Leaf area index and aboveground biomass estimation of an alpine peatland with a UAV multi-sensor approach

References

• Aase, J. K. 1978. “Relationship Between Leaf Area and Dry Matter in Winter Wheat 1.” Agronomy Journal 70 (4): 563–27. https://doi.org/10.2134/agronj1978.00021962007000040011x.
   Web of Science ®Google Scholar
• Anders, N., J. Valente, R. Masselink, and S. Keesstra. 2019. “Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds.” Drones 3 (3): 61. https://doi.org/10.3390/drones3030061.
   Google Scholar
• Antala, M., R. Juszczak, C. Van der Tol, and A. Rastogi. 2022. “Impact of Climate Change-Induced Alterations in Peatland Vegetation Phenology and Composition on Carbon Balance.” Science of the Total Environment 827:154294. https://doi.org/10.1016/j.scitotenv.2022.154294.
   PubMed Web of Science ®Google Scholar
• ARPA Veneto. 2023. Meteorological Data. https://wwwold.arpa.veneto.it/bollettini/storico/Mappa_2022_TEMP.htm?t=BL.
   Google Scholar
• Bajocco, S., F. Ginaldi, D. Morelli, M. Scaglione, D. Fanchini, E. Raparelli, S. U. M. Bregaglio, and F. Savian. 2022. “On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library.” Remote Sensing 14 (15): 3554. https://doi.org/10.3390/rs14153554.
   Web of Science ®Google Scholar
• Bauer, D. F. 1972. “Constructing Confidence Sets Using Rank Statistics.” Journal of the American Statistical Association 67 (339): 687–690. https://doi.org/10.1080/01621459.1972.10481279.
   Web of Science ®Google Scholar
• Benjamini, Y., and Y. Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society, Series B 57 (1): 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
   Google Scholar
• Biondi, E., C. Blasi, M. Allegrezza, M. Anzellotti, M. M. Azzella, E. Carli, S. Casavecchia, et al. 2014. “Plant Communities of Italy: The Vegetation Prodrome.” Plant Biosystems 148 (4): 728–814. https://doi.org/10.1080/11263504.2014.948527.
   Web of Science ®Google Scholar
• Boochs, F., G. Kupfer, K. Dockter, and W. Kuhbauch. 1990. “Shape of the Red Edge as Vitality Indicator for Plants.” Plants International Journal of Remote Sensing 11 (10): 1741–1753. https://doi.org/10.1080/01431169008955127.
   Web of Science ®Google Scholar
• Bragazza, L. 1996. “Delimitation of the Aerobic Peat Layer in a Sphagnum Mire on the Southern Alps.” Oecologia montana 5:41–46.
   Google Scholar
• Bragazza, L. 2006. “A Decade of Plant Species Changes on a Mire in the Italian Alps: Vegetation-Controlled or Climate-Driven Mechanisms?” Climatic Change 77:415–429. https://doi.org/10.1007/s10584-005-9034-x.
   Web of Science ®Google Scholar
• Bragazza, L., and R. Gerdol. 1999. “Hydrology, Groundwater Chemistry and Peat Chemistry in Relation to Habitat Conditions in a Mire on the South – Eastern Alps of Italy.” Plant Ecology 144 (2): 243–256. https://doi.org/10.1023/A:1009868113976.
   Web of Science ®Google Scholar
• Bragazza, L., H. Rydin, and R. Gerdol. 2005. “Multiple Gradients in Mire Vegetation: A Comparison of a Swedish and And Italian Bog.” Plant Ecology 177 (2): 223–236. https://doi.org/10.1007/s11258-005-2182-2.
   Web of Science ®Google Scholar
• Bratsch, S., H. Epstein, M. Buchhorn, D. Walker, and H. Landes. 2017. “Relationships Between Hyperspectral Data and Components of Vegetation Biomass in Low Arctic Tundra Communities at Ivotuk, Alaska.” Environmental Research Letters 12 (2): 025003. https://doi.org/10.1088/1748-9326/aa572e.
   Web of Science ®Google Scholar
• Breusch, T. S., and A. R. Pagan. 1979. “A Simple Test for Heteroscedasticity and Random Coefficient Variation.” Econometrica 47 (5): 1287–1294. https://doi.org/10.2307/1911963.
   Web of Science ®Google Scholar
• Cabezas, J., M. Galleguillos, A. Valdés, J. P. Fuentes, C. Pérez, and J. F. Perez-Quezada. 2015. “Evaluation of Impacts of Management in an Anthropogenic Peatland Using Field and Remote Sensing Data.” Ecosphere 6 (12): 1–24. https://doi.org/10.1890/ES15-00232.1.
   Web of Science ®Google Scholar
• Campbell, G. S. 1986. “Extinction Coefficients for Radiation in Plant Canopies Calculated Using an Ellipsoidal Inclination Angle Distribution.” Agricultural and Forest Meteorology 36 (4): 317–321. https://doi.org/10.1016/0168-1923(86)90010-9.
   Web of Science ®Google Scholar
• Crabbe, R. A., D. W. Lamb, C. Edwards, K. Andersson, and D. Schneider. 2019. “A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape.” Remote Sensing 11 (7): 872. https://doi.org/10.3390/rs11070872.
   Web of Science ®Google Scholar
• Cunliffe, A. M., J. J. Assmann, G. Daskalova, J. T. Kerby, and I. H. Myers-Smith. 2020. “Aboveground Biomass Corresponds Strongly with Drone-Derived Canopy Height but Weakly with Greenness (NDVI) in a Shrub Tundra Landscape.” Environmental Research Letters 15 (12): 125004. https://doi.org/10.1088/1748-9326/aba470.
   Web of Science ®Google Scholar
• Czapiewski, S. 2022. “Assessment of the Applicability of UAV for the Creation of Digital Surface Model of a Small Peatland.” Frontiers in Earth Science 10:834923. https://doi.org/10.3389/feart.2022.834923.
   Web of Science ®Google Scholar
• Czapiewski, S., and D. Szumińska. 2021. “An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021.” Land 11 (1): 1–14. https://doi.org/10.3390/land11010024.
   Web of Science ®Google Scholar
• Dayal, K. R., S. Durrieu, S. Alleaume, F. Revers, E. Larmanou, J. P. Renaud, and M. Bouvier. 2020. “Scan Angle Impact on Lidar-Derived Metrics Used in Aba Models for Prediction of Forest Stand Characteristics: A Grid Based Analysis.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 43:975–982. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-975-2020.
   Google Scholar
• Fang, H., and S. Liang. 2014. “Leaf Area Index Models”. In Reference Module in Earth Systems and Environmental Sciences, Elsevier reference collections. https://doi.org/10.1016/B978-0-12-409548-9.09076-X.
   Google Scholar
• FAO. 2020. Peatlands Mapping and Monitoring – Recommendations and Technical Overview. Rome. https://doi.org/10.4060/ca8200en.
   Google Scholar
• Fox, J., and G. Monette. 1992. “Generalized Collinearity Diagnostics.” Journal of the American Statistical Association 87 (417): 178–183. https://doi.org/10.2307/2290467.
   Web of Science ®Google Scholar
• Gao, T., X. Yang, Y. Jin, H. Ma, J. Li, H. Yu, Q. Yu, X. Zheng, and B. Xu. 2013. “Spatio-Temporal Variation in Vegetation Biomass and Its Relationships with Climate Factors in the Xilingol Grasslands, Northern China.” PLoS ONE 8 (12): e83824. https://doi.org/10.1371/journal.pone.0083824.
   PubMed Web of Science ®Google Scholar
• Garisoain, R., C. Delire, B. Decharme, and L. Gandois. 2022. “Seasonality of Sphagnum LAI in a Mountainous Peatland (Pyrenees, France)”. Proceedings of EGU General Assembly 2022, Wien.
   Google Scholar
• Gerdol, R., and M. Tomaselli. 1984. “Evaluation of Vegetation Classifications from Peatlands in the Dolomites (S-Alps).” Studia Geobotanica 4:25–39. http://hdl.handle.net/10077/14992.
   Google Scholar
• Gerdol, R., M. Tomaselli, and L. Bragazza. 1994. “A Floristic-Ecologic Classification of Five Mire Sites in the Montane Subalpine Belt of South Tyrol (S Alps, Italy).” Phyton Annales Rei Botanicae 34 (1): 35–56.
   Web of Science ®Google Scholar
• Gilalbert, M., J. González-Piqueras, F. García-Haro, and J. Meliá. 2002. “A Generalized Soil-Adjusted Vegetation Index.” Remote Sensing of Environment 82 (2–3): 303–310. https://doi.org/10.1016/S0034-4257(02)00048-2.
   Web of Science ®Google Scholar
• Gitelson, A. A., and M. N. Merzlyak. 1997. “Remote Estimation of Chlorophyll Content in Higher Plant Leaves.” International Journal of Remote Sensing 18 (12): 2691–2697. https://doi.org/10.1080/014311697217558.
   Web of Science ®Google Scholar
• Houldcroft, C. J., C. L. Campbell, I. J. Davenport, R. J. Gurney, and N. Holden. 2005. “Measurement of Canopy Geometry Characteristics Using LiDar Laser Altimetry: A Feasibility Study.” IEEE Transactions on Geoscience and Remote Sensing 43 (10): 2270–2282. https://doi.org/10.1109/TGRS.2005.856639.
   Web of Science ®Google Scholar
• Huang, S., L. Tang, J. P. Hupy, Y. Wang, and G. Shao. 2021. “A Commentary Review on the Use of Normalized Difference Vegetation Index (NDVI) in the Era of Popular Remote Sensing.” Journal of Forestry Research 32 (1): 1–6. https://doi.org/10.1007/s11676-020-01155-1.
   Web of Science ®Google Scholar
• Huete, A. R., H. Liu, and W. J. D. van Leeuwen. 1997. “The Use of Vegetation Indices in Forested Regions: Issues of Linearity and Saturation”. IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development, 4: 1966–1968. https://doi.org/10.1109/IGARSS.1997.609169.
   Google Scholar
• Iglhaut, J., C. Cabo, S. Puliti, L. Piermattei, J. O’Connor, and J. Rosette. 2019. “Structure from Motion Photogrammetry in Forestry: A Review.” Current Forestry Reports 5 (3): 155–168. https://doi.org/10.1007/s40725-019-00094-3.
   Web of Science ®Google Scholar
• Joosten, H., and D. Clarke. 2002. Wise Use of Mires and Peatlands – Background and Principles, Including a Framework for Decision-Making. Saarijärvi: International Mire Conservation Group and International Peat Society.
   Google Scholar
• Kimmel, K., and Ü. Mander. 2010. “Ecosystem Services of Peatlands: Implications for Restoration.” Progress in Physical Geography 34 (4): 491–514. https://doi.org/10.1177/0309133310365595.
   Web of Science ®Google Scholar
• Lai, Z., Y. Wang, S. Guo, X. Meng, J. Li, W. Li, and S. Han. 2022. “Laser Reflectance Feature Assisted Accurate Extrinsic Calibration for Non-Repetitive Scanning LiDar and Camera Systems.” Optics Express 30 (10): 16242–16263. https://doi.org/10.1364/OE.453449.
   PubMed Web of Science ®Google Scholar
• Le Maire, G., C. François, and E. Dufrene. 2004. “Towards Universal Broad Leaf Chlorophyll Indices Using PROSPECT Simulated Database and Hyperspectral Reflectance Measurements.” Remote Sensing of Environment 89 (1): 1–28. https://doi.org/10.1016/j.rse.2003.09.004.
   Web of Science ®Google Scholar
• le Maire, G. C., K. Francois, D. Soudani, J.-Y. Berveiller, N. Pontailler, H. Breda, H. Genet, H. Davi, and E. Dufrêne. 2008. “Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index and Leaf Canopy Biomass.” Remote Sensing of Environment 112 (10): 3846–3864. https://doi.org/10.1016/j.rse.2008.06.005.
   Web of Science ®Google Scholar
• Lindsay, R. 2018. ““Peatland (Mire Types): Based on Origin and Behavior of Water, Peat Genesis, Landscape Position, and climate”.” In The Wetland Book (Part II): Distribution, Description and Conservation, edited by C. M. Finlayson, G. R. Milton, R. Prentice, and N. C. Davidson, 251–273. Heidelberg: Springer Netherlands. https://doi.org/10.1007/978-94-007-4001-3_279.
   Google Scholar
• Liu, J., A. K. Skidmore, S. Jones, T. Wang, M. Heurich, X. Zhu, and Y. Shi. 2018. “Large Off-Nadir Scan Angle of Airborne LiDar Can Severely Affect the Estimates of Forest Structure Metrics.” ISPRS Journal of Photogrammetry and Remote Sensing 136:13–25. https://doi.org/10.1016/j.isprsjprs.2017.12.004.
   Web of Science ®Google Scholar
• Lopatin, J., T. Kattenborn, M. Galleguillos, J. F. Perez-Quezada, and S. Schmidtlein. 2019. “Using Aboveground Vegetation Attributes as Proxies for Mapping Peatland Belowground Carbon Stocks.” Remote Sensing of Environment 231:111217. https://doi.org/10.1016/j.rse.2019.111217.
   Web of Science ®Google Scholar
• Lovell, J. L., D. L. B. Jupp, G. J. Newnham, N. C. Coops, and D. S. Culvenor. 2005. “Simulation Study for Finding Optimal Lidar Acquisition Parameters for Forest Height Retrieval.” Forest Ecology and Management 214 (1–3): 398–412. https://doi.org/10.1016/j.foreco.2004.07.077.
   Web of Science ®Google Scholar
• Lu, D. 2006. “The Potential and Challenge of Remote Sensing‐Based Biomass Estimation.” International Journal of Remote Sensing 27 (7): 1297–1328. https://doi.org/10.1080/01431160500486732.
   Web of Science ®Google Scholar
• Luscombe, D. J., K. Anderson, N. Gatis, A. Wetherelt, E. Grand‐Clement, and R. E. Brazier. 2015. “What Does Airborne LiDar Really Measure in Upland Ecosystems?” Ecohydrology 8 (4): 584–594. https://doi.org/10.1002/eco.1527.
   Web of Science ®Google Scholar
• Maljanen, M., B. D. Sigurdsson, J. Guðmundsson, H. Óskarsson, J. T. Huttunen, and P. J. Martikainen. 2010. “Greenhouse Gas Balances of Managed Peatlands in the Nordic Countries – Present Knowledge and Gaps.” Biogeosciences 7: 2711–2738. https://doi.org/10.5194/bg-7-2711-2010.
   Web of Science ®Google Scholar
• Malone, C. R. 1968. “Determination of Peak Standing Crop Biomass of Herbaceous Shoots by the Harvest Method.” The American Midland Naturalist 79 (2): 429–435. https://doi.org/10.2307/2423188.
   Web of Science ®Google Scholar
• McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. https://doi.org/10.1080/01431169608948714.
   Web of Science ®Google Scholar
• McPartland, M. Y., E. S. Kan, M. J. Falkowski, R. Kolka, M. R. Turetsky, B. Palik, and R. A. Montgomery. 2019. “The Response of Boreal Peatland Community Composition and NDVI to Hydrologic Change, Warming, and Elevated Carbon Dioxide.” Global Change Biology 25 (1): 93–107. https://doi.org/10.1111/gcb.14465.
   PubMed Web of Science ®Google Scholar
• Meyer, G. E., and J. C. Neto. 2008. “Verification of Color Vegetation Indices for Automated Crop Imaging Applications.” Computers and Electronics in Agriculture 63 (2): 282–293. https://doi.org/10.1016/j.compag.2008.03.009.
   Web of Science ®Google Scholar
• Minasny, B., Ö. Berglund, J. Connolly, C. Hedley, F. de Vries, A. Gimona, B. Kempen, et al. 2019. “Digital Mapping of Peatlands: A Critical Review.” Earth Science Review 196:102870. https://doi.org/10.1016/j.earscirev.2019.05.014.
   Web of Science ®Google Scholar
• Miura, N., T. F. Koyanagi, S. Yokota, and S. Yamada. 2019. “Can UAV LiDar Derive Vertical Structure of Herbaceous Vegetation on Riverdike?” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4:127–132. https://doi.org/10.5194/isprs-annals-IV-2-W5-127-2019.
   Google Scholar
• Mohammedshum, A. A., and L. Kooistra. 2015. “Mapping Tree Distribution and LAI in Peatlands Using Field Methods and Imaging Spectroscopy: A Case Study for the Haaksbergerveen, the Netherlands.” International Journal of Remote Sensing 36 (18): 4535–4549. https://doi.org/10.1080/01431161.2015.1084064.
   Web of Science ®Google Scholar
• Moore, T. R., J. L. Bubier, S. E. Frolking, P. M. Lafleur, and N. T. Roulet. 2002. “Plant Biomass and Production and CO2 Exchange in an Ombrotrophic Bog.” Journal of Ecology 90 (1): 25–36. https://doi.org/10.1046/j.0022-0477.2001.00633.x.
   Web of Science ®Google Scholar
• Murphy, M. T., A. McKinley, and T. R. Moore. 2009. “Variations in Above-And Below-Ground Vascular Plant Biomass and Water Table on a Temperate Ombrotrophic Peatland.” Botany 87 (9): 845–853. https://doi.org/10.1139/B09-052.
   Google Scholar
• Navarro, A., M. Young, B. Allan, P. Carnell, P. Macreadie, and D. Ierodiaconou. 2020. “The Application of Unmanned Aerial Vehicles (UAVs) to Estimate Above-Ground Biomass of Mangrove Ecosystems.” Remote Sensing of Environment 242:111747. https://doi.org/10.1016/j.rse.2020.111747.
   Web of Science ®Google Scholar
• Niu, Y., L. Zhang, H. Zhang, W. Han, and X. Peng. 2019. “Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery.” Remote Sensing 11 (11): 1261. https://doi.org/10.3390/rs11111261.
   Web of Science ®Google Scholar
• Pajula, R., and A. H. Purre. 2021. “NDVI as a Proxy to Estimate the CO2 Fluxes in Peatlands: Example of Alkaline Fen.” 16th International Peatland Congress, Virtual Event.
   Google Scholar
• Palozzi, J., and Z. Lindo. 2017. “Boreal Peat Properties Link to Plant Functional Traits of Ecosystem Engineers.” Plant and Soil 418: 1–15. https://doi.org/10.1007/s11104-017-3291-0.
   Web of Science ®Google Scholar
• Pignatti, S. 1982. Flora d’Italia. Bologna, Italia: Edagricole.
   Google Scholar
• Pope, R. M., and E. S. Fry. 1997. “Absorption Spectrum (380–700 Nm) of Pure Water. II. Integrating Cavity Measurements.” Applied Optics 36 (33): 8710–8723. https://doi.org/10.1364/AO.36.008710.
   PubMed Web of Science ®Google Scholar
• Poto, L., J. Gabrieli, S. J. Crowhurst, P. G. Appleby, P. Ferretti, N. Surian, G. Cozzi, et al. 2013. “The First Continuous Late Glacial–Holocene Peat Bog Multi-Proxy Record from the Dolomites (NE Italian Alps).” Quaternary International 306:71–79. https://doi.org/10.1016/j.quaint.2013.05.001.
   Web of Science ®Google Scholar
• Qin, H., C. Wang, X. Xi, J. Tian, and G. Zhou. 2017. “Simulating the Effects of the Airborne Lidar Scanning Angle, Flying Altitude, and Pulse Density for Forest Foliage Profile Retrieval.” Applied Sciences 7 (7): 712. https://doi.org/10.3390/app7070712.
   Google Scholar
• Räsänen, A., S. Juutinen, M. Aurela, and T. Virtanen. 2019. “Predicting Aboveground Biomass in Arctic Landscapes Using Very High Spatial Resolution Satellite Imagery and Field Sampling.” International Journal of Remote Sensing 40 (3): 1175–1199. https://doi.org/10.1080/01431161.2018.1524176.
   Web of Science ®Google Scholar
• Räsänen, A., S. Juutinen, M. Kalacska, M. Aurela, P. Heikkinen, K. Mäenpää, A. Rimali, and T. Virtanen. 2020. “Peatland Leaf-Area Index and Biomass Estimation with Ultra-High Resolution Remote Sensing.” GIScience & Remote Sensing 57 (7): 943–964. https://doi.org/10.1080/15481603.2020.1829377.
   Web of Science ®Google Scholar
• Räsänen, A., J. Wagner, G. Hugelius, and T. Virtanen. 2021. “Aboveground Biomass Patterns Across Treeless Northern Landscapes.” International Journal of Remote Sensing 42 (12): 4536–4561. https://doi.org/10.1080/01431161.2021.1897187.
   Web of Science ®Google Scholar
• Rastogi, A., S. Bandopadhyay, M. Strózecki, and R. Juszczak. 2018. “Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices”. ITM Web Conferences 23:00030. https://doi.org/10.1051/itmconf/20182300030.
   Google Scholar
• Rastogi, A., M. Stróżecki, H. M. Kalaji, D. Łuców, M. Lamentowicz, and R. Juszczak. 2019. “Impact of Warming and Reduced Precipitation on Photosynthetic and Remote Sensing Properties of Peatland Vegetation.” Environmental and Experimental Botany 160:71–80. https://doi.org/10.1016/j.envexpbot.2019.01.005.
   Web of Science ®Google Scholar
• Richardson, J. J., L. M. Moskal, and S. H. Kim. 2009. “Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LIDAR.” Agricultural and Forest Meteorology 149 (6–7): 1152–1160. https://doi.org/10.1016/j.agrformet.2009.02.007.
   Web of Science ®Google Scholar
• Roussel, J.-R., D. Auty, N. C. Coops, P. Tompalski, T. R. H. Goodbody, A. S. Meador, J.-F. Bourdon, F. De Boissieu, and A. Achim. 2020. “lidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data.” Remote Sensing of Environment 251:112061. https://doi.org/10.1016/j.rse.2020.112061.
   Web of Science ®Google Scholar
• Royston, P. 1982. “Algorithm as 181: The W Test for Normality.” Applied Statistics 31 (2): 176–180. https://doi.org/10.2307/2347986.
   Web of Science ®Google Scholar
• Rydin, H., and J. K. Jeglum. 2006. The Biology of Peatlands. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198528722.001.0001.
   Google Scholar
• Šimanauskienė, R., R. Linkevičienė, M. Bartold, K. Dąbrowska‐Zielińska, G. Slavinskienė, D. Veteikis, and J. Taminskas. 2019. “Peatland Degradation: The Relationship Between Raised Bog Hydrology and Normalized Difference Vegetation Index.” Ecohydrology 12 (8): e2159. https://doi.org/10.1002/eco.2159.
   Web of Science ®Google Scholar
• Sofonia, J. J., S. Phinn, C. Roelfsema, F. Kendoul, and Y. Rist. 2019. “Modelling the Effects of Fundamental UAV Flight Parameters on LiDar Point Clouds to Facilitate Objectives-Based Planning.” ISPRS Journal of Photogrammetry and Remote Sensing 149:105–118. https://doi.org/10.1016/j.isprsjprs.2019.01.020.
   Web of Science ®Google Scholar
• Steenvoorden, J., H. Bartholomeus, and J. Limpens. 2023. “Less is More: Optimizing Vegetation Mapping in Peatlands Using Unmanned Aerial Vehicles (UAVs).” International Journal of Applied Earth Observation and Geoinformation 117:103220. https://doi.org/10.1016/j.jag.2023.103220.
   Web of Science ®Google Scholar
• Tang, Z., J. Guo, Y. Xiang, X. Lu, Q. Wang, H. Wang, M. Cheng, et al. 2022. “Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index.” Agronomy 12 (7): 1729. https://doi.org/10.3390/agronomy12071729.
   Google Scholar
• Tanneberger, F., C. Tegetmeyer, S. Busse, A. Barthelmes, S. Shumka, A. Moles Mariné, K. Jenderedjian, et al. 2017. “The Peatland Map of Europe.” Mires and Peat 19:1–17. https://doi.org/10.19189/MaP.2016.OMB.264.
   Web of Science ®Google Scholar
• Tian, L., Y. Qu, and J. Qi. 2021. “Estimation of Forest LAI Using Discrete Airborne LiDar: A Review.” Remote Sensing 13 (12): 2408. https://doi.org/10.3390/rs13122408.
   Web of Science ®Google Scholar
• Tuittila, E.-S., S. Juutinen, S. Frolking, M. Väliranta, A. M. Laine, A. Miettinen, M.-L. Seväkivi, A. Quillet, and P. Merilä. 2013. “Wetland Chronosequence as a Model of Peatland Development: Vegetation Succession, Peat and Carbon Accumulation.” The Holocene 23 (1): 25–35. https://doi.org/10.1177/0959683612450197.
   Web of Science ®Google Scholar
• Turetsky, M. R., B. Benscoter, S. Page, G. Rein, G. R. van der Werf, and A. Watts. 2015. “Global Vulnerability of Peatlands to Fire and Carbon Loss.” Nature Geoscience 8 (1): 11–14. https://doi.org/10.1038/ngeo2325.
   Web of Science ®Google Scholar
• Vescovo, L., and D. Gianelle. 2008. “Using the MIR Bands in Vegetation Indices for the Estimation of Grassland Biophysical Parameters from Satellite Remote Sensing in the Alps Region of Trentino (Italy).” Advances in Space Research 41 (11): 1764–1772. https://doi.org/10.1016/j.asr.2007.07.043.
   Web of Science ®Google Scholar
• Wang, Y., and H. Fang. 2020. “Estimation of LAI with the LiDar Technology: A Review.” Remote Sensing 12 (20): 3457. https://doi.org/10.3390/rs12203457.
   Web of Science ®Google Scholar
• Ward, S. E., R. D. Bardgett, N. P. McNamara, J. K. Adamson, and N. J. Ostle. 2007. “Long-Term Consequences of Grazing and Burning on Northern Peatland Carbon Dynamics.” Ecosystems (New York, N Y) 10 (7): 1069–1083. https://doi.org/10.1007/s10021-007-9080-5.
   Web of Science ®Google Scholar
• Wu, C., Z. Niu, Q. Tang, and W. Huang. 2008. “Estimating Chlorophyll Content from Hyperspectral Vegetation Indices: Modeling and Validation.” Agricultural and Forest Meteorology 148 (8–9): 1230–1241. https://doi.org/10.1016/j.agrformet.2008.03.005.
   Web of Science ®Google Scholar
• Yousefi Lalimi, F., S. Silvestri, L. J. Moore, and M. Marani. 2017. “Coupled Topographic and Vegetation Patterns in Coastal Dunes: Remote Sensing Observations and Ecomorphodynamic Implications.” JGR Biogeosciences 122 (1): 119–130. https://doi.org/10.1002/2016JG003540.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., and J. R. Miller. 1999. “Land Cover Mapping at BOREAS Using Red Edge Spectral Parameters from CASI Imagery.” Journal of Geophysical Research: Atmospheres 104 (D22): 27921–27933. https://doi.org/10.1029/1999JD900161.
   Web of Science ®Google Scholar
• Zenmuse L1 User Manual. 2021. https://dl.djicdn.com/downloads/Zenmuse_L1/20210518/Zenmuse_L1%20_User%20Manual_EN_1.pdf.
   Google Scholar
• Zeybek, M., and İ. Şanlıoğlu. 2019. “Point Cloud Filtering on UAV Based Point Cloud.” Measurement 133:99–111. https://doi.org/10.1016/j.measurement.2018.10.013.
   Web of Science ®Google Scholar
• Zhang, K., S. C. Chen, D. Whitman, M. L. Shyu, J. Yan, and C. Zhang. 2003. “A Progressive Morphological Filter for Removing Nonground Measurements from Airborne LIDAR Data.” IEEE Transactions on Geoscience and Remote Sensing 41 (4): 872–882. https://doi.org/10.1109/TGRS.2003.810682.
   Web of Science ®Google Scholar
• Zhang, W., J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan. 2016. “An Easy-To-Use Airborne LIDAR Data Filtering Method Based on Cloth Simulation.” Remote Sensing 8 (6): 501. https://doi.org/10.3390/rs8060501.
   Web of Science ®Google Scholar
• Zheng, G., and L. M. Moskal. 2009. “Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors.” Sensors 9 (4): 2719–2745. https://doi.org/10.3390/s90402719.
   PubMed Web of Science ®Google Scholar
• Zhu, X., and D. Liu. 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.
   Web of Science ®Google Scholar