Solar photovoltaic (PV) technology has been widely used as a major source of renewable energy. Vietnam is especially active in installing solar energy systems. The total installed solar PV capacity in Vietnam has significantly increased since 2019, but the spatial evolution of solar panels is yet to be discussed. Therefore, this study aims to reveal the shift that occurred in solar photovoltaic planning sites in Vietnam from 2019 to 2022. Solar PV maps were produced from Sentinel-2 imagery via a deep learning segmentation model. Land cover maps, terrain slope, solar power potential, population density, and power grid datasets were compared to the locations of the detected PV sites each year to reveal a shift in the solar farm planning sites. The result show that the deep learning model achieved satisfactory performance. The observed shift in the PV installation sites suggests that for the first two years, large solar farms were built on suitable land near the electricity grid, while smaller PVs were constructed at locations less suitable for solar energy production in 2021 and 2022. These findings suggest that the shift in solar PV planning in Vietnam was caused by the availability of suitable land with an appropriate energy transfer capacity and the participation of smaller-scale PV operators. Full article

Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately assess the damage characteristics. The buildings identified as Major damage in the Japanese damage certification survey contain damage with various characteristics. If Major damage is treated as a single class, the parameters of SAR images will vary greatly, and the relationship between building damage and SAR images would not be properly evaluated. Therefore, it is necessary to divide Major damage buildings into more detailed classes. In this study, the Major damage buildings were newly classified into five damage classes, to correctly evaluate the relationship between building damage characteristics and SAR imagery. The proposed damage classification is based on Japanese damage assessment data and field photographs, and is classified according to the dominant damage characteristics of the building, such as collapse and damage to walls and roofs. We then analyzed the backscattering characteristics of SAR images for each classified damage class. We used ALOS-2 PALSAR-2 images observed before and after the 2016 Kumamoto earthquake in Mashiki Town, where many buildings were damaged by the earthquake. Then, we performed the analysis using two indices, the correlation coefficient R and the coherence differential value γ_dif, and the damage class. The results indicate that the backscattering characteristics of SAR images show different trends in each damage class. The R tended to decrease for large deformations such as collapsed buildings. The γ_dif was likely to be sensitive not only to collapsed buildings but also to damage with relatively small deformation, such as distortion and tilting. In addition, it was suggested that the ground displacement near the earthquake fault affected the coherence values. Full article

Turbid water is known to affect aquatic ecosystems. If the spread of turbid water can be predicted, it is expected to lead to the prediction of damage caused by turbid water in rich aquatic ecosystems and aquaculture farms, and to countermeasures against turbid water. In this study, we developed a method for predicting the area of high-turbidity water using machine learning with satellite-observed total suspended solids (TSS) product and relatively readily available meteorological and oceanographic data (rainfall, wind direction and speed, atmospheric pressure, and tide level) in the past and evaluated it for the Kuma River estuary of the Yatsushiro Sea in Japan. The results showed that the highest accuracy was obtained using random forest regression, with a coefficient of determination of 0.552, when the area of high-turbidity water based on the previous day’s TSS product and hourly meteorological and oceanographic data from the previous day were used as inputs. The most important factor for the prediction was the area of high-turbidity water, followed by wind, and tide level, but the effect of rainfall was small, which was probably due to the flood-control function of the river. Our future work will be to evaluate the applicability of the method to other areas, improve the accuracy, and predict the distribution area. Full article

The cultivation of Chinese cabbage is a crucial source of daily vegetable supply for both human consumption and livestock feed, particularly in East Asian countries. However, changes in global climate and land usage have resulted in significant shifts in the ecological conditions suitable for Chinese cabbage production, thereby threatening its productivity. To address this issue, this study was conducted to map the bio-geophysical suitability of Chinese cabbage in East Asia (Japan, Northeast China, South Korea, and North Korea) from 2001 to 2020. This study integrated six key factors—temperature, rainfall, photosynthetically active radiation (PAR), soil nitrogen, soil pH, and soil texture—into a seasonal and monthly bio-geophysical suitability assessment using a GIS-based Analytic Hierarchy Process–Multiple-Criteria Decision-Making Analysis (AHP-MCDA). The levels of bio-geophysical suitability were categorized into four levels: optimal, suitable, marginal, and unsuitable. The findings of the study firstly indicate that summer is the optimal season for Chinese cabbage cultivation, as it was found to have the highest level of optimal suitability among the four seasons in East Asia. South Korea has the largest percentage of optimal and suitable areas compared to the other three countries. Secondly, this study also conducted a comparison analysis between bio-geophysical suitability and Normalized Difference Vegetation Index (NDVI) over 20 years, and the results show good consistency between the two indicators, with the highest R² value being 0.61. Thirdly, the comparison between bio-geophysical suitability and production data in two villages in Japan demonstrates that an increase in suitability from 0.28 to 0.32 indicates a significant increase in production. Production would stay stable even with further increases in suitability. Finally, two case studies with monthly comparisons of bio-geophysical suitability across Japan and East Asia in 2020 provide an effective benchmark for determining optimal sowing and harvest times. This study’s results can provide important insights into the trade of Chinese cabbage and support the development of agricultural insurance programs both for farmers and insurance companies. Furthermore, this approach may also be applicable for the assessment of the suitability of other crops. Full article

This study presents the feasibility of estimating the ice thickness of the Shirase Glacier using the synthetic aperture interferometric radar altimeter (SIRAL) on board the CryoSat-2 and the interannual variation of the ice thickness of the Shirase Glacier in 2011–2020. The SIRAL data were converted to ice thickness by assuming hydrostatic equilibrium, and the results showed that the ice thickness decreased from the grounding line to the terminus of the glacier. Furthermore, the ice thickness decreased 30 km downstream from the grounding line of the glacier in 2012 and 2017, and decreased 55 km and 60 km downstream from the grounding line of the glacier at other times, which was attributed to the discharge of landfast ice and the retreat of the glacier terminus. When the flow of glacial ice can be reasonably approximated as an incompressible fluid, and the law of conservation of mass can be applied to the ice stream, the multiple of the velocity and the underlying ice thickness under a constant ice density can be shown to correspond to the equation of continuity. Consequently, this study revealed that the ice thickness decreases with accelerating flow velocity, which is coincident with past outflow events. Full article

Road markings are reflective features on roads that provide important information for safe and smooth driving. With the rise of autonomous vehicles (AV), it is necessary to represent them digitally, such as in high-definition (HD) maps generated by mobile mapping systems (MMSs). Unfortunately, MMSs are expensive, paving the way for the use of low-cost alternatives such as low-cost light detection and ranging (LiDAR) sensors. However, low-cost LiDAR sensors produce sparser point clouds than their survey-grade counterparts. This significantly reduces the capabilities of existing deep learning techniques in automatically extracting road markings, such as using convolutional neural networks (CNNs) to classify point cloud-derived imagery. A solution would be to provide a more suitable loss function to guide the CNN model during training to improve predictions. In this work, we propose a modified loss function—focal combo loss—that enhances the capability of a CNN to extract road markings from sparse point cloud-derived images in terms of accuracy, reliability, and versatility. Our results show that focal combo loss outperforms existing loss functions and CNN methods in road marking extractions in all three aspects, achieving the highest mean F1-score and the lowest uncertainty for the two distinct CNN models tested. Full article

A spatial time series geostatistical deconvolution/fusion model (STGDFM), as one of spatiotemporal data fusion model, combines Dense time series data with a Coarse-scale (i.e., DC data) and Sparse time series data with a Fine-scale (i.e., SF data) to generate Synthetic Dense time series data with a Fine-scale (i.e., SDF data). Specifically, STGDFM uses a geostatistics-based spatial time series modeling to capture the temporal trends included in time series DC data. This study evaluated the prediction performance of STGDFM for abrupt changes in reflectance due to disasters in spatiotemporal data fusion, and a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM) were selected as comparative models. For the applicability assessment, flood and wildfire were selected as case studies. In the case of flood, MODIS-like data (240 m) with spatial resolution converted from Landsat data and Landsat data (30 m) were used as DC and SF data, respectively. In the case of wildfire, MODIS and Landsat data were used as DC and SF data, respectively. The case study results showed that among the three spatiotemporal fusion models, STGDFM presented the best prediction performance with 0.894 to 0.979 at the structure similarity and 0.760 to 0.872 at the R-squared values in the flood- and wildfire-affected areas. Unlike STARFM and ESTARFM that adopt the assumptions for reflectance changes, STGDFM combines the temporal trends using time series DC data. Therefore, STGDFM could capture the abrupt changes in reflectance due to the flood and wildfire. These results indicate that STGDFM can be used for cases where satellite images of appropriate temporal and spatial resolution are difficult to acquire for disaster monitoring. Full article

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future. Full article