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

doi:10.3832/ifor4527-017

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

Can Vatandaslar^(1-2) , Ferhat Bolat⁽³⁾, Saygin Abdikan⁽⁴⁾, Pinar Pamukcu-Albers⁽⁵⁾, Caner Satiral⁽⁶⁾

iForest - Biogeosciences and Forestry, Volume 17, Issue 5, Pages 277-285 (2024)
doi: https://doi.org/10.3832/ifor4527-017
Published: Sep 27, 2024 - Copyright © 2024 SISEF

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Flooded 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 radar- or optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error ≈ 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.

Keywords

SAR Mosaics, Landsat-8, Normalized Difference Vegetation Index (NDVI), Aboveground Biomass and Carbon Stocks, Carbon Density Maps, Bottomland Forests, National Parks, Igneada

Authors’ address

(1)

0000-0001-5552-5670
Faculty of Forestry, Artvin Coruh University, 08100 Artvin (Turkey)

(2)

0000-0001-5552-5670
Warnell School of Forestry and Natural Resources, University of Georgia, Athens 30602, GA (USA)

(3)

0000-0003-2655-5023
Faculty of Forestry, Çankiri Karatekin University, 18200 Çankiri (Turkey)

(4)

0000-0002-3310-352X
Department of Geomatics Engineering, Hacettepe University, Sihhiye, Ankara (Turkey)

(5)

0000-0003-1935-1815
Department of Geography, University of Bonn, 53115 Bonn (Germany)

(6)

0000-0003-3818-109X
Forestry Research and Application Center, Artvin Coruh University, 08100 Artvin (Turkey)

Corresponding author

Can Vatandaslar
canvatandaslar@artvin.edu.tr

Citation

Vatandaslar C, Bolat F, Abdikan S, Pamukcu-Albers P, Satiral C (2024). Modeling aboveground carbon in flooded forests using synthetic aperture radar data: a case study from a natural reserve in Turkish Thrace. iForest 17: 277-285. - doi: 10.3832/ifor4527-017

Academic Editor

Matteo Garbarino

Paper history

Received: Nov 24, 2023
Accepted: Jun 28, 2024

First online: Sep 27, 2024
Publication Date: Oct 31, 2024
Publication Time: 3.03 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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