Kentsel alanlarda ağaç yükseklik bilgilerinin ICESat-2/ATLAS ve İHA verilerinden elde edilmesi

Yıl 2024, Cilt: 9 Sayı: 1, 86 - 96, 15.04.2024

Müge Ağca , Efdal Kaya , Ali İhsan Daloğlu , Aslıhan Yücel , Sercan Yalçınkaya

https://doi.org/10.29128/geomatik.1384320

Öz

Günümüzde nüfusun artması ile birlikte kentsel alanlar hızlı bir şekilde büyümektedir. Kentsel alanlardaki ağaçların belirlenmesi ve yükseklik bilgilerinin elde edilmesi karbon salınımlarının belirlenmesi, gölge, hava kirliliği gibi birçok disiplin tarafından önemsenen çalışmalar için büyük önem arz etmektedir. Bu çalışmanın genel amacı İnsansız Hava Aracı ve uzaysal ICESat-2/ATLAS sistemlerinden elde edilen veriler kullanılarak kentsel alanlarda ağaç yüksekliklerinin belirlenmesidir. Çalışma alanı olarak İzmir ilinin Balçova ilçesi seçilmiştir. Çalışmada ileri teknolojilerden elde edilen veriler arazide yerinde toplanan veriler ile karşılaştırılmıştır. Kentsel alanda ICESat-2 sisteminden elde edilen yükseklik bilgilerinin doğruluğu RMSE, MSE, MAE, ME, R2, Pearson korelasyon katsayısı, Spearman korelasyon katsayısı ve Kendall korelasyon katsayıları hesaplanarak nicel olarak değerlendirilmiştir. Ayrıca çalışma alanına ait yüksek kalite ve doğrulukta topografik veriler ve ortofoto oluşturulmuştur. Sonuç olarak, yapılan tüm istatistiksel analizler değerlendirildiğinde hem ICESat-2/ATLAS verilerinin (R2: 0.97) hem de İHA verilerinin (R2: 0.98) kentsel alanlarda ağaç yüksekliklerinin belirlenmesinde başarılı sonuçlar verdiği görülmüştür. Bu çalışmada ülkemizde henüz çok yeni bir veri seti olan ICESat-2/ATLAS verilerinin ağaç yükseklik bilgilerinin çıkarılmasındaki performansı analiz edilmiştir. Çalışmadan elde edilen sonuçlar ileride yapılacak olan benzer çalışmalara altlık olacak niteliktedir.

Anahtar Kelimeler

İHA, ICESat-2/ATLAS, Ağaç yüksekliği, Lazer, Foton

Kaynakça

• Abdullah, S., Rashid, M. F. A., Tahar, K. N., & Osoman, M. A. (2021). Tree Crown Mapping based on unmanned aerial vehicle (UAV) towards a green-sustainable residential. Plannıng Malaysia Journal, 19(2), 97-107. https://doi.org/10.21837/pm.v19i16.955
• Agca, M., & Daloglu, A. I. (2023). Local Geoid height calculations with GNSS, airborne, and spaceborne Lidar data. The Egyptian Journal of Remote Sensing and Space Science, 26(1), 85-93. https://doi.org/10.1016/j.ejrs.2022.12.009
• Ağca, M. (2020). PALS, ICESat/GLAS ve ICESat-2 Lazer Sistemleri ve Kullanım Alanları. Geomatik, 5(1), 27-35. https://doi.org/10.29128/geomatik.560344
• Ağca, M., Gültekin, N., & Kaya, E. (2020). İnsansız hava aracından elde edilen veriler ile kaya düşme potansiyelinin değerlendirilmesi: Adam Kayalar örneği, Mersin. Geomatik, 5(2), 134-145. https://doi.org/10.29128/geomatik.595574
• Alexander, C., Korstjens, A. H., & Hill, R. A. (2018). Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models. International Journal of Applied Earth Observation and Geoinformation, 65, 105-113. https://doi.org/10.1016/j.jag.2017.10.009
• Anderson, C. T., Dietz, S. L., Pokswinski, S. M., Jenkins, A. M., Kaeser, M. J., Hiers, J. K., & Pelc, B. D. (2021). Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests. Forest Ecology and Management, 491, 119118. https://doi.org/10.1016/j.foreco.2021.119118
• Barazzetti, L., Scaioni, M., & Remondino, F. (2010). Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation. The Photogrammetric Record, 25(132), 356-381. https://doi.org/10.1111/j.1477-9730.2010.00599.x
• Bendea, H., Boccardo, P., Dequal, S., Giulio Tonolo, F., Marenchino, D., & Piras, M. (2008). Low cost UAV for post-disaster assessment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8), 1373-1379.
• Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., González-Dugo, V., & Fereres, E. (2009b). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(6), 6.
• Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009a). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722-738. https://doi.org/10.1109/TGRS.2008.2010457
• Chiabrando, F., Nex, F., Piatti, D., & Rinaudo, F. (2011). UAV and RPV systems for photogrammetric surveys in archaelogical areas: two tests in the Piedmont region (Italy). Journal of Archaeological Science, 38(3), 697-710. https://doi.org/10.1016/j.jas.2010.10.022
• Chou, T. Y., Yeh, M. L., Chen, Y. C., & Chen, Y. H. (2010). Disaster monitoring and management by the unmanned aerial vehicle technology. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(7B), 137–142.
• Da Cunha Neto, E. M., Rex, F. E., Veras, H. F. P., Moura, M. M., Sanquetta, C. R., Käfer, P. S., ... & Dalla Corte, A. P. (2021). Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest. Urban Forestry & Urban Greening, 63, 127197. https://doi.org/10.1016/j.ufug.2021.127197
• Dirik, H., Erdoğan, R., Altınçekiç, H. S., & Altınçekiç, H. (2014). Kent Ağaçlarının İşlevleri, Koruma Önemi ve Değer Belirleme Yaklaşımları. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 15(2), 161-174. http://dx.doi.org/10.17474/acuofd.74718
• Durgun, H., Çoban, H. O., & Eker, M. (2022). İnsansız hava aracıyla elde edilen hava fotoğraflarından kızılçam ağaçlarının çap ve boylarının ölçümü ve gövde hacminin tahmini. Turkish Journal of Forestry, 23(4), 255-267. https://doi.org/10.18182/tjf.1199567
• Fiorillo, F., Jiménez Fernández-Palacios, B., Remondino, F., & Barba, S. (2015). 3D Surveying and modelling of the Archaeological Area of Paestum, Italy. Virtual Archaeology Review, 4(8), 55-60. https://doi.org/10.4995/var.2013.4306
• Grenzdörffer, G. J., Engel, A., & Teichert, B. (2008). The photogrammetric potential of low-cost UAVs in forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 31(B3), 1207-1213.
• Gruen, A., Huang, X., Qin, R., Du, T., Fang, W., Boavida, J., & Oliveira, A. (2013). Joint processing of UAV imagery and terrestrial mobile mapping system data for very high resolution city modeling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 175-182. https://doi.org/10.5194/isprsarchives-XL-1-W2-175-2013
• Hao, J., Li, X., Wu, H., Yang, K., Zeng, Y., Wang, Y., & Pan, Y. (2023). Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR. Geodesy and Geodynamics, 14(3), 292-303. https://doi.org/10.1016/j.geog.2022.11.008
• Kaya, Y., & Polat, N. (2023). A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences, 8(1), 52-62. https://doi.org/10.26833/ijeg.1035037
• Kohoutek, T. K., & Eisenbeiss, H. (2012). Processing of UAV based range imaging data to generate detailed elevation models of complex natural structures. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 405-410. https://doi.org/10.5194/isprsarchives-XXXIX-B1-405-2012
• Konolige, K., & Agrawal, M. (2008). FrameSLAM: From bundle adjustment to real-time visual mapping. IEEE Transactions on Robotics, 24(5), 1066-1077. https://doi.org/10.1109/TRO.2008.2004832
• Ma, Y., Xu, N., Liu, Z., Yang, B., Yang, F., Wang, X. H., & Li, S. (2020). Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets. Remote Sensing of Environment, 250, 112047. https://doi.org/10.1016/j.rse.2020.112047
• Manyoky, M., Theiler, P., Steudler, D., & Eisenbeiss, H. (2012). Unmanned aerial vehicle in cadastral applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 57-62. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-57-2011
• Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., ... & Zwally, J. (2017). The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sensing of Environment, 190, 260-273. https://doi.org/10.1016/j.rse.2016.12.029
• Mielcarek, M., Stereńczak, K., & Khosravipour, A. (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71, 132-143. https://doi.org/10.1016/j.jag.2018.05.002
• Molina, P., Colomina, I., Vitoria, T., Silva, P. F., Skaloud, J., Kornus, W., ... & Aguilera, C. (2012). Searching lost people with UAVs: the system and results of the close-search project. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 441-446. https://doi.org/10.5194/isprsarchives-XXXIX-B1-441-2012
• Montoya, R. C., D'Amato, A. W., Messier, C., & Nolet, P. (2023). Mapping temperate forest stands using mobile terrestrial LiDAR shows the influence of forest management regimes on tree mortality. Forest Ecology and Management, 544, 121194. https://doi.org/10.1016/j.foreco.2023.121194
• Najatishendi, E., Ergene, E. M., Uzar, M., & Şanlı, F. B. (2022). Production of flood risk maps: Ayancık Stream Example. Mersin Photogrammetry Journal, 4(1), 14-23. https://doi.org/10.53093/mephoj.1123378
• Narine, L. L., Popescu, S. C., & Malambo, L. (2019a). Synergy of ICESat-2 and Landsat for mapping forest aboveground biomass with deep learning. Remote Sensing, 11(12), 1503. https://doi.org/10.3390/rs11121503
• Narine, L. L., Popescu, S. C., & Malambo, L. (2020). Using ICESat-2 to estimate and map forest aboveground biomass: A first example. Remote Sensing, 12(11), 1824. https://doi.org/10.3390/rs12111824
• Narine, L. L., Popescu, S., Neuenschwander, A., Zhou, T., Srinivasan, S., & Harbeck, K. (2019b). Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data. Remote Sensing of Environment, 224, 1-11. https://doi.org/10.1016/j.rse.2019.01.037
• Narine, L. L., Popescu, S., Zhou, T., Srinivasan, S., & Harbeck, K. (2009). Mapping forest aboveground biomass with a simulated ICESat-2 vegetation canopy product and Landsat data. Annals of Forest Research, 62(1), 69-86.
• Neitzel, F., & Klonowski, J. (2012). Mobile 3D mapping with a low-cost UAV system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 39-44. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-39-2011
• Neuenschwander, A. L., & Magruder, L. A. (2019). Canopy and terrain height retrievals with ICESat-2: A first look. Remote Sensing, 11(14), 1721. https://doi.org/10.3390/rs11141721
• Nie, S., Wang, C., Xi, X., Luo, S., Li, G., Tian, J., & Wang, H. (2018). Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Optics Express, 26(10), A520-A540. https://doi.org/10.1364/OE.26.00A520
• Nofrizal, A. Y., Sonobe, R., Hiroto, Y., Morita, A., & Ikka, T. (2022). Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest. International Journal of Engineering and Geosciences, 7(3), 221-228. https://doi.org/10.26833/ijeg.953188
• Noor, N. M., Abdullah, A. A. A., Abdullah, A., Ibrahim, I., & Sabeek, S. (2019). 3D city modeling using Multırotor drone for city heritage conservation. Planning Malaysia Journal, 17(1), 338 –349. https://doi.org/10.21837/pm.v17i9.610
• Oczipka, M., Bemmann, J., Piezonka, H., Munkabayar, J., Ahrens, B., Achtelik, M., & Lehmann, F. (2009). Small drones for geo-archaeology in the steppes: locating and documenting the archaeological heritage of the Orkhon Valley in Mongolia. In Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX, 7478, 53-63. https://doi.org/10.1117/12.830404
• Pang, X., Chen, Y., Ji, Q., Li, G., Shi, L., Lan, M., & Liang, Z. (2022). An Improved Algorithm for the Retrieval of the Antarctic Sea Ice Freeboard and Thickness from ICESat-2 Altimeter Data. Remote Sensing, 14(5), 1069. https://doi.org/10.3390/rs14051069
• Qin, H., Zhou, W., Qian, Y., Zhang, H., & Yao, Y. (2022). Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data. Urban Forestry & Urban Greening, 76, 127728. https://doi.org/10.1016/j.ufug.2022.127728
• Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2012). UAV photogrammetry for mapping and 3D modeling–current status and future perspectives. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 25-31. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-25-2011
• Rinaudo, F., Chiabrando, F., Lingua, A., & Spano, A. (2012). Archaeological site monitoring: UAV photogrammetry can be an answer. The International archives of the photogrammetry, Remote Sensing and Spatial İnformation Sciences, 39, 583-588.
• Saliu, I. S., Satyanarayana, B., Fisol, M. A. B., Wolswijk, G., Decannière, C., Lucas, R., ... & Dahdouh-Guebas, F. (2021). An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs. Estuarine, Coastal and Shelf Science, 248, 106971. https://doi.org/10.1016/j.ecss.2020.106971
• Simurda, C., Magruder, L. A., Markel, J., Garvin, J. B., & Slayback, D. A. (2022). ICESat-2 applications for investigating emerging volcanoes. Geosciences, 12(1), 40. https://doi.org/10.3390/geosciences12010040
• Xing, Y., Huang, J., Gruen, A., & Qin, L. (2020). Assessing the performance of ICESat-2/ATLAS multi-channel photon data for estimating ground topography in forested terrain. Remote Sensing, 12(13), 2084. https://doi.org/10.3390/rs12132084
• Yılmaz, H. M., Aktan, N., Çolak, A., & Alptekin, A. (2022). The use of unmanned aerial vehicle (UAV) data in village development plans: A case study of Aksaray Yaylak Village. Mersin Photogrammetry Journal, 4(2), 68-72. https://doi.org/10.53093/mephoj.1202261
• Zang, J., Ni, W., & Zhang, Y. (2023). Spatially-explicit mapping annual oil palm heights in peninsular Malaysia combining ICESat-2 and stand age data. Remote Sensing of Environment, 295, 113693. https://doi.org/10.1016/j.rse.2023.113693
• Zarco-Tejada, P. J., Guillén-Climent, M. L., Hernández-Clemente, R., Catalina, A., González, M. R., & Martín, P. (2013). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171, 281-294. https://doi.org/10.1016/j.agrformet.2012.12.013
• Zhang, G., Chen, W., & Xie, H. (2019). Tibetan Plateau's lake level and volume changes from NASA's ICESat/ICESat‐2 and Landsat Missions. Geophysical Research Letters, 46(22), 13107-13118. https://doi.org/10.1029/2019GL085032