Comparison of vegetation canopy height model (for estimating biomass in a mangrove forest in the Colombian Caribbean
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This work integrates satellite-LIDAR to compare the evaluation of high-resolution carbon stock in a mangrove forest (Rincón Mosquito) in the coastal area of the Caribbean Sea. The height of vegetation resulting from the extraction of a height model of the EROS-B satellite vegetation and an airborne LIDAR vs. field data was compared. Satellites offer an opportunity to monitor changes in forest carbon caused by deforestation and degradation, just as new aerial methods, especially the LIDAR method, offer a way to estimate forest carbon density, which helps in the development of lines base for carbon inventories. After calculating the total biomass from the satellite and LIDAR data, the biomass measurement taken in the field was compared. In terms of small scales it can be concluded that the sensor with greater precision is the LIDAR by having a level of spatial resolution that allows great detail of the tree mass., The use of stereo satellite pairs is viable in large areas and not in small areas , where particularly in recent years, data taken with unmanned aerial vehicles seems to be a more viable alternative in availability and cost.
Aslan, A., Rahman, A. F., y Robeson, S. M. (2018). Investigating the use of Alos Prism data in detecting mangrove succession through canopy height estimation. Ecological Indicators, 87, 136-143. doi: https://doi.org/10.1016/j.ecolind.2017.12.008
Asner, G. P. (2009). Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environmental Research Letters, 4(3), 034009. doi: 10.1088/1748-9326/4/3/034009
Asner, G. P., y Mascaro, J. (2014). Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric. Remote Sensing of Environment, 140, 614-624. doi: https://doi.org/10.1016/j.rse.2013.09.023
Brown, S., y Lugo, A. E. (1984). Biomass of Tropical Forests: A New Estimate Based on Forest Volumes. Science, 223(4642), 1290. doi: 10.1126/science.223.4642.1290
Brown, S., Pearson, T., Slaymaker, D., Ambagis, S., Moore, N., Novelo, D., y Sabido, W. (2005). Creating a virtual tropical forest from three-dimensional aerial imagery to estimate carbon stocks. Ecological applications, 15(3), 1083-1095. doi: 10.1890/04-0829
Cabrera, E. (2011). Protocolo de procesamiento digital de imágenes para la cuantificación de la deforestación en Colombia, Nivel subnacional escala gruesa y fina. Bogotá, DC: IDEAM.
Cottam, G., y Curtis, J. T. (1956). The Use of Distance Measures in Phytosociological Sampling. Ecology, 37(3), 451-460. doi: 10.2307/1930167
Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87-99. doi: 10.1007/s00442-005-0100-x
Eggleston, S., Buendia, L., Miwa, K., Ngara, T., y Tanabe, K. (2006). 2006 IPCC guidelines for national greenhouse gas inventories (Vol. 5): Institute for Global Environmental Strategies Hayama, Japan.
Fuentes, J., Varga, D., y Pintó, J. (2018). The Use of High-Resolution Historical Images to Analyse the Leopard Pattern in the Arid Area of La Alta Guajira, Colombia. Geosciences, 8(10), 366.
Fuentes, J. E., Bolaños, J. A., y Rozo, D. M. (2012). Modelo digital de superficie a partir de imágenes de satélite IKONOS para el análisis de áreas de inundación en santa marta, Colombia. Boletín de Investigaciones Marinas y Costeras - INVEMAR, 41, 251-266.
García Arbeláez, C., Barrera, X., Gómez, R., y Suárez Castaño, R. (2015). El ABC de los compromisos de Colombia para la Cop 21. Bogotá: WWF-Colombia.
García, M., Saatchi, S., Ustin, S., y Balzter, H. (2018). Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. International Journal of Applied Earth Observation and Geoinformation, 66, 159-173. doi: https://doi.org/10.1016/j.jag.2017.11.017
Goldbergs, G., Levick, S. R., Lawes, M., y Edwards, A. (2018a). Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. Remote Sensing of Environment, 205, 141-150. doi: https://doi.org/10.1016/j.rse.2017.11.010
Goldbergs, G., Maier, S. W., Levick, S. R., y Edwards, A. (2018b). Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas. Remote Sensing, 10(2), 161.
Goldbergs, G., Maier, S. W., Levick, S. R., y Edwards, A. (2019). Limitations of high-resolution satellite stereo imagery for estimating canopy height in Australian tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 75, 83-95. doi: https://doi.org/10.1016/j.jag.2018.10.021
Hung, C., Bryson, M., y Sukkarieh, S. (2012). Multi-class predictive template for tree crown detection. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 170-183. doi: https://doi.org/10.1016/j.isprsjprs.2012.01.009
Immitzer, M., Stepper, C., Böck, S., Straub, C., y Atzberger, C. (2016). Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock. Forest Ecology and Management, 359, 232-246. doi: https://doi.org/10.1016/j.foreco.2015.10.018
Lefsky, M. A., Cohen, W. B., Harding, D. J., Parker, G. G., Acker, S. A., y Gower, S. T. (2002a). Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography, 11(5), 393-399. doi: 10.1046/j.1466-822x.2002.00303.x
Lefsky, M. A., Cohen, W. B., Parker, G. G., y Harding, D. J. (2002b). Lidar Remote Sensing for Ecosystem Studies: Lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience, 52(1), 19-30. doi: 10.1641/0006-3568(2002)052[0019:lrsfes]2.0.co;2
Maltamo, M., Næsset, E., y Vauhkonen, J. (2014). Forestry applications of airborne laser scanning. Concepts and case studies. Manag For Ecosys, 27, 460.
Meddens, A. J. H., Vierling, L. A., Eitel, J. U. H., Jennewein, J. S., White, J. C., y Wulder, M. A. (2018). Developing 5 m resolution canopy height and digital terrain models from WorldView and ArcticDEM data. Remote Sensing of Environment, 218, 174-188. doi: https://doi.org/10.1016/j.rse.2018.09.010
Menon, S., Denman, K. L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P. M., . . . Holland, E. (2007). Couplings between changes in the climate system and biogeochemistry: Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).
Mielcarek, M., Stereńczak, K., y Khosravipour, A. (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71, 132-143. doi: https://doi.org/10.1016/j.jag.2018.05.002
Mitchell, K. (2010). Quantitative analysis by the point-centered quarter method. arXiv preprint arXiv:1010.3303.
Nfotabong-Atheull, A., Din, N., y Dahdouh-Guebas, F. (2013). Qualitative and Quantitative Characterization of Mangrove Vegetation Structure and Dynamics in a Peri-urban Setting of Douala (Cameroon): An Approach Using Air-Borne Imagery. Estuaries and Coasts, 36(6), 1181-1192. doi: 10.1007/s12237-013-9638-8
Rugnitz, M., Chacón, M., y Porro, R. (2009). Guía para la Determinación de Carbono en Pequeñas Propiedades Rurales. Centro Mundial Agroforestal (ICRAF)/Consorcio Iniciativa Amazónica (IA). Lima, Perú.
Sullivan, F. B., Ducey, M. J., Orwig, D. A., Cook, B., y Palace, M. W. (2017). Comparison of lidar- and allometry-derived canopy height models in an eastern deciduous forest. Forest Ecology and Management, 406, 83-94. doi: https://doi.org/10.1016/j.foreco.2017.10.005
Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., y Vopěnka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7(3), 62.
Wallace, L., Lucieer, A., Watson, C., y Turner, D. (2012). Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sensing, 4(6), 1519-1543.
White, J. C., Wulder, M. A., Vastaranta, M., Coops, N. C., Pitt, D., y Woods, M. (2013). The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning. Forests, 4(3), 518-536.
Yin, D., y Wang, L. (2019). Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges. Remote Sensing of Environment, 223, 34-49. doi: https://doi.org/10.1016/j.rse.2018.12.034
Zhu, X., Hou, Y., Weng, Q., y Chen, L. (2019). Integrating UAV optical imagery and LiDAR data for assessing the spatial relationship between mangrove and inundation across a subtropical estuarine wetland. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 146-156. doi: https://doi.org/10.1016/j.isprsjprs.2019.01.021