ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

Year 2023, Volume: 23 Issue: 3, 220 - 229, 06.12.2023

Emre Aktürk

https://doi.org/10.17475/kastorman.1394895

Abstract

Çalışmanın amacı: Bu çalışmada, 2020 ve 2022 yılları için Orman Kanopi Örtüsü Tahmin Modeli (CCEM) ile Orman Örtü Örtüsü (FCC) haritalarının üretilmesinde gündüz ve gece segmentlerinin kullanılmasının tahmin başarısı üzerine etkisi araştırılmaktadır.
Çalışma alanı: Çalışma alanı, geniş ormanlık alanlarıyla bilinen, Louisiana eyaletine bitişik, Texas eyaletinin güneydoğu bölgesinde yer alan ve güney sahil şeridine yakın olan 17 birbirine bağlı ilçeyi kapsamaktadır.
Materyal ve yöntem: Çalışma, ICESat-2/ATLAS verilerinden alınan hem gündüz hem de gece segmentlerini kapsayacak şekilde, orman kanopi örtüsü haritalarını CCEM kullanarak tahmin başarısı kapsamlı bir karşılaştırmasını gerçekleştirdi.
Temel sonuçlar: Çalışmanın bulguları, gece segmentlerinden elde edilen FCC haritalarının, sırasıyla 2020 ve 2022 yılları için 0.77 ve 0.83 daha yüksek kappa katsayıları göstererek, gündüz segmentlerinden elde edilenlere göre daha üstün olduğunu ortaya koymaktadır. Ek olarak, gündüz ve gece segmentlerinden türetilen haritalar için FCC tahmin sınıflarının başarıları arasında belirgin farklılıklar gözlemlenmiştir.
Araştırma vurguları: Bu çalışma, gece segmentlerinden türetilen FCC haritalarının, gündüz segmentlerinden türetilenlere göre daha doğru sonuçlar verdiğini ortaya çıkan önemli bir bulguyu ortaya çıkartmıştır. Çalışma ayrıca, özellikle Orta Seviyede Orman Kanopi Örtüsü (MFCC) sınıfından elde edilen daha düşük FCC sınıflandırma başarısını tespit etmiştir

Keywords

Kanopi Örtüsü, ICESat-2, ATL08, Google Earth Engine, Veri Toplama Zaman

Assessing the Influence of Acquisition Time in Forest Canopy Cover Estimation Using ICESat-2 ATL08 Dataset

Abstract

Aim of study: This study investigates the estimation success of using day and night segments in producing Forest Canopy Cover (FCC) maps with the Canopy Cover Estimation Model (CCEM) for the years 2020 and 2022.
Area of study: The study area covers 17 interconnected counties situated in the southeastern part of Texas state, adjacent to the state of Louisiana, and near the southern coastlines, known for their extensive forested areas.
Material and methods: This study incorporated both day and night acquisition segments from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data for a comprehensive comparison of their effectiveness in mapping the forest canopy cover using the CCEM.
Main results: The study’s findings reveal that night segment-derived FCC maps outperform those derived from day segments, showing higher kappa coefficients of 0.77 and 0.83 for the years 2020 and 2022, respectively. In addition, notable differences were observed among classes of FCC estimations successes for day and night segment-derived maps.
Research highlights: This study introduces a significant finding that the FCC maps derived from night segments yield more accurate results than those derived from day segments. The study further discovers a notable difference in the forest canopy cover classification success, particularly with a lower accuracy observed in the Moderate Forest Canopy Cover (MFCC) category.

Keywords

Canopy cover, ICESat-2, ATL08, Google Earth Engine, Acquisition Time

References

• Akturk, E., Altunel, A. O., Atesoglu, A., Seki, M. & Erpay, S. (2023a). How good is TanDEM-X 50 m forest/non-forest map? Product validation using temporally corrected geo-browser supplied imagery through Collect Earth. International Journal of Geographical Information Science, 37(5), 1041-1068.
• Akturk, E., Popescu, S.C. & Malambo, L. (2023b). ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform. Sensors, 23, 3394.
• Arnold, M., Powell, B., Shanley, P. & Sunderland, T. C. (2011). Forests, biodiversity and food security. The international forestry review, 13(3), 259-264.
• Avery T.E. & Burkart H.E. (1994). Forest measurements. McGraw-Hill, New York, 331.
• Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L. & Smets, B. (2020). Copernicus global land cover layers—collection 2. Remote Sensing, 12(6), 1044.
• FAO (2014). Global Forest Resources Assessment, Country Report, Italy, Food and Agriculture Organization, Rome.
• Herzfeld, U. C., McDonald, B. W., Wallin, B. F., Neumann, T. A., Markus, T., Brenner, A. & Field, C. (2013). Algorithm for detection of ground and canopy cover in micropulse photon-counting lidar altimeter data in preparation for the ICESat-2 mission. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2109-2125.
• Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M. & Brumby, S. P., (2021, July). Global land use/land cover with Sentinel 2 and deep learning. In 2021 IEEE international geoscience and remote sensing symposium IGARSS, 4704-4707.
• Korhonen, L., Korhonen, K. T., Rautiainen, M. & Stenberg, P. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fenn, 40(4), 577-588.
• Latif, Q. S., Saab, V. A., Dudley, J. G., Markus, A. & Mellen-McLean, K. (2020). Development and evaluation of habitat suitability models for nesting white-headed woodpecker (Dryobates albolarvatus) in burned forest. PloS One, 15(5), e0233043.
• Lausch, A., Erasmi, S., King, D. J., Magdon, P. & Heurich, M. (2017). Understanding forest health with remote sensing-part II—A review of approaches and data models. Remote Sensing, 9(2), 129.
• Liu, M. & Popescu, S. (2022). Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data. Remote Sensing of Environment, 280, 113172.
• Luo, H., Wang, L., Wu, C. & Zhang, L. (2018). An improved method for impervious surface mapping incorporating LiDAR data and high-resolution imagery at different acquisition times. Remote Sensing, 10(9), 1349.
• 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.
• McPherson, E. G., Simpson, J. R., Xiao, Q. & Wu, C. (2011). Million trees Los Angeles canopy cover and benefit assessment. Landscape and Urban Planning, 99(1), 40-50.
• Narine, L. L., Popescu, S., Neuenschwander, A., Zhou, T., Srinivasan, S. & Harbeck, K. (2019a). Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data. Remote Sensing of Environment, 224, 1-11.
• Narine, L. L., Popescu, S. C. & Malambo, L. (2019b). Synergy of ICESat-2 and Landsat for mapping forest aboveground biomass with deep learning. Remote Sensing, 11(12), 1503.
• 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.
• Narine, L., Malambo, L., & Popescu, S. (2022). Characterizing canopy cover with ICESat-2: A case study of southern forests in Texas and Alabama, USA. Remote Sensing of Environment, 281, 113242.
• Nasiri, V., Darvishsefat, A. A., Arefi, H., Griess, V. C., Sadeghi, S. M. M. & Borz, S. A. (2022). Modeling forest canopy cover: A synergistic use of Sentinel-2, aerial photogrammetry data, and machine learning. Remote Sensing, 14(6), 1453.
• Neuenschwander, A. & Pitts, K., (2019). The ATL08 land and vegetation product for the ICESat-2 Mission. Remote sensing of Environment, 221, 247-259.
• Neuenschwander, A., Guenther, E., White, J. C., Duncanson, L. & Montesano, P. (2020). Validation of ICESat-2 terrain and canopy heights in boreal forests. Remote Sensing of Environment, 251, 112110.
• Neuenschwander, A., Pitts, K., Jelley, B., Robbins, J., Klotz, B., Popescu, S., et al. (2021). Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) algorithm theoretical basis document (ATBD) for land-vegetation along-track products (ATL08). Applied Research Laboratory, University of Texas, Austin, TX.
• Pyngrope, O. R., Kumar, M., Pebam, R., Singh, S. K., Kundu, A. & Lal, D. (2021). Investigating forest fragmentation through earth observation datasets and metric analysis in the tropical rainforest area. SN Applied Sciences, 3(7), 705.
• Qin, Y., Xiao, X., Wigneron, J. P., Ciais, P., Brandt, M., Fan, L., et al. (2021). Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nature Climate Change, 11(5), 442-448.
• Smith, A. M., Falkowski, M. J., Hudak, A. T., Evans, J. S., Robinson, A. P., & Steele, C. M. (2009). A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Canadian Journal of Remote Sensing, 35(5), 447-459.
• Stojanova, D., Panov, P., Gjorgjioski, V., Kobler, A. & Džeroski, S. (2010). Estimating vegetation height and canopy cover from remotely sensed data with machine learning. Ecological Informatics, 5(4), 256-266.
• Tang, H., Armston, J., Hancock, S., Marselis, S., Goetz, S., & Dubayah, R. (2019). Characterizing global forest canopy cover distribution using spaceborne lidar. Remote Sensing of Environment, 231, 111262.
• The National Aeronautics and Space Administration (2023). Earth Data ATLAS/ICESat-2 L3A Land and Vegetation Height V005. Available at: https://search.earthdata.nasa.gov (accessed on: 20 April 2023).
• Varvia, P., Korhonen, L., Bruguière, A., Toivonen, J., Packalen, P., Maltamo, M., et al. (2022). How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?. Remote Sensing of Environment, 280, 113174.
• Wu, D. L., Chae, J. H., Lambert, A. & Zhang, F. F. (2011). Characteristics of CALIOP attenuated backscatter noise: implication for cloud/aerosol detection. Atmospheric Chemistry and Physics, 11(6), 2641-2654.
• Yin, C., He, B., Yebra, M., Quan, X., Edwards, A. C., Liu, X. & Liao, Z. (2020). Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sensing of Environment, 236, 111454.