Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Impact Assessment of Forest Development on Net Primary Production using Satellite Image Spatial-temporal Fusion and CASA-Model

위성영상 시공간 융합과 CASA 모형을 활용한 산지 개발사업의 식생 순일차생산량에 대한 영향 평가

  • Jin, Yi-Hua (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Zhu, Jing-Rong (Graduate School, Seoul National University) ;
  • Sung, Sun-Yong (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Lee, Dong-Ku (Department of Landscape Architecture and Rural System Engineering, Seoul National University)
  • 김예화 (서울대학교 협동과정조경학) ;
  • 주경영 (서울대학교 대학원) ;
  • 성선용 (서울대학교 협동과정조경학) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Received : 2017.05.30
  • Accepted : 2017.07.16
  • Published : 2017.08.31
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Abstract

As the "Guidelines for GHG Environmental Assessment" was revised, it pointed out that the developers should evaluate GHG sequestration and storage of the developing site. However, the current guidelines only taking into account the quantitative reduction lost within the development site, and did not consider the qualitative decrease in the carbon sequestration capacity of forest edge produced by developments. In order to assess the quantitative and qualitative effects of vegetation carbon uptake, the CASA-NPP model and satellite image spatial-temporal fusion were used to estimate the annual net primary production in 2005 and 2015. The development projects between 2006 and 2014 were examined for evaluate quantitative changes in development site and qualitative changes in surroundings by development types. The RMSE value of the satellite image fusion results is less than 0.1 and approaches 0, and the correlation coefficient is more than 0.6, which shows relatively high prediction accuracy. The NPP estimation results range from 0 to $1335.53g\;C/m^2$ year before development and from 0 to $1333.77g\;C/m^2$ year after development. As a result of analyzing NPP reduction amount within the development area by type of forest development, the difference is not significant by type of development but it shows the lowest change in the sports facilities development. It was also found that the vegetation was most affected by the edge vegetation of industrial development. This suggests that the industrial development causes additional development in the surrounding area and indirectly influences the carbon sequestration function of edge vegetaion due to the increase of the edge and influx of disturbed species. The NPP calculation method and results presented in this study can be applied to quantitative and qualitative impact assessment of before and after development, and it can be applied to policies related to greenhouse gas in environmental impact assessment.

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References

  1. Adam MC..Kneeshaw D and Beckley TM. 2012. Forestry and road development: direct and indirect impacts from an Aboriginal perspective. Ecology and Society. 17(4), 1.
  2. Brown ME..Lary DJ..Vrieling A..Stathakis D. and Mussa H. 2008. Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing. 29(24), 7141-7158. https://doi.org/10.1080/01431160802238435
  3. Chai T. and Draxler RR. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?Arguments against avoiding RMSE in the literature, Geosci. Model Dev. 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  4. Doraiswamy PC..Sinclair TR..Hollinger S.. Akhmedov B..Stern A. and Prueger J. 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sens.Environ. 97, 192-202. https://doi.org/10.1016/j.rse.2005.03.015
  5. Filed CB..Randerson JT. and Malmstrom CM. 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment. 51, 74-88. https://doi.org/10.1016/0034-4257(94)00066-V
  6. FLAASH M. 2009. Atmospheric Correction Module: QUAC and FLAASH User's Guide, Version 4.7. Boulder, CO, USA.
  7. Frantz D..Stellmes M..Roder A..Udelhoven T..Mader S. and Hill J, 2016. Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium and Coarse Resolution Inputs. IEEE Transactions on Geoscience and Remote Sensing, 54(7), 4153-4164. https://doi.org/10.1109/TGRS.2016.2537929
  8. Gao F..Masek J..Schwaller M. and Hall F, 2006. On the blending of the Landsat and MODIS surface reflectance: predictiong daily Landsat surface reflectance, IEEE Transaction on Geoscience and Remote Sensing, 44(8), 2207-2218. https://doi.org/10.1109/TGRS.2006.872081
  9. Goward SN..Cruickshanks GD. and Hope AS. 1985. Observed Relation between Thermal Emission and Reflected Spectral Radiance of a Complex Vegetated Landscape. Remote Sensing of Environment. 18, 137-146. https://doi.org/10.1016/0034-4257(85)90044-6
  10. Huete A..Didan K..Muira T..Rodriguez E P..Gao F. and Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(12), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  11. Hwang SI and Park SH. 2011. A Comparative Study on Estimation Methodologies of Carbon Sequestration Amount by Vegetation for Environmental Impact Assessment on Development Projects. Journal of Environmental Impact Assessment. 20(4), 477-487.
  12. Jin YH.Jeong SG. Jeong SG.Lee DK. 2015. Assessment on the Forest Conservation Value Considering Forest Ecosystem Services-The case of Gapyung-gun. Journal of Environmental Impact Assessment. 24(5), 420-431. (in Korean with English summary) https://doi.org/10.14249/eia.2015.24.5.420
  13. 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. (in Korean with English summary) https://doi.org/10.13087/kosert.2016.19.1.155
  14. Laurance WF..Nascimento HEM..Laurance SG..Andrade A..Ewers RM..Harms KE..Luizao RCC. and Ribeiro JE. 2007. Habitat Fragmentation, Variable Edge Effects, and the Landscape-Divergence Hypothesis. PLoS ONE. 2(10), e1017. https://doi.org/10.1371/journal.pone.0001017
  15. Lee H. and Noh S. 2013. Advanced Statistical Analysis: Theory and Practice. Moonwoosa.
  16. Malingreau JP. 1989. The vegetation index and the study of vegetation dynamics. Ispra Courses. Springer Netherlands.
  17. Marsett RC..Qi J..Heilman P..Biedenbender SH..Watsonm MC..Amer S..Weltz M.. Coodrich D. and Marsett R. 2006. Remote Sensing for Grassland Management in the Arid Southwest. Rangeland Ecology & Management. 59(5), 530-540. https://doi.org/10.2111/05-201R.1
  18. McRoberts R. and Tomppo E. 2007. Remote Sensing support for national forest inventories. Remote Sensing of Environment. 110(4), 412-419. https://doi.org/10.1016/j.rse.2006.09.034
  19. Ministry of Environment. 2011. Guidelines for Environmental Assessment of Greenhouse Gases. (in Korean with English summary)
  20. Morisette JT..Heinsch FA. and Running SW. 2006. Monitoring Global Vegetation using Moderate Resolution Satellites. Eos Trans. AGU. 87(50), 568-568. https://doi.org/10.1029/2006EO500009
  21. Na S..Hong S..Kim Y. and Lee K. 2014. Estimation of Corn and Soybean Yields Based on MODIS Data and CASA Model in Iowa and Illinois. USA. Korean J. Soil Sci. Fert. 47(2), 92-99. https://doi.org/10.7745/KJSSF.2014.47.2.092
  22. Na SI..Park JH. and Park JK. 2012. Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI. Journal of the Korean Society of Agricultural Engineers. 54(3), 141-148. https://doi.org/10.5389/KSAE.2012.54.3.141
  23. Noemi G. 2010. Estimating Maize Grain Yield from Crop Biophysical Parameters Using Remote Sensing. Digital Commons@University of Nebraska-Lincoln.
  24. Pat S. and Chavez J. 1996. Image-Based Atmospheric Corrections-Revisited and Improved. Photogrammetric Engineering and Remote Sensing. 62, 1025-1036.
  25. Potter CS..Randerson JT. and Field CB. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cy. 7, 811-841. https://doi.org/10.1029/93GB02725
  26. Ren JQ..Chen ZX..Zhou QB. and Tang HJ. 2008. Regional Yield Estimation for Winter Wheat with MODIS-NDVI Data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation. 10, 403-413. https://doi.org/10.1016/j.jag.2007.11.003
  27. Rojas O. 2007. Operational Maize Yield Model Development and Validation Based on Remote Sensing and Agro-meteorological Data in Kenya. Int.J.Remote Sens. 28(1), 3775-3793. https://doi.org/10.1080/01431160601075608
  28. Roy DP..Ju J..Lewis P..Schaaf C..Gao F..Hansen M. and Lindquist E. 2008. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment. 112(6), 3112-3130. https://doi.org/10.1016/j.rse.2008.03.009
  29. Running SW. and Coughlan JC. 1988. A general model of forest ecosystem processes for regional applications I: Hydrologic balance, canopy gas exchange and primary production processes. Ecological Modelling. 42(2), 125-154. https://doi.org/10.1016/0304-3800(88)90112-3
  30. Sung SY.Lee DK.Mo YW. 2015. Comparison of Carbon Stock Between Forest Edge and Core by Using Connectivity Analysis. Journal of Korean Society of Rural Planning. 21(4), 27-33. (in Korean with English summary) https://doi.org/10.7851/ksrp.2015.21.4.027
  31. Tao F..Yokozawa M..Zhang Z..Xu Y. and Hayashi Y. 2005. Remote Sensing of Crop Production in China by Production Efficiency Models: Models Comparisons, Estimates and Uncertainties. Ecological Modelling. 183, 385-396. https://doi.org/10.1016/j.ecolmodel.2004.08.023
  32. Wald L. 2002. Data fusion: definitions and architectures: fusion of images of different spatial resolution. Presses des MINES.
  33. Xu JR..Dong JH. and Yang HB. 2015. Land cover change and its impact on net primary productivity in China's typical temperate grassland system in the past 25 years: A study of the Huangfuchuan Watershed. Remote sensing for land and resources. 27(2), 118-125.
  34. Zhu X..Chen J..Gao F..Chen X. and 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
  35. Zhu X..Helmer EH..Gao F..Liu D..Chen J. and 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