The 2016 Kumamoto earthquake caused crustal deformation along the ruptured fault. In addition, local surface displacements, which cannot be explained only by the mainshock fault movement, have also been reported previously. In this study, we report local post-seismic surface displacement around the Ezu lake and the Suizenji area using synthetic aperture radar interferometry of ALOS-2/PALSAR-2 data. Based on the hydrological structure beneath the area and the observed drawdown of the water level, the local displacement is likely caused by the groundwater migration associated with the 2016 Kumamoto earthquake.

Recently, to assess climate change, multi-angular optical remote sensing, especially satellite remote sensing, has attracted attention for precise monitoring of vegetation activity. Most ground objects have anisotropic properties such that the radiance of an object varies under different illumination and observation angles. Such optical properties are defined by Bi-directional Reflectance Distribution Functions (BRDF). Using BRDFs, one can estimate the three-dimensional structure of a target such as tree height or crown shape, which is difficult to estimate based solely on one-directional optical remote sensing.

This review classified recent BRDF studies of vegetation into three categories: (1) observational studies, (2) simulation studies based on physical models, and (3) simulation studies based on semi-empirical models. Next, we discussed the present status of each category. We described some difficulties of integrating each category to develop BRDF studies: (1) observational data insufficiency and (2) impossibility of measuring many parameters used in BRDF models. To overcome these problems, we proposed a new BRDF observation system using an unmanned aerial vehicle (UAV). Additionally, we introduced a Bidirectional Reflectance Simulator (BiRS), which can estimate BRDFs based on observable parameters alone.

Gross primary production (GPP) capacity is defined as GPP under low stress, and the algorithm for its estimation was developed by Thanyapraneedkul et al. (2012) using a light response curve. The idea behind this algorithm is that the light response curve under low stress is related to chlorophyll content. The parameter is estimated from a vegetation index derived from satellite observations of the green chlorophyll index for five vegetation types: grass, needleleaf deciduous trees, needleleaf evergreen trees, broadleaf deciduous trees, and cropland (paddy fields). Global GPP capacity estimations require modifications to include additional vegetation types, such as closed and open shrubs, which account for approximately 13 % of global land cover.

In this study, the open and closed shrub parameters in the GPP capacity estimation algorithm were determined using AmeriFlux data and satellite data. The optimal parameter for maximum photosynthesis estimation at 2000 PAR (μmol m^-2 s^-1) of open shrubs was similar to that of grass, but for closed shrubs it differed. We concluded that grass and open shrubs could be combined into a single group, and plant functional types in this study and the prior study (Thanyapraneedkul, et al., 2012) could be divided into two categories: grass and woody plants. From this, estimation formula’s parameters were determined for the two categories. Additionally, seasonal changes in GPP capacity were investigated, based on AmeriFlux data and the MODIS GPP product (MOD17A2). GPP capacity and AmeriFlux GPP observations were nearly identical if the vegetation did not experience high stress levels. Also, our results indicated that GPP capacity reflected drought conditions.