Korean Journal of Environmental Biology (환경생물)

Korean Society of Environmental Biology (한국환경생물학회)
권호 표지
DOI QR코드

DOI QR Code

Application of satellite remote sensing-based vegetation index for evaluation of transplanted tree status

이식수목의 현황 평가를 위한 위성영상 기반 원격탐사 식생지수 적용 연구

  • Mi Na Choi (Division of Ecological Assessment, National Institute of Ecology) ;
  • Do-Hun Lee (Division of Ecological Assessment, National Institute of Ecology) ;
  • Moon-Jeong Jang (Division of Ecological Assessment, National Institute of Ecology) ;
  • Dong Ju Kim (Division of Ecological Assessment, National Institute of Ecology) ;
  • Sun Mi Lee (Division of Ecological Assessment, National Institute of Ecology) ;
  • Yoon Jung Moon (Division of Ecological Assessment, National Institute of Ecology) ;
  • Yong Sung Kwon (Division of Ecological Assessment, National Institute of Ecology)
  • 최미나 (국립생태원 환경영향평가팀) ;
  • 이도훈 (국립생태원 환경영향평가팀) ;
  • 장문정 (국립생태원 환경영향평가팀) ;
  • 김동주 (국립생태원 환경영향평가팀) ;
  • 이선미 (국립생태원 환경영향평가팀) ;
  • 문윤정 (국립생태원 환경영향평가팀) ;
  • 권용성 (국립생태원 환경영향평가팀)
  • Received : 2022.11.17
  • Accepted : 2023.03.06
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Forest destruction is an inevitable result of the development processes. According to the environmental impact assessment, over 10% of the destroyed trees need to be recycled and transplanted to minimize the impact of forest destruction. However, the rate of successful transplantation is low, leading to a high rate of tree death. This is attributable to a lack of consideration for environmental factors when choosing a temporary site for transplantation and inadequate management. To monitor transplanted trees, a field survey is essential; however, the spatio-temporal aspect is limited. This study evaluated the applicability of remote sensing for the effective monitoring of transplanted trees. Vegetation indices based on satellite remote sensing were derived to detect time-series changes in the status of the transplanted trees at three temporary transplantation sites. The mortality rate and vitality of transplanted trees before and after the transplant have a similar tendency to the changes in the vegetation indicators. The findings of this study showed that vegetation indices increased after transplantation of trees and decreased as the death rate increased and vitality decreased over time. This study presents a method for assessing newly transplanted trees using satellite images. The approach of utilizing satellite photos and the vegetation index is expected to detect changes in trees that have been transplanted across the country and help to manage tree transplantation for the environmental impact assessment.

우리나라는 산림이 64%에 이르기 때문에 개발사업에 의한 산림훼손이 불가피하다. 이에 대한 방안으로 환경영향평가 제도에서는 훼손되는 수목량의 10%를 재활용 및 이식하도록 하고 있다. 그러나 환경적 요건이 고려되지 않아 이식성공률이 저조하고 가이식장 운영이 잘 되지 않아 수목이 고사하는 등 문제가 지속적으로 발생하고 있다. 이러한 실태를 파악하기 위해서는 현장조사가 필수적이나 시간 및 공간적 한계가 존재한다. 본 연구에서는 원격탐사 기반의 식생지수를 적용하여 개발사업으로 인해 발생하는 이식수목 현황의 시계열적 변화를 탐지하고 원격탐사의 적용성 평가를 목적으로 한다. 이를 위해 위성영상을 분석하여 가이식장 면적을 구축하고 이식 전, 후 식생지수의 시계열 변화를 분석하여 식생 상태를 도출하였다. 연구 결과는 현장조사를 통한 이식수목의 고사율 및 활력도와 위성영상 기반으로 한 가이식 전 후의 식생지수 변화 분석의 결과가 유사한 경향성을 나타내었다. 이에 따라 가이식장에 수목 이식 후에는 가이식장 범위의 녹색 식물의 상대적 분포량과 활동성이 증가하고 시간이 지남에 따라 수목 고사 및 활력도 감소로 인해 낮아지는 것을 규명하였다. 본 연구를 통해 위성영상에 기반한 이식수목 평가 방법을 제시하였으나, 실제 평가에 적용하기 위해서는 보다 정량적인 방법론을 개발할 필요가 있을 것으로 사료된다. 본 연구는 원격탐사 기법인 위성영상과 식생지수를 활용하여 보다 전국에 분포한 이식수목의 변화를 탐지하여, 개발사업으로 인해 시행되는 환경영향평가 제도의 수목 이식이 제대로 수행되고 산림 파괴에 효과적인 저감 대책을 마련하는 데 기여할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국립생태원 수탁연구 '육상풍력 환경모니터링 및 환류체계 구축 연구(NIE-C-2022-90)'와 한국환경산업기술원의 'ICT 기반 환경영향평가 의사결정 지원 기술개발사업(2020002990001)'의 지원을 받아 연구되었습니다.

References

  1. American Association of Nurserymen. 1996. American Standards for Nursery Stock. ANSI Z60.1-1996. Amer. Assn. of Nurserymen. Washington, D.C.
  2. Anand A, SK Singh and S Kanga. 2018. Estimating the change in forest cover density and predicting NDVI for west Singhbhum using linear regression. Int. J. Environ. Rehabil. Conserv. 9:193-203. https://doi.org/10.31786/09756272.18.9.1.125
  3. Bae SH and SD Lee. 2019. A study on the timing of management through comparison of growth rate by year of tree in the ecological restoration site - Focused on the deciduous tree. pp. 85-86. In: Proceedings of the Korean Society of Environment and Ecology Conference. Korean Society of Environment and Ecology Conference. Korean Society of Environment and Ecology.
  4. Ban YU, KS Joo, HK Jeong and GH Hwang. 2010. Improvement of EIS documentation & consultation process through expert survey method. J. Environ. Impact Assess. 19:141-151.
  5. Bannari A, D Morin, F Bonn and A Huete. 1995. A review of vegetation indices. Remote Sens. Rev. 13:95-120. https://doi.org/10.1080/02757259509532298
  6. Brantley ST, JC Zinnert and DR Young. 2011. Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sens. Environ. 115:514-523. https://doi.org/10.1016/j.rse.2010.09.020
  7. Campbell MJ, PE Dennison, JW Tune, SA Kannenberg, KL kerr, BF Codding and WR Anderegg. 2020. A multi-sensor, multi-scale approach to mapping tree mortality in woodland ecosystems. Remote Sens. Environ. 245:111853. https://doi.org/10.1016/j.rse.2020.111853
  8. Cho HR, SB Kim and D Oh. 2009. Cost effectiveness depending on the native tree treatment methods. J. Korean Env. Res. Tech. 12:51-62.
  9. Cho KJ, JG Choi, YM Park, YI Song, H Sagong, SB Lee, JC Jung and YS Im. 2008. Achievement and Development of EIA Over the Last 30 Years. KEI Research Report. RE-12. Korea Environment Institute. Sejong, Korea.
  10. Cho NW. 2020. An empirical study on the institutional change of environmental impact assessment: Focused on agreed terms. Ph.D. Dissertation. Yonsei University. Seoul. Korea. p. 172.
  11. Drusch M, U del Bello, S Carlier, O Colin, V Fernandez, F Gascon, B Hoersch, C Isola, P Laverinti, P Martimort, A Meygret, F Spoto, O Sy, F Marchese and P Bargellini. 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120:25-36. https://doi.org/10.1016/j.rse.2011.11.026
  12. Furniss TJ, VR Kane, AJ Larson and JA Lutz. 2020. Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty. Remote Sens. Environ. 237:111497. https://doi.org/10.1016/j.rse.2019.111497
  13. Gilman EF. 1989. Plant form in relation to root spread. J. Environ. Hort. 7:88-90. https://doi.org/10.24266/0738-2898-7.3.88
  14. Gilman EF. 1990. Tree root growth and development. II. Response to culture, management and planting. J. Environ. Hort. 8:220-227. https://doi.org/10.24266/0738-2898-8.4.220
  15. Gilman EF and ME Kane. 1991. Growth dynamics following planting of cultivars of Juniperus chinensis. J. Am. Soc. Hortic. Sci. 116:637-641. https://doi.org/10.21273/JASHS.116.4.637
  16. Gilman EF, RJ Black and B Dehgan. 1998. Irrigation volume and frequency and tree size affect establishment rate. J. Arboric. 24:1-9.
  17. Gitelson AA, YJ Kaufman and MN Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
  18. Gutierrez-Rodriguez M, JA Escalante-Estrada, MT Rodriguez Gonzalez and MP Reynolds. 2006. Canopy reflectance indices and its relationship with yield in common bean plants (Phaseolus vulgaris L.) with phosphorous supply. Int. J. Agric. Biol. 8:203-207.
  19. Hansen MC, YE Shimabukuro, P Potapov and K Pittman. 2008. Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover. Remote Sens. Environ. 112:3784-3793. https://doi.org/10.1016/j.rse.2008.05.012
  20. Hati JP, S Goswami, S Samanta, N Pramanick, SD Majumdar, NR Chaube, A Misra and S Hazra. 2021. Estimation of vegetation stress in the mangrove forest using AVIRIS-NG airborne hyperspectral data. Model Earth Syst. Environ. 7:1877-1889. https://doi.org/10.1007/s40808-020-00916-5
  21. Hawrylo P, B Bednarz, P Wezyk and M Szostak. 2018. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. Eur. J. Remote Sens. 51:194-204. https://doi.org/10.1080/22797254.2017.1417745
  22. Huete A, K Didan and WV Leeuwen. 1999. MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document Version 3.1. University of Arizona. Tucson, AZ.
  23. Huete A, K Didan, WV Leeuwen, T Miura and E Glenn. 2010. MODIS vegetation indices. pp. 579-602. In: Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer. New York.
  24. Huete AR. 1988. A soil-adjusted vegetation index(SAVI). Remote Sens. Environ. 25:295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  25. Huete AR. 2012. Vegetation indices, remote sensing and forest monitoring. Geogr. Compass 6:513-532. https://doi.org/10.1111/j.1749-8198.2012.00507.x
  26. Jamal M and S Mandal. 2016. Monitoring forest dynamics and landslide susceptibility in Mechi-Balason interfluves of Darjiling Himalaya, West Bengal using forest canopy density model (FCDM) and Landslide Susceptibility Index model (LSIM). Model Earth Syst. Environ. 2:1-17. https://doi.org/10.1007/s40808-016-0243-2
  27. Jeong MH, SG Jeong, CG Jang and JY Sin. 2015. Analysis of tree vitality according to root state of transplanted trees. pp. 127-128. In: Proceedings of the Korean Institute of Landscape Architecture Conference. Korean Institute of Landscape Architecture Conference.
  28. Jiang Z, AR Huete, J Chen, Y Chen, J Li, G Yan and X Zhang. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101: 366-378. https://doi.org/10.1016/j.rse.2006.01.003
  29. Kerr JT and M Ostrovsky. 2003. From space to species: ecological applications for remote sensing. Trends. Ecol. Evol. 18:299-305. https://doi.org/10.1016/S0169-5347(03)00071-5
  30. Kim CS and ZS Kim. 2012. Effects of cutting time, auxin treatment, and cutting position on rooting of the green-wood cuttings and growth characteristics of transplanted cuttings in the adult Prunus yedoensis. Hortic. Sci. Technol. 30:129-136. https://doi.org/10.7235/hort.2012.11041
  31. Kim OK. 2005. The transplantation method of natural vegetation community in development area-A case study of Yong-In Dongbaek District. Department of Landscape Architecture, Graduate School of Urban Science, University of Seoul. Seoul. p.167.
  32. Knipling EB. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1:155-159. https://doi.org/10.1016/S0034-4257(70)80021-9
  33. Korea Environment Institute. 2017. Review and Writing Manual for Environmental Impact Statement. (Ver.2.0). Korea Environment Institute. Sejong, Korea. p. 21.
  34. Korea Land Development Corporation. 1994. The Study of Utilizing Wild Plants in Land Development District - Focused on New Town (Bundang and Ilsan). Korea Land Development Corporation Report. Korea Land Development Corporation. p. 113.
  35. Kureel N, J Sarup, S Matin, S Goswami and K Kureel. 2022. Modelling vegetation health and stress using hypersepctral remote sensing data. Model Earth Syst. Environ. 8:733-748. https://doi.org/10.1007/s40808-021-01113-8
  36. Langley SK, HM Cheshire and KS Humes. 2001. A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. J. Arid Environ. 49:401-411. https://doi.org/10.1006/jare.2000.0771
  37. Lee GS and YW Choi. 2019. Analysis of cropland spectral properties and vegetation index using UAV. J. Korean Assoc. Geogr. Inf. Stud. 22:86-101. https://doi.org/10.11108/kagis.2019.22.4.086
  38. Lee SC, BY Jo and SH Choi. 2015. A study of establishment ratio of native tree transplant. J. Korean Inst. Landsc. Archit. 43:23-29. https://doi.org/10.9715/KILA.2015.43.2.023
  39. Lee SD and HK Kang. 2012. Transplantation method of damage ecosystem associated with development of the borrow pits. Korean J. Environ. Ecol. 26:394-405.
  40. Lee SM, BJ Kim, DH Lee, MH Jang, JK Kim, HR Kim, SH Woo, SH Yoo, TH Lee, YJ Moon, SW Park and SH Kim. 2020. Operational Improvement of Temporary Transplant Site in Environmental Impact Assessment(I). National Institute of Ecology. Seocheon, Korea.
  41. Lee SM, BJ Kim, DH Lee, MH Jang, JK Kim, HR Kim, SH Woo, SH Yoo, TH Lee, YJ Moon, SW Park and SH Kim. 2021. Operational improvement of temporary transplant site in Environmental Impact Assessment(II). National Institute of Ecology. Seocheon, Korea.
  42. Lees T, G Tseng, C Atzberger, S Reece and S Dadson. 2022. Deep learning for vegetation health forecasting: A case study in Kenya. Remote Sens. 14:698. https://doi.org/10.3390/rs14030698
  43. Li J and B Chen. 2020. Global revisit interval analysis of Landsat-8-9 and Sentinel-2a-2b data for terrestrial monitoring. Sensors 20:6631. https://doi.org/10.3390/s20226631
  44. Li X, W Yuan and W Dong. 2021. A machine learning method for predicting vegetation indices in China. Remote Sens. 13:1147. https://doi.org/10.3390/rs13061147
  45. Lorant MM, SP Davydov, H Kropp, HD Alexander, MC Mack, SM Natali and NS Zimov. 2018. Vegetation indices do not capture forest cover variation in upland Siberian larch forests. Remote Sens. 10:1686. https://doi.org/10.3390/rs10111686
  46. Na SJ, IS Kim, JH Kim and DH Lee. 2014. Growth characteristics of Pinus densiflora seedlings by root pruning intensity. J. Agric. Life Sci. 48:15-21. https://doi.org/10.14397/jals.2014.48.1.15
  47. Nordberg ML and J Evertson. 2005. Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery. Land Degrad. Dev. 16:139-149. https://doi.org/10.1002/ldr.660
  48. Obregon MA, G Rodrigues, MJ Costa, M Potes and AM Silva. 2019. Validation of ESA Sentinel-2 L2A aerosol optical thickness and columnar water vapour during 2017-2018. Remote Sens. 11:1649. https://doi.org/10.3390/rs11141649
  49. Park CM. 2002. The restoration technique of native forest resources on the development land applied in the new campus of Kyushu University, Japan. J. Korea Env. Res. Tech. 5:50-57.
  50. Pettorelli N, JO Vik, A Mysterud, JM Gaillard, CJ Tucker and NC Stenseth. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends. Ecol. Evol. 20:503-510. https://doi.org/10.1016/j.tree.2005.05.011
  51. Purevdorj TS, R Tateishi, T Ishiyama and Y Honda. 1998. Relationships between percent vegetation cover and vegetation indices. Int. J. Remote Sens. 19:3519-3535. https://doi.org/10.1080/014311698213795
  52. Qi J, F Cabot, MS Moran and G Dedieu. 1995. Biophysical parameter estimations using multidirectional spectral measurements. Remote Sens. Environ. 54:71-83. https://doi.org/10.1016/0034-4257(95)00102-7
  53. Rouse JW, RH Haas, JA Schel and DW Deering. 1974. Monitoring vegetation systems in the great plains with ERTS. pp. 309-317. In: Third Earth Resources Technology Satellite-1 Symposium, Volume I: Technical Presentations Section A, NASA SP-351 (Freden SC, EP Mercanti and M Becker, eds.). National Aeronautics and Space Administration. Washington, D.C.
  54. Rulinda CM, A Dilo, W Bijker and A Stein. 2012. Characterising and quantifying vegetative drought in East Africa using fuzzy modelling and NDVI data. J. Arid. Environ. 78:169-178. https://doi.org/10.1016/j.jaridenv.2011.11.016
  55. Running SW, CO Justice, V Salomonson, D Hall, J Barker, YJ Kaufmann and D Carneggie. 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. Int. J. Remote Sens. 15:3587-3620. https://doi.org/10.1080/01431169408954346
  56. Saleem MH, J Potgieter and KM Arif. 2019. Plant disease detection and classification by deep learning. Plants 8:468. https://doi.org/10.3390/plants8110468
  57. Son MB, JH Chung, YG Lee and SJ Kim. 2021. A comparative analysis of vegetation and agricultural monitoring of Terra MODIS and Sentinel-2 NDVIs. J. Korean Soc. Agric. Eng. 63:101-115. https://doi.org/10.5389/KSAE.2021.63.6.101
  58. Verbesselt J, A Robinson, C Stone and D Culvenor. 2009. Forecasting tree mortality using change metrics derived from MODIS satellite data. For. Ecol. Manage. 258:1166-1173. https://doi.org/10.1016/j.foreco.2009.06.011
  59. Wang FM, JF Huang, YL Tang and XZ Wang. 2007. New vegetation index and its application in estimating leaf area index of rice. Rice Sci. 14:195-203. https://doi.org/10.1016/S1672-6308(07)60027-4
  60. Wang Z, T Wang, R Darvishzadeh, AK Skidmore, S Jones, L Suarez, W Woodgate, U Heiden, M Heurich and J Hearne. 2016. Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote Sens. 8:491. https://doi.org/10.3390/rs8060491
  61. Watson G. 1985. Tree size affects root regeneration and top growth after transplanting. J. Arboric. 11:37-40. https://doi.org/10.48044/jauf.1985.009
  62. Watson GW. 1986. Cultural practices can influence root development for better transplanting success. J. Environ. Hortic. 4:32-34. https://doi.org/10.24266/0738-2898-4.1.32
  63. Watson WT. 2005. Influence of tree size on transplant establishment and growth. HortTechnology 15:118-122. https://doi.org/10.21273/HORTTECH.15.1.0118