Indian forests using multi-source data sets with machine learning algorithm. Ecological Informatics, 65, 101392. https://doi.org/10.1016/j.ecoinf.2021.101392
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Goparaju, L., Madugundu, R., Ahmad, F., Sinha, D. & Singh, J. S. (2021). Temporal dynamics of above-ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data. Proceedings of the Indian National Science Academy, 87(3), 499–513. https://doi.org/10.1007/s43538-021-00046-1
Harding, D., Lefsky, M., Parker, G., & Blair, J. (2001). Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sensing of Environment, 76(3), 283–297. https://doi.org/10.1016/s0034-4257(00)00210-8
Huang, X., Cheng, F., Wang, J., Duan, P., & Wang, J. (2023). Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14. https://doi.org/10.1109/tgrs.2023.3240848
Lee, S., Ni-Meister, W., Yang, W., & Chen, Q. (2011). Physically based vertical vegetation structure retrieval from ICESat data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA. Remote Sensing of Environment, 115(11), 2776–2785. https://doi.org/10.1016/j.rse.2010.08.026
Lefsky, M. A. (2009). Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. Journal of Applied Remote Sensing, 3(1), 033505. https://doi.org/10.1117/1.3082116
Lefsky, M. A. (2010). A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophysical Research Letters, 37(15). https://doi.org/10.1029/2010gl043622
Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito‐Santo, F., . . . de Oliveira, R. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32(22). https://doi.org/10.1029/2005gl023971
Lefsky, M. A., Harding, D., Cohen, W., Parker, G., & Shugart, H. (1999). Surface Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern Maryland, USA. Remote Sensing of Environment, 67(1), 83–98. https://doi.org/10.1016/s0034-4257(98)00071-6
Lefsky, M. A., Hudak, A. T., Cohen, W. B., & Acker, S. (2005). Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest. Remote Sensing of Environment, 95(4), 532–548. https://doi.org/10.1016/j.rse.2005.01.010
Lefsky, M. A., Keller, M., Pang, deCamargo, & Hunter. (2007). Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms. Journal of Applied Remote Sensing, 1(1), 013537. https://doi.org/10.1117/1.2795724
Liaw, A., Wiener, M. (2002). Classification and regression by random forest. The R Journal, R News 2, 18-22. Retrieved from https://journal.r-project.org/articles/RN-2002-022/
Li, Z. Bi, S. Hao, S. Cui, Y. (2021). Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis. Ecological Indicators, 142 (2022) 109246. https://doi.org/10.1016/j.ecolind.2022.109246
Lorey T. (1878). Die mittlere Bestandeshöhe. Allgemeine Forst- und Jagdzeitung, 54: 149-155. (in German).
Lu, D., & Jiang, X. (2024). A brief overview and perspective of using airborne Lidar data for forest biomass estimation. International Journal of Image and Data Fusion, 1–24. https://doi.org/10.1080/19479832.2024.2309615
Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., . . . Saah, D. (2012). Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. International Journal of Forestry Research, 2012, 1–16. https://doi.org/10.1155/2012/436537
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(2), 211–219. https://doi.org/10.1016/j.jag.2007.11.004
Mora, B., Wulder, M., White, J., & Hobart, G. (2013). Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR. Remote Sensing, 5(5), 2308–2326. https://doi.org/10.3390/rs5052308
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, 48, e2021GL093799. https://doi.org/10.1029/2021GL093799
Nelson, R., Ranson, K., Sun, G., Kimes, D., Kharuk, V., & Montesano, P. (2009). Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sensing of Environment, 113(3), 691–701. https://doi.org/10.1016/j.rse.2008.11.010
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Integration of ICESat/GLAS data and random forest to estimate canopy height and biomass in Central Indian Forest. (2025). Journal of Applied and Natural Science, 17(1), 52-64. https://doi.org/10.31018/jans.v17i1.5860
