[논문]PLS 회귀분석을 이용한 미래 육상 식생의 생산성 예측

최철현; 박경훈; 정성관

doi:10.11108/kagis.2017.20.1.042

[국내논문] PLS 회귀분석을 이용한 미래 육상 식생의 생산성 예측
Predicting Future Terrestrial Vegetation Productivity Using PLS Regression 원문보기

한국지리정보학회지 = Journal of the Korean Association of Geographic Information Studies, v.20 no.1, 2017년, pp.42 - 55

최철현 (국립생태원 생태보전연구실) , 박경훈 (창원대학교 환경공학과) , 정성관 (경북대학교 조경학과)

초록
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식생의 기후 적응력은 지역에 따른 상황 및 공간적 패턴이 다르게 나타나기 때문에 픽셀 스케일의 접근이 필요하다. 본 연구에서는 위성영상 기반 식생지수에 대해 PLS 회귀분석을 적용하여 식생의 생산성에 영향을 미치는 기후요인을 평가하고 남한지역의 미래 산림 생산성을 예측하였다. 그 결과, 최고강수분기의 평균기온(Bio8), 최저강수분기의 평균기온(Bio9), 최저강수월의 강수량(Bio14) 변수가 식생의 생산성에 높은 영향을 미치는 것으로 분석되었다. 미래 기후시나리오 자료를 이용하여 예측된 2050년의 식생 생산성은 전체적으로 감소하는 것으로 나타났으며, 특히 고지대에서 크게 감소하는 것으로 분석되었다. 이러한 결과는 기후에 민감한 지역의 식생에 대한 생산성 모니터링과 미래 기후변화로 인한 산림 탄소 저장량의 변화를 평가하는데 있어 유용하게 활용될 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

Since the phases and patterns of the climate adaptability of vegetation can greatly differ from region to region, an intensive pixel scale approach is required. In this study, Partial Least Squares (PLS) regression on satellite image-based vegetation index is conducted for to assess the effect of climate factors on vegetation productivity and to predict future productivity of forests vegetation in South Korea. The results indicate that the mean temperature of wettest quarter (Bio8), mean temperature of driest quarter (Bio9), and precipitation of driest month (Bio14) showed higher influence on vegetation productivity. The predicted 2050 EVI in future climate change scenario have declined on average, especially in high elevation zone. The results of this study can be used in productivity monitoring of climate-sensitive vegetation and estimation of changes in forest carbon storage under climate change.

주제어

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제안 방법

Since the phases and patterns of the climate adaptability of vegetation may greatly differ from region to region, an intensive pixel scale approach is required. In this study, a satellite-based vegetation index and 19 bioclimatic variables were used to evaluate the climatic sensitivity of vegetation at the pixel scale, and importance of bioclimatic variables on vegetation productivity were analyzed. The results indicate that Bio8, Bio9 and Bio14 variables showed high influence on the EVI of the entire forest area.
In this study, major climate factors influencing vegetation productivity are determined using MODIS vegetation index and climate data. Furthermore, we predicted future productivity in order to identify areas vulnerable to climate change.
In this study, the MODIS EVI(MOD13Q1) data were used to evaluate the vegetation productivity. MOD13Q1 data are provided every 16 days at 250m spatial resolution.

대상 데이터

, 2015). The DEM data was obtained from the 30m-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER) Global Digital Elevation Model Version 2(GDEM V2) provided by the NASA. The analysis was conducted using the 'spgwr' package of R(Team, 2013; Bivand et al.
The spatial scope of the study is South Korea, located in middle-latitudes with a temperate climate zone and four distinctive seasons. South Korea has a small land area, but its complex geography, seasonal changes and many different types of biomes make the climatic influence quite different from region to region.

이론/모형

When a regression coefficients was derived by the PLS regression analysis, future EVI can be estimated using future bioclimatic variables. In this study, future bioclimatic variables in 2050 of the RCP 8.5 scenario(HadGEM2-AO) provided by Worldclim-Global Climate Data(www.worldclim.org) were used. The PLS regression analysis was conducted for each pixel in order to consider differences of the climatic sensitivity for the each biome types and regions.
Generally, the spatial interpolation of temperature is invariably influenced by elevation(DeGaetano and Belcher, 2007). To consider variations of temperature by elevation and local topographic differences, the Geographically Weighted Regression (GWR) method was used in this study. GWR sets the elevation derived from the Digital Elevation Model (DEM) as the independent variable, and the monthly maximum temperature and monthly minimum temperature in the AWS records as the dependent variable.

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