Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 883-887, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-883-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 883-887, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-883-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jun 2016

23 Jun 2016

PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET

Hidetake Hirayama1, Mizuki Tomita2, and Keitarou Hara2 Hidetake Hirayama et al.
  • 1Graduate School of Tokyo University of Information Sciences, 4-1Onaridai, Wakaba-ku, Chiba, 265-8501 Japan
  • 2Tokyo University of Information Sciences, 4-1Onaridai, Wakaba-ku, Chiba, 265-8501 Japan

Keywords: MODIS, Beech, Prediction modelling, Climate change

Abstract. The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC),warmth index (WI), winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.