Modeling aboveground carbon in flooded forests using synthetic aperture radar data: a case study from a natural reserve in Turkish Thrace

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dc.authorid0000-0001-5552-5670en_US
dc.authorid0000-0001-5552-5670en_US
dc.authorid0000-0003-2655-5023en_US
dc.authorid0000-0002-3310-352Xen_US
dc.authorid0000-0003-1935-1815en_US
dc.authorid0000-0003-3818-109Xen_US
dc.contributor.authorVatandaşlar, Can
dc.contributor.authorBolat, Ferhat
dc.contributor.authorAbdikan, Saygın
dc.contributor.authorPamukçu Albers, Pınar
dc.contributor.authorSatıral, Caner
dc.date.accessioned2024-12-11T13:01:54Z
dc.date.available2024-12-11T13:01:54Z
dc.date.issued2024
dc.departmentAÇÜen_US
dc.description.abstractFlooded forests are rare and highly dynamic ecosystems, yet they can store a significant amount of carbon because of their ability to produce biomass rapidly. Estimation and mapping of the carbon that is stored in flooded forests are challenging tasks through the use of optical remote sensing because these ecosystems are often located in moist regions where clouds can interfere with data acquisition and image interpretation. This study models the aboveground carbon (AGC) stocks of a flooded forest in Turkish Thrace with synthetic aperture radar (SAR) data, which are less affected by weather and illumination conditions compared to optical imagery. Forest management plan data, including inventory records of 229 sample plots, a detailed forest cover map, and stand tables of the 2,119-ha Igneada Longoz Forest, were used to calculate AGC and to develop spatially explicit models based on ALOS/PALSAR-2 (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) and Landsat-8 images. The results indicated that the horizontally transmitted and horizontally received (HH) and cross-polarization ratio (CPR) bands of ALOS/PALSAR were the most influential variables in the linear and nonlinear regression models. The models did not include any variables from either radaror optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error approximate to 26%), the linear model yielded negative estimations in several land cover classes (e.g., dune, forest opening, degraded forest). AGC stock was estimated and mapped using the nonlinear model in these cases. The density map revealed that Igneada Longoz Forest stored 279,258.9 t AGC, with a mean and standard deviation of 124 +/- 115.4 t C ha-1. AGC density varied significantly depending on stand types and management units across the forest, and carbon hotspots accumulated in the northern and southern sites of the study area, primarily composed of ash and alder seed stands. The models and maps that this study developed are expected to help in the rapid and cost-effective assessment of AGC stored in flooded forest ecosystems across the temperate climate zone.
dc.identifier.doi10.3832/ifor4527-017
dc.identifier.endpage285en_US
dc.identifier.issn1971-7458
dc.identifier.issue5en_US
dc.identifier.startpage277en_US
dc.identifier.urihttp://dx.doi.org/10.3832/ifor4527-017
dc.identifier.urihttps://hdl.handle.net/11494/5195
dc.identifier.volume17en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisheriForesten_US
dc.relation.ispartofBiogeosciences and Forestry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSAR Mosaicsen_US
dc.subjectLandsat-8en_US
dc.subjectNormalized Difference Vegetation Index (NDVI)en_US
dc.subjectAboveground Biomass and Carbon Stocksen_US
dc.subjectCarbon Density Mapsen_US
dc.subjectBottomland Forestsen_US
dc.subjectNational Parksen_US
dc.subjectIgneadaen_US
dc.titleModeling aboveground carbon in flooded forests using synthetic aperture radar data: a case study from a natural reserve in Turkish Thraceen_US
dc.typeArticle

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