Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

dc.creatorJOSE LUIS HERNANDEZ STEFANONI
dc.creatorMIGUEL ANGEL CASTILLO SANTIAGO
dc.creatorJean Francois Mas
dc.creatorCharlotte Wheeler
dc.creatorJuan Andrés Mauricio
dc.creatorFernando de Jesús Tun Dzul
dc.creatorSTEPHANIE PATRICIA GEORGE CHACON
dc.creatorGABRIELA REYES PALOMEQUE
dc.creatorBLANCA GUADALUPE CASTELLANOS BASTO
dc.creatorRaúl Vaca
dc.creatorJUAN MANUEL DUPUY RADA
dc.date2020
dc.date.accessioned2023-07-21T19:19:25Z
dc.date.available2023-07-21T19:19:25Z
dc.identifierhttp://cicy.repositorioinstitucional.mx/jspui/handle/1003/1884
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7737436
dc.descriptionReliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates.
dc.formatapplication/pdf
dc.languageeng
dc.relationinfo:eu-repo/semantics/datasetDOI/10.1186/s13021-020-00151-6
dc.relationcitation:Hernández-Stefanoni, J. L., Castillo-Santiago, M. Á., Mas, J. F., Wheeler, C. E., Andres-Mauricio, J., Tun-Dzul, F., ... & Dupuy, J. M. (2020). Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data. Carbon balance and management, 15(1), 1-17.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceCarbon balance and management, 15(1), 1-17.
dc.subjectinfo:eu-repo/classification/Autores/CLIMATIC WATER DEFICIT
dc.subjectinfo:eu-repo/classification/Autores/FOREST BIOMASS
dc.subjectinfo:eu-repo/classification/Autores/L-BAND SAR
dc.subjectinfo:eu-repo/classification/Autores/RANDOM FOREST
dc.subjectinfo:eu-repo/classification/Autores/TEXTURE ANÁLISIS
dc.subjectinfo:eu-repo/classification/Autores/YUCATAN PENINSULA
dc.subjectinfo:eu-repo/classification/cti/2
dc.subjectinfo:eu-repo/classification/cti/24
dc.subjectinfo:eu-repo/classification/cti/2417
dc.subjectinfo:eu-repo/classification/cti/241715
dc.subjectinfo:eu-repo/classification/cti/241715
dc.titleImproving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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