DOI QR코드

DOI QR Code

Analyzing Difference of Urban Forest Edge Vegetation Condition by Land Cover Types Using Spatio-temporal Data Fusion Method

시공간 위성영상 융합기법을 활용한 도시 산림 임연부 인접 토지피복 유형별 식생 활력도 차이 분석

  • Sung, Woong Gi (Graduate School, Seoul National University) ;
  • Lee, Dong Kun (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Jin, Yihua (Agricultural College of Yanbian University)
  • 성웅기 (서울대학교 대학원) ;
  • 이동근 (서울대학교 조경지역시스템공학부) ;
  • 김예화 (연변대학교 농학원)
  • Received : 2017.11.30
  • Accepted : 2018.05.30
  • Published : 2018.06.30

Abstract

The importance of monitoring and assessing the status of urban forests in the aspect of urban forest management is emerging as urban forest edges increase due to urbanization and human impacts. The purpose of this study was to investigate the status of vegetation condition of urban forest edge that is affected by different land cover types using $NDVI_{max}$ images derived from FSDAF (Flexible Spatio-temporal DAta Fusion). Among 4 land cover types,roads had the greatest effect on the forest edge, especially up to 30m, and it was found to affect up to 90m in Seoul urban forest. It was also found that $NDVI_{max}$ increased with distance away from the forest edge. The results of this study are expected to be useful for assessing the effects of land cover types and land cover change on forest edges in terms of urban forest monitoring and urban forest management.

Acknowledgement

Supported by : 한국환경산업기술원

References

  1. Akamphon S, Akamphon K. 2014. Cost and benefit tradeoffs in using a shade tree for residential building energy saving. The international journal published by the Thai Society of Higher Education Institutes on Environment. 7: 19-24.
  2. Alignier A, Deconchat M. 2013. Patterns of forest vegetation responses to edge effect as revealed by a continuous approach. Annals of Forest Science. 70(6): 601-609. https://doi.org/10.1007/s13595-013-0301-0
  3. Baker TP, Jordan GJ, Steel EA, Fountain-Jones NM, Wardlaw TJ, Baker SC. 2014. Microclimate through space and time: micro climatic variation at the edge of regeneration forests over daily, yearly and decadal time scales. Forest Ecology and Management. 334: 174-184. https://doi.org/10.1016/j.foreco.2014.09.008
  4. Baldi G, Nosetto MD, Aragon R, Aversa F, Paruelo JM, Jobbagy EG. 2008. Long-term satellite NDVI data sets: Evaluating their ability to detect ecosystem functional changes in South America. Sensors. 8: 5397-5425. https://doi.org/10.3390/s8095397
  5. Briber BM, Hutyra LR, Reinmann AB, Raciti SM, Dearborn VK, Holden CE, Dunn AL. 2015. Tree productivity enhanced with conversion from forest to urban land covers. Plos One. 10: e0136237. https://doi.org/10.1371/journal.pone.0136237
  6. Chai T, Draxler RR. 2014. Root mean square error (RMSE) or mean absolute error (MAE) Arguments against avoiding RMSE in the literature. Geoscientific Model Development. 7(3): 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  7. Chaplin-Kramer R, Ramler I, Sharp R, Haddad NM, Gerber JS, West PC, Mandle L, Engstrom P, Baccini A, Sim S, Mueller C, King H. 2015. Degradation in carbon stocks near tropical forest edges. Nature Communications. 6: 1-6.
  8. Davies-Colley RJ, Payne GW, van Elswijk M. 2000. Microclimate gradients across a forest edge. New Zealand Journal of Ecology. 24(2): 111-121.
  9. Delgado JD, Arroyo NL, Arevalo JR, Fernandez-Palacios JM. 2007. Edge effects of roads on temperature, light, canopy cover, and canopy height in laurel and pine forests (Tenerife, Canary Islands). Landscape and Urban Planning. 81: 328-340. https://doi.org/10.1016/j.landurbplan.2007.01.005
  10. Escobedo FJ, Nowak DJ. 2009. Spatial heterogeneity and air pollution removal by an urban forest. Landscape and Urban Planning. 90: 102-110. https://doi.org/10.1016/j.landurbplan.2008.10.021
  11. FLAASH M. 2009. Atmospheric Correction Module: QUAC and FLAASH User's Guide, Version 4.7, Boulder, CO, USA.
  12. Gobster PH, Westphal LM. 2004. The human dimensions of urban greenways: Planning for recreation and related experiences. Landscape and Urban Planning. 68: 147-165. https://doi.org/10.1016/S0169-2046(03)00162-2
  13. Gulci S, Akay AE, Oguz H, Gulci N. 2017. Assessment of the Road Impacts on Coniferous Species Within the Road-Effect Zone Using NDVI Analysis Approach. Fresenius Environmental Bulletin. 26: 1654-1662.
  14. Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzalez A, Holt RD, Lovejoy TE, Sexton JO, Austin MP, Collins CD, Cook WM, Damschen EI, Ewers RM, Foster RM, Jenkins CN, King AJ, Laurance WF, Levey DJ, Margules CR, Melbourne BA, Nicholls AO, Orrock JL, Song D, Townshend JR. 2015. Habitat fragmentation and its lasting impact on Earth's ecosystems. Science Advances. 1:e1500052-e1500052. https://doi.org/10.1126/sciadv.1500052
  15. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO Townshend JRG. 2013. High-Resolution Global Maps of 21st Century forest cover change. Science. 342: 850-854. https://doi.org/10.1126/science.1244693
  16. Hernandez-Santana V, Asbjornsen H, Sauer T, Isenhart T, Schilling K, Schultz R. 2011. Enhanced transpiration by riparian buffer trees in response to advection in a humid temperate agricultural landscape. Forest Ecology and Management. 261: 1415-1427. https://doi.org/10.1016/j.foreco.2011.01.027
  17. Hofmeister J, Hosek J, Brabec M, Hedl R, Modry M. 2013. Strong influence of long-distance edge effect on herb-layer vegetation in forest fragments in an agricultural landscape. Perspective in Plant Ecology Evolution and Systematics. 15: 293-303. https://doi.org/10.1016/j.ppees.2013.08.004
  18. Kim EY, Song WK, Yoon EJ, Jung HJ. 2016. Definition of Invasive Disturbance Species and its Influence Factor: Review. Journal of the Korea Society of Environmental Restoration Technology. 19(1): 155-170. [Korean Literature] https://doi.org/10.13087/kosert.2016.19.1.155
  19. Kim SH, Jang DH. 2014. Analysis of forest types and estimation of the forest carbon stocks using landsat satellite images in Chungcheongnam-do, South Korea. Journal of The Korean Association of Regional Geographers. 20(2): 206-216. [Korean Literature]
  20. Kim JY, Jang DH. 2015. An application of remote sensing data for the land surface characteristics analysis of ecological stream restoration area in Daejeon stream. Journal of the Association of Korean Geographers. 4(2): 231-240. [Korean Literature] https://doi.org/10.25202/JAKG.4.2.5
  21. Kuttler W. 2008. The Urban Climate: Basic and Applied Aspects. In: Marzluff JM. Shulenberger E, Endlicher W, Alberti M, Bradley G, Ryan C, ZumBrunnen C, Simon U. Urban Ecology. Springer; p. 233-248.
  22. Lee H, Noh S. 2013. Advanced Statistical Analysis: Theory and Practice. Moonwoosa Publishing. [Korean Literature]
  23. Li F, Song G, Liujun Z, Yanan Z, Di L. 2017. Urban vegetation phenology analysis using high spatio-temporal NDVI time series. Urban Forestry & Urban Greening. 25: 43-57. https://doi.org/10.1016/j.ufug.2017.05.001
  24. Liu D, Zhu X. 2012. An enhanced physical method for downscaling thermal infrared radiance. IEEE Geoscience and Remote Sensing Letters. 9(4): 690-694. https://doi.org/10.1109/LGRS.2011.2178814
  25. MacLean MG. 2017. Edge influence detection using aerial LiDAR in Northeastern US deciduous forests. Ecological Indicators. 72: 310-314. https://doi.org/10.1016/j.ecolind.2016.08.034
  26. Magnago LFS, Edwards DP, Edwards FA, Magrach A, Martins SV, Laurance WF. 2014. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. Journal of Ecology. 102: 475-485. https://doi.org/10.1111/1365-2745.12206
  27. Magnago LFS, Magrach A, Barlow J, Schaefer CEGR, Laurance WF, Martins SV, Edwards DP. 2017. Do fragment size and edge effects predict carbon stocks in trees and lianas in tropical forests?. Functional Ecology. 31: 542-552. https://doi.org/10.1111/1365-2435.12752
  28. Markon BCJ. 2001. Survey USG. Seven year phenological record of Alaskan ecoregions derived from Advanced Very High Resolution Radiometer normalized difference vegetation index data. Open-File Report 01-11 U.S. Department of the Interior.
  29. Numata I, Cochrane MA, Souza Jr CM, Sales MH. 2011. Carbon emissions from deforestation and forest fragmentation in the Brazilian Amazon. Environmental Research Letters. 6: 44003. https://doi.org/10.1088/1748-9326/6/4/044003
  30. Riedel SM, Epstein HE. 2005. Edge effects on vegetation and soils in a Virginia old-field. Plant and Soil. 270: 13-22. https://doi.org/10.1007/s11104-004-1012-y
  31. Riutta T, Slade EM, Morecroft MD, Bebber DP, Malhi Y. 2014. Living on the edge: quantifying the structure of a fragmented vforest landscape in England. Landscape Ecology. 29: 949-961. https://doi.org/10.1007/s10980-014-0025-z
  32. Sentinel Hub [Internet]. Laboratory for geographical information systems, Ltd. [cited 2017 Nov 20]. Available from: https://www.sentinel-hub.com/max_service
  33. Shojanoori R, Shafri HZM. 2017. Review on the use of remote sensing for urban forest monitoring Review on the Use of Remote Sensing for Urban Forest Monitoring. Arboriculture & urban forestry. 42(6): 400-417.
  34. Sung HY, Park OJ. 2000. A study on distribution and change of NDVI with land-cover change in city of Sungnam. The journal of GIS Association of Korea. 8(2): 275-288
  35. Sung SY, Lee DK, Mo YW. 2015. Comparison of Carbon Stock Between Forest Edge and Core by Using Connectivity Analysis. Korean Society of Rural Planning. 21(4): 27-33. [Korean Literature] https://doi.org/10.7851/ksrp.2015.21.4.027
  36. Weng Q, Fu P, Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment. 145: 55-67. https://doi.org/10.1016/j.rse.2014.02.003
  37. Wu M, Huang W, Niu Z, Wang C. 2015. Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data. Sensors. 15: 24002-24025. https://doi.org/10.3390/s150924002
  38. Wu M, Wu C, Huang W, Niu Z, Wang C, Li W, Hao P. 2016. An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Information Fusion. 31: 14-25. https://doi.org/10.1016/j.inffus.2015.12.005
  39. Xun B, Yu D, Liu Y, Hao R, Sun Y. 2014. Quantifying isolation effect of urban growth on key ecological areas. Ecological Engineering. 69: 46-54. https://doi.org/10.1016/j.ecoleng.2014.03.041
  40. Zhu X, Chen J, Gao F, Chen X, Masek JG. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment. 114(11): 2610-2623. https://doi.org/10.1016/j.rse.2010.05.032
  41. Zhu X, Helmer EH, Gao F, Liu D, Chen J, Lefsky MA. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment. 172: 165-177. https://doi.org/10.1016/j.rse.2015.11.016