Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

DOI QR Code

A Comparative Study of Vegetation Phenology Using High-resolution Sentinel-2 Imagery and Topographically Corrected Vegetation Index

고해상도 Sentinel-2 위성 자료와 지형효과를 고려한 식생지수 기반의 산림 식생 생장패턴 비교

  • Seungheon Yoo (Department of Landscape Architecture and Rural Systems Engineering, Seoul National University) ;
  • Sungchan Jeong (Interdisciplinary Program in Landscape Architecture, Seoul National University)
  • 유승헌 (서울대학교 농업생명과학대학 조경.지역시스템공학부 조경학전공) ;
  • 정성찬 (서울대학교 환경대학원 협동과정 조경학)
  • Received : 2024.01.29
  • Accepted : 2024.05.07
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Land Surface Phenology (LSP) plays a crucial role in understanding vegetation dynamics. The near-infrared reflectance of vegetation (NIRv) has been increasingly adopted in LSP studies, being recognized as a robust proxy for gross primary production (GPP). However, NIR v is sensitive to the terrain effects in mountainous areas due to artifacts in NIR reflectance cannot be canceled out. Because of this, estimating phenological metrics in mountainous regions have a substantial uncertainty, especially in the end of season (EOS). The topographically corrected NIRv (TCNIRv) employs the path length correction (PLC) method, which was deduced from the simplification of the radiative transfer equation, to alleviate limitations related to the terrain effects. TCNIRv has been demonstrated to estimate phenology metrics more accurately than NIRv, especially exhibiting improved estimation of EOS. As the topographic effect is significantly influenced by terrain properties such as slope and aspect, our study compared phenology metrics estimations between south-facing slopes (SFS) and north-facing slopes (NFS) using NIRv and TCNIRv in two distinct mountainous regions: Gwangneung Forest (GF) and Odaesan National Park (ONP), representing relatively flat and rugged areas, respectively. The results indicated that TCNIR v-derived EOS at NFS occurred later than that at SFS for both study sites (GF : DOY 266.8/268.3 at SFS/NFS; ONP : DOY 262.0/264.8 at SFS/NFS), in contrast to the results obtained with NIRv (GF : DOY 270.3/265.5 at SFS/NFS; ONP : DOY 265.0/261.8 at SFS/NFS). Additionally, the gap between SFS and NFS diminished after topographic correction (GF : DOY 270.3/265.5 at SFS/NFS; ONP : DOY 265.0/261.8 at SFS/NFS). We conclude that TCNIRv exhibits discrepancy with NIR v in EOS detection considering slope orientation. Our findings underscore the necessity of topographic correction in estimating photosynthetic phenology, considering slope orientation, especially in diverse terrain conditions.

개엽기, 낙엽기 추정은 식물 생태 주기를 이해하는 데 매우 중요한 역할을 한다. 식물의 근적외선 반사(NIRv)는 일차생산량(GPP)의 강력한 대리지표로 밝혀져 식물계절학 연구에 활발하게 가용되는 추세이다. 하지만 지형에 의한 반사도 왜곡 효과가 상쇄되지 않아 산악 지역의 지형 왜곡 효과에 민감하며 낙엽기를 추정하는 데 성능이 떨어진다. 지형 보정 NIRv(TCNIRv)는 지형 왜곡 효과와 관련된 한계점을 완화하기 위해 경로 길이 보정 방법을 사용한다. TCNIRv는 낙엽기에 대해 NIRv 보다 더 정확한 값을 추정할 수 있다는 사실이 확인되었다. 지형 보정은 경사 및 사면 방향 같은 지형 속성과 연관성이 크기 때문에, 이번 연구에서는 광릉 수목원과 오대산 국립공원 같이 비교적 상이한 지형 특성을 가진 두 산악 지역을 대상으로 남사면과 북사면에서의 예측 결과를 비교하였다. 결과적으로, 두 연구지에서 TCNIRv 를 이용해 예측한 낙엽기는 북사면에서 남사면보다 지연되었고 (광릉 수목원: SFS/NFS - DOY 266.8/268.3; 오대산 국립공원: SFS/NFS - DOY 262/264.8), 이는 NIRv 의 결과와는 반대되는 예측 결과였다 (광릉 수목원: SFS/NFS - DOY 270.3/265.5; 오대산 국립공원: SFS/NFS - DOY 265/261.8). 또한 지형 보정 이후 남사면와 북사면 간의 낙엽기의 차이가 감소했다는 사실도 알 수 있었다 (광릉 수목원: SFS/NFS - DOY 270.3/265.5; 오대산 국립공원: SFS/NFS - DOY 265/261.8). 우리는 사면방향에 따라 식물 생장기를 예측했을 때, TCNIRv 를 이용한 낙엽기 추정에서 NIRv 를 이용해 예측한 결과와 차이점을 가진다고 결론 내렸다. 이로써 다양한 지형 조건에서 사면 별 식물 생장기를 추정하는 데 지형 보정이 필수적이라는 사실을 강조한다.

Keywords

Acknowledgement

We thank for Prof. Youngryel Ryu internal discussion. This research was supported by the Technology Development Project for Creation and Management of Ecosystem based Carbon Sinks (202300218237) through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE).

References

  1. Badgley, G., C. B. Field, and J. A. Berry, 2017: Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances 3(3), e1602244. https://doi.org/doi:10.1126/sciadv.1602244
  2. Baldocchi, D. D., Y. Ryu, B. Dechant, E. Eichelmann, K. Hemes, S. Ma, C. R. Sanchez, R. Shortt, D. Szutu, and A. Valach, 2020: Outgoing near-infrared radiation from vegetation scales with canopy photosynthesis across a spectrum of function, structure, physiological capacity, and weather. Journal of Geophysical Research: Biogeosciences 125(7), e2019JG005534.
  3. Caparros-Santiago, J. A., V. Rodriguez-Galiano, and J. Dash, 2021: Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 171, 330-347. https://doi.org/10.1016/j.isprsjprs.2020.11.019
  4. Chen, R., G. Yin, G. Liu, J. Li, and A. Verger, 2020: Evaluation and normalization of topographic effects on vegetation indices. Remote Sensing 12(14), 2290. https://www.mdpi.com/2072-4292/12/14/2290
  5. Chen, R., G. Yin, W. Zhao, B. Xu, Y. Zeng, G. Liu, and A. Verger, 2022: TCNIRv: Topographically corrected near-infrared reflectance of vegetation for tracking gross primary production over mountainous areas. IEEE Transactions on Geoscience and Remote Sensing 60, 1-10. https://doi.org/10.1109/TGRS.2022.3149655
  6. Chen, R., G. Yin, W. Zhao, K. Yan, S. Wu, D. Hao, and G. Liu, 2023a: Topographic correction of optical remote sensing images in mountainous areas: A systematic review. IEEE Geoscience and Remote Sensing Magazine, 2-22. https://doi.org/10.1109/MGRS.2023.3311100
  7. Chen, R., G. Yin, G. Liu, Y. Yang, C. Wang, Q. Xie, and A. Verger, 2023b: Correction of illumination effects on seasonal divergent NIRv photosynthetic phenology. Agricultural and Forest Meteorology, 339, 109542. https://doi.org/10.1016/j.agrformet.2023.109542
  8. Dash, J., and B. O. Ogutu, 2016: Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems. Progress in Physical Geography 40(2), 322-351. https://doi.org/10.1177/0309133316639403
  9. de Beurs, K. M., and G. M. Henebry, 2004: Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89(4), 497-509. https://doi.org/10.1016/j.rse.2003.11.006
  10. Florinsky, I., T. Skrypitsyna, and O. Luschikova, 2018: Comparative accuracy of the AW3D30 DSM, ASTER GDEM, and SRTM1 DEM: A case study on the Zaoksky testing ground, Central European Russia. Remote Sensing Letters 9(7), 706-714. https://doi.org/10.1080/2150704X.2018.1468098
  11. Guerrero, F. J. D. T., A. Hinojosa-Corona, T. G. Kretzschmar, 2016: A comparative study of NDVI values between north- and south-facing slopes in a semiarid mountainous region. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(12), 5350-5356. https://doi.org/10.1109/JSTARS.2016.2618393
  12. Horn, B. K., 1981: Hill shading and the reflectance map. Proceedings of the IEEE 69(1), 14-47. https://doi.org/10.1109/PROC.1981.11918
  13. Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira, 2002: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  14. Karami, M., A. Westergaard-Nielsen, S. Normand, U. A. Treier, B. Elberling, and B. U. Hansen, 2018: A phenology-based approach to the classification of Arctic tundra ecosystems in Greenland. ISPRS Journal of Photogrammetry and Remote Sensing 146, 518-529. https://doi.org/10.1016/j.isprsjprs.2018.11.005
  15. Rodriguez-Galiano, V. F., J. Dash, and P. M. Atkinson, 2015: Characterising the land surface phenology of Europe using decadal MERIS data. Remote Sensing 7(7), 9390-9409. https://www.mdpi.com/2072-4292/7/7/9390 https://doi.org/10.3390/rs70709390
  16. Wang, H., D. Yakir, E. Rotenberg, 2023: Assessing the effectiveness of a central flux tower in representing the spatial variations in gross primary productivity in a semi-arid pine forest. Agricultural and Forest Meteorology 333, 109415. https://doi.org/10.1016/j.agrformet.2023.109415
  17. Wang, R., J. M. Chen, Z. Liu, Z., A. Arain, 2017: Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests. ISPRS Journal of Photogrammetry and Remote Sensing 130, 187-201. https://doi.org/10.1016/j.isprsjprs.2017.05.017
  18. Wang, X., M. P. Dannenberg, D. Yan, M. O. Jones, J, S. Kimball, D. J. P. Moore, W. J. D. van Leeuwen, K. Didan, and W. K. Smith, 2020: Globally consistent patterns of asynchrony in vegetation phenology derived from optical, microwave, and fluorescence satellite data. Journal of Geophysical Research: Biogeosciences 125(7), e2020JG005732. https://doi.org/10.1029/2020JG005732
  19. Wang, X., J. Xiao, X. Li, G. Cheng, M. Ma, G. Zhu, M. Altaf Arain, T. Andrew Black, and R. S. Jassal, 2019: No trends in spring and autumn phenology during the global warming hiatus. Nature Communications 10(1), 2389. https://doi.org/10.1038/s41467-019-10235-8
  20. Wen, J., Q. Liu, Q. Liu, Q. Xiao, Q. and X. Li, 2009: Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China. International Journal of Remote Sensing 30(11), 2875-2896. https://doi.org/10.1080/01431160802558618
  21. White, M. A., P. E. Thornton, and S. W. Running, 1997: A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 11(2), 217-234. https://doi.org/10.1029/97GB00330
  22. Yang, Y., R. Chen, G. Yin, C. Wang, G. Liu, A. Verger, A. Descals, I. Filella, and J. Penuelas, 2022: Divergent performances of vegetation indices in extracting photosynthetic phenology for northern deciduous broadleaf forests. IEEE Geoscience and Remote Sensing Letters 19, 1-5. https://doi.org/10.1109/LGRS.2022.3182405
  23. Yang, Y., and F. Fan, 2023: Land surface phenology and its response to climate change in the Guangdong-Hong Kong-Macao Greater Bay Area during 2001-2020. Ecological Indicators 154, 110728. https://doi.org/10.1016/j.ecolind.2023.110728
  24. Yin, G., A. Li, S. Wu, W. Fan, Y. Zeng, K. Yan, B., Xu, J. Li, and Q. Liu, 2018: PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment 215, 184-198. https://doi.org/10.1016/j.rse.2018.06.009
  25. Yin, G., A. Verger, I. Filella, A. Descals, and J. Penuelas, 2020a: Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices. Geophysical Research Letters 47(18), e2020GL089167.
  26. Yin, G., L. Ma, W. Zhao, Y. Zeng, B. Xu, and S. Wu, 2020b: Topographic correction for Landsat 8 OLI vegetation reflectances through path length correction: A comparison between explicit and implicit methods. IEEE Transactions on Geoscience and Remote Sensing 58(12), 8477-8489. https://doi.org/10.1109/TGRS.2020.2987985
  27. Zeng, L., B. D. Wardlow, D. Xiang, S. Hu, and D. Li, 2020: A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment 237, 111511. https://doi.org/10.1016/j.rse.2019.111511
  28. Zhang, Y., R. Commane, S. Zhou, A. P. Williams, and P. Gentine, 2020: Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nature Climate Change 10(8), 739-743. https://doi.org/10.1038/s41558-020-0806-0