Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests

References

• Abbas, S., Sing Wong, M., Jin, W., Shahzad, N., & Muhammad Irteza, S. (2020). Approaches of satellite remote sensing for the assessment of above-ground biomass across tropical forests: Pan-tropical to national scales. Remote Sensing, 12(20), 3351.
   Web of Science ®Google Scholar
• Alberdi, I., Vallejo Bombín, R., Gabriel Álvarez González, J., Condés Ruiz, S., González Ferreiro, E., Guerrero García, S., Hernández Mateo, L., Martínez Jáuregui, M., Montes Pita, F., de Oliveira Rodríguez, N., Pasalodos-Tato, M., Robla, E., Ruiz-González, A. D., Sánchez-González, M., Sandoval, V., San Miguel, A., Sixto, H., & Cañellas, I. (2017). The multi-objective Spanish national forest inventory. Forest Systems, 26(2), e04S. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA): 14. https://doi.org/10.5424/fs/2017262-10577
   Web of Science ®Google Scholar
• Álvarez-González, J. G., Cañellas, I., Alberdi, I., Gadow, K. V., & Ruiz-González, A. D. (2014). National forest inventory and forest observational studies in Spain: Applications to forest modeling. Forest Ecology and Management 316, 54–20. Elsevier. https://doi.org/10.1016/j.foreco.2013.09.007.
   Web of Science ®Google Scholar
• Arnan, A., De Bruin, S., Herold, M., Quegan, S., Labriere, N., Rodriguez-Veiga, P., Avitabile, V., Santoro, M., Mitchard, E. T., Ryan, C. M., Phillips, O. L., Willcock, S., Verbeeck, H., Carreiras, J., Hein, L., Schelhaas, M.-J., Pacheco-Pascagaza, A. M., da Conceição Bispo, P. … Gilani, H. (2022). A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment 272, 112917. Elsevier. https://doi.org/10.1016/j.rse.2022.112917.
   Web of Science ®Google Scholar
• Bastos, A., Ciais, P., Sitch, S., Aragão, L. E., Chevallier, F., Fawcett, D., Rosan, T. M., Saunois, M., Günther, D., & Perugini, L. (2022). On the use of earth observation to support estimates of national greenhouse gas emissions and sinks for the global stocktake process: Lessons learned from ESA-CCI RECCAP2. Carbon Balance and Management, 17(1), 1–16. BioMed Central.
   PubMed Web of Science ®Google Scholar
• Bolton, D. K., Coops, N. C., & Wulder, M. A. (2013). Measuring forest structure along productivity gradients in the Canadian boreal with Small-Footprint Lidar. Environmental Monitoring and Assessment, 185(8), 6617–6634. Springer.
   PubMed Web of Science ®Google Scholar
• Büntgen, U., Krusic, P. J., Piermattei, A., Coomes, D. A., Esper, J., Myglan, V. S., Kirdyanov, A. V., Julio Camarero, J., Crivellaro, A., & Körner, C. (2019). Limited capacity of tree growth to mitigate the global greenhouse effect under predicted warming. Nature Communications, 10(1), 1–6. Nature Publishing Group.
   PubMedGoogle Scholar
• Cao, L., Coops, N. C., Innes, J. L., Sheppard, S. R., Liyong, F., Ruan, H., & She, G. (2016). Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data. Remote Sensing of Environment, 178, 158–171. https://doi.org/10.1016/j.rse.2016.03.012
   Web of Science ®Google Scholar
• Domingo, D., Alonso, R., Teresa Lamelas, M., Luis Montealegre, A., Rodríguez, F., & de la Riva, J. (2019). Temporal transferability of pine forest attributes modeling using low-density airborne laser scanning data. Remote Sensing, 11(3), 261. https://doi.org/10.3390/rs11030261
   Web of Science ®Google Scholar
• Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C. A., Botequim, B., Rodríguez-Gonzálvez, P., González-Ferreiro, E., & Guerra-Hernández, J. (2021). Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in mediterranean forests. Remote Sensing, 13(12), Multidisciplinary Digital Publishing Institute: 2279. https://doi.org/10.3390/rs13122279
   Web of Science ®Google Scholar
• Dubayah, R. O., Armston, J., Healey, S. P., Yang, Z., Patterson, P. L., Saarela, S., Stahl, G., Duncanson, L., & Kellner, J. R. (2022). GEDI L4B Gridded Aboveground Biomass Density, Version 2. ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2017
   Google Scholar
• Eggleston, S., Buendia, L., Miwa, K., Ngara, T., & Tanabe, K. (2006). IPCC guidelines for national greenhouse gas inventories. In Institute for Global Environmental Strategies (IGES). https://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html
   Google Scholar
• Espejo, A., Federici, S., Green, C., Amuchastegui, N., d’Annunzio, R., Balzter, H., Bholanath, P., Brack, C., Brewer, C., & Birigazzi, L. (2020). Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and guidance from the global forest observations initiative, edition 3.0. UN Food and Agriculture Organization.
   Google Scholar
• Fahey, T. J., Woodbury, P. B., Battles, J. J., Goodale, C. L., Hamburg, S. P., Ollinger, S. V., & Woodall, C. W. (2010). Forest carbon storage: Ecology, management, and policy. Frontiers in Ecology and the Environment, 8(5), 245–252. Wiley Online Library. https://doi.org/10.1890/080169
   Web of Science ®Google Scholar
• Gea-Izquierdo, G., Fernández de Uña, L., & Cañellas, I. (2013). Growth projections reveal local vulnerability of mediterranean oaks with rising temperatures. Forest Ecology and Management 305, 282–293. Elsevier. https://doi.org/10.1016/j.foreco.2013.05.058.
   Web of Science ®Google Scholar
• Guerra-Hernández, J., Bastos Görgens, E., García-Gutiérrez, J., Carlos Estraviz Rodriguez, L., Tomé, M., & González-Ferreiro, E. (2016). Comparison of ALS based models for estimating aboveground biomass in three types of mediterranean forest. European Journal of Remote Sensing, 49(1), 185–204. https://doi.org/10.5721/EuJRS20164911
   Web of Science ®Google Scholar
• Guerra-Hernández, J., Botequim, B., Bujan, S., Jurado-Varela, A., Alberto Molina-Valero, J., Martínez-Calvo, A., & Pérez-Cruzado, C. (2022). Interpreting the uncertainty of model-based and cesign-based estimation in downscaling estimates from NFI data: A case-study in extremadura (Spain). GIScience & Remote Sensing, 59(1), 686–704. Taylor &Francis.
   Web of Science ®Google Scholar
• Guerra-Hernández, J., Narine, L. L., Pascual, A., Gonzalez-Ferreiro, E., Botequim, B., Malambo, L., Neuenschwander, A., Popescu, S. C., & Godinho, S. (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. Taylor & Francis. https://doi.org/10.1080/15481603.2022.2115599
   Web of Science ®Google Scholar
• Harris, N. L., Gibbs, D. A., Baccini, A., Birdsey, R. A., De Bruin, S., Farina, M., Fatoyinbo, L., Hansen, M. C., Herold, M., & Houghton, R. A. (2021). Global maps of twenty-first century forest carbon fluxes. Nature Climate Change, 11(3), 234–240. Nature Publishing Group.
   Web of Science ®Google Scholar
• Houghton, R. A., & Nassikas, A. A. (2017). Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochemical Cycles, 31(3), 456–472. Wiley Online Library. https://doi.org/10.1002/2016GB005546
   Web of Science ®Google Scholar
• Hudak, A. T., Fekety, P. A., Kane, V. R., Kennedy, R. E., Filippelli, S. K., Falkowski, M. J., Tinkham, W. T., Smith, A. M., Crookston, N. L., & Domke, G. M. (2020). A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA. Environmental Research Letters, 15(9), 095003. IOP Publishing. https://doi.org/10.1088/1748-9326/ab93f9
   Web of Science ®Google Scholar
• Hudak, A. T., Strand, E. K., Vierling, L. A., Byrne, J. C., Eitel, J. U., Martinuzzi, S., & Falkowski, M. J. (2012). Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sensing of Environment 123, 25–40. Elsevier. https://doi.org/10.1016/j.rse.2012.02.023.
   Web of Science ®Google Scholar
• Hunka N et al . (2023). On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake. Environ. Res. Lett., 18(12), 124042 10.1088/1748-9326/ad0b60
   Web of Science ®Google Scholar
• Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29(5), 1339–1366. https://doi.org/10.1080/01431160701736489
   Web of Science ®Google Scholar
• Jiang, M., Medlyn, B. E., Drake, J. E., Duursma, R. A., Anderson, I. C., Barton, C. V., Boer, M. M., Carrillo, Y., Castañeda-Gómez, L., & Collins, L. (2020). The Fate of Carbon in a Mature Forest under Carbon Dioxide Enrichment. Nature, 580(7802), 227–231. Nature Publishing Group.
   PubMed Web of Science ®Google Scholar
• Jurado-Varela, A., & Guerra-Hernández, J. (2022). ‘Extensión del Cuarto Inventario Forestal Nacional (IFN 4) mediante técnicas LiDAR en Extremadura.’. Revista Montes. https://dialnet.unirioja.es/servlet/articulo?codigo=8581306
   Google Scholar
• Karimon, N., Herold, M., De Sy, V., De Bruin, S., Araza, A., Málaga, N., Gamarra, J. G., Hergoualc’h, K., Pekkarinen, A., Ramirez, C., Morales-Hidalgo, D., & Tavani, R. (2022). Exploring characteristics of national forest inventories for integration with global space-based forest biomass data. Science of the Total Environment 850, Elsevier: 157788. https://doi.org/10.1016/j.scitotenv.2022.157788.
   PubMed Web of Science ®Google Scholar
• Keenan, R. J., Reams, G. A., Achard, F., de Freitas, J. V., Grainger, A., & Lindquist, E. (2015). Dynamics of global forest area: Results from the FAO global forest resources assessment 2015. Forest Ecology and Management. 352, 9–20. https://doi.org/10.1016/j.foreco.2015.06.014
   Web of Science ®Google Scholar
• Khati, U., Lavalle, M., & Singh, G. (2021). The role of time-series L-Band SAR and GEDI in mapping sub-tropical above-ground biomass. Frontiers in Earth Science, 9(October), 752254. https://doi.org/10.3389/feart.2021.752254
   Google Scholar
• Kilpeläinen, A., & Heli, P. (2022). Carbon sequestration and storage in European forests. In L. Hetemäki, J. Kangas, & H. Peltola (Eds.), Forest bioeconomy and climate change (pp. 113–128). Springer International Publishing. https://doi.org/10.1007/978-3-030-99206-4_6
   Google Scholar
• Kumar, L., & Mutanga, O. (2017). Remote sensing of above-ground biomass. Multidisciplinary Digital Publishing Institute.
   Google Scholar
• Labrière, N., Davies, S. J., Disney, M. I., Duncanson, L. I., Herold, M., Lewis, S. L., Phillips, O. L., Quegan, S., Saatchi, S. S., & Schepaschenko, D. G. (2022). Toward a forest biomass reference measurement system for remote sensing applications. Global Change Biology. Wiley Online Library.
   Google Scholar
• Lister, A. J., Andersen, H., Frescino, T., Gatziolis, D., Healey, S., Heath, L. S., Liknes, G. C., McRoberts, R., Moisen, G. G., Nelson, M., Riemann, R., Schleeweis, K., Schroeder, T. A., Westfall, J., & Wilson, B. T. (2020). Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory. Forests, 11(12), Multidisciplinary Digital Publishing Institute: 1364. https://doi.org/10.3390/f11121364
   Web of Science ®Google Scholar
• Liu, S., Brandt, F., Nord-Larsen, T., Chave, J., Reiner, F., Lang, N., Tong, X., Ciais, P., Igel, C., Pascual, A., & Guerra-Hernandez, J. (2023). The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Science Advances, 9(37). https://doi.org/10.1126/sciadv.adh4097
   Web of Science ®Google Scholar
• Luyssaert, S., Schulze, E., Börner, A., Knohl, A., Hessenmöller, D., Law, B. E., Ciais, P., & Grace, J. (2008). Old-growth forests as global carbon sinks. Nature, 455(7210), 213–215. Nature Publishing Group. https://doi.org/10.1038/nature07276
   PubMed Web of Science ®Google Scholar
• MAGRAMA. (2017). 4rd Spanish National Forest Inventory, Ministry of Agriculture, Food and Environment. Ministerio de Agricultura, Alimentación y Medio Ambiente.
   Google Scholar
• MAPA. (2018). Mapa Forestal de España. Escala 1:25.000. Ministerio de Agricultura,Pesca y Alimentación. Dirección General de Desarrollo Rural, Innovación y Política Forestal. https://www.mapa.gob.es/es/desarrollo-rural/temas/politica-forestal/inventario-cartografia/mapa-forestal-espana/metodologia_mfe_25.aspx.
   Google Scholar
• Marino, E., Luis Tomé, J., Hernando, C., Guijarro, M., & Madrigal, J. (2022). Transferability of airborne LiDAR data for canopy fuel mapping: Effect of pulse density and model formulation. Fire, 5(5), 126. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/fire5050126
   Google Scholar
• Mauro, F., Ritchie, M., Wing, B., Frank, B., Monleon, V., Temesgen, H., & Hudak, A. (2019). Estimation of changes of forest structural attributes at three different spatial aggregation levels in northern california using multitemporal LiDAR. Remote Sensing, 11(8), MDPI: 923. https://doi.org/10.3390/rs11080923
   Web of Science ®Google Scholar
• McRoberts, R. E. (2006). A model-based approach to estimating forest area. Remote Sensing of Environment, 103(1), 56–66. Elsevrier.
   Web of Science ®Google Scholar
• McRoberts, R. E., Næsset, E., Gobakken, T., Chirici, G., Condés, S., Hou, Z., Saarela, S., Chen, Q., Ståhl, G., & Walters, B. F. (2018). Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications. Canadian Journal of Forest Research, 48(6), 642–649. NRC Research Press.
   Google Scholar
• McRoberts, R. E., Næsset, E., Saatchi, S., & Quegan, S. (2022). Statistically rigorous, model-based inferences from maps. Remote Sensing of Environment 279, Elsevier: 113028. https://doi.org/10.1016/j.rse.2022.113028.
   Web of Science ®Google Scholar
• McRoberts, R. E., & Tomppo, E. O. (2007). Remote sensing support for national forest inventories. Remote Sensing of Environment, 110(4), 412–419. Elsevier.
   Web of Science ®Google Scholar
• MITECO. (2020). 4rd Spanish National Forest Inventory in Extremadura. Ministerio Para La Transición Ecológica y El Reto Demográfico. https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/inventario-forestal-nacional/publicaciones.aspx.
   Google Scholar
• Molina-Valero, J. A., Julio Camarero, J., Gabriel Alvarez-Gonzalez, J., Cerioni, M., Hevia, A., Sanchez-Salguero, R., Martin-Benito, D., & Perez-Cruzado, C. (2021). Mature Forests Hold Maximum Live Biomass Stocks. Forest Ecology and Management 480, Elsevier: 118635. https://doi.org/10.1016/j.foreco.2020.118635.
   Web of Science ®Google Scholar
• Montero, G., Ruiz-Peinado, R., & Munoz, M. (2005). Producción de Biomasa y Fijación de CO2 Por Los Bosques Españoles. INIA-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria.
   Google Scholar
• Pascual A, Guerra-Hernández J, Cosenza D N and Sandoval-Altelarrea V. (2021). Using enhanced data co-registration to update Spanish National Forest Inventories (NFI) and to reduce training data under LiDAR-assisted inference. International Journal of Remote Sensing, 42(1), 126–147. 10.1080/01431161.2020.1813346
   Web of Science ®Google Scholar
• Pérez-Cruzado, C., Mohren, G. M., Merino, A., & Rodríguez-Soalleiro, R. (2012). Carbon balance for different management practices for fast growing tree species planted on former pastureland in Southern Europe: A case study using the CO2Fix Model. European Journal of Forest Research, 131(6), 1695–1716. Springer. https://doi.org/10.1007/s10342-012-0609-6
   Web of Science ®Google Scholar
• Persson, H. J., Ekström, M., & Ståhl, G. (2022). Quantify and account for field reference errors in forest remote sensing studies. Remote Sensing of Environment 283, Elsevier: 113302. https://doi.org/10.1016/j.rse.2022.113302.
   Web of Science ®Google Scholar
• Persson, H. J., & Ståhl, G. (2020). Characterizing uncertainty in forest remote sensing studies. Remote Sensing, 12(3), 3. Multidisciplinary Digital Publishing Institute: 505. https://doi.org/10.3390/rs12030505
   Web of Science ®Google Scholar
• Poudel, K. P., Flewelling, J. W., & Temesgen, H. (2018). Predicting volume and biomass change from multi-temporal lidar sampling and remeasured field inventory data in panther creek watershed, Oregon, USA. Forests, 9(1), MDPI: 28. https://doi.org/10.3390/f9010028
   PubMed Web of Science ®Google Scholar
• Ricardo, R.-P., Montero González, G., & Del Rio, M. (2012). Biomass models to estimate carbon stocks for hardwood tree species. Forest Systems, 21(1), 42–52. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria(INIA). https://doi.org/10.5424/fs/2112211-02193
   Web of Science ®Google Scholar
• Routa, J., Kilpeläinen, A., Ikonen, V.-P., Asikainen, A., Venäläinen, A., & Peltola, H. (2019). Effects of intensified silviculture on timber production and its economic profitability in Boreal Norway Spruce and scots pine stands under changing climatic conditions. Forestry: An International Journal of Forest Research, 92(5), 648–658. Oxford University Press.
   Google Scholar
• Rozendaal, D. M., Requena Suarez, D., De Sy, V., Avitabile, V., Sarah Carter, C. A. Y., Alvarez-Davila, E., Anderson-Teixeira, K., Araujo-Murakami, A., & Arroyo, L. (2022a). Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC Default Values for tropical and subtropical forests. Environmental Research Letters, 17(1), 014047. IOP Publishing.
   Web of Science ®Google Scholar
• Rozendaal, D. M., Requena Suarez, D., De Sy, V., Avitabile, V., Sarah Carter, C. A. Y., Alvarez-Davila, E., Anderson-Teixeira, K., Araujo-Murakami, A., & Arroyo, L. (2022b). Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC default values for tropical and subtropical forests. Environmental Research Letters, 17(1), 014047. IOP Publishing.
   Web of Science ®Google Scholar
• Ruiz-Peinado, R., Del Rio, M., & Montero, G. (2011). New models for estimating the carbon sink capacity of Spanish softwood species. Forest Systems, 20(1), 176–188. https://doi.org/10.5424/fs/2011201-11643
   Web of Science ®Google Scholar
• Saarela, S., Wästlund, A., Holmström, E., Appiah Mensah, A., Holm, S., Nilsson, M., Fridman, J., & Ståhl, G. (2020). Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors. Forest Ecosystems, 7(1), 1–17. Springer. https://doi.org/10.1186/s40663-020-00245-0
   Web of Science ®Google Scholar
• Santoro, M., & Cartus, O. (2021). Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2, centre for environmental data analysis. june 2021. htt p://D x.Doi.Org/1 0.5285/84403d09cef3485883158f4df2989b0c.
   Google Scholar
• Schall, P., Schulze, E.-D., Fischer, M., Ayasse, M., & Ammer, C. (2018). Relations between forest management, stand structure and productivity across different types of central European forests. Basic & Applied Ecology 32, 39–52. Elsevier. https://doi.org/10.1016/j.baae.2018.02.007.
   Web of Science ®Google Scholar
• Sequeda, M. M. (2017). Forestación de Tierras Agrarias En Extremadura. In 7 Congreso Forestal Español. Sociedad Española de Ciencias Forestales. http://Secforestales.Org/Publicaciones/Index.Php/Congresosforestales/Article/View/19381
   Google Scholar
• Sophie, D., Donoghue, D. N., & Galiatsatos, N. (2020). The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity. International Journal of Applied Earth Observation and Geoinformation 92, 102160. Elsevier:. https://doi.org/10.1016/j.jag.2020.102160.
   Web of Science ®Google Scholar
• Tompalski, P., White, J. C., Coops, N. C., & Wulder, M. A. (2019). Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data. Remote Sensing of Environment. 227, 110–124. https://doi.org/10.1016/j.rse.2019.04.006
   Web of Science ®Google Scholar
• Vangi, E., D’Amico, G., Francini, S., Borghi, C., Giannetti, F., Corona, P., Marchetti, M., Travaglini, D., Pellis, G., & Vitullo, M. (2022). LARGE-SCALE high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool. Environmental Modelling & Software. 105580. https://doi.org/10.1016/j.envsoft.2022.105580
   Web of Science ®Google Scholar
• Yang, L., Liang, S., & Zhang, Y. (2020). A new method for generating a global forest aboveground biomass map from multiple high-level satellite products and ancillary information. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13, 2587–2597. IEEE. https://doi.org/10.1109/JSTARS.2020.2987951.
   Web of Science ®Google Scholar
• Zhao, K., & Popescu, S. (2009). Lidar-based mapping of leaf area Index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment, 113(8), 1628–1645. https://doi.org/10.1016/j.rse.2009.03.006
   Web of Science ®Google Scholar