In the face of rising the seas, relying solely on seawalls and other reinforced coastlines is unsustainable due to the high costs associated with their construction and upkeep and unforeseen effects on habitats. Restoring and conserving coastal habitats can be more affordable, long-term solutions for protecting the coast. Yet, basic knowledge about where, how, and for whom habitats decrease the risk of coastal disasters is typically lacking from decision-makers. This study investigates how climate change may affect coastal areas and how natural coastal habitats may help protect them. We studied two small tropical islands, Bintan and Seribu Islands, Indonesia. The research applied the InVEST Coastal Vulnerability model to calculate the hazard index. To assess the effect on the vulnerable population, a grid system for the coastal population was created and overlaid with the hazard index. The comprehensive analysis indicates that if the coastal habitat is lost in the future, Bintan and Seribu Islands will face a severe threat from rising sea levels, with 96% and 63% of the inhabitants being highly exposed to climate hazards. Furthermore, the model shows that preserving natural coastal habitats by 2040 could help protect approximately 104 and 17 km of coastline areas in Bintan and Seribu Islands, respectively. Ecosystems offer safeguards to certain thinly inhabited, distant coastal villages, certain of those with a large, vulnerable population. Thus, this study highlights that natural coastal habitat is essential in climate change adaptation. Enhancing and safeguarding these natural habitats is crucial for mitigating climate-related hazards and ensuring community resilience.

Understanding physical processes in nature, including the occurrence of slow-onset natural disasters such as droughts and landslides, requires knowledge of the change in soil moisture between two points in time. The study was conducted on a relatively bare soil, and the change in soil moisture was examined with an index called Normalized radar Backscatter soil Moisture Index (NBMI) using Sentinel-1 satellite data. Along with soil moisture measured with a probe on the ground, a study of correlation with satellite imagery was conducted using a Multiple Linear Regression (MLR) model. Furthermore, the Dubois model was used to predict soil moisture. Results have shown that NBMI on a logarithmic scale provides a good representation of soil moisture change with R²~86%. The MLR model showed a positive correlation of soil moisture with the co-polarized backscatter coefficient, but an opposite correlation with the surface roughness and angle of incidence. The results of the Dubois model showed poor correlation of 44.37% and higher RMSE error of 17.1, demonstrating the need for detailed and accurate measurement of surface roughness as a prerequisite for simulating the model. Of the three approaches, index-based measurement has been shown to be the most rapid for understanding soil moisture change and has the potential to be used for understanding some mechanisms of natural disasters under similar soil conditions.

The utilization of multi-sensor and constellation satellite data with appropriate geometric and radiometric calibration and validation is required for effective satellite data applications for various monitoring tasks. In this paper, we present the research and development of optical calibration sites using both natural surface and ground point-source-mirror reflectors constructed at The Center for Research and Application of Satellite Remote Sensing of Yamaguchi University (YUCARS), Japan. The YUCARS calibration sites experimented with GRUS-1A (Axelspace Corporation, Tokyo, Japan) and PlanetScope (Planet Lab, San Francisco, CA, USA) images for the verification of radiometric and geometric performance following the harmonized reflectance product. The top of atmosphere (TOA) radiance and reflectance of optical micro-satellite imageries were simulated by MODTRAN6 based on the in-situ data of the ground point-source-mirror reflector, ground surface and atmospheric measurements. The YUCARS mirror arrays were used to verify geometric accuracy and better band co-registration. The TOA reflectance derived from the ground measurements and acquired by satellite instruments were correlated to derive harmonization coefficients. The results show an improvement in image accuracy and harmonization of the different sensor data for the multi-temporal application. The preliminary results show that the mirror-arrays method can efficiently solve the limits of the external environment, time, and space. Furthermore, it can be used for improving radiometric performance and image quality using deblurring from a point spread function created from YUCARS mirror arrays.