Estimation of Aboveground Biomass of Maritime Pine (Pinus pinaster Ait.) Plantations Using Sentinel-1 and Sentinel-2 Satellite Images in Bartın

Yıl 2024, Cilt: 5 Sayı: 1, 15 - 27, 28.03.2024

Eren Gürsoy Özdemir , Aziz Demiralay Batuhan Şahin

https://doi.org/10.48123/rsgis.1327406

Cited By: 1

Öz

Forests are a crucial component in the world ecosystem, covering approximately one-third of the earth's surface, hosting more than half of the biodiversity on the planet, holding a significant amount of carbon released into the atmosphere, and strongly impacting climate change. Accurate forest biomass estimation is essential in reducing carbon emissions and increasing carbon sink areas. With the development of satellite technologies and remote sensing systems, estimating the Above Ground Biomass (AGB) with active and passive systems has become possible. In this study, the effects of band and vegetation index values on Above Ground Biomass (AGB) estimation were investigated in Maritime pine (Pinus pinaster Ait.) reforestation areas in Bartın using data from the Sentinel-1 radar and Sentinel-2 optical satellite provided free of charge to researchers by the European Space Agency (ESA), along with the Multiple Linear Regression (MLR) and Random Forest (RF) methods. The relationships between AGB values obtained from ground sample plot data and the satellite data were examined, and 16 models were developed. The best results for AGB estimation were achieved using the model that incorporated the Sentinel-1 VH backscatter value, the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2, and the RF method (R2=0.61, RMSE= 49.412 t/ha).

Anahtar Kelimeler

Biomass, Above ground biomass, Sentinel-1, Sentinel-2, Maritime pine

Bartın’daki Sahil Çamı (Pinus pinaster Ait.) Ağaçlandırma Alanlarında Sentinel-1 ve Sentinel-2 Uydu Görüntüleri Kullanılarak Toprak Üstü Biyokütlenin Kestirilmesi

Öz

Ormanlar, yaklaşık olarak yeryüzünün üçte birini kaplayan, gezegendeki biyoçeşitliliğin yarısından fazlasına ev sahipliği yapan, atmosfere salınan karbonun önemli bir miktarını tutan, iklim değişimi konusunda da güçlü bir etkiye sahip dünya ekosistemindeki çok önemli bir bileşendir. Ormanlık alanların biyokütlesinin doğru bir şekilde kestirilmesi, karbon salınımlarının azaltılması ve karbon yutak alanlarının artırılması kapsamında büyük önem taşımaktadır. Uydu teknolojilerinin ve uzaktan algılama sistemlerinin gelişmesiyle birlikte aktif ve pasif sistemler ile Toprak Üstü Biyokütlenin (TÜB) kestiriminin yapılması mümkün hale gelmiştir. Bu çalışmada, Bartın’daki sahil çamı (Pinus pinaster Ait.) ağaçlandırmalarında, Avrupa Uzay Ajansı (ESA) tarafından araştırmacılara ücretsiz sunulan Sentinel-1 radar, Sentinel-2 optik uydu verileri ile Çoklu Doğrusal Regresyon (ÇDR) ve Rastgele Orman (RO) yöntemlerinden yararlanılarak bant ve bitki örtüsü indeksi değerlerinin TÜB kestirimine etkileri ve yersel örnekleme alan verilerinden elde edilen TÜB değerleri ile ilişkileri araştırılmaktadır. 16 modelin geliştirildiği çalışmada, Sentinel-1 VH geri saçılım değeri, Sentinel-2’den türetilmiş normalize edilmiş fark bitki örtüsü indeksi değeri (NDVI) füzyonu ve RO yöntemi kullanıldığı model ile TÜB kestiriminde en iyi sonuç elde edilmiştir (R2=0.61, RMSE= 49.412 t/ha).

Anahtar Kelimeler

Biyokütle, Toprak üstü biyokütle, Sentinel-1, Sentinel-2, Sahil çamı

Destekleyen Kurum

TÜBİTAK BİDEB

Teşekkür

Çalışma, TÜBİTAK BİDEB 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında 2021/2 dönemi 1919B012106792 başvuru nolu proje olarak desteklenmiştir. Desteklerinden dolayı TÜBİTAK BİDEB ‘e ve uydu görüntülerinin ücretsiz olarak temin edilmesinde sağladığı imkanlardan dolayı Avrupa Uzay Ajansına (ESA) teşekkür ederiz.

Kaynakça

• Bao, N., Li, W., Gu, X., & Liu, Y. (2019). Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sensing, 11(23), 2855. https://doi.org/10.3390/rs11232855
• Bonan, G.B. (2008). Forests and climate change: forcings, feedbacks and the climate benefits of forests. Science, 320, 1444–1449. https://doi.org/10.1126/science.1155121
• Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
• Çepel, N., Dündar, M., & Günel, A. (1977). Türkiye’nin önemli yetişme bölgelerinde saf sarıçam ormanlarının gelişimi ile bazı edafik ve fizyografik etmenler arasındaki ilişkiler (Proje No: TOAG 154). TÜBİTAK, Tarım ve Ormancılık Araştırma Grubu, TÜBİTAK Yayınları No:354, TOAG Seri No: 65, Ankara.
• Cheng, W., Yang, C., Zhou, J., Zhou, W., & Liu, Y. (2009). Research summary of forest volume quantitative estimation based on remote sensing technology. Journal of Anhui Agricultural Sciences, 37, 7746–7750.
• Cox, P., Betts, R., & Jones, C. (2000). Erratum: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408, 750. https://doi.org/10.1038/35047138
• David, R. M., Rosser, N. J., & Donoghue Daniel, N. M. (2022). Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery. Remote Sensing of Environment, 282, 113232. https://doi.org/10.1016/j.rse.2022.113232
• Dixon, R. K., Brown, S. A., Houghton, R. A., Solomon, A. M., Trexler, M. C., & Wisniewski, J. (1994) Carbon Pools and Flux of Global Forest Ecosystems. Science, 263, 185-190. http://dx.doi.org/10.1126/science.263.5144.185
• Dobson, M. C., Ulaby, F. T., LeToan, T., Beaudoin, A., Kasischke, E. S., & Christensen, N. (1992). Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 412–415. https://doi.org/10.1109/36.134090
• Eckert, S. (2012). Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data. Remote Sensing, 4(4), 810–829. http://dx.doi.org/10.3390/rs4040810
• Flores-Anderson, A. I., Herndon, K. E., Thapa, R. B., & Cherrington, E. (2019). The SAR handbook: Comprehensive methodologies for forest monitoring and biomass estimation (No. MSFC-E-DAA-TN67454). https://gis1.servirglobal.net/TrainingMaterials/SAR/SARHB_FullRes.pdf
• Foody, G. M., Boyd, D. S., & Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463–474. https://doi.org/10.1016/S0034-4257(03)00039-7
• George-Chacón, S. P., Milodowski, D. T., Dupuy, J. M., Mas, J.-F., Williams, M., Castillo-Santiago, M. A., & Hernández-Stefanoni, J. L. (2022). Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula. Geocarto International, 37(25), 7659–7680. https://doi.org/10.1080/10106049.2021.1980619
• Georgopoulos, N., Sotiropoulos, C., Stefanidou, A., & Gitas, I. Z. (2022). Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain. Forests, 13, 2157. https://doi.org/10.3390/f13122157
• Ghasemi, N., Sahebi, M. R., & Mohammadzadeh, A. (2013). Biomass Estimation of a Temperate Deciduous Forest Using Wavelet Analysis. IEEE Transactions on Geoscience and Remote Sensing, 51(2), 765–776. https://doi.org/10.1109/TGRS.2012.2205260
• Ghosh, P., Mandal, D., Bhattacharya, A., Nanda, M. K., & Bera, S. (2018). Assessing Crop Monitoring Potential of Sentinel-2 in A Spatio-Temporal Scale. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-5, 227–231. https://doi.org/10.5194/isprs-archives-XLII-5-227-2018
• Guerra-Hernández, J., Narine, L. L., Pascual, A., Gonzalez-Ferreiro, E., Botequim, B., Malambo, L., Neuenschwander, A., Popescu, S. C., & Godinho, S. (2022). Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests. GIScience & Remote Sensing, 59(1), 1509–1533. https://doi.org/10.1080/15481603.2022.2115599
• Günel, A. (1981). Orman Hasılat Bilgisi. İstanbul Üniversitesi Yayınları.
• Güner, Ş. T., Özel, C., Türkkan, M. & Akgül, S. (2019). Türkiye’deki sahilçamı ağaçlandırmalarında ağaç bileşenlerine ait karbon yoğunluklarının değişimi. Ormancılık Araştırma Dergisi, 6(2) , 167-176.
• 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, 106596. https://doi.org/10.1016/j.compag.2021.106596
• Güverçin, İ., & Günlü, A. (2023). Saf Kızılçam (Pinus brutia Ten.) Meşcerelerinde Aktif ve Pasif Uydu Görüntüleri Kullanılarak Topraküstü Biyokütlenin Tahmin Edilmesi (Anamur Orman İşletme Şefliği Örneği). Bartın Orman Fakültesi Dergisi, 25(1), 177–191. https://doi.org/10.24011/barofd.1261299
• Hamdan, O., Aziz, H. K., & Rahman, K. A. (2011). Remotely Sensed L-Band SAR Data for Tropical Forest Biomass Estimation. Journal of Tropical Forest Science, 23(3), 318–327.
• Kandemir, A., & Mataracı T. (2018). Pinus L. In A. Güner, A. Kandemir, Y. Menemen, H. Yıldırım, S. Aslan, G. Ekşi, I. Güner & A. Ö. Çimen (Eds.), Illustrated Flora of Turkey 2 (pp. 324–354). Nezahat Gökyiğit Botanical Garden Press.
• Keleş, S., Günlü, A., & Ercanli, I. (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. https://doi.org/10.1016/B978-0-12-822931-6.00008-3
• Li, C., Li, Y., & Li, M. (2019). Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China. Forests, 10(2), 104. http://dx.doi.org/10.3390/f10020104
• Li, Y., Li, M., Li, C., & Liu, Z. (2020). Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific Reports, 10, 9952. https://doi.org/10.1038/s41598-020-67024-3
• Liu, Y. A., Gong, W. S., Xing, Y. Q., Hu, X. Y., & Ong, J. Y. (2019). Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 277–289. https://doi.org/10.1016/j.isprsjprs.2019.03.016
• Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63–105. https://doi.org/10.1080/17538947.2014.990526
• Meteoroloji Genel Müdürlüğü. (2023, 20 Haziran). Resmi İstatistikler. Meteoroloji Genel Müdürlüğü (MGM). 20 Haziran 2023’de https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=BARTIN adresinden alındı.
• Monnet, J. M., Chanussot, J., & Berger, F. (2011). Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning. IEEE Geoscience and Remote Sensing Letters, 8(3), 580-84. https://doi.org/10.1109/LGRS.2010.2094179
• Naik, P., Dalponte, M., & Bruzzone, L. (2021). Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data. Remote Sensing, 13(7), 1282. https://doi.org/10.3390/rs13071282
• Nasirzadehdizaji, R., Balik Sanli, F., Abdikan, S., Cakir, Z., Sekertekin, A., & Ustuner, M. (2019). Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage. Applied Sciences, 9(4), 655. https://doi.org/10.3390/app9040655
• Nuthammachot, N., Askar, A., Stratoulias, D., & Wicaksono, P. (2022). Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation. Geocarto International, 37(2), 366-376.
• Nelson, R., Ranson, K. J., Sun, G., Kimes, D. S., Kharuk, V., & Montesano, P. (2009). Estimating Siberian Timber Volume Using MODIS and ICESat/GLAS. Remote Sensing of Environment, 113(3), 691-701.
• Omar, H., Misman, M., & Kassim, A. (2017). Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia. Applied Sciences, 7(7), 675. http://dx.doi.org/10.3390/app7070675
• Pham, T. D., Yokoya, N., Xia, J., Ha, N. T., Le, N. N., Nguyen, T. T. T., Dao, T. H., … Takeuchi, W. (2020). Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam. Remote Sensing, 12(8), 1334. http://dx.doi.org/10.3390/rs12081334
• Santoro, M., Cartus, O., Fransson, J. E. S., & Wegmüller, U. (2019). Complementarity of X-, C-, and L-band SAR Backscatter Observations to Retrieve Forest Stem Volume in Boreal Forest. Remote Sensing, 11, 1563. https://doi.org/10.3390/rs11131563
• Schmidt, M., Carter, J., Stone, G., & O’Reagain, P. (2016). Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland. Remote Sensing, 8(12), 989. http://dx.doi.org/10.3390/rs8120989
• Şimşek, Y., Tubukçu, M., Toplu, F., Akkan, A., & Avcıoğlu, E. (1985). Türkiye'de ithal edilen hızlı büyüyen yabancı türlerin büyümeleri üzerine araştırmalar. Ormancılık Araştırma Enstitüsü Yayınları.
• Tavasoli, N., & Arefi, H. (2021). Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning. Environmental Sciences Proceedings, 5(1), 13. https://doi.org/10.3390/IECG2020-07916
• Theofanous, N., Irene, C., Giorgos, M., Christos D., Natalia, V., & Sofia, S. (2021). Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions. Forests, 12(7), 902. https://doi.org/10.3390/f12070902
• Tolunay, D., Makineci, E., Şahin, A., Özturna, A. G., Pehlivan, S., & Abdelkaim, M. A. (2017). İstanbul-Durusu Kumul Alanlarındaki Sahil Çamı (Pinus pinaster Ait.) ve Fıstık Çamı (Pinus pinea L.) Ağaçlandırmalarında Karbon Birikimi (TÜBİTAK TOVAG Proje No: 114O797).
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://dx.doi.org/10.1016/0034-4257(79)90013-0
• Ürgenç. S. (1972). Hızlı gelişen bazı egzotik (yabancı) iğne yapraklı ağaç türlerinin Türkiye’ye ithali ve yetiştirilmesi imkânları üzerine araştırmalar (Yayın No. 1750/188). İ.Ü. Orman Fakültesi Yayınları.
• Vaglio Laurin, G., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., & Papale, D. (2016). Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates. Remote Sensing, 9(1), 18. http://dx.doi.org/10.3390/rs9010018
• Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T., & Tien Bui, D. (2018). Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sensing, 10(2), 172. http://dx.doi.org/10.3390/rs10020172
• Vatandaşlar, C., & Abdikan, S. (2022). Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape. Journal of Forestry Research, 33, 827–838. https://doi.org/10.1007/s11676-021-01363-3
• Vickers, D., Thomas, C., Pettijohn, J., Martin, J., & Law, B. (2012). Five years of carbon fluxes and inherent water-use efficiency at two semi-arid pine forests with different disturbance histories. Tellus B: Chemical and Physical Meteorology, 64(1), 17159. https://doi.org/10.3402/tellusb.v64i0.17159
• Wang, X., Shao, G., Chen, H., Lewis, B. J., Qi, G., Yu, D., Zhou, L., & Dai, L. (2013). An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China. Environmental Management, 52, 612–620. https://doi.org/10.1007/s00267-013-0089-6
• Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, R. B., & Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 189-201. https://doi.org/10.1016/j.isprsjprs.2019.06.007
• Yadav, S., Padalia, H., Sinha, S. K., Srinet, R., & Chauhan, P. (2021). Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest. Remote Sensing Applications: Society and Environment, 21, 100462. https://doi.org/10.1016/j.rsase.2020.100462