291
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest

, , , , &
Pages 1304-1338 | Received 02 Nov 2023, Accepted 15 Jan 2024, Published online: 02 Feb 2024

References

  • Adam, M., M. Urbazaev, C. Dubois, and C. Schmullius. 2020. “Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters.” Remote Sensing 12 (23): 3948. https://doi.org/10.3390/rs12233948.
  • Arévalo, P., A. Baccini, C. E. Woodcock, P. Olofsson, and W. S. Walker. 2023. “Continuous Mapping of Aboveground Biomass Using Landsat Time Series.” Remote Sensing of Environment 288:113483. https://doi.org/10.1016/j.rse.2023.113483.
  • Asner, G. P. 2011. “Painting the World REDD: Addressing Scientific Barriers to Monitoring Emissions from Tropical Forests.” Environmental Research Letters 6 (2): 021002. https://doi.org/10.1088/1748-9326/6/2/021002.
  • Baccini, A. G. S. J., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, et al. 2012. “Estimated Carbon Dioxide Emissions from Tropical Deforestation Improved by Carbon-Density Maps.” Nature Climate Change 2 (3): 182–185. https://doi.org/10.1038/nclimate1354.
  • Baghdadi, N., G. Le Maire, J. S. Bailly, K. Osé, Y. Nouvellon, M. Zribi, C. Lemos, and R. Hakamada. 2014. “Evaluation of ALOS/PALSAR L-Band Data for the Estimation of Eucalyptus Plantations Aboveground Biomass in Brazil.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (8): 3802–3811. https://doi.org/10.1109/JSTARS.2014.2353661.
  • Baghdadi, N., G. Le Maire, I. Fayad, J. S. Bailly, Y. Nouvellon, C. Lemos, and R. Hakamada. 2013. “Testing Different Methods of Forest Height and Aboveground Biomass Estimations from ICESat/GLAS Data in Eucalyptus Plantations in Brazil.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (1): 290–299. https://doi.org/10.1109/JSTARS.2013.2261978.
  • Bartlett, P., Y. Freund, W. S. Lee, and R. E. Schapire. 1998. “Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods.” Annals of Statistics 26 (5): 1651–1686. https://doi.org/10.1214/aos/1024691352.
  • Beck, J., B. Wirt, S. Luthcke, M. Hofton, and J. Armston. 2021. “Global Ecosystem Dynamics Investigation (GEDI) Level 02 User Guide. Document Version 2.0, April 2021. U.S. Geological Survey, Earth Resources Observation and Science Center (Sioux Falls.” South Dakota, USA. https://lpdaac.usgs.gov/documents/986/GEDI02_UserGuide_V2.pdf.
  • Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” Isprs Journal of Photogrammetry & Remote Sensing 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
  • Bennett, A. C., T. D. Penman, S. K. Arndt, S. H. Roxburgh, and L. T. Bennett. 2020. “Climate More Important Than Soils for Predicting Forest Biomass at the Continental Scale.” Ecography 43 (11): 1692–1705. https://doi.org/10.1111/ecog.05180.
  • Brahma, B., A. J. Nath, G. W. Sileshi, and A. K. Das. 2018. “Estimating Biomass Stocks and Potential Loss of Biomass Carbon Through Clear Felling of Rubber Plantations.” Biomass & bioenergy 115:88–96. https://doi.org/10.1016/j.biombioe.2018.04.019.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
  • Brown, S. 2002. “Measuring Carbon in Forests: Current Status and Future Challenges.” Environmental Pollution 116 (3): 363–372. https://doi.org/10.1016/S0269-7491(01)00212-3.
  • Champion, S. H. G., S. K. Seth. 1968. “A Revised Survey of the Forest Types of India A Revis.” Surveys Forest Types India.
  • Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Fölster, et al. 2005. “Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests.” Oecologia 145 (1): 87–99. https://doi.org/10.1007/s00442-005-0100-x.
  • Chen, L., C. Ren, G. Bao, B. Zhang, Z. Wang, M. Liu, W. Man, and J. Liu. 2022. “Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDar Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region.” Remote Sensing 14 (12): 2743. https://doi.org/10.3390/rs14122743.
  • Chhabra, A., and V. K. Dadhwal. 2004. “Assessment of Major Pools and Fluxes of Carbon in Indian Forests.” Climatic Change 64 (3): 341–360. https://doi.org/10.1023/B:CLIM.0000025740.50082.e7.
  • Chhabra, A., S. Palria, and V. K. Dadhwal. 2002. “Spatial Distribution of Phytomass Carbon in Indian Forests.” Global Change Biology 8 (12): 1230–1239. https://doi.org/10.1046/j.1365-2486.2002.00552.x.
  • Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, et al., 2013. Carbon and Other Biogeochemical Cycles. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 465–570.
  • Climate Fund, G. 2017. Terms of Reference for the Pilot Programme for REDD+. Results-Based Payments. https://www.greenclimate.fund/sites/default/files/document/terms-reference-pilot-programme-redd-results-based-payments.pdf.
  • Das, B., S. K. Patnaik, R. Bordoloi, A. Paul, and O. P. Tripathi. 2022. “Prediction of Forest Aboveground Biomass Using an Integrated Approach of Space-Based Parameters, and Forest Inventory Data.” Geology, Ecology & Landscapes 1–13. https://doi.org/10.1080/24749508.2022.2139484.
  • Dorado-Roda, I., A. Pascual, S. Godinho, C. A. Silva, B. Botequim, P. Rodríguez-Gonzálvez, E. González-Ferreiro, and J. Guerra-Hernández. 2021. “Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests.” Remote Sensing 13 (12): 2279. https://doi.org/10.3390/rs13122279.
  • Dubayah, R., J. Armston, S. P. Healey, J. M. Bruening, P. L. Patterson, J. R. Kellner, L. Duncanson, et al. 2022. “GEDI Launches a New Era of Biomass Inference from Space.” Environmental Research Letters 17 (9): 095001. https://doi.org/10.1088/1748-9326/ac8694.
  • Dubayah, R., J. Armston, J. R. Kellner, L. Duncanson, S. P. Healey, P. L. Patterson, S. Hancock, et al. 2022. GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1. Oak Ridge, Tennessee, USA: ORNL DAAC.
  • Dubayah, R., J. B. Blair, J. Beck, B. Wirt, J. Armston, M. Hofton, S. Luthcke, and H. Tang. 2021. “GLOBAL Ecosystem Dynamics Investigation (GEDI) Level 2 User Guide for SDPS PGEVersion 3 (P003) of GEDI L2A Data and SDPS PGEVersion 3 (P003) of GEDI L2B Data.” 3:1–25.
  • Dubayah, R., J. B. Blair, S. Goetz, L. Fatoyinbo, M. Hansen, S. Healey, M. Hofton, et al. 2020. “The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth’s Forests and Topography.” Science of Remote Sensing 1:100002. https://doi.org/10.1016/j.srs.2020.100002. September 2019.
  • Duncanson, L., J. R. Kellner, J. Armston, R. Dubayah, D. M. Minor, S. Hancock, S. P. Healey, et al. 2022. “Aboveground Biomass Density Models for Nasa’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission.” Remote Sensing of Environment 270:112845. https://doi.org/10.1016/j.rse.2021.112845.
  • El Hajj, M., N. Baghdadi, I. Fayad, G. Vieilledent, J. S. Bailly, and D. Ho Tong Minh. 2017. “Interest of Integrating Spaceborne LiDar Data to Improve the Estimation of Biomass in High Biomass Forested Areas.” Remote Sensing 9 (3): 213. https://doi.org/10.3390/rs9030213.
  • El Hajj, M., N. Baghdadi, N. Labrière, J. S. Bailly, and L. Villard. 2019. “Mapping of Aboveground Biomass in Gabon.” Comptes Rendus Geoscience 351 (4): 321–331. https://doi.org/10.1016/j.crte.2019.01.001.
  • ESA, “Sentinel-1,” 2023a https://sentinel.esa.int/web/sentinel/missions/sentinel-1.
  • ESA, “Sentinel-2,” 2023b https://sentinel.esa.int/web/sentinel/missions/sentinel-2.
  • Fang, J., G. Yu, L. Liu, S. Hu, and F. S. Chapin III. 2018. “Climate Change, Human Impacts, and Carbon Sequestration in China.” Proceedings of the National Academy of Sciences 115 (16): 4015–4020. https://doi.org/10.1073/pnas.1700304115.
  • FAO. 2010. “Global Forest Resources Assessment 2010.” FAO FORESTRY PAPER 163, FAO FORESTRY PAPER. Food and Agriculture Organization of The United Nations.
  • Fararoda, R., R. S. Reddy, G. Rajashekar, T. R. K. Chand, C. S. Jha, and V. K. Dadhwal. 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.
  • Farr, T., P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, et al. 2007. “The Shuttle Radar Topography Mission: Reviews of Geophys., 45.” Reviews of Geophysics 45 (2): 1–13. https://doi.org/10.1029/2005RG000183.
  • Fayad, I., N. Baghdadi, C. Alcarde Alvares, J. L. Stape, J. S. Bailly, H. F. Scolforo, I. R. Cegatta, M. Zribi, and G. Le Maire. 2021. “Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data.” Remote Sensing 13 (11): 2136. https://doi.org/10.3390/rs13112136.
  • Fayad, I., N. Baghdadi, S. Guitet, J. S. Bailly, B. Hérault, V. Gond, M. El Hajj, and D. H. T. Minh. 2016. “Aboveground Biomass Mapping in French Guiana by Combining Remote Sensing, Forest Inventories and Environmental Data.” International Journal of Applied Earth Observation and Geoinformation 52:502–514. https://doi.org/10.1016/j.jag.2016.07.015.
  • Fayad, I., N. Baghdadi, and K. Lahssini. 2022. “An Assessment of the GEDI Lasers’ Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area.” Remote Sensing 14 (13): 2969. https://doi.org/10.3390/rs14132969.
  • Forest Survey of India. 1996. Volume Equations for Forests of India, Nepal and Bhutan. Dehradun, India: Director, Forest Survey.
  • Forkuor, G., J.-B.-B. Zoungrana, K. Dimobe, B. Ouattara, K. P. Vadrevu, and J. E. Tondoh. 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.
  • FSI, 2019. India State of Forest Report 2019. Forest Survey of India. Govt. of India, Ministry of Environment & Forest, Dehraduna, India.
  • Fund, B. 2020. “ISFL Emission Reductions (ER) Program Requirements.” https://www.biocarbonfund-isfl.org/sites/default/files/2023-01/ISFL%20ER%20Program%20Requirements_V1.3_2023.pdf.
  • Ghosal, K., S. Das Bhattacharya, and P. K. Paul. 2022. “Estimation of Aboveground Forest Biomass in Himalayan Region of West Bengal, India Using IRS P6 LISS-IV Data.” Arabian Journal of Geosciences 15 (7): 601. https://doi.org/10.1007/s12517-022-09898-3.
  • Gibbs, H. K., S. Brown, J. O. Niles, and J. A. Foley. 2007. “Monitoring and Estimating Tropical Forest Carbon Stocks: Making REDD a Reality.” Environmental Research Letters 2 (4): 045023. https://doi.org/10.1088/1748-9326/2/4/045023.
  • Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. https://doi.org/10.1016/j.patrec.2005.08.011.
  • Google, 2022. Sentinel-2 Cloud Masking with s2cloudless. https://developers.google.com/earth-engine/tutorials/community/sentinel-2s2cloudless.
  • Guerra-Hernández, J., L. L. Narine, A. Pascual, E. Gonzalez-Ferreiro, B. Botequim, L. Malambo, A. Neuenschwander, S. C. Popescu, and S. Godinho. 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.
  • Haripriya, G. S. 2000. “Estimates of Biomass in Indian Forests.” Biomass and Bioenergy 19 (4): 245–258. https://doi.org/10.1016/S0961-9534(00)00040-4.
  • Hengl, T., J. Mendes de Jesus, G. B. M. Heuvelink, M. Ruiperez Gonzalez, M. Kilibarda, A. Blagotić, W. Shangguan, et al. 2017. “SoilGrids250m: Global Gridded Soil Information Based on Machine Learning.” PloS One 12 (2): e0169748. https://doi.org/10.1371/journal.pone.0169748.
  • Hill, T. C., M. Williams, A. A. Bloom, E. T. A. Mitchard, C. M. Ryan, and B. Bond-Lamberty. 2013. “Are Inventory Based and Remotely Sensed Above-Ground Biomass Estimates Consistent?” PloS One 8 (9): e74170. https://doi.org/10.1371/journal.pone.0074170.
  • Hossain, M. D., and D. Chen. 2019. “Segmentation for Object-Based Image Analysis (OBIA): A Review of Algorithms and Challenges from Remote Sensing Perspective.” Isprs Journal of Photogrammetry & Remote Sensing 150 (February): 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009.
  • Houghton, R., N. Greenglass, A. Baccini, A. Cattaneo, S. Goetz, J. Kellndorfer, N. Laporte, and W. Walker. 2010. “The Role of Science in Reducing Emissions from Deforestation and Forest Degradation (REDD).” Carbon Management 1 (2): 253–259. https://doi.org/10.4155/cmt.10.29.
  • Houghton, R. A., F. Hall, and S. J. Goetz. 2009. “Importance of Biomass in the Global Carbon Cycle.” Journal of Geophysical Research: Biogeosciences 114 (G2): 437–442. n/a-n/a. indices for determining soybean and corn growth parameters. Photogramm. Eng. Remote Sens. https://doi.org/10.1029/2009JG000935.
  • Jung, M., A. Arnell, X. de Lamo, S. García-Rangel, M. Lewis, J. Mark, C. Merow, et al. 2021. “Areas of Global Importance for Conserving Terrestrial Biodiversity, Carbon and Water.” Nature Ecology & Evolution 5 (11): 1499–1509. https://doi.org/10.1038/s41559-021-01528-7.
  • Kanmegne Tamga, D., H. Latifi, T. Ullmann, R. Baumhauer, J. Bayala, and M. Thiel. 2022. “Estimation of Aboveground Biomass in Agroforestry Systems Over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data.” Sensors 23 (1): 349. https://doi.org/10.3390/s23010349.
  • Karan, S. K., and L. Hamelin. 2020. “Towards Local Bioeconomy: A Stepwise Framework for High-Resolution Spatial Quantification of Forestry Residues.” Renewable and Sustainable Energy Reviews 134:110350. https://doi.org/10.1016/j.rser.2020.110350.
  • Kaul, M., V. K. Dadhwal, and G. M. J. Mohren. 2009. “Land use change and net C flux in Indian forests.” Forest Ecology and Management 258 (2): 100–108. https://doi.org/10.1016/j.foreco.2009.03.049.
  • Kellner, J. R., J. Armston, and L. Duncanson. 2021. “Algorithm Theoretical Basis Document (ATBD) for GEDI Level-4A (L4A) Footprint Level Aboveground Biomass Density.”
  • Kellner, J. R., J. Armston, and L. Duncanson. 2022. “Algorithm theoretical basis document for GEDI footprint aboveground biomass density.” Earth & Space Science 9:e2022EA002516. https://doi.org/10.1029/2022EA002516.
  • Kishwan, J., R. Pandey, and V. K. Dadhwal. 2009. India’s Forest and Tree Cover: Contribution as a Carbon Sink. Dehradun, Uttarakhand, India: Indian Counc. For. Res. Educ.
  • Kumari, K., and S. Kumar, “Machine Learning Based Modeling for Forest Aboveground Biomass Retrieval,” in 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), Hyderabad, India. IEEE, 2023, vol. 1, pp. 1–4.
  • Kumar, L., and O. Mutanga. 2017. “Remote Sensing of Above-Ground Biomass.” Remote Sensing 9 (9): 935–938. https://doi.org/10.3390/rs9090935.
  • Lahssini, K., N. Baghdadi, G. Le Maire, and I. Fayad. 2022. “Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates Over Tropical Forests.” Remote Sensing 14 (24): 6264. https://doi.org/10.3390/rs14246264.
  • Laurin, G. V., N. Puletti, W. Hawthorne, V. Liesenberg, P. Corona, D. Papale, Q. Chen, and R. Valentini. 2016. “Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data.” Remote Sensing of Environment 176:163–176.
  • Liang, M., L. Duncanson, J. A. Silva, and F. Sedano. 2023. “Quantifying Aboveground Biomass Dynamics from Charcoal Degradation in Mozambique Using GEDI Lidar and Landsat.” Remote Sensing of Environment 284:113367. https://doi.org/10.1016/j.rse.2022.113367.
  • Li, W., Z. Niu, R. Shang, Y. Qin, L. Wang, and H. Chen. 2020. “High-Resolution Mapping of Forest Canopy Height Using Machine Learning by Coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 Data.” International Journal of Applied Earth Observation and Geoinformation 92:102163. https://doi.org/10.1016/j.jag.2020.102163.
  • Liu, A., X. Cheng, and Z. Chen. 2021. “Performance Evaluation of GEDI and ICESat-2 Laser Altimeter Data for Terrain and Canopy Height Retrievals.” Remote Sensing of Environment 264:112571. https://doi.org/10.1016/j.rse.2021.112571.
  • Lone, J. M., T. Sivasankar, R. Pebam, K. K. Sarma, M. A. Qadir, and P. L. N. Raju. 2018. (PDF) Comparison of C-Band Sentinel-1 and L-Band ALOSPALSAR-2 Data for Aboveground Forest Biomass Estimation Over Nongkhyllem Forest Reserve and Wildlife Sanctuary. Meghalaya, India: Seminar on Advances in Remote Sensing & GIS Applications.
  • Ma, H., L. Mo, T. W. Crowther, D. S. Maynard, J. van den Hoogen, B. D. Stocker, C. Terrer, and C. M. Zohner. 2021. “The Global Distribution and Environmental Drivers of Aboveground versus Belowground Plant Biomass.” Nature Ecology & Evolution 5 (8): 1110–1122. https://doi.org/10.1038/s41559-021-01485-1.
  • Mitchard, E. T., S. S. Saatchi, A. Baccini, G. P. Asner, S. J. Goetz, N. L. Harris, and S. Brown. 2013. “Uncertainty in the Spatial Distribution of Tropical Forest Biomass: A Comparison of Pan-Tropical Maps.” Carbon Balance and Management 8 (1): 10. https://doi.org/10.1186/1750-0680-8-10.
  • Mullissa, A., A. Vollrath, C. Odongo-Braun, B. Slagter, J. Balling, Y. Gou, N. Gorelick, and J. Reiche. 2021. “Sentinel-1 Sar Backscatter Analysis Ready Data Preparation in Google Earth Engine.” Remote Sensing 13 (10): 1954. https://doi.org/10.3390/rs13101954.
  • Musthafa, M., and G. Singh. 2022. “Improving Forest Above-Ground Biomass Retrieval Using Multi-Sensor L- and C- Band SAR Data and Multi-Temporal Spaceborne LiDAR Data.” Frontiers in Forests and Global Change 5:822704. https://doi.org/10.3389/ffgc.2022.822704.
  • Nandy, S., R. Singh, S. Ghosh, T. Watham, S. P. S. Kushwaha, A. S. Kumar, and V. K. Dadhwal. 2017. “Neural Network-Based Modelling for Forest Biomass Assessment.” Carbon Management 8 (4): 305–317. https://doi.org/10.1080/17583004.2017.1357402.
  • Nandy, S., R. Srinet, and H. Padalia. 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 (14): e2021GL093799. https://doi.org/10.1029/2021GL093799.
  • Oliveira, P. V., X. Zhang, B. Peterson, and J. P. Ometto. 2023. “Using Simulated GEDI Waveforms to Evaluate the Effects of Beam Sensitivity and Terrain Slope on GEDI L2A Relative Height Metrics Over the Brazilian Amazon Forest.” Science of Remote Sensing 7:100083. https://doi.org/10.1016/j.srs.2023.100083.
  • Ou, G., C. Li, Y. Lv, A. Wei, H. Xiong, H. Xu, and G. Wang. 2019. “Improving Aboveground Biomass Estimation of Pinus Densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison.” Remote Sensing 11 (7): 738. https://doi.org/10.3390/rs11070738.
  • Pan, Y., R. A. Birdsey, J. Fang, R. Houghton, P. E. Kauppi, W. A. Kurz, O. L. Phillips, et al. 2011. “A Large and Persistent Carbon Sink in the World’s Forests.” Science 333 (6045): 988–993. https://doi.org/10.1126/science.1201609.
  • Paoli, G. D., L. M. Curran, and J. W. F. Slik. 2008. “Soil Nutrients Affect Spatial Patterns of Aboveground Biomass and Emergent Tree Density in Southwestern Borneo.” Oecologia 155 (2): 287–299. https://doi.org/10.1007/s00442-007-0906-9.
  • Pascual, A., J. Guerra-Hernández, J. Armston, D. M. Minor, L. I. Duncanson, P. B. May, J. R. Kellner, and R. Dubayah. 2023. “Assessing the Performance of Nasa’s GEDI L4A Footprint Aboveground Biomass Density Models Using National Forest Inventory and Airborne Laser Scanning Data in Mediterranean Forest Ecosystems.” Forest Ecology and Management 538:120975. https://doi.org/10.1016/j.foreco.2023.120975.
  • Pelletier, J., N. Ramankutty, and C. Potvin. 2011. “Diagnosing the Uncertainty and Detectability of Emission Reductions for REDD + Under Current Capabilities: An Example for Panama.” Environmental Research Letters 6 (2): 024005. https://doi.org/10.1088/1748-9326/6/2/024005.
  • Piao, S., H. Yue, X. Wang, and F. Chen. 2022. “Estimation of Carbon Sinks in Terrestrial Ecosystems in China: Methods, Progress and Prospects.” Science China Earth Sciences 2021–2197. https://doi.org/10.1360/SSTe-2021-0197.
  • Poggio, L., L. M. De Sousa, N. H. Batjes, G. Heuvelink, B. Kempen, E. Ribeiro, and D. Rossiter. 2021. “SoilGrids 2.0: Producing Soil Information for the Globe with Quantified Spatial Uncertainty.” Soil 7 (1): 217–240. https://doi.org/10.5194/soil-7-217-2021.
  • Pourrahmati, M. R., N. N. Baghdadi, A. A. Darvishsefat, M. Namiranian, I. Fayad, J. S. Bailly, and V. Gond. 2015. “Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (11): 5246–5261. https://doi.org/10.1109/JSTARS.2015.2478478.
  • Ramachandran, N., and O. Dikshit, 2022, July. Forest Aboveground Biomass Estimation from Airborne L-Band SAR Data Using Machine Learning. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia (pp. 6403–6405). IEEE.
  • Reddy, C. S., C. S. Jha, P. G. Diwakar, and V. K. Dadhwal. 2015. “Nationwide Classification of Forest Types of India Using Remote Sensing and GIS.” Environmental Monitoring and Assessment 187 (12): 777. https://doi.org/10.1007/s10661-015-4990-8.
  • Rodda, S. R., R. R. Nidamanuri, R. Fararoda, T. Mayamanikandan, and G. Rajashekar. 2023. “Evaluation of Height Metrics and Above-Ground Biomass Density from GEDI and ICESat-2 Over Indian Tropical Dry Forests Using Airborne LiDAR Data.” The Journal of the Indian Society of Remote Sensing 1–16. https://doi.org/10.1007/s12524-023-01693-1.
  • Rodríguez-Veiga, P., J. Wheeler, V. Louis, K. Tansey, and H. Balzter. 2017. “Quantifying Forest Biomass Carbon Stocks from Space.” Current Forestry Reports 3 (1): 1–18. https://doi.org/10.1007/s40725-017-0052-5.
  • Roy, P. S., P. Meiyappan, P. K. Joshi, M. P. Kale, V. K. Srivastav, S. K. Srivasatava, M. D. Behera, et al. 2016. “Decadal Land Use and Land Cover Classifications Across India, 1985, 1995, 2005.” International Journal of Offshore and Polar Engineering. https://doi.org/10.3334/ORNLDAAC/1336.
  • Saatchi, S. S., N. L. Harris, S. Brown, M. Lefsky, E. T. A. Mitchard, W. Salas, B. R. Zutta, et al. 2011. “Benchmark Map of Forest Carbon Stocks in Tropical Regions Across Three Continents.” Proceedings of the National Academy of Sciences of the United States of America 108 (24): 9899–9904. https://doi.org/10.1073/pnas.1019576108.
  • Sathayea, J., and A. Reddy. 2013. Integrating Ecology and Economy in India. Global Greenhouse Regime, Who Pays? Science, Economics and North-South Politics in the Climate Change Conventions. No. GTZ-844. Tokio (Japan): UNU 191
  • Sexton, J. O., X.-P. Song, M. Feng, P. Noojipady, A. Anand, C. Huang, D.-H. Kim, et al. 2013. “Global, 30-M Resolution Continuous Fields of Tree Cover: Landsat-Based Rescaling of MODIS Vegetation Continuous Fields with Lidar-Based Estimates of Error.” International Journal of Digital Earth 6 (5): 427–448. https://doi.org/10.1080/17538947.2013.786146.
  • Shendryk, Y. 2022. “Fusing GEDI with Earth Observation Data for Large Area Aboveground Biomass Mapping.” International Journal of Applied Earth Observation and Geoinformation 115:103108. https://doi.org/10.1016/j.jag.2022.103108.
  • Shendryk, Y., R. Davy, and P. Thorburn. 2021. “Integrating Satellite Imagery and Environmental Data to Predict Field-Level Cane and Sugar Yields in Australia Using Machine Learning.” Field Crops Research 260:107984. https://doi.org/10.1016/J.FCR.2020.107984.
  • Sileshi, G. W. 2014. “A Critical Review of Forest Biomass Estimation Models, Common Mistakes and Corrective Measures.” Forest Ecology and Management 329:237–254. https://doi.org/10.1016/j.foreco.2014.06.026.
  • Silveira, E. M. O., S. Henrique G Silva, F. W. Acerbi-Junior, M. C. Carvalho, L. Marcelo T Carvalho, J. Roberto S Scolforo, and M. A. Wulder. 2019. “Object-Based Random Forest Modelling of Aboveground Forest Biomass Outperforms a Pixel-Based Approach in a Heterogeneous and Mountain Tropical Environment.” International Journal of Applied Earth Observation and Geoinformation 78:175–188. https://doi.org/10.1016/j.jag.2019.02.004.
  • Silveira, E. M. O., V. C. Radeloff, S. Martinuzzi, G. J. M. Pastur, J. Bono, N. Politi, L. Lizarraga, et al. 2023. “Nationwide Native Forest Structure Maps for Argentina Based on Forest Inventory Data, SAR Sentinel-1 and Vegetation Metrics from Sentinel-2 Imagery.” Remote Sensing of Environment 285:113391. https://doi.org/10.1016/j.rse.2022.113391.
  • Sinergise, 2022. Sentinel Hub’s Cloud Detector for Sentinel-2 Imagery. https://github.com/sentinel-hub/sentinel2-cloud-detector.
  • Singh, C., L. Wang-Erlandsson, I. Fetzer, J. Rockström, and R. van der Ent. 2020. “Rootzone Storage Capacity Reveals Drought Coping Strategies Along Rainforest-Savanna Transitions.” Environmental Research Letters 15 (12): 124021. https://doi.org/10.1088/1748-9326/abc377.
  • Skakun, S., J. Wevers, C. Brockmann, G. Doxani, M. Aleksandrov, M. Batič, D. Frantz, et al. 2022. “Cloud Mask Intercomparison eXercise (CMIX): An Evaluation of Cloud Masking Algorithms for Landsat 8 and Sentinel-2.” Remote Sensing of Environment 274:112990. https://doi.org/10.1016/j.rse.2022.112990.
  • Soudani, K., N. Delpierre, D. Berveiller, G. Hmimina, G. Vincent, A. Morfin, and É. Dufrêne. 2021. “Potential of C-Band Synthetic Aperture Radar Sentinel-1 Time-Series for the Monitoring of Phenological Cycles in a Deciduous Forest.” International Journal of Applied Earth Observation and Geoinformation: ITC Journal 104:102505. https://doi.org/10.1016/j.jag.2021.102505.
  • Tamiminia, H., B. Salehi, M. Mahdianpari, and T. Goulden. 2023. “State-Wide Forest Canopy Height and Aboveground Biomass Map for New York with 10 M Resolution, Integrating GEDI, Sentinel-1, and Sentinel-2 Data.” Ecological Informatics 79:102404. https://doi.org/10.1016/j.ecoinf.2023.102404.
  • Tan, K., S. Piao, C. Peng, and J. Fang. 2007. “Satellite-Based Estimation of Biomass Carbon Stocks for Northeast China’s Forests Between 1982 and 1999.” Forest Ecology and Management 240 (1–3): 114–121. https://doi.org/10.1016/j.foreco.2006.12.018.
  • Thenkabail, P., A. Ward, J. Lyon, and C. Merry. 1994. “Thematic mapper vegetation indices for determining soybean and corn growth parameters. In Photogrammetric engineering and remote sensing, Vol. 60, no. 4, pp. 437–442. Bethesda, MD, United States: American Society for Photogrammetry and Remote Sensing.
  • Vafaei, S., J. Soosani, K. Adeli, H. Fadaei, H. Naghavi, T. D. Pham, and D. T. Bui. 2018. “Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran).” Remote Sensing 10 (2): 172. https://doi.org/10.3390/rs10020172.
  • Valderrama-Landeros, L., F. Flores-Verdugo, R. Rodríguez-Sobreyra, J. M. Kovacs, and F. Flores-de-Santiago. 2021. “Extrapolating Canopy Phenology Information Using Sentinel-2 Data and the Google Earth Engine Platform to Identify the Optimal Dates for Remotely Sensed Image Acquisition of Semiarid Mangroves.” Journal of Environmental Management 279:111617. https://doi.org/10.1016/j.jenvman.2020.111617.
  • Venter, M., O. Venter, W. Edwards, M. I. Bird, and R. Zang. 2015. “Validating Community-Led Forest Biomass Assessments.” PloS One 10 (6): e0130529. https://doi.org/10.1371/journal.Pone.0130529.
  • Vikaspedia. 2023. “Natural Resources, Table 1.2 Agro-climatic regions/zones in India.” https://vikaspedia.in/agriculture/crop-production/weather-information/agro-climatic-zones-in-india.
  • Vorster, A. G., P. H. Evangelista, A. E. L. Stovall, and S. Ex. 2020. “Variability and Uncertainty in Forest Biomass Estimates from the Tree to Landscape Scale: The Role of Allometric Equations.” Carbon Balance and Management 15 (1): 8. https://doi.org/10.1186/s13021-020-00143-6.
  • Wang, C., A. J. Elmore, I. Numata, M. A. Cochrane, L. Shaogang, J. Huang, Y. Zhao, and Y. Li. 2022. “Factors Affecting Relative Height and Ground Elevation Estimations of GEDI Among Forest Types Across the Conterminous USA.” GIScience & Remote Sensing 59 (1): 975–999. https://doi.org/10.1080/15481603.2022.2085354.
  • Wang, Y., Y. Peng, X. Hu, and P. Zhang. 2023. “Fine-Resolution Forest Height Estimation by Integrating ICESat-2 and Landsat 8 OLI Data with a Spatial Downscaling Method for Aboveground Biomass Quantification.” Forests 14 (7): 1414. https://doi.org/10.3390/f14071414.
  • Xu, Y., S. Ding, P. Chen, H. Tang, H. Ren, and H. Huang. 2023. “Horizontal Geolocation Error Evaluation and Correction on Full-Waveform LiDar Footprints via Waveform Matching.” Remote Sensing 15 (3): 776. https://doi.org/10.3390/rs15030776.
  • Yadav, B. K. V., and S. Nandy. 2015. “Mapping Aboveground Woody Biomass Using Forest Inventory, Remote Sensing and Geostatistical Techniques.” Environmental Monitoring and Assessment 187 (5): 308. https://doi.org/10.1007/s10661-015-4551-1.
  • Yanai, R., C. Wayson, D. Lee, A. Espejo, J. L. Campbell, M. B. Green, J. M. Zukswert, et al. 2020. “Improving Uncertainty in Forest Carbon Accounting for REDD+ Mitigation Efforts.” Environmental Research Letters 15 (12): 124002. https://doi.org/10.1088/1748-9326/abb96f.
  • Yang, Q., C. Niu, X. Liu, Y. Feng, Q. Ma, X. Wang, H. Tang, and Q. Guo. 2023. “Mapping High-Resolution Forest Aboveground Biomass of China Using Multisource Remote Sensing Data.” GIScience & Remote Sensing 60 (1): 2203303. https://doi.org/10.1080/15481603.2023.2203303.
  • Zhang, Y., S. Liang, and L. Yang. 2019. “A Review of Regional and Global Gridded Forest Biomass Datasets.” Remote Sensing 11 (23): 2744. https://doi.org/10.3390/rs11232744.
  • Zhang, Y., N. Wang, Y. Wang, and M. Li. 2023. “A New Strategy for Improving the Accuracy of Forest Aboveground Biomass Estimates in an Alpine Region Based on Multi-Source Remote Sensing.” GIScience & Remote Sensing 60 (1): 2163574. https://doi.org/10.1080/15481603.2022.2163574.
  • Zheng, D., J. Rademacher, J. Chen, T. Crow, M. Bresee, J. Le Moine, and S. R. Ryu. 2004. “Estimating Aboveground Biomass Using Landsat 7 ETM+ Data Across a Managed Landscape in Northern Wisconsin, USA.” Remote Sensing of Environment 93 (3): 402–411. https://doi.org/10.1016/j.rse.2004.08.008.
  • Zupanc, A., 2017. Improving Cloud Detection with Machine Learning. https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-earning-c09dc5d7cf13. (Accessed August 5, 2023).

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.