Assessing Carbon Stocks in A Tropical Forest Ecosystem of High Conservation Value: A Case Study in Bac Kan Province, Vietnam
Main Article Content
Abstract
Carbon stocks (CS) is a crucial factor in understanding the carbon cycle and enhancing forest quality monitoring, supporting sustainable forestry management and policy adjustment. This study shows a non-invasive method for estimating aboveground biomass (AGB) and CS, integrating multi-source remote sensing, topography and field data. The tropical forest ecosystem in Bac Kan province was chosen because this is the province with the largest forest cover in Vietnam and is considering participating in the global carbon credit market. The Cubist model was developed using Sentinel-1A SAR data, indices from Landsat-9 OLI-2 images and topographic data. The accuracy of Cubist models is evaluated through statistical indicators such as: Root mean square deviation (RMSE), Coefficient of determination (R2) and Mean absolute error (MAE). Among 130 Cubist prediction models, the Model 1 demonstrated the highest accuracy for estimating AGB in the study area. Results indicate that the accumulated carbon stock is primarily concentrated in Evergreen broadleaved forest on soil mountain (EBFS) accounting for about 40 or approximately 2.6 million MgC; Mixed wood-bamboo forest on soil mountain (MWBF) accounting for about 23%, or 1.5 million MgC; and Plantation on soil mountain (PFS) accounting for about 22%, or 1.5 million MgC. The study provides a comprehensive view of forest ecosystem conservation and improves monitoring, protection and restoration of high-value forests.
Keywords:
Remote sensing Tropical forest, Carbon stocks, High conservation value, Bac Kan province.
References
[1] J. Sánchez, M. D. Curt, N. Robert, J. Fernández, Chapter Two - Biomass Resources, The Role of Bioenergy in the Bioeconomy, Elsevier, 2019, pp. 25-111, https://doi.org/10.1016/B978-0-12-813056-8.00002-9.
[2] K. Adla, K. Dejan, D. Neira, Š. Dragana, Chapter 9 - Degradation of Ecosystems and Loss of Ecosystem Services, One Health, 2022, pp. 281-327, https://doi.org/10.1016/B978-0-12-822794-7.00008-3.
[3] M. A. Njana, B. Mbilinyi, Z. Eliakimu, The Role of Forests in the Mitigation of Global Climate Change: Emprical Evidence from Tanzania, Environmental Challenges, Vol. 4, 2021, pp. 100170, https://doi.org/10.1016/j.envc.2021.100170.
[4] G. He, Z. Zhang, Q. Zhu, W. Wang, W. Peng, and Y. Cai, Estimating Carbon Sequestration Potential of Forest and Its Influencing Factors at Fine Spatial-Scales: A Case Study of Lushan City in Southern China, Int. J. Environ. Res. Public. Health, Vol. 19, No. 15, 2022, pp. 9184, https://doi.org/10.3390/ijerph19159184.
[5] A. Raihan, R. A. Begum, M. N. M. Said, J. J. Pereira, Assessment of Carbon Stock in Forest Biomass and Emission Reduction Potential in Malaysia, Forests, Vol. 12, No. 10, 2021, pp. 1294, https://doi.org/10.3390/f12101294.
[6] B. Mackey, C. F. Kormos, H. Keith, W. R. Moomaw, R. A. Houghton, R. A. Mittermeier, D. Hole, S. Hugh, Understanding the Importance of Primary Tropical Forest Protection As A Mitigation Strategy, Mitigation and Adaptation Strategies for Global Change, Vol. 25, No. 5, 2020, pp. 763-787, https://doi.org/10.1007/s11027-019-09891-4.
[7] M. Siraj, Forest Carbon Stocks In Woody plants of Chilimo-Gaji Forest, Ethiopia: Implications of managing Forests for Climate Change Mitigation, South African Journal of Botany, Vol. 127, 2019, pp. 213-219, https://doi.org/10.1016/j.sajb.2019.09.003.
[8] S. K. Behera, N. Sahu, A. K. Mishra, S. S. Bargali, M. D. Behera, R. Tuli, Aboveground Biomass and Carbon Stock Assessment in Indian Tropical Deciduous Forest And Relationship with Stand Structural Attributes, Ecol. Eng., Vol. 99, 2017, pp. 513-524, https://doi.org/10.1016/j.ecoleng.2016.11.046.
[9] H. K. Gibbs, S. Brown, J. O. Niles, J. A. Foley, Monitoring and Estimating Tropical Forest Carbon Stocks: Making REDD a reality, Environ Research Lettes, Vol. 2, No. 4, 2007, pp. 045023, https://doi.org/10.1088/1748-9326/2/4/045023.
[10] T. D. Pham, K. Yoshino, D. T. Bui, Biomass Estimation of Sonneratia Caseolaris (L.) Engler at A Coastal Area of Hai Phong city (Vietnam) using ALOS-2 PALSAR Imagery and GIS-based Multi-Layer Perceptron Neural Networks, GIScience Remote Sensing, Vol. 54, No. 3, 2017, pp. 329-353, https://doi.org/10.1080/15481603.2016.1269869.
[11] T. V. Pham, T. A. T. Do, H. D. Tran, A. N. T. Do, Assessing the Impact of Ecological Security and Forest Fire Susceptibility on Carbon Stocks in Bo Trach District, Quang Binh Province, Vietnam, Ecological Informatics, Vol. 74, 2023, pp. 101962, https://doi.org/10.1016/j.ecoinf.2022.101962.
[12] Y. Jiao, D. Wang, X. Yao, S. Wang, T. Chi, Y. Meng, Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation, Remote Sensing, Vol. 15, No. 5, 2023, pp. 1410, https://doi.org/10.3390/rs15051410.
[13] M. V. Pham, T. M. Pham, Q. V. V. Du, Q. T. Bui, A. V. Tran, H. M. Pham, T. N. Nguyen, Integrating Sentinel-1A SAR data and GIS to Estimate Aboveground Biomass and Carbon Accumulation for Tropical Forest Types in Thuan Chau district, Vietnam, Remote Sensing Applications: Society and Environment, Vol. 14, 2019, pp. 148-157, https://doi.org/10.1016/j.rsase.2019.03.003.
[14] D. T. Nhung, N. D. Hung, P. N. Hai, D. D. L. Phuong, N. T. D. My, P. V. Manh, Estimation of Above Ground Biomass Using A Combination of Integrated Remote Sensing and Machine Learning Algorithms: A Case Study in the Western Nghe An Biosphere Reserve, Vietnam, Journal of Forestry Science and Technology, Vol. 12, No. 4, 2023, pp. 81-92, https://doi.org/10.55250/jo.vnuf.12.4.2023.081-092 (in Vietnamese).
[15] R. Showstack, Landsat 9 Satellite Continues Half-Century of Earth Observations: Eyes in the Sky Serve as A Valuable Tool for Stewardship, BioScience, Vol. 72, No. 3, 2022, pp. 226-232, https://doi.org/10.1093/biosci/biab145.
[16] Y. Hu, Y. Nie, Z. Liu, G. Wu, W. Fan, Improving the Potential of Coniferous Forest Aboveground Biomass Estimation by Integrating C- and L-Band SAR Data with Feature Selection and Non-Parametric Model, Remote Sensing, Vol. 15, No. 17, 2023, pp. 4194, https://doi.org/10.3390/rs15174194.
[17] M. H. Pham, T. H. Do, V. M. Pham, Q. T. Bui, Mangrove Forest Classification and Aboveground Biomass Estimation Using An Atom Search Algorithm and Adaptive Neuro-Fuzzy Inference System, PLOS ONE, Vol. 15, No. 5, 2020, pp. e0233110, https://doi.org/10.1371/journal.pone.0233110.
[18] H. Yu, Y. Wu, L. Niu, Y. Chai, Q. Feng, W. Wang, T. Liang, A Method to Avoid Spatial Overfitting in Estimation of Grassland Above-Ground Biomass on the Tibetan Plateau, Ecological Indicators, Vol. 125, 2021, pp. 107450, https://doi.org/10.1016/j.ecolind.2021.107450.
[19] A. T. N. Dang, S. Nandy, R. Srinet, N. V. Luong, S. Ghosh, A. Senthil Kumar, Forest Aboveground Biomass Estimation Using Machine Learning Regression Algorithm in Yok Don National Park, Vietnam, Ecological Informatics, Vol. 50, 2019, pp. 24-32, https://doi.org/10.1016/j.ecoinf.2018.12.010.
[20] S. D. Madundo, E. W. Mauya, C. J. Kilawe, Comparison of Multi-Source Remote Sensing Data for Estimating and Mapping Above-Ground Biomass in the West Usambara Tropical Montane Forests, Scientific African, Vol. 21, 2023, pp. e01763, https://doi.org/10.1016/j.sciaf.2023.e01763.
[21] Q. T. Bui, Q. T. Pham, V. M. Pham, V. T. Tran, D. H. Nguyen, Q. H. Nguyen, H. D. Nguyen, N. T. Do, V. M. Vu, Hybrid Machine Learning Models for Aboveground Biomass Estimations, Ecological Informatics, Vol. 79, 2024, pp. 102421, https://doi.org/10.1016/j.ecoinf.2023.102421.
[22] People's Committee of Bac Kan Province, Summary Report on Bac Kan Province Planning for the Period 2021-2030, Vision to 2050, http://tnmt.backan.gov.vn/uploads/news/2024_05/22.5.2023.01bao-cao-tong-hop-qht-bac-kan-pdf, (accessed on: January 5th, 2024) (in Vietnamese).
[23] UNFCCC, Vietnam Briefing Discusses the COP26 and Vietnam’s Climate Action Plan Including Its Commitment to Achieve Net-Zero Carbon Emissions by 2050, https://iucn.org/sites/default/files/2022-12/eng-deliver-vn-cop26-committments-on-re-final, (accessed on: January 5th, 2024).
[24] B. Brede et al., Non-destructive Estimation of Individual Tree Biomass: Allometric Models, Terrestrial and UAV Laser Scanning, Remote Sensing of Environment, Vol. 280, 2022, pp. 113180, https://doi.org/10.1016/j.rse.2022.113180.
[25] G. A. Blackburn, Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves, International Journal of Remote Sensing, Vol. 19, No. 4, 1998, https://doi.org/10.1080/014311698215919.
[26] A. A. Gitelson, Y. J. Kaufman, R. Stark, D. Rundquist, Novel Algorithms for Remote Estimation of Vegetation Fraction, Remote Sensing of Environment, Vol. 80, No. 1, 2002, https://doi.org/10.1016/S0034-4257(01)00289-9.
[27] C. Buschmann, E. Nagel, In Vivo Spectroscopy and Internal Optics of Leaves As Basis for Remote Sensing of Vegetation, International Journal of Remote Sensing, Vol. 14, No. 4, 1993, https://doi.org/10.1080/01431169308904370.
[28] N. H. Broge, E. Leblanc, Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density, Remote Sensing of Environment, Vol. 76, No. 2, 2001, https://doi.org/10.1016/s0034-4257(00)00197-8.
[29] N. Kaveh, A. Ebrahimi, E. Asadi, Comparative Analysis of Random Forest, Exploratory Regression, and Structural Equation Modeling for Screening Key Environmental Variables in Evaluating Rangeland Above-Ground Biomass, Ecological Informatics, Vol. 77, 2023, pp. 102251, https://doi.org/10.1016/j.ecoinf.2023.102251.
[30] O. Mutanga, E. Adam, M. A. Cho, High Density Biomass Estimation for Wetland Vegetation Using Worldview-2 Imagery and Random Forest Regression Algorithm, International Journal of Applied Earth Observation and Geoinformation, Vol. 18, 2012, pp. 399-406, https://doi.org/10.1016/j.jag.2012.03.012.
[31] J. R. Quinlan, Learning with Continuous Classes, Proceedings of Australian Joint Conference on Artificial Intelligence, 1992, pp. 343-348.
[32] L. Brilli, M. Chiesi, C. Brogi, R. Magno, L. Arcidiaco, L. Bottai, G. Tagliaferri, M. Bindi, F. Maselli, Combination of Ground and Remote Sensing Data to Assess Carbon Stock Changes in the Main Urban Park of Florence, Urban Forestry & Urban Greening, Vol. 43, 2019, pp. 126377, https://doi.org/10.1016/j.ufug.2019.126377.
[33] K. Mokany, R. J. Raison, A. S. Prokushkin, Critical Analysis of Root: Shoot Ratios in Terrestrial Biomes, Global Change Biology, Vol. 12, No. 1, 2006, pp. 84-96, https://doi.org/10.1111/j.1365-2486.2005.001043.x.
[34] H. Shiferaw, T. Kassawmar, G. Zeleke, Above and Belowground Woody-Biomass and Carbon Stock Estimations at Kunzila watershed, Northwest Ethiopia, Trees, Forests and People, Vol. 7, 2022, pp. 100204, https://doi.org/10.1016/j.tfp.2022.100204.
[35] K. Prabhakara, W. D. Hively, G. W. McCarty, Evaluating the Relationship Between Biomass, Percent Groundcover and Remote Sensing Indices Across Six Winter Cover Crop Fields in Maryland, United States, International Journal of Applied Earth Observation and Geoinformation, Vol. 39, 2015, pp. 88-102, https://doi.org/10.1016/j.jag.2015.03.002.
[2] K. Adla, K. Dejan, D. Neira, Š. Dragana, Chapter 9 - Degradation of Ecosystems and Loss of Ecosystem Services, One Health, 2022, pp. 281-327, https://doi.org/10.1016/B978-0-12-822794-7.00008-3.
[3] M. A. Njana, B. Mbilinyi, Z. Eliakimu, The Role of Forests in the Mitigation of Global Climate Change: Emprical Evidence from Tanzania, Environmental Challenges, Vol. 4, 2021, pp. 100170, https://doi.org/10.1016/j.envc.2021.100170.
[4] G. He, Z. Zhang, Q. Zhu, W. Wang, W. Peng, and Y. Cai, Estimating Carbon Sequestration Potential of Forest and Its Influencing Factors at Fine Spatial-Scales: A Case Study of Lushan City in Southern China, Int. J. Environ. Res. Public. Health, Vol. 19, No. 15, 2022, pp. 9184, https://doi.org/10.3390/ijerph19159184.
[5] A. Raihan, R. A. Begum, M. N. M. Said, J. J. Pereira, Assessment of Carbon Stock in Forest Biomass and Emission Reduction Potential in Malaysia, Forests, Vol. 12, No. 10, 2021, pp. 1294, https://doi.org/10.3390/f12101294.
[6] B. Mackey, C. F. Kormos, H. Keith, W. R. Moomaw, R. A. Houghton, R. A. Mittermeier, D. Hole, S. Hugh, Understanding the Importance of Primary Tropical Forest Protection As A Mitigation Strategy, Mitigation and Adaptation Strategies for Global Change, Vol. 25, No. 5, 2020, pp. 763-787, https://doi.org/10.1007/s11027-019-09891-4.
[7] M. Siraj, Forest Carbon Stocks In Woody plants of Chilimo-Gaji Forest, Ethiopia: Implications of managing Forests for Climate Change Mitigation, South African Journal of Botany, Vol. 127, 2019, pp. 213-219, https://doi.org/10.1016/j.sajb.2019.09.003.
[8] S. K. Behera, N. Sahu, A. K. Mishra, S. S. Bargali, M. D. Behera, R. Tuli, Aboveground Biomass and Carbon Stock Assessment in Indian Tropical Deciduous Forest And Relationship with Stand Structural Attributes, Ecol. Eng., Vol. 99, 2017, pp. 513-524, https://doi.org/10.1016/j.ecoleng.2016.11.046.
[9] H. K. Gibbs, S. Brown, J. O. Niles, J. A. Foley, Monitoring and Estimating Tropical Forest Carbon Stocks: Making REDD a reality, Environ Research Lettes, Vol. 2, No. 4, 2007, pp. 045023, https://doi.org/10.1088/1748-9326/2/4/045023.
[10] T. D. Pham, K. Yoshino, D. T. Bui, Biomass Estimation of Sonneratia Caseolaris (L.) Engler at A Coastal Area of Hai Phong city (Vietnam) using ALOS-2 PALSAR Imagery and GIS-based Multi-Layer Perceptron Neural Networks, GIScience Remote Sensing, Vol. 54, No. 3, 2017, pp. 329-353, https://doi.org/10.1080/15481603.2016.1269869.
[11] T. V. Pham, T. A. T. Do, H. D. Tran, A. N. T. Do, Assessing the Impact of Ecological Security and Forest Fire Susceptibility on Carbon Stocks in Bo Trach District, Quang Binh Province, Vietnam, Ecological Informatics, Vol. 74, 2023, pp. 101962, https://doi.org/10.1016/j.ecoinf.2022.101962.
[12] Y. Jiao, D. Wang, X. Yao, S. Wang, T. Chi, Y. Meng, Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation, Remote Sensing, Vol. 15, No. 5, 2023, pp. 1410, https://doi.org/10.3390/rs15051410.
[13] M. V. Pham, T. M. Pham, Q. V. V. Du, Q. T. Bui, A. V. Tran, H. M. Pham, T. N. Nguyen, Integrating Sentinel-1A SAR data and GIS to Estimate Aboveground Biomass and Carbon Accumulation for Tropical Forest Types in Thuan Chau district, Vietnam, Remote Sensing Applications: Society and Environment, Vol. 14, 2019, pp. 148-157, https://doi.org/10.1016/j.rsase.2019.03.003.
[14] D. T. Nhung, N. D. Hung, P. N. Hai, D. D. L. Phuong, N. T. D. My, P. V. Manh, Estimation of Above Ground Biomass Using A Combination of Integrated Remote Sensing and Machine Learning Algorithms: A Case Study in the Western Nghe An Biosphere Reserve, Vietnam, Journal of Forestry Science and Technology, Vol. 12, No. 4, 2023, pp. 81-92, https://doi.org/10.55250/jo.vnuf.12.4.2023.081-092 (in Vietnamese).
[15] R. Showstack, Landsat 9 Satellite Continues Half-Century of Earth Observations: Eyes in the Sky Serve as A Valuable Tool for Stewardship, BioScience, Vol. 72, No. 3, 2022, pp. 226-232, https://doi.org/10.1093/biosci/biab145.
[16] Y. Hu, Y. Nie, Z. Liu, G. Wu, W. Fan, Improving the Potential of Coniferous Forest Aboveground Biomass Estimation by Integrating C- and L-Band SAR Data with Feature Selection and Non-Parametric Model, Remote Sensing, Vol. 15, No. 17, 2023, pp. 4194, https://doi.org/10.3390/rs15174194.
[17] M. H. Pham, T. H. Do, V. M. Pham, Q. T. Bui, Mangrove Forest Classification and Aboveground Biomass Estimation Using An Atom Search Algorithm and Adaptive Neuro-Fuzzy Inference System, PLOS ONE, Vol. 15, No. 5, 2020, pp. e0233110, https://doi.org/10.1371/journal.pone.0233110.
[18] H. Yu, Y. Wu, L. Niu, Y. Chai, Q. Feng, W. Wang, T. Liang, A Method to Avoid Spatial Overfitting in Estimation of Grassland Above-Ground Biomass on the Tibetan Plateau, Ecological Indicators, Vol. 125, 2021, pp. 107450, https://doi.org/10.1016/j.ecolind.2021.107450.
[19] A. T. N. Dang, S. Nandy, R. Srinet, N. V. Luong, S. Ghosh, A. Senthil Kumar, Forest Aboveground Biomass Estimation Using Machine Learning Regression Algorithm in Yok Don National Park, Vietnam, Ecological Informatics, Vol. 50, 2019, pp. 24-32, https://doi.org/10.1016/j.ecoinf.2018.12.010.
[20] S. D. Madundo, E. W. Mauya, C. J. Kilawe, Comparison of Multi-Source Remote Sensing Data for Estimating and Mapping Above-Ground Biomass in the West Usambara Tropical Montane Forests, Scientific African, Vol. 21, 2023, pp. e01763, https://doi.org/10.1016/j.sciaf.2023.e01763.
[21] Q. T. Bui, Q. T. Pham, V. M. Pham, V. T. Tran, D. H. Nguyen, Q. H. Nguyen, H. D. Nguyen, N. T. Do, V. M. Vu, Hybrid Machine Learning Models for Aboveground Biomass Estimations, Ecological Informatics, Vol. 79, 2024, pp. 102421, https://doi.org/10.1016/j.ecoinf.2023.102421.
[22] People's Committee of Bac Kan Province, Summary Report on Bac Kan Province Planning for the Period 2021-2030, Vision to 2050, http://tnmt.backan.gov.vn/uploads/news/2024_05/22.5.2023.01bao-cao-tong-hop-qht-bac-kan-pdf, (accessed on: January 5th, 2024) (in Vietnamese).
[23] UNFCCC, Vietnam Briefing Discusses the COP26 and Vietnam’s Climate Action Plan Including Its Commitment to Achieve Net-Zero Carbon Emissions by 2050, https://iucn.org/sites/default/files/2022-12/eng-deliver-vn-cop26-committments-on-re-final, (accessed on: January 5th, 2024).
[24] B. Brede et al., Non-destructive Estimation of Individual Tree Biomass: Allometric Models, Terrestrial and UAV Laser Scanning, Remote Sensing of Environment, Vol. 280, 2022, pp. 113180, https://doi.org/10.1016/j.rse.2022.113180.
[25] G. A. Blackburn, Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves, International Journal of Remote Sensing, Vol. 19, No. 4, 1998, https://doi.org/10.1080/014311698215919.
[26] A. A. Gitelson, Y. J. Kaufman, R. Stark, D. Rundquist, Novel Algorithms for Remote Estimation of Vegetation Fraction, Remote Sensing of Environment, Vol. 80, No. 1, 2002, https://doi.org/10.1016/S0034-4257(01)00289-9.
[27] C. Buschmann, E. Nagel, In Vivo Spectroscopy and Internal Optics of Leaves As Basis for Remote Sensing of Vegetation, International Journal of Remote Sensing, Vol. 14, No. 4, 1993, https://doi.org/10.1080/01431169308904370.
[28] N. H. Broge, E. Leblanc, Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density, Remote Sensing of Environment, Vol. 76, No. 2, 2001, https://doi.org/10.1016/s0034-4257(00)00197-8.
[29] N. Kaveh, A. Ebrahimi, E. Asadi, Comparative Analysis of Random Forest, Exploratory Regression, and Structural Equation Modeling for Screening Key Environmental Variables in Evaluating Rangeland Above-Ground Biomass, Ecological Informatics, Vol. 77, 2023, pp. 102251, https://doi.org/10.1016/j.ecoinf.2023.102251.
[30] O. Mutanga, E. Adam, M. A. Cho, High Density Biomass Estimation for Wetland Vegetation Using Worldview-2 Imagery and Random Forest Regression Algorithm, International Journal of Applied Earth Observation and Geoinformation, Vol. 18, 2012, pp. 399-406, https://doi.org/10.1016/j.jag.2012.03.012.
[31] J. R. Quinlan, Learning with Continuous Classes, Proceedings of Australian Joint Conference on Artificial Intelligence, 1992, pp. 343-348.
[32] L. Brilli, M. Chiesi, C. Brogi, R. Magno, L. Arcidiaco, L. Bottai, G. Tagliaferri, M. Bindi, F. Maselli, Combination of Ground and Remote Sensing Data to Assess Carbon Stock Changes in the Main Urban Park of Florence, Urban Forestry & Urban Greening, Vol. 43, 2019, pp. 126377, https://doi.org/10.1016/j.ufug.2019.126377.
[33] K. Mokany, R. J. Raison, A. S. Prokushkin, Critical Analysis of Root: Shoot Ratios in Terrestrial Biomes, Global Change Biology, Vol. 12, No. 1, 2006, pp. 84-96, https://doi.org/10.1111/j.1365-2486.2005.001043.x.
[34] H. Shiferaw, T. Kassawmar, G. Zeleke, Above and Belowground Woody-Biomass and Carbon Stock Estimations at Kunzila watershed, Northwest Ethiopia, Trees, Forests and People, Vol. 7, 2022, pp. 100204, https://doi.org/10.1016/j.tfp.2022.100204.
[35] K. Prabhakara, W. D. Hively, G. W. McCarty, Evaluating the Relationship Between Biomass, Percent Groundcover and Remote Sensing Indices Across Six Winter Cover Crop Fields in Maryland, United States, International Journal of Applied Earth Observation and Geoinformation, Vol. 39, 2015, pp. 88-102, https://doi.org/10.1016/j.jag.2015.03.002.