Anand, A., Pandey, P. C., Petropoulos, G. P., Pavlides, A., Srivastava, P. K., Sharma, J. K., & Malhi, R. K. M. (2020). Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative. Remote Sensing, 12(4), 597. https://doi.org/10.3390/rs12040597

Askar, N. N., Phairuang, W., Wicaksono, P., & Sayektiningsih, T. (2018). Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery. Journal of Sensors, 2018, 6745629. https://doi.org/10.1155/2018/6745629

Baniya, B., Tang, Q., Huang, Z., Sun, S., & Techato, K. A. (2018). Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal. Forests, 9(6), 329. https://doi.org/10.3390/f9060329

Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and Future Köppen-Geiger Climate Classification Maps at 1-km Resolution. Scientific Data, 5(1), 180214. https://doi.org/10.1038/sdata.2018.214

Berra, E. F., Brandelero, C., Pereira, R. S., Sebem, E., Goergen, L. C. D. G., Benedetti, A. C. P., & Lippert, D. B. (2012). Total Wood Volume Estimation of Eucalyptus Species by Images of Landsat Satellite. Ciência Florestal, 22(4), 853-864. https://doi.org/10.5902/198050987566

Bolat, N. (2019). ÇAKÜ Orman Fakültesi Araştırma ve Uygulama Ormanında Göktürk-2 uydu görüntüsü kullanılarak topraküstü biyokütle ve karbon depolama miktarlarının belirlenmesi [Master’s thesis, Çankırı Karatekin University]. (in Turkish)

Bulut, S., Sivrikaya, F., & Günlü, A. (2022). Evaluating Statistical and Combine Method to Predict Stand Above-ground Biomass Using Remotely Sensed Data. Arabian Journal of Geosciences, 15(9), 838. https://doi.org/10.1007/s12517-022-10140-3

Çepel, N. (2003). Ekolojik sorunlar ve çözümleri. TÜBİTAK. (in Turkish)

Chen, L., Ren, C., Zhang, B., Wang, Z., & Xi, Y. (2018). Estimation of Forest Above-ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery. Forests, 9(10), 582. https://doi.org/10.3390/f9100582

Cusack, D. F., Axsen, J., Shwom, R., Hartzell-Nichols, L., White, S., & Mackey, K. R. (2014). An Interdisciplinary Assessment of Climate Engineering Strategies. Frontiers in Ecology and the Environment, 12(5), 280-287. https://doi.org/10.1890/130030

Dos Reis, A. A., Carvalho, M. C., de Mello, J. M., Gomide, L. R., Ferraz Filho, A. C., & Acerbi Junior, F. W. (2018). Spatial Prediction of Basal Area and Volume in Eucalyptus Stands Using Landsat TM Data: An Assessment of Prediction Methods. New Zealand Journal of Forestry Science, 48(1), 1. https://doi.org/10.1186/s40490-017-0108-0

Dvorakova, K., Heiden, U., Pepers, K., Staats, G., van Os, G., & van Wesemael, B. (2023). Improving soil Organic Carbon Predictions from a Sentinel–2 Soil Composite by Assessing Surface Conditions and Uncertainties. Geoderma, 429, 116128. https://doi.org/10.1016/j.geoderma.2022.116128

Dvorakova, K., Shi, P., Limbourg, Q., & van Wesemael, B. (2020). Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues. Remote Sensing, 12(12), 1913. https://doi.org/10.3390/rs12121913

FMP (2022). Forest management plans and inventory data of Burmahanyayla Planning Unit. General Directorate of Forestry.

Gardin, L., Chiesi, M., Fibbi, L., & Maselli, F. (2021). Mapping Soil Organic Carbon in Tuscany through the Statistical Combination of Ground Observations with Ancillary and Remote Sensing Data. Geoderma, 404, 115386. https://doi.org/10.1016/j.geoderma.2021.115386

Goodale, C. L., Apps, M. J., Birdsey, R. A., Field, C. B., Heath, L. S., Houghton, R. A., ... & Shvidenko, A. Z. (2002). Forest Carbon Sinks in the Northern Hemisphere. Ecological Applications, 12(3), 891-899. https://doi.org/10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2

Gou, Y., Ryan, C. M., & Reiche, J. (2022). Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model. Remote Sensing, 14(2), 404. https://doi.org/10.3390/rs14020404

Güner, Ş. T., Diamantopoulou, M. J., Poudel, K. P., Çömez, A., & Özçelik, R. (2022). Employing Artificial Neural Network for Effective Biomass Prediction: An Alternative Approach. Computers and Electronics in Agriculture, 192, 1065960. https://doi.org/10.1016/j.compag.2021.106596

Günlü, A., & Ercanlı, İ. (2020). Artificial Neural Network Models by ALOS PALSAR Data for Aboveground Stand Carbon Predictions of Pure Beech Stands: A Case Study from Northern of Turkey. Geocarto International, 35(1), 17-28. https://doi.org/10.1080/10106049.2018.1499817

Günlü, A., Ercanli, I., Başkent, E. Z., & Çakır, G. (2014). Estimating Aboveground Biomass Using Landsat TM Imagery: A Case Study of Anatolian Crimean Pine Forests in Turkey. Annals of Forest Research, 57(2), 289-298. https://doi.org/10.15287/afr.2014.278

Günlü, A., Keleş, S., Ercanli, İ., & Şenyurt, M. (2021). Estimation of aboveground stand carbon using Landsat 8 OLI satellite ımage: A case study from Turkey. In P. K. Shit, H. R. Pourghasemi, P. Das, & G. S. Bhunia (Eds.), Spatial Modeling in Forest Resources Management (pp. 385-403). Springer, Cham.

Imran, A. B., & Ahmed, S. (2018). Potential of Landsat-8 Spectral Indices to Estimate Forest Biomass. International Journal of Human Capital in Urban Management, 3(4), 303-314.

Jiang, F., Sun, H., Chen, E., Wang, T., Cao, Y., & Liu, Q. (2022). Above-ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images. Remote Sensing, 14(22), 5734. https://doi.org/10.3390/rs14225734

Kahriman, A., Sönmez, T., Yavuz, M., Şahin, A., Yılmaz, S., Uzun, M., Kumaş, G., & Genç, Y. (2016). Antalya ve Mersin Yöresi Saf Kızılçam Meşcerelerinde Hasılat Araştırmaları, (TÜBİTAK-TOVAG, Project No: 112O808). (in Turkish)

Kauranne, T., Joshi, A., Gautam, B., Manandhar, U., Nepal, S., Peuhkurinen, J., Hämäläinen, J., Junttila, V., Gunia K., Latva-Käyrä, P., Kolesnikov, A., Tegel, K., & Leppänen, V. (2017). LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon. Remote Sensing, 9(2), 154. https://doi.org/10.3390/rs9020154

Keleş, S., Günlü, A., & Ercanli, İ. (2021). Estimating aboveground stand carbon by combining Sentinel-1 and Sentinel-2 satellite data: A case study from Turkey. In P. K. Shit, H. R. Pourghasemi, P. P. Adhikary, G. S. Bhunia, & V. P. Sati (Eds.), Forest Resources Resilience and Conflicts (pp. 117-126). Elsevier.

Labrecque, S., Fournier, R. A., Luther, J. E., & Piercey, D. (2006). A Comparison of Four Methods to Map Biomass from Landsat-TM and Inventory Data in Western Newfoundland. Forest Ecology and Management, 226(1-3), 129-144. https://doi.org/10.1016/j.foreco.2006.01.030

Lal, R. (2008). Carbon Sequestration. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1492), 815-830. https://doi.org/10.1098/rstb.2007.2185

Li, Y., Han, N., Li, X., Du, H., Mao, F., Cui, L., Liu, T., & Xing, L. (2018). Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China. Remote Sensing, 10(6), 898. https://doi.org/10.3390/rs10060898

Myeong, S., Nowak, D. J., & Duggin, M. J. (2006). A Temporal Analysis of Urban Forest Carbon Storage using remote sensing. Remote Sensing of Environment, 101(2), 277-282. https://doi.org/10.1016/j.rse.2005.12.001

Nurda, N., Noguchi, R., & Ahamed, T. (2020). Forest Productivity and Carbon Stock Analysis from Vegetation Phenological Indices Using Satellite Remote Sensing in Indonesia. Asia-Pacific Journal of Regional Science, 4(3), 657-690. https://doi.org/10.1007/s41685-020-00163-7

Ou, G., Li, C., Lv, Y., Wei, A., Xiong, H., Xu, H., & Wang, G. (2019). Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison. Remote Sensing, 11(7), 738. https://doi.org/10.3390/rs11070738

Perry, E., Sheffield, K., Crawford, D., Akpa, S., Clancy, A., & Clark, R. (2022). Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series. Remote Sensing, 14(13), 3071. https://doi.org/10.3390/rs14133071

Pungpa, S., Chumkiew, S., & Piyatadsananon, P. (2023). Estimation of aboveground biomass and carbon stock using remote sensing data in Sakaerat Environmental Research Station, Thailand. In W. Boonpook, Z. Lin, P. Meksangsouy, & P. Wetchayont (Eds.) Applied Geography and Geoinformatics for Sustainable Development (pp. 207-215). Springer, Cham.

Saglam, S., Ozdemir, E., Ozkan, U. Y., Demirel, T., & Makineci, E. (2020). Biomass Estimation of Aboveground Tree Components for Turkey Oak (Quercus cerris L.) in South-eastern Turkey. Environmental Monitoring and Assessment, 192(7), 418. https://doi.org/10.1007/s10661-020-08386-z

Sakici, O. E., Seki, M., & Saglam, F. (2018). Above-ground Biomass and Carbon Stock Equations for Crimean Pine Stands in Kastamonu Region of Turkey. Fresenius Environmental Bulletin, 27(10), 7079-7089.

Situmorang, J. P., Sugianto, S., & Darusman, D. (2016). Estimation of Carbon Stock Stands Using EVI and NDVI Vegetation Index in Production Forest of Lembah Seulawah Sub-District, Aceh Indonesia. Aceh International Journal of Science and Technology, 5(3), 126-139. https://dx.doi.org/10.13170/aijst.5.3.5836

Sivrikaya, F., & Bozali, N. (2012). Karbon Depolama Kapasitesinin Belirlenmesi: Türkoğlu Planlama Birimi Örneği. Bartın Orman Fakültesi Dergisi, 14(1), 69-76. (in Turkish)

Sivrikaya, F., & Işık, M. (2022). Comparison of Biomass Estimation Approaches Based on Inventory Data: A Case Study in Kapıkaya Forest, Turkey. Environmental Engineering & Management Journal, 21(5), 737-743.

Sivrikaya, F., Baskent, E. Z., & Bozali, N. (2013). Spatial Dynamics of Carbon Storage: A Case Study from Turkey. Environmental Monitoring and Assessment, 185(11), 9403-9412. https://doi.org/10.1007/s10661-013-3260-x

Sivrikaya, F., Keleş, S., & Çakir, G. (2007). Spatial Distribution and Temporal Change of Carbon Storage in Timber Biomass of Two Different Forest Management Units. Environmental Monitoring and Assessment, 132(1), 429-438. https://doi.org/10.1007/s10661-006-9545-6

Tavasoli, N., Arefi, H., Samiei-Esfahany, S., & Ronoud, Q. (2019). Modelling the Amount of Carbon Stock Using Remote Sensing in Urban Forest and Its Relationship with Land Use Change. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(4), 1051-1058. https://doi.org/10.5194/isprs-archives-XLII-4-W18-1051-2019

Turgut, R., & Günlü, A. (2022). Estimating Aboveground Biomass Using Landsat 8 OLI Satellite Image in Pure Crimean Pine (Pinus nigra J.F. Arnold subsp. pallasiana (Lamb.) Holmboe) Stands: A Case from Turkey. Geocarto International, 37(3), 720-734. https://doi.org/10.1080/10106049.2020.1737971

Wallis, C. I., Crofts, A. L., Inamdar, D., Arroyo-Mora, J. P., Kalacska, M., Laliberté, É., & Vellend, M. (2023). Remotely Sensed Carbon Content: The Role of Tree Composition and Tree Diversity. Remote Sensing of Environment, 284, 113333. https://doi.org/10.1016/j.rse.2022.113333

Wang, S., Zhou, M., Zhuang, Q., & Guo, L. (2021). Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China. Remote Sensing, 13(20), 4106. https://doi.org/10.3390/rs13204106

Wang, S., Zhuang, Q., Jin, X., Yang, Z., & Liu, H. (2020). Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data. Remote Sensing, 12(7), 1115. https://doi.org/10.3390/rs12071115

Wharton, E. H., & Griffith, D. M. (1993). Methods to estimate total forest biomass for extensive forest inventories: Applications in the northeastern US. US Department of Agriculture, Forest Service.

Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang L., & Zhang, X. (2019). Remote Sensing of the Terrestrial Carbon Cycle: A Review of Advances over 50 Years. Remote Sensing of Environment, 233, 111383. https://doi.org/10.1016/j.rse.2019.111383