Bosbeheer in perspectief: een blik vanuit de ruimte

Adame, P., Cañellas, I., Moreno-Fernández, D., Packalen, T., Hernández, L., & Alberdi, I. (2020). Analyzing the Joint Effect of Forest Management and Wildfires on Living Biomass and Carbon Stocks in Spanish Forests. Forests, 11(11), Article 11. https://doi.org/10.3390/f11111219 Aertsen, W., Kint, V., van Orshoven, J., Özkan, K., & Muys, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119-1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007 Agentschap voor Natuur en Bos. (2020, juni 16). Hoe gebeuren de metingen? Agentschap voor Natuur en Bos. https://www.natuurenbos.be/beleid-wetgeving/natuurbeheer/bosinventaris/…metingen Ajani, J. I., Keith, H., Blakers, M., Mackey, B. G., & King, H. P. (2013). Comprehensive carbon stock and flow accounting: A national framework to support climate change mitigation policy. Ecological Economics, 89, 61-72. https://doi.org/10.1016/j.ecolecon.2013.01.010 Alberdi, I., Cañellas, I., & Vallejo, R. (2017). The Spanish National Forest Inventory: History, development, challenges and perspectives. Pesquisa Florestal Brasileira, 37. https://doi.org/10.4336/2017.pfb.37.91.1337 Alcasena, F., Rodrigues, M., Gelabert, P., Ager, A., Salis, M., Ameztegui, A., Cervera, T., & Vega-García, C. (2021). Fostering Carbon Credits to Finance Wildfire Risk Reduction Forest Management in Mediterranean Landscapes. Land, 10(10), Article 10. https://doi.org/10.3390/land10101104 Alcasena, F., Salis, M., Nauslar, N. J., Aguinaga, A. E., & Vega-García, C. (2016). Quantifying economic losses from wildfires in black pine afforestations of northern Spain. Forest Policy and Economics, 73, 153-167. https://doi.org/10.1016/j.forpol.2016.09.005 Ameztegui, A., Rodrigues, M., & Granda, V. (2022). Uncertainty of biomass stocks in Spanish forests: A comprehensive comparison of allometric equations. European Journal of Forest Research, 141(3), 395-407. https://doi.org/10.1007/s10342-022-01444-w Ascaso-Sastrón, E., López-Villalba, P., Vicens-Ferrer, M., & Herms-Canellas, I. (z.d.). Some soil quality indicators of the soils of Catalonia. Bates, D., Maechler, M., Bolker [aut, B., cre, Walker, S., Christensen, R. H. B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., Fox, J., Bauer, A., simulate.formula), P. N. K. (shared copyright on, Tanaka, E., & Jagan, M. (2024). lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4 (1.1-35.3) [Software]. https://cran.r-project.org/web/packages/lme4/index.html Bellassen, V., & Luyssaert, S. (2014). Carbon sequestration: Managing forests in uncertain times. Nature, 506(7487), Article 7487. https://doi.org/10.1038/506153a Berben, J. (1983). Dendrometrische studie van de Corsikaanse den. Genk: LISEC. Bhat, Y., Kripa, M. K., & Dadhwal, V. K. (2023). Validation of ESA-CCI Forest Biomass Products over India: Methodological and Data Challenges and Results. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-023-01741-w Boisvenue, C., & Running, S. W. (2006). Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century. Global Change Biology, 12(5), 862-882. https://doi.org/10.1111/j.1365-2486.2006.01134.x Bolar, K. (2019). STAT: Interactive Document for Working with Basic Statistical Analysis (0.1.0) [Software]. https://cran.r-project.org/web/packages/STAT/index.html Bradford, J. B., Jensen, N. R., Domke, G. M., & D’Amato, A. W. (2013). Potential increases in natural disturbance rates could offset forest management impacts on ecosystem carbon stocks. Forest Ecology and Management, 308, 178-187. https://doi.org/10.1016/j.foreco.2013.07.042 Brockerhoff, E. G., Barbaro, L., Castagneyrol, B., Forrester, D. I., Gardiner, B., González-Olabarria, J. R., Lyver, P. O., Meurisse, N., Oxbrough, A., Taki, H., Thompson, I. D., van der Plas, F., & Jactel, H. (2017). Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodiversity and Conservation, 26(13), 3005-3035. https://doi.org/10.1007/s10531-017-1453-2 Bulut, O. (2021, januari 11). Effective Feature Selection: Recursive Feature Elimination Using R. Medium. https://towardsdatascience.com/effective-feature-selection-recursive-fe…148ff998e4f7 Cervera, T.; Baiges, T.; Rabascall, X.; et al. 2022. Methodology for calculating the impact of forest management on ecosystem services: carbon, water and biodiversity. Forest Ownership Centre, Santa Perpètua de Mogoda. 61 pp. 73 Chen, L., Wang, Y., Ren, C., Zhang, B., & Wang, Z. (2019a). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sensing, 11(4), Article 4. https://doi.org/10.3390/rs11040414 Chen, L., Wang, Y., Ren, C., Zhang, B., & Wang, Z. (2019b). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sensing, 11(4), Article 4. https://doi.org/10.3390/rs11040414 Chéret, V., & Denux, J.-P. (2011). Analysis of MODIS NDVI Time Series to Calculate Indicators of Mediterranean Forest Fire Susceptibility. GIScience & Remote Sensing, 48(2), 171-194. https://doi.org/10.2747/1548-1603.48.2.171 Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147-159. https://doi.org/10.1016/0034-4257(89)90023-0 CREAF. (z.d.). Allometr App. AllometrApp. Geraadpleegd 5 december 2023, van https://laboratoriforestal.creaf.cat/allometrapp/ CTFC. (2021). Forest Management in the Catalan territory. NetForest. https://www.ctfc.cat/docs/IO5_CatalanWeek.pdf?fbclid=IwAR2g22V4tXHs8oCt… Howmf4FFaUUt3U90EoIS6kQoedLs0 Cutler, F. original by L. B. and A., & Wiener, R. port by A. L. and M. (2022). randomForest: Breiman and Cutler’s Random Forests for Classification and Regression (4.7-1.1) [Software]. https://cran.rproject.org/web/packages/randomForest/index.html Dagnelie, P., Palm, R., Rondeux, J., & Thill, A. (1985). Tables de cubage des arbres et des peuplements forestiers. Gembloux: Les presses agronomiques de Gembloux. Darst, B. F., Malecki, K. C., & Engelman, C. D. (2018). Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genetics, 19(1), 65. https://doi.org/10.1186/s12863-018-0633-8 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 Delalay, M., Tiwari, V., Ziegler, A. D., Gopal, V., & Passy, P. (2019). Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: Operational method and its implementation for a mountainous area of Nepal. Journal of Applied Remote Sensing, 13(1), 014530. https://doi.org/10.1117/1.JRS.13.014530 Denghsheng Lu, Qui Chen, Guangxing Wang, Emilio Moran, Mateus Batistella, Maozhen Zhang, Gaia Vaglio Laurin, & David Saah. (2012, april 4). Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. https://www.hindawi.com/journals/ijfr/2012/436537/ Duane, A., Pique, M., Castellnou, M., & Brotons, L. (2015). Predictive modelling of fire occurrences from different fire spread patterns in Mediterranean landscapes. International Journal of Wildland Fire, 24. https://doi.org/10.1071/WF14040 Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., & Silva, C. (2020). The Global Ecosystem Dynamics Investigation: Highresolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing, 1, 100002. https://doi.org/10.1016/j.srs.2020.100002 Dupire, S., Curt, T., Bigot, S., & Fréjaville, T. (2019). Vulnerability of forest ecosystems to fire in the French Alps. European Journal of Forest Research, 138(5), 813-830. https://doi.org/10.1007/s10342-019- 01206-1 Elvira, N. J., Lloret, F., Jaime, L., Margalef-Marrase, J., Pérez Navarro, M. Á., & Batllori, E. (2021). Species climatic niche explains post-fire regeneration of Aleppo pine (Pinus halepensis Mill.) under compounded effects of fire and drought in east Spain. Science of The Total Environment, 798, 149308. https://doi.org/10.1016/j.scitotenv.2021.149308 esa. (z.d.). Sentinel-2—Missions—Sentinel Online. Sentinel Online. Geraadpleegd 10 april 2023, van https://copernicus.eu/missions/sentinel-2 European Commission. (z.d.). Causes of climate change. Geraadpleegd 6 mei 2024, van https://climate.ec.europa.eu/climate-change/causes-climate-change_en 74 European Space Agency. (z.d.-a). Biomass. ESA Climate Office. Geraadpleegd 9 april 2024, van https://climate.esa.int/en/projects/biomass/ European Space Agency. (z.d.-b). Introducing Copernicus. Geraadpleegd 1 april 2024, van https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Introdu… European Space Agency. (z.d.-c). Sentinel-1—Mission guide. Sentinel Online. Geraadpleegd 8 april 2024, van https://copernicus.eu/missions/sentinel-1 European Space Agency. (z.d.-d). User Guides—Sentinel-1 SAR - Acquisition Modes—Sentinel Online. Sentinel Online. Geraadpleegd 8 april 2024, van https://copernicus.eu/user-guides/sentinel-1- sar/acquisition-modes European Space Agency. (2022). Sentinel-2 MSI Level-2A BOA Reflectance [dataset]. https://doi.org/10.5270/S2_-znk9xsj FAO. (z.d.). Overview | REDD+ Reducing Emissions from Deforestation and Forest Degradation | Food and Agriculture Organization of the United Nations. Geraadpleegd 1 april 2023, van https://www.fao.org/redd/overview/en/ FAO. (2022). The State of the World’s Forests 2022. FAO. https://doi.org/10.4060/cb9360en forestWISE. (z.d.). FIRE-RES: living lab Catalonia-Spain. FIRE RES. Geraadpleegd 6 mei 2024, van https://fire-res.eu/living-lab/catalonia-spain/ Forkuor, G., Benewinde Zoungrana, J.-B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020a). 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 Forkuor, G., Benewinde Zoungrana, J.-B., Dimobe, K., Ouattara, B., Vadrevu, K. P., & Tondoh, J. E. (2020b). 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 Forsius, M., Holmberg, M., Junttila, V., Kujala, H., Schulz, T., Paunu, V.-V., Savolahti, M., Minunno, F., Akujärvi, A., Bäck, J., Grönroos, J., Heikkinen, R. K., Karvosenoja, N., Mäkelä, A., Mikkonen, N., Pekkonen, M., Rankinen, K., & Virkkala, R. (2023). Modelling the regional potential for reaching carbon neutrality in Finland: Sustainable forestry, energy use and biodiversity protection. Ambio. https://doi.org/10.1007/s13280-023-01860-1 Francini, S., D’Amico, G., Vangi, E., Borghi, C., & Chirici, G. (2022). Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy. Sensors, 22(5), Article 5. https://doi.org/10.3390/s22052015 Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., Qian, J., & Yang, J. (2023). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models (4.1-8) [Software]. https://cran.r-project.org/web/packages/glmnet/index.html Garcia-Gonzalo, J., Peltola, H., Briceño-elizondo, E., & Kellomäki, S. (2007a). Changed thinning regimes may increase carbon stock under climate change: A case study from a Finnish boreal forest. Climatic Change, 81(3), 431-454. https://doi.org/10.1007/s10584-006-9149-8 Garcia-Gonzalo, J., Peltola, H., Briceño-elizondo, E., & Kellomäki, S. (2007b). Changed thinning regimes may increase carbon stock under climate change: A case study from a Finnish boreal forest. Climatic Change, 81(3), 431-454. https://doi.org/10.1007/s10584-006-9149-8 gencat. (2024). Basic indicators of Catalonia. Forest fires. https://www.idescat.cat/indicadors/?id=basics&n=10545&tema=media&lang=en Gobierno de España. (2020). 50 years of wildfire knowledge and prevention in Spain. https://www.miteco.gob.es/es/biodiversidad/temas/incendios-forestales/f…- 526033.pdf Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R. A., & Sun, M. (2009). Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance and Management, 4(1), 2. https://doi.org/10.1186/1750-0680-4-2 Google Earth. (z.d.). Geraadpleegd 9 april 2024, van https://earth.google.com/web/@50.76016652,3.48772321,111.53777842a,604…. 24176764h,0t,0r/data=OgMKATA Goos, P. (2017). Inleiding tot statistiek en kansrekenen. Acco. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 75 Gschwantner, T., Alberdi, I., Balázs, A., Bauwens, S., Bender, S., Borota, D., Bosela, M., Bouriaud, O., Cañellas, I., Donis, J., Freudenschuß, A., Hervé, J.-C., Hladnik, D., Jansons, J., Kolozs, L., Korhonen, K. T., Kucera, M., Kulbokas, G., Kuliešis, A., … Zell, J. (2019). Harmonisation of stem volume estimates in European National Forest Inventories. Annals of Forest Science, 76(1), Article 1. https://doi.org/10.1007/s13595-019-0800-8 Gurung, M. B., Bigsby, H., Cullen, R., & Manandhar, U. (2015a). Estimation of carbon stock under different management regimes of tropical forest in the Terai Arc Landscape, Nepal. Forest Ecology and Management, 356, 144-152. https://doi.org/10.1016/j.foreco.2015.07.024 Gurung, M. B., Bigsby, H., Cullen, R., & Manandhar, U. (2015b). Estimation of carbon stock under different management regimes of tropical forest in the Terai Arc Landscape, Nepal. Forest Ecology and Management, 356, 144-152. https://doi.org/10.1016/j.foreco.2015.07.024 Hamner, B., Frasco, M., & LeDell, E. (2018). Metrics: Evaluation Metrics for Machine Learning (0.1.4) [Software]. https://cran.r-project.org/web/packages/Metrics/index.html Hastie, T., & Tibshirani, R. (1986). Generalized Additive Models. Statistical Science, 1(3), 297-310. https://doi.org/10.1214/ss/1177013604 Hijmans, R. J., Etten, J. van, Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., Fasoli, B., Forrest, D., Ghosh, A., Golicher, D., Gray, J., Greenberg, J. A., Hiemstra, P., Hingee, K., Ilich, A., Geosciences, I. for M. A., … Wueest, R. (2023). raster: Geographic Data Analysis and Modeling (3.6-26) [Software]. https://cran.r-project.org/web/packages/raster/index.html Holloway, V., & Giandomenico, E. (2009). The History of REDD. Hoscilo, A., Aneta, L., Ziolkowski, D., Stereńczak, K., Lisańczuk, M., Schmullius, C., & Carsten, P. (2018). Forest Aboveground Biomass Estimation Using a Combination of Sentinel-1 and Sentinel-2 Data. 9026-9029. https://doi.org/10.1109/IGARSS.2018.8517965 INFORMA. (2022, oktober 13). Informa forests—Informa Forests. https://informa-forests.eu/ IPCC. (2003). Good practice guidance for land use, land-use change and forestry /The Intergovernmental Panel on Climate Change. Ed. By Jim Penman (J. Penman, Red.). IPCC. (2007). FAQ 1.3—AR4 WGI Chapter 1: Historical Overview of Climate Change Science. What is the Greenhouse Effect. https://archive.ipcc.ch/publications_and_data/ar4/wg1/en/faq-1-3.html Ispikoudis, S., Zianis, D., Tziolas, E., Damianidis, C., Rapti, D., Tsiros, E., Michalakis, D., & Karteris, A. (2024). Assessment of Forest Biomass and Carbon Storage in Habitat 9340 Quercus ilex L. to Support Management Decisions for Climate Change Mitigation. Sustainability, 16(4), Article 4. https://doi.org/10.3390/su16041403 Jandl, R., Bauhus, J., Bolte, A., Schindlbacher, A., & Schüler, S. (2015). Effect of Climate-Adapted Forest Management on Carbon Pools and Greenhouse Gas Emissions. Current Forestry Reports, 1(1), 1-7. https://doi.org/10.1007/s40725-015-0006-8 Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D. W., Minkkinen, K., & Byrne, K. A. (2007). How strongly can forest management influence soil carbon sequestration? Geoderma, 137(3), 253-268. https://doi.org/10.1016/j.geoderma.2006.09.003 Kay, H., Lucas, R., Quegan, S., de Bruin, S., Araza, A., & Herold, M. (2019). PRODUCT VALIDATION & INTERCOMPARISON REPORT YEAR 1 VERSION 1.0. 1, 51. Kern, C. C., Kenefic, L. S., Kuehne, C., Weiskittel, A. R., Kaschmitter, S. J., D’Amato, A. W., Dey, D. C., Kabrick, J. M., Palik, B. J., & Schuler, T. M. (2021a). Relative influence of stand and site factors on aboveground live-tree carbon sequestration and mortality in managed and unmanaged forests. Forest Ecology and Management, 493, 119266. https://doi.org/10.1016/j.foreco.2021.119266 Kern, C. C., Kenefic, L. S., Kuehne, C., Weiskittel, A. R., Kaschmitter, S. J., D’Amato, A. W., Dey, D. C., Kabrick, J. M., Palik, B. J., & Schuler, T. M. (2021b). Relative influence of stand and site factors on aboveground live-tree carbon sequestration and mortality in managed and unmanaged forests. Forest Ecology and Management, 493, 119266. https://doi.org/10.1016/j.foreco.2021.119266 KMI. (2024). Klimaatatlas. KMI. https://www.meteo.be/nl/klimaat/klimaat-vanbelgie/klimaatatlas/klimaatkaarten/neerslag/neerslaghoeveelheid/jaarlijks Koch, B. (2010). Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 581-590. https://doi.org/10.1016/j.isprsjprs.2010.09.001 Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., & Hunt, 76 T. (2023). caret: Classification and Regression Training (6.0-94) [Software]. https://cran.rproject.org/web/packages/caret/index.html Kun, Z., DellaSala, D., Keith, H., Kormos, C., Mercer, B., Moomaw, W. R., & Wiezik, M. (2020). Recognizing the importance of unmanaged forests to mitigate climate change. GCB Bioenergy, 12(12), 1034- 1035. https://doi.org/10.1111/gcbb.12714 Kursa, M. B., & Rudnicki, W. R. (2022). Boruta: Wrapper Algorithm for All Relevant Feature Selection (8.0.0) [Software]. https://cran.r-project.org/web/packages/Boruta/index.html Kursa, M., & Rudnicki, W. (2010). Feature Selection with Boruta Package. Journal of Statistical Software, 36, 1-13. https://doi.org/10.18637/jss.v036.i11 Kycko, M., Zagajewski, B., Kluczek, M., Tardà, A., Pineda, L., Palà, V., & Corbera, J. (2022). Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia. Remote Sensing, 14(21), Article 21. https://doi.org/10.3390/rs14215531 Lang, N., Jetz, W., & Wegner, J. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7, 1-12. https://doi.org/10.1038/s41559-023-02206-6 Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR (arXiv:2107.07431). arXiv. https://doi.org/10.48550/arXiv.2107.07431 Lang, N., Schindler, K., & Wegner, J. D. (2022). ETH_GlobalCanopyHeight_10m_2020_version1: Global canopy height map for the year 2020 derived from Sentinel-2 and GEDI [dataset]. ETH Zurich. https://doi.org/10.3929/ethz-b-000609802 Lasco, R., & Pulhin, F. (2003). Philippine forest ecosystems and climate change: Carbon stocks, rate of sequestration and the Kyoto Protocol. Annals of Tropical Research, 25. Laurin, G. V., Balling, J., Corona, P., Mattioli, W., Papale, D., Puletti, N., Rizzo, M., Truckenbrodt, J., & Urban, M. (2018). Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data. Journal of Applied Remote Sensing, 12(1), 016008. https://doi.org/10.1117/1.JRS.12.016008 Legendre, P., & Fortin, M. J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80(2), 107-138. https://doi.org/10.1007/BF00048036 Liao, C., Luo, Y., Fang, C., & Li, B. (2010). Ecosystem Carbon Stock Influenced by Plantation Practice: Implications for Planting Forests as a Measure of Climate Change Mitigation. PLOS ONE, 5(5), e10867. https://doi.org/10.1371/journal.pone.0010867 Liaw, A., & Wiener, M. (2002a). Classification and Regression by randomForest. 2. Liaw, A., & Wiener, M. (2002b). Classification and Regression by randomForest. 2. Lin, H.-T., Lam, T. Y., von Gadow, K., & Kershaw, J. A. (2020). Effects of nested plot designs on assessing stand attributes, species diversity, and spatial forest structures. Forest Ecology and Management, 457, 117658. https://doi.org/10.1016/j.foreco.2019.117658 Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Vaglio Laurin, G., & Saah, D. (2012). Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. International Journal of Forestry Research, 2012, e436537. https://doi.org/10.1155/2012/436537 Luyssaert, S., Marie, G., Valade, A., Chen, Y.-Y., Njakou Djomo, S., Ryder, J., Otto, J., Naudts, K., Lansø, A. S., Ghattas, J., & McGrath, M. J. (2018). Trade-offs in using European forests to meet climate objectives. Nature, 562(7726), Article 7726. https://doi.org/10.1038/s41586-018-0577-1 Luyssaert, S., Schulze, E.-D., Börner, A., Knohl, A., Hessenmöller, D., Law, B. E., Ciais, P., & Grace, J. (2008). Old-growth forests as global carbon sinks. Nature, 455(7210), Article 7210. https://doi.org/10.1038/nature07276 Marzano, R., Lingua, E., & Garbarino, M. (2012). Post-fire effects and short-term regeneration dynamics following highseverity crown fires in a Mediterranean forest. iForest - Biogeosciences and Forestry. https://doi.org/10.3832/ifor0612-005 Mngadi, M., Odindi, J., & Mutanga, O. (2021). The Utility of Sentinel-2 Spectral Data in Quantifying AboveGround Carbon Stock in an Urban Reforested Landscape. Remote Sensing, 13(21), Article 21. https://doi.org/10.3390/rs13214281 Mohajane, M., Costache, R., Karimi, F., Bao Pham, Q., Essahlaoui, A., Nguyen, H., Laneve, G., & Oudija, F. (2021). Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 129, 107869. https://doi.org/10.1016/j.ecolind.2021.107869 77 Mohd Zaki, N. A., Latif, Z. A., & Suratman, M. N. (2018). Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: Integration of field-based and remotely sensed estimates. International Journal of Remote Sensing, 39(8), 2312-2340. https://doi.org/10.1080/01431161.2017.1421793 Moradi, F., Sadeghi, S. M. M., Heidarlou, H. B., Deljouei, A., Boshkar, E., & Borz, S. A. (2022). Aboveground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data. Annals of Forest Research, 65(1), 165-182. https://doi.org/10.15287/afr.2022.2390 Morsdorf, F., Mårell, A., Koetz, B., Cassagne, N., Pimont, F., Rigolot, E., & Allgöwer, B. (2010). Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sensing of Environment, 114(7), 1403-1415. https://doi.org/10.1016/j.rse.2010.01.023 Muhandisin, A. (2023, oktober 12). Understanding Wrapper Methods in Machine Learning: A Guide to Feature Selection. Medium. https://arismuhandisin.medium.com/understanding-wrapper-methodsin-machine-learning-a-guide-to-feature-selection-23f71059abf8 Mutanga, O., Masenyama, A., & Sibanda, M. (2023). Spectral saturation in the remote sensing of highdensity 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 Nabuurs, G.-J., Verkerk, P. J., Schelhaas, M.-J., González Olabarria, J. R., Trasobares, A., Cienciala, E., & European Forest Institute. (2018). Climate-Smart Forestry: Mitigation impacts in three European regions (From Science to Policy) [From Science to Policy]. European Forest Institute. https://doi.org/10.36333/fs06 Nagler, P. L., Daughtry, C. S. T., & Goward, S. N. (2000). Plant Litter and Soil Reflectance. Remote Sensing of Environment, 71(2), 207-215. https://doi.org/10.1016/S0034-4257(99)00082-6 Nocentini, S., Travaglini, D., & Muys, B. (2022). Managing Mediterranean Forests for Multiple Ecosystem Services: Research Progress and Knowledge Gaps. Current Forestry Reports, 8(2), 229-256. https://doi.org/10.1007/s40725-022-00167-w Noormets, A., Epron, D., Domec, J. C., McNulty, S. G., Fox, T., Sun, G., & King, J. S. (2015). Effects of forest management on productivity and carbon sequestration: A review and hypothesis. Forest Ecology and Management, 355, 124-140. https://doi.org/10.1016/j.foreco.2015.05.019 Nuthammachot, N., Askar, A., Stratoulias, D., & Wicaksono, P. (2022). Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation. Geocarto International, 37(2), 366-376. https://doi.org/10.1080/10106049.2020.1726507 Olivares-Cabello, C., Chaparro, D., Vall-llossera, M., & Camps, A. (2021). Monitoring Forest Above-Ground Biomass from Multifrequency Vegetation Optical Depth: A Preliminary Study. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6012-6015. https://doi.org/10.1109/IGARSS47720.2021.9553770 Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. C. (2012). Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275, 117-129. https://doi.org/10.1016/j.foreco.2012.03.003 Open ICGC — QGIS Python Plugins Repository. (z.d.). Geraadpleegd 20 april 2023, van https://plugins.qgis.org/plugins/OpenICGC/ Otsu, K., Pla, M., Duane, A., Cardil, A., & Brotons, L. (2019). Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery. Drones, 3(4), Article 4. https://doi.org/10.3390/drones3040080 Pebesma, E. (2023). sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/sf.pdf Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 7, e6876. https://doi.org/10.7717/peerj.6876 Perin, J., Bauwens, S., Pitchugin, M., Lejeune, P., Hébert, J., & ANB. (2018). National Forest Accounting Plan of Belgium. https://www.cncnkc.be/sites/default/files/report/file/national_forest_accounting_plan_-_belgium.pdf Picard, R. R., & Cook, R. D. (1984). Cross-Validation of Regression Models. Journal of the American Statistical Association, 79(387), 575-583. https://doi.org/10.1080/01621459.1984.10478083 Piqué,M; Castellnou, M.; Valor, T.; Pagés, J.; Larrañaga, A.; Miralles, M.; Cervera, T. 2011. Integració del risc de grans incendis forestals (GIF) en la gestió forestal: Incendis tipus i vulnerabilitat de le estructures forestals al foc de capçades. Sèrie: Orientacions de gestió forestal sostenible per a 78 Catalunya (ORGEST) Centre de la Propietat Forestal. Departament d'Agricultura, Ramaderia, Pesca, Alimentació i Medi Natural Generalitat de Catalunya. Piralilou, S. T., Einali, G., Ghorbanzadeh, O., Gudiyangada, T., Gholamnia, K., Blaschke, T., & Ghamisi, P. (2022). A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions. Remote Sensing, 14, 672. https://doi.org/10.3390/rs14030672 Pires Coelho, A. J., Ribeiro Matos, F. A., Villa, P. M., Heringer, G., Pontara, V., de Paula Almado, R., & Alves Meira-Neto, J. A. (2022). Multiple drivers influence tree species diversity and above-ground carbon stock in second-growth Atlantic forests: Implications for passive restoration. Journal of Environmental Management, 318, 115588. https://doi.org/10.1016/j.jenvman.2022.115588 Prabhakaran, S. (2018, juni 7). Feature Selection—Ten Effective Techniques with Examples | ML+. Machine Learning Plus. https://www.machinelearningplus.com/machine-learning/featureselection/ Pradhan, B., Dini Hairi Bin Suliman, M., & Arshad Bin Awang, M. (2007). Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 16(3), 344-352. https://doi.org/10.1108/09653560710758297 Probst, P., Wright, M. N., & Boulesteix, A.-L. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3), e1301. https://doi.org/10.1002/widm.1301 Provincie Vlaams-Brabant. (2023). Brabantse Wouden. Provincie Vlaams-Brabant. https://www.vlaamsbrabant.be/nl/natuur-en-milieu/brabantse-wouden Pukkala, T. (2017a). Does management improve the carbon balance of forestry? Forestry: An International Journal of Forest Research, 90(1), 125-135. https://doi.org/10.1093/forestry/cpw043 Pukkala, T. (2017b). Does management improve the carbon balance of forestry? Forestry: An International Journal of Forest Research, 90(1), 125-135. https://doi.org/10.1093/forestry/cpw043 Puletti, N., Galluzzi, M., Grotti, M., & Ferrara, C. (2021). Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data. Remote Sensing Applications: Society and Environment, 24, 100620. https://doi.org/10.1016/j.rsase.2021.100620 Puliti, S., Hauglin, M., Breidenbach, J., Montesano, P., Neigh, C. S. R., Rahlf, J., Solberg, S., Klingenberg, T. F., & Astrup, R. (2020). Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data. Remote Sensing of Environment, 236, 111501. https://doi.org/10.1016/j.rse.2019.111501 Quataert, P., Van der Aa, B., & Verschelde, P. (2011). Opstellen van tarieven voro Inlandse eik en beuk in Vlaanderen ten behoe van het berekenen van houtvolumes. Statistische evaluatie van de regressiemodellen en overzicht van de resultaten (technisch rapport deel III). Instituut voor Natuuren Bosonderzoek, Brussel; Rapporten van het Instituut voor Natuur- en Bosonderzoek 2011 (18). Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., & Balzter, H. (2017a). Quantifying Forest Biomass Carbon Stocks From Space. Current Forestry Reports, 3(1), 1-18. https://doi.org/10.1007/s40725- 017-0052-5 Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., & Balzter, H. (2017b). Quantifying Forest Biomass Carbon Stocks From Space. Current Forestry Reports, 3(1), 1-18. https://doi.org/10.1007/s40725- 017-0052-5 Rosas-Chavoya, M., López-Serrano, P. M., Vega-Nieva, D. J., Hernández-Díaz, J. C., Wehenkel, C., & Corral-Rivas, J. J. (2023). Estimating Above-Ground Biomass from Land Surface Temperature and Evapotranspiration Data at the Temperate Forests of Durango, Mexico. Forests, 14(2), Article 2. https://doi.org/10.3390/f14020299 Rüetschi, M., Schaepman, M. E., & Small, D. (2018). Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland. Remote Sensing, 10(1), Article 1. https://doi.org/10.3390/rs10010055 Ruiz-Peinado, R., Bravo-Oviedo, A., López-Senespleda, E., Bravo, F., & Río, M. D. (2017a). Forest management and carbon sequestration in the Mediterranean region: A review. Forest Systems, 26(2), Article 2. https://doi.org/10.5424/fs/2017262-11205 Ruiz-Peinado, R., Bravo-Oviedo, A., López-Senespleda, E., Bravo, F., & Río, M. D. (2017b). Forest management and carbon sequestration in the Mediterranean region: A review. Forest Systems, 26(2), Article 2. https://doi.org/10.5424/fs/2017262-11205 79 Sabatini, F. M., de Andrade, R. B., Paillet, Y., Ódor, P., Bouget, C., Campagnaro, T., Gosselin, F., Janssen, P., Mattioli, W., Nascimbene, J., Sitzia, T., Kuemmerle, T., & Burrascano, S. (2019). Trade-offs between carbon stocks and biodiversity in European temperate forests. Global Change Biology, 25(2), 536-548. https://doi.org/10.1111/gcb.14503 Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I., Wegmüller, U., & Wiesmann, A. (2011). Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements. Remote Sensing of Environment, 115(2), 490-507. https://doi.org/10.1016/j.rse.2010.09.018 Sarker, Md. L. R., Nichol, J., Iz, H. B., Ahmad, B. B., & Rahman, A. A. (2013). Forest Biomass Estimation Using Texture Measurements of High-Resolution Dual-Polarization C-Band SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 51(6), 3371-3384. https://doi.org/10.1109/TGRS.2012.2219872 Schwaiger, F., Poschenrieder, W., Biber, P., & Pretzsch, H. (2019). Ecosystem service trade-offs for adaptive forest management. Ecosystem Services, 39, 100993. https://doi.org/10.1016/j.ecoser.2019.100993 Selkimäki, M., & González-Olabarria, J. R. (2017). Assessing Gully Erosion Occurrence in Forest Lands in Catalonia (Spain). Land Degradation & Development, 28(2), 616-627. https://doi.org/10.1002/ldr.2533 Simonson, W., Ruiz-Benito, P., Valladares, F., & Coomes, D. (2016). Modelling above-ground carbon dynamics using multi-temporal airborne lidar: Insights from a Mediterranean woodland. Biogeosciences, 13(4), 961-973. https://doi.org/10.5194/bg-13-961-2016 Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology, 12(5), 1779- 1792. https://doi.org/10.1007/s13762-015-0750-0 Soriano-Luna, M. D. los Á., Ángeles-Pérez, G., Guevara, M., Birdsey, R., Pan, Y., Vaquera-Huerta, H., Valdez-Lazalde, J. R., Johnson, K. D., & Vargas, R. (2018). Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production. Forests, 9(8), Article 8. https://doi.org/10.3390/f9080490 Stiers, M., Willim, K., Seidel, D., Ehbrecht, M., Kabal, M., Ammer, C., & Annighöfer, P. (2018). A quantitative comparison of the structural complexity of managed, lately unmanaged and primary European beech (Fagus sylvatica L.) forests. Forest Ecology and Management, 430, 357-365. https://doi.org/10.1016/j.foreco.2018.08.039 Storms, I., Verdonck, S., Verbist, B., Willems, P., & De Geest, P. (2022). Quantifying climate change effects on future forest biomass availability using yield tables improved by mechanistic scaling. Science of The Total Environment, 833(155189). Szpakowski, D. M., & Jensen, J. L. R. (2019). A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sensing, 11(22), Article 22. https://doi.org/10.3390/rs11222638 T. Vashum, K., & Jayakumar, S. (2012). Methods to estimate Above-Ground Biomass and Carbon Stock in Natural Forests—A Review. https://doi.org/10.4172/2157-7625.1000116 Taherdoost, H. (2016). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research (SSRN Scholarly Paper 3205035). https://doi.org/10.2139/ssrn.3205035 Tamiminia, H., Salehi, B., Mahdianpari, M., & Goulden, T. (2024). 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 Teixeira-Pinto, A. (2021). 4 Lasso Regression | Machine Learning for Biostatistics. The University of Sydney. https://bookdown.org/tpinto_home/Regularisation/lasso-regression.html Thammanu, S., Han, H., Marod, D., Srichaichana, J., & Chung, J. (2021). Above-ground carbon stock and REDD+ opportunities of community-managed forests in northern Thailand. PLOS ONE, 16(8), e0256005. https://doi.org/10.1371/journal.pone.0256005 Thom, H. C. S. (1958). A NOTE ON THE GAMMA DISTRIBUTION. Monthly Weather Review, 86(4), 117- 122. https://doi.org/10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2 Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S. R., & Schmullius, C. (2014). Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, 23(3), 297-310. https://doi.org/10.1111/geb.12125 80 Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., & Fu, W. (2023). Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects. Forests, 14(6), Article 6. https://doi.org/10.3390/f14061086 Tilman, D., Reich, P., Phillips, H., Menton, M., Patel, A., Vos, E., Peterson, D., & Knops, J. (2000). Fire Suppression and Ecosystem Carbon Storage. Ecology, 81(10), 2680-2685. https://doi.org/10.1890/0012-9658(2000)081[2680:FSAECS]2.0.CO;2 Toda, S. (2006). Forests and forest sector in Catalonia. https://www.semanticscholar.org/paper/Forestsand-forest-sector-in-Catalonia-Toda/355876e823c611c556fc105963aa1e958adff0a8 Torre-Tojal, L., Bastarrika, A., Boyano, A., Lopez-Guede, J. M., & Graña, M. (2022a). Above-ground biomass estimation from LiDAR data using random forest algorithms. Journal of Computational Science, 58, 101517. https://doi.org/10.1016/j.jocs.2021.101517 Torre-Tojal, L., Bastarrika, A., Boyano, A., Lopez-Guede, J. M., & Graña, M. (2022b). Above-ground biomass estimation from LiDAR data using random forest algorithms. Journal of Computational Science, 58, 101517. https://doi.org/10.1016/j.jocs.2021.101517 Tuanmu, M.-N., Viña, A., Bearer, S., Xu, W., Ouyang, Z., Zhang, H., & Liu, J. (2010). Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sensing of Environment, 114, 1833-1844. https://doi.org/10.1016/j.rse.2010.03.008 UNESCAP. (z.d.). Fact Sheet: REDD and REDD+. https://www.unescap.org/sites/default/files/51.%20FSREDD-and-REDD.pdf UNFCCC. (z.d.). What is the Kyoto Protocol? Geraadpleegd 30 september 2023, van https://unfccc.int/kyoto_protocol Vanhellemont, M., Leyman, A., Govaere, L., De Keersmaeker, L., & Vandekerkhove, K. (2024). Site-specific additionality in aboveground carbon sequestration in set-aside forests in Flanders (northern Belgium). Frontiers in Forests and Global Change, 7. https://doi.org/10.3389/ffgc.2024.1236203 Vayreda, J., Martinez-Vilalta, J., Gracia, M., & Retana, J. (2012a). Recent climate changes interact with stand structure and management to determine changes in tree carbon stocks in Spanish forests. Global Change Biology, 18(3), 1028-1041. https://doi.org/10.1111/j.1365-2486.2011.02606.x Vayreda, J., Martinez-Vilalta, J., Gracia, M., & Retana, J. (2012b). Recent climate changes interact with stand structure and management to determine changes in tree carbon stocks in Spanish forests. Global Change Biology, 18(3), 1028-1041. https://doi.org/10.1111/j.1365-2486.2011.02606.x Vilppo, T., Diaz-Yáñez, O., & Mola-Yudego, B. (2016). Sustainable Fuelwood Management in West Africa. ResearchGate. https://www.researchgate.net/figure/Measurement-of-DBH-in-different-sit…a-level-ground-b-slope-c-uneven_fig12_311614491 VPO. (2017). Digitale bodemkaart van het Vlaams Gewest: Bodemtypes, substraten,…. www.vlaanderen.be. https://www.vlaanderen.be/DataCatalogRecord/5c129f2d-4498-4bc3-8860- 01cb2d513f8f Weiss, M., & Baret, F. (2016). Sentinel 2 Toolbox Level 2 Products: LAI, FAPAR, FCOVER; Wen, B., Blondeel, H., Landuyt, D., & Verheyen, K. (2022). A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe. Forest Ecology and Management, 522, 120465. https://doi.org/10.1016/j.foreco.2022.120465 Wilson, N., & Bradstock, R. (2022). Past Logging and Wildfire Increase above Ground Carbon Stock Losses from Subsequent Wildfire. Fire, 5(1), Article 1. https://doi.org/10.3390/fire5010026 Wood, S. (2023). mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation (1.9-1) [Software]. https://cran.r-project.org/web/packages/mgcv/index.html Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC. Wood, S. N. (2011). Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(1), 3-36. https://doi.org/10.1111/j.1467-9868.2010.00749.x World resource institute. (z.d.). Forest Carbon Stocks | Global Forest Review. Forest Carbon Stocks. Geraadpleegd 5 oktober 2023, van https://research.wri.org/gfr/biodiversity-ecological-servicesindicators/forest-carbon-stocks WorldData. (2023). Climate details in Belgium. Worlddata.Info. https://www.worlddata.info/europe/belgium/climate.php Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., & Zhang, X. (2019). Remote sensing of the terrestrial 81 carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 233, 111383. https://doi.org/10.1016/j.rse.2019.111383 Yommy, A. S., Liu, R., Wu, & Shuang. (2015). SAR Image Despeckling Using Refined Lee Filter. 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2, 260-265. https://doi.org/10.1109/IHMSC.2015.236 Zhang, H., Zhang, Z., Liu, K., Huang, C., & Dong, G. (2023). Integrating land use management with tradeoffs between ecosystem services: A framework and application. Ecological Indicators, 149, 110193. https://doi.org/10.1016/j.ecolind.2023.110193 Duane, A., Pique, M., Castellnou, M., & Brotons, L. (2015). Predictive modelling of fire occurrences from different fire spread patterns in Mediterranean landscapes. International Journal of Wildland Fire, 24. https://doi.org/10.1071/WF14040 Zhu, X., & Liu, D. (2015). Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 222-231. https://doi.org/10.1016/j.isprsjprs.2014.08.014