Japan Geoscience Union Meeting 2021

Presentation information

[J] Poster

H (Human Geosciences ) » H-TT Technology & Techniques

[H-TT18] Environmental Remote Sensing

Sat. Jun 5, 2021 5:15 PM - 6:30 PM Ch.11

convener:Naoko Saitoh(Center for Environmental Remote Sensing), Hitoshi Irie(Center for Environmental Remote Sensing, Chiba University), Hiroto Shimazaki(National Institute of Technology, Kisarazu College), Teppei Ishiuchi(Miyagi University)

5:15 PM - 6:30 PM

[HTT18-P04] Evaluation of heterogeneity in growth of black pines planted for artificial coastal forests in the Sendai Plain

*Takuto Kajiwara1, Masayuki Kawahigashi2 (1.Graduate Student of Tokyo Metropolitan University, 2.Tokyo Metropolitan University)


Keywords:satellite image, vegetation index, tree planting, embankment materials

1. Introduction
The 2011 tsunami caused by the Great East Japan Earthquake hit the coastal area, resulting in fatal damage to the black pine coastal forest in the Sendai Plain, which has been developed since the 17th century. Although the Forestry Agency immediately launched a coastal forest restoration project in the damaged area after the tsunami, there were many drawbacks to grow plants, such as high salinity due to seawater inundation and high groundwater level. The Forestry Agency constructed an embankment to raise basement for planting using excavated soil from the hilly areas surrounding the Sendai Plain. Seedlings of black pines planted on the basement showed uneven growth rates in the area after several years. Since the constant growth of trees has been required to function as embankment for alleviation of tsunami impact, negative factors should be urgently removed to promote their satisfactory growth. The present growth condition was evaluated by the normalized difference vegetation index (NDVI) calculated by satellite images.
2. Research method
The survey site was set in the Arabama Coastal Forest, Wakabayashi-Ku, Sendai City, Miyagi Prefecture. Totally 35 plots with different planting years were targeted to calculate NDVI. The NDVI value was calculated from satellite images taken by Sentinel-2 on 20 August, 2020. Distribution of NDVI values was mapped using ArcMap 10.5.1. The range of NDVI values in each planting plot was estimated by the kernel density estimation, which is an algorithm to estimate a probability density by extrapolating data of finite samples, based on the NDVI values of all pixels. Statistical significance for differences in normality, homoscedasticity, and mean values was evaluated to compare the non-uniformity of the growth status at each site.
3. Results and Discussion
There were statistically significant differences in NDVI values between planting plots established in the same year, indicating that different growth rate of black pines even under same growth condition. Again, the spatial distribution of NDVI differed depending on planting plots and the growth of the black pine varied even in the same plot with same planting year. Since there is no difference in the climatic conditions, such as sunshine and precipitation between planting plots, the differences in the growth of black pine could be growth factors in soil affected by microtopography and micro-scale variation in soil. Therefore, constant and detail surveys are necessary to elucidate the cause of the growth differences in black pines.