Saf Kızılçam (Pinus brutia Ten.) Meşcerelerinde Aktif ve Pasif Uydu Görüntüleri Kullanılarak Topraküstü Biyokütlenin Tahmin Edilmesi (Anamur Orman İşletme Şefliği Örneği)

Yıl 2023, Cilt: 25 Sayı: 1, 177 - 191, 15.04.2023

İzzet Güverçin , Alkan Günlü

https://doi.org/10.24011/barofd.1261299

Cited By: 2

Öz

Bu çalışmanın amacı; saf kızılçam (Pinus brutia Ten.) meşcrelerinde aktif (Sentinel-1A) ve pasif (Landsat 8 OLI) uydu görüntüleri ile bazı topoğrafik veriler kullanılarak topraküstü biyokütlenin tahmin edilmesidir. Çalışmada toplam 404 adet örnek alan verisi kullanılmıştır. Bu örnek alan verilerinin 323 (%80) modellerin oluşturulmasında ve 81 (%20) ise modellerin test edilmesinde kullanılmıştır. Her bir örnek alana ilişkin topraküstü biyokütle değerleri allometrik denklem kullanılarak hesaplanmıştır. Ayrıca her bir örnek alana ilişkin Landsat 8 OLI uydu görüntüsünden bant reflektans, vejetasyon indis ve tekstür değerleri, Sentinel-1A uydu görüntüsünün her iki polarizasyonu (VV ve VH) için parlaklık ve geri yansıtım değerleri ile Alos-Palsar uydu görüntüsünden üretilen Sayısal Yükseklik Model (SYM) verisinden yükselti, eğim ve bakı değerleri hesaplanmıştır. Topraküstü biyokütle ile Landsat 8 OLI, Sentinel-1A ve SAM verisinden elde edilen değişkenler arasındaki ilişkiler regresyon analizi ile modellenmiştir. Toplam 22 farklı regresyon modeli geliştirilmiştir. Geliştirilen modeller arasında en iyi ilişki (R2= 0,509 ; Sy.x= 28,39), Landsat 8 OLI uydu görüntüsünün bant reflektans değerleri, vejetasyon indisleri, tekstür değerleri, Sentinel-1A uydu görüntüsünün iki polarizasyona ilişkin parlaklık değerleri ile yükselti ve bakının bağımsız değişkenler olarak yer aldığı modelle elde edilmiştir.

Anahtar Kelimeler

Topraküstü biyomas, Sentinel 1A, Landsat 8 OLI, allometrik denklem, kızılçam

Teşekkür

Bu makale, Doç. Dr. Alkan GÜNLÜ danışmanlığında Yüksek Lisans öğrencisi İzzet GÜVERÇİN tarafından hazırlanan “Saf Kızılçam Meşcerelerinde Sentinel-1A ve Landsat 8 OLI Uydu Görüntüsü Kullanılarak Topraküstü Biyokütlenin Tahmin Edilmesi (Anamur Orman İşletme Şefliği Örneği)” adlı Yüksek Lisans tezinden üretilmiştir. Çalışmada kullanılan envanter verilerinin temin edilmesinde destek veren Orman Genel Müdürlüğüne ve Orman İdaresi ve Planlama Dairesi Başkanlığı’na teşekkür ederiz.

Estimating Aboveground Biomass Using Active and Passive Satel-lite Image in Pure Calabrian Pine (Pinus brutia Ten.) Stands (A Case Study in Anamur Forest Planning Unit)

Öz

The aim of this study is to estimate aboveground biomass in pure Calabrian pine (Pinus brutia Ten.) stands using active (Sentinel-1A) and passive (Landsat 8 OLI) satellite images and some topographic data. Sample plot data of a total of 404 sample areas were used in the study. Of these sample plot data, 323 (80%) were used to create models and 81 (20%) to test models. Aboveground biomass values for each sample plot were calculated using the allometric equation. In addition, band reflectance, vegetation indices and texture values from Landsat 8 OLI satellite image for each sample plot, brightness and backscattering values for both polarizations (VV and VH) of Sentinel-1A satellite image, and the elevation, slope and aspect values were calculated from the Digital Elevation Model (DEM) data produced from the Alos-Palsar satellite image. Relationships between aboveground biomass and variables obtained from Landsat 8 OLI, Sentinel-1A and SAM data were modelled regression analysis. A total of 22 different regression models were developed. The best success among the developed models was obtained with the model (R2= 0.509; Sy.x= 28.39) in which the band reflectance values, vegetation indices and texture values of the Landsat 8 OLI satellite image, the brightness values of the two polarizations of the Sentinel-1A satellite image, elevation and aspect are included as independent variables.

Anahtar Kelimeler

Abaveground biomass, Sentinel 1A, Landsat 8 OLI, allometric equation, calabrian pine

Kaynakça

• Anonim (2016). Orman Genel Müdürlüğü, Anamur Orman Amenajman Planı 2016-2025. Mersin Orman Bölge Müdürlüğü, Ankara: OGM, p.220.
• Askar, N.N., Phairuang, W., Wicaksono, P. and Sayektiningsih, T. (2018). Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery Hindawi Journal of Sensors, 1-11.
• Baloloy, A. B., Blanco, A. C., Candido, C. G., Argamosa, R. L., Dumalag, J. C., Dimapilis, L. C. and Paringit, E. C. (2018). Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: rapideye, planetscope and sentinel-2. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4(3).
• Birth, G. S., and McVey, G. R. (1968). Measuring the color of growing turf with a reflectance spectrophotometer 1. Agronomy Journal, 60(6): 640-643.
• Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing, 19(4): 657-675.
• Brown, S. (2002). Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3): 363-372.
• Brown, S. and Gaston, G. (1995). Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical Africa. In African greenhouse gas emission inventories and mitigation options: Forestry, land-use change, and agriculture, pp. 51-62. Springer, Dordrecht.
• Brown, S. and Iverson, L.R. (1992). Biomass estimates for tropical forests. World Resource Review, 4 (3): 366-384.
• Brown, S., Gillespie, A.J. and Lugo, A.E. (1989). Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science, 35(4): 881-902.
• Chen, D.W., Stow, D.A. and Gong, P. (2004). Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. International Journal of Remote Sensing, 25: 2177 - 2192.
• Crippen, R.E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34, 71–73.
• DeVries, B., Pratihast, A. K., Verbesselt, J., Kooistra, L., and Herold, M. 2016. Characterizing forest change using community-based monitoring data and Landsat time series. PloS one, 11(3).
• Dube, T. and Mutanga, O. (2015). Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in Umgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 36-46.
• Eckert, S. (2012). Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sensing, 4(4): 810-829.
• Foody, G.M. (2003). Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, 24(20): 4035-4046.
• Frazier, R.J., Coops, N.C., Wulder, M.A. (2015). Boreal Shield forest disturbance and recovery trends using Landsat time series. Remote Sens. Environ., 170, 317–327.
• Gallaun, H., Zanchi, G., Nabuurs, G.J., Hengeveld, G., Schardt, M. and Verkerk, P.J. (2010). EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. Forest Ecology and Management, 260(3): 252-261.
• Gasparri, N. I., Parmuchi, M. G., Bono, J., Karszenbaum, H. and Montenegro, C. L. (2010). Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments, 74(10): 1262-1270.
• Gitelson, A.A., Gritz, U. and Merzlyak, M.N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271-282.
• Goel, N.S. and Qin, W. (1994). Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews, 10(4), 309-347.
• Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton R.A. and Sun, M. (2009). Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management, 4(1): 1-7.
• Günlü, A., Ercanlı, İ., Sönmez, T. and Başkent, E.Z. (2014). Prediction of some stand parameters using pan-sharpened IKONOS satellite image. European Journal of Remote Sensing, 47(1): 329-342.
• Güverçin, İ. (2022). Saf kızılçam meşcerelerinde Sentinel-1A ve Landsat 8 OLI uydu görüntüsü kullanılarak topraküstü biyokütlenin tahmin edilmesi (Anamur orman işletme şefliği örneği) (Yüksek lisans tezi). Erişim adresi: https://tez.yok.gov.tr/UlusalTezMerkezi
• Hall, R.J., Skakun, R.S. and Arsenault E.J. (2006). Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground Biomass and stand volume. Forest Ecology and Management, 225: 378–390.
• Hardisky, M.A., Klemas, V. and Smart, R.M. (1983). The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 49: 77 – 83.
• Heiskanen, J. (2006). Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(6): 1135-1158.
• Houghton, R.A. (2005). Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6): 945-958.
• Huete, A.R. (1988). A Soil Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25:295-309.
• Jiang, Z.Y., Huete, A.R., Didan, K. and Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845.
• Kalinowski, A. and Oliver, S. (2004). ASTER mineral index processing manual. Remote Sensing Applications, Geoscience Australia, pp. 37-36.
• Kaufman, Y.J. and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2): 261-270.
• Keleş, S., Günlü, A. and Ercanli, İ. (2021). Estimating aboveground stand carbon by combining Sentinel-1 and Sentinel-2 satellite data: a case study from Turkey. In Forest Resources Resilience and Conflicts pp. 117-126, Elsevier.
• Key, C.H. and Benson, N.C. (2006). Landscape assessment (LA). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, p. LA-1-55, 164.
• Khan, N. M., Rastoskuev, V. V., Sato, Y. and Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1-3): 96-109.
• Kumar, K. K., Nagai, M., Witayangkurn, A., Kritiyutanant, K. and Nakamura, S. (2016). Above ground biomass assessment from combined optical and SAR remote sensing data in Surat Thani Province, Thailand. Journal of Geographic Information System, 8(04): 506.
• Li, J. J., Wang, X. R., Wang, X. J., Ma, W. C. and Zhang, H. (2009). Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecological Complexity, 6(4): 413-420.
• Li, S., and Chen, X. (2014). A New Bare-Soıl Index for rapid mapping developing areas using landsat 8 data. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2(4).
• Li, Y., Li, M., Liu, Z. and Li, C. (2020). Combining Kriging interpolation to improve the accuracy of forest aboveground biomass estimation using remote sensing data. IEEE Access, 8.
• Liu, H.Q. and Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE transactions on geoscience and remote sensing, 33(2), 457-465.
• López-Serrano, P. M., Cárdenas Domínguez, J. L., Corral-Rivas, J. J., Jiménez, E., López-Sánchez, C. A. and Vega-Nieva, D. J. 2020. Modeling of aboveground biomass with Landsat 8 OLI and machine learning in temperate forests. Forests, 11(1): 11.
• Lu, D. (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12): 2509-2525.
• Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 27(7): 1297–1328.
• Lu, D., Mausel, P., Brondizio, E. and Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1–3): 149–167.
• Luo, K., Wei, Y., Du, J., Liu, L., Luo, X., Shi, Y., Pei, X., Lei, N., Song, C., Li, J. and Tang, X. (2021). Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. Journal of Forestry Research, 1-12.
• Maynard, C. L., Lawrence, R. L., Nielsen, G. A. and Decker, G. (2007). Modeling vegetation amount using bandwise regression and ecological site descriptions as an alternative to vegetation indices. GIScience and Remote Sensing, 44(1): 68-81.
• Mukherjee, N. R., and Samuel, C. (2016). Assessment of the temporal variations of surface water bodies in and around Chennai using Landsat imagery. Indian Journal of Science and Technology, 9(18): 1-7.
• Muukkonen, P. and Heiskanen, J. (2005). Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote sensing of Environment, 99(4): 434-447.
• Nelson, R., Krabill, W. and Tonelli, J. (1988). Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, 24(2): 247-267.
• Nelson, R.F., Kimes, D.S., Salas, W.A. and Routhier, M. (2000). Secondary forest age and tropical forest biomass estimation using thematic mapper ımagery: single-year tropical forest age classes, a surrogate for standing biomass, cannot be reliably identified using single-date tm imagery. Bioscience, 50(5): 419-431.
• Norovsuren, B., Tseveen, B., Batomunkuev, V. and Renchin, T. (2019). Estimation for forest biomass and coverage using Satellite data in small scale area, Mongolia. In IOP Conference Series: Earth and Environmental Science (Vol. 320, No. 1, p. 012019). IOP Publishing.
• Nuthammachot, N., Askar, A., Stratoulias, D. and Wicaksono, P. (2022). Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation. Geocarto International, 37(2): 366-376.
• Penuelas, J., Baret, F. and Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2): 221-230.
• Phua, M. H., Johari, S. A., Wong, O. C., Ioki, K., Mahali, M., Nilus, R., Coomes, D. A., Maycock, C.R. and Hashim, M. (2017). Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests. Forest Ecology and Management, 406: 163-171.
• Qi, J., Chehbouni, A.L., Huete, A.R., Kerr, Y.H. and Sorooshian, S. (1994). A modified soil adjusted vegetation index (MSAVI). Remote Sensing of Environment 48, 119–126.
• Rouse, J.W., Hass, R.H., Shell, J.A. and Deering, D.W. (1974). Monitoring vegetation systems in the great plains with ERTS-l. Proceedings, 3rd Earth Resources Technology Satellite Symposium, 1: 309-317.
• Roy, P.S. and Ravan, S.A. (1996). Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. Journal of Biosciences, 21(4): 535-561.
• Sader, S.A., Waide, R.B., Lawrence, W. T. and Joyce, A.T. (1989). Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data. Remote Sensing of Environment, 28: 143-198.
• Santos, J.R., Freitas, C.D., Araujo, L.S., Dutra, L.V., Mura, J.C., Gama, F.F., Soler, L.D. and Sant’anna, S. J. (2003). Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. Remote Sensing of Environment, 87: 482-493.
• Schroeder, P., Brown, S., Mo, J., Birdsey, R. and Cieszewski, C. (1997). Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science, 43(3): 424-434.
• Shao, Z. and Zhang, L. (2016). Estimating forest aboveground biomass by combining optical and SAR data: a case study in Genhe, Inner Mongolia, China. Sensors, 16(6): 834.
• SPSS. (2007). Institute Inc. SPSS Base 15.0 User’s Guide.
• Sripada, R. P. (2005). Determining in-season nitrogen requirements for corn using aerial color-infrared photography. North Carolina State University.
• Sripada, R. P., Heiniger, R. W., White, J. G. and Meijer, A. D. (2006). Aerial color infrared photography for determining early in‐season nitrogen requirements in corn. Agronomy Journal, 98(4): 968-977.
• Steininger, M.K. (2000). Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21(6-7): 1139-1157.
• Şahin, A. (2015). Mersin yöresi saf Kızılçam (Pinus brutia Ten.) meşcerelerinde hasılat araştırmaları (Doktora tezi). Erişim adresi: https://tez.yok.gov.tr/UlusalTezMerkezi
• Thenkabail, P. S., Stucky, N., Griscom, B. W., Ashton, M. S., Diels, J., Van der Meer, B. and Enclona, E. (2004). Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing, 25(23): 5447-5472.
• Tiwari, A.K. (1994). Mapping forest biomass through digital processing of IRS-IA data. International Journal of Remote Sensing, 15(9): 1849-1866.
• Tomppo, E., Nilsson, M., Rosengren, M., Aalto, P. and Kennedy, P. (2002). Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass. Remote Sensing of Environment, 82(1): 156-171.
• Tucker, C.J. (1980). A spectral method for determining the percentage of green herbage material in clipped sample. Remote Sensing of Environment, 9(2): 175–181.
• Turgut, R. and 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 Internatıonal, 37(3) : 720–734.
• Van Deventer, A. P., Ward, A. D., Gowda, P. H. and Lyon, J. G. (1997). Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering and Remote Sensing, 63, 87-93.
• Wang, C., Chen, J., Wu, J., Tang, Y., Shi, P., Black, T.A. and Zhu, K. (2017). A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12.
• Xie, S., Wang, W., Meng, J., Zhao, T. and Huang, G. (2017). Estimation of forest stand parameters using SPOT-5satellite images and topographic information. Preprints.https://doi.org/10.20944/preprints201710.0017.v1
• Xie, Y., Sha, Z., Yu, M., Bai, Y. and Zhang, L. (2009). A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecological Modelling, 220(15): 1810-1818.
• Yan, F., Wu, B. and Wang, Y. (2015). Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China. Agricultural and Forest Meteorology, 200: 119-128.
• Zhao, F., Guo, Q. and Kelly, M. (2012). Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA. Agricultural and Forest Meteorology, 165: 64-72.
• Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J. and Ryu, S.R. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3): 402-411.
• Zheng, G., Chen, J. M., Tian, Q. J., Ju, W. M. and Xia, X. Q. (2007). Combining remote sensing imagery and forest age inventory for biomass mapping. Journal of Environmental Management, 85(3): 616-623.
• Zianis, D. and Mencuccini, M. (2004). On simplifying allometric analyses of forest biomass. Forest Ecology And Management, 187(2-3): 311-332.
• Zimble, D. A., Evans, D. L., Carlson, G. C., Parker, R. C., Grado, S. C. and Gerard, P. D. (2003). Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote sensing of Environment, 87(2-3): 171-182.