Fine resolution basin database using 10m DEM at the area along Sanriku coast of Japan

Rias-type bays are one of the most common coasts in Japan where aquacultures have been active due to sheltered geological shape with a deep bottom. The huge tsunami hit Sanriku Coast consisting of open rias-type bays near the epicenter facing Pacific Ocean on 11 March 2011. For recovering Sanriku Coast, it is important to include sustainability in its program. Satoumi is defined as the human use and management of coastal seas for high productivity while maintaining high biodiversity. Therefore, we proposed Satoumi approach to an open rias-type bay, Shizugawa Bay, in southern Sanriku Coast. We conducted scientific researches on mapping of coastal habitats and aquaculture facilities, hydrography, and material flows of nutrients, a minor element (Fe) and organic matters in the bay including those from the rivers and from the offshore waters. At the same time, Committee for Shizugawa Bay Management of Fishermen’s Cooperative of Miyagi Prefecture decided to decrease in aquaculture facilities for sustainable development of aquaculture. Based on these data, a physical-biological coupling model was used for calculating the number of aquaculture facilities that are suitable not only for yields but also for environments. These researches were established on strong collaborations among a fishermen’s’ cooperative, local governments and scientists. Results of this practice may help to realize sustainable coastal use of a rias-type bay.

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Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

Remote Sensing ◽

10.3390/rs13040606 ◽

2021 ◽

Vol 13 (4) ◽

pp. 606

Author(s):

Tee-Ann Teo ◽

Yu-Ju Fu

Keyword(s):

Satellite Images ◽

Traditional Approach ◽

Spectral Band ◽

Super Resolution ◽

High Temporal Resolution ◽

Landsat 8 ◽

Fusion Model ◽

Image Blending ◽

Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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Uncovering dynamic evolution in the plastid genome of seven Ligusticum species provides insights into species discrimination and phylogenetic implications

Scientific Reports ◽

10.1038/s41598-020-80225-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Can Yuan ◽

Xiufen Sha ◽

Miao Xiong ◽

Wenjuan Zhong ◽

Yu Wei ◽

...

Keyword(s):

Plastid Genome ◽

Species Discrimination ◽

Chloroplast Genomes ◽

Genomic Arrangement ◽

Phylogenetic Implications ◽

Solid Foundation ◽

Conserved Gene ◽

Fine Resolution ◽

Genome Scale ◽

First Time

AbstractLigusticum L., one of the largest members in Apiaceae, encompasses medicinally important plants, the taxonomic statuses of which have been proved to be difficult to resolve. In the current study, the complete chloroplast genomes of seven crucial plants of the best-known herbs in Ligusticum were presented. The seven genomes ranged from 148,275 to 148,564 bp in length with a highly conserved gene content, gene order and genomic arrangement. A shared dramatic decrease in genome size resulted from a lineage-specific inverted repeat (IR) contraction, which could potentially be a promising diagnostic character for taxonomic investigation of Ligusticum, was discovered, without affecting the synonymous rate. Although a higher variability was uncovered in hotspot divergence regions that were unevenly distributed across the chloroplast genome, a concatenated strategy for rapid species identification was proposed because separate fragments inadequately provided variation for fine resolution. Phylogenetic inference using plastid genome-scale data produced a concordant topology receiving a robust support value, which revealed that L. chuanxiong had a closer relationship with L. jeholense than L. sinense, and L. sinense cv. Fuxiong had a closer relationship to L. sinense than L. chuanxiong, for the first time. Our results not only furnish concrete evidence for clarifying Ligusticum taxonomy but also provide a solid foundation for further pharmaphylogenetic investigation.

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MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3052886 ◽

2021 ◽

pp. 1-5

Author(s):

Rui Li ◽

Chenxi Duan ◽

Shunyi Zheng ◽

Ce Zhang ◽

Peter M. Atkinson

Keyword(s):

Semantic Segmentation ◽

Remotely Sensed ◽

Remotely Sensed Images ◽

Fine Resolution

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Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

Remote Sensing ◽

10.3390/rs13030434 ◽

2021 ◽

Vol 13 (3) ◽

pp. 434

Author(s):

Ana del Águila ◽

Dmitry S. Efremenko

Keyword(s):

Radiative Transfer ◽

Single Scattering ◽

Atmospheric Composition ◽

Radiative Transfer Model ◽

Transfer Model ◽

Absorption Bands ◽

Acceleration Techniques ◽

Scattering Approximation ◽

Fine Resolution ◽

Single Scattering Approximation

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.

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Extensive Evaluation of a Continental-Scale High-Resolution Hydrological Model Using Remote Sensing and Ground-Based Observations

Remote Sensing ◽

10.3390/rs13071247 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1247

Author(s):

Bowen Zhu ◽

Xianhong Xie ◽

Chuiyu Lu ◽

Tianjie Lei ◽

Yibing Wang ◽

...

Keyword(s):

Remote Sensing ◽

Hydrological Modeling ◽

Infiltration Capacity ◽

Modeling Framework ◽

Large Domain ◽

Continental Scale ◽

Extensive Evaluation ◽

Mean Differences ◽

Fine Resolution

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.

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Geocoding cryptosporidiosis cases in Ireland (2008–2017)—development of a reliable, reproducible, multiphase geocoding methodology

Irish Journal of Medical Science (1971 -) ◽

10.1007/s11845-020-02468-0 ◽

2021 ◽

Author(s):

Lisa Domegan ◽

Patricia Garvey ◽

Paul McKeown ◽

Howard Johnson ◽

Paul Hynds ◽

...

Keyword(s):

Infectious Disease ◽

Spatial Data ◽

Application Programming Interface ◽

Epidemiological Studies ◽

National Standards ◽

Small Areas ◽

Infectious Disease Reporting ◽

Health Related ◽

Application Programming ◽

Fine Resolution

Abstract Background Geocoding (the process of converting a text address into spatial data) quality may affect geospatial epidemiological study findings. No national standards for best geocoding practice exist in Ireland. Irish postcodes (Eircodes) are not routinely recorded for infectious disease notifications and > 35% of dwellings have non-unique addresses. This may result in incomplete geocoding and introduce systematic errors into studies. Aims This study aimed to develop a reliable and reproducible methodology to geocode cryptosporidiosis notifications to fine-resolution spatial units (Census 2016 Small Areas), to enhance data validity and completeness, thus improving geospatial epidemiological studies. Methods A protocol was devised to utilise geocoding tools developed by the Health Service Executive’s Health Intelligence Unit. Geocoding employed finite-string automated and manual matching, undertaken sequentially in three additive phases. The protocol was applied to a cryptosporidiosis notification dataset (2008–2017) from Ireland’s Computerised Infectious Disease Reporting System. Outputs were validated against devised criteria. Results Overall, 92.1% (4266/4633) of cases were successfully geocoded to one Small Area, and 95.5% (n = 4425) to larger spatial units. The proportion of records geocoded increased by 14% using the multiphase approach, with 5% of records re-assigned to a different spatial unit. Conclusions The developed multiphase protocol improved the completeness and validity of geocoding, thus increasing the power of subsequent studies. The authors recommend capturing Eircodes ideally using application programming interface for infectious disease or other health-related datasets, for more efficient and reliable geocoding. Where Eircodes are not recorded/available, for best geocoding practice, we recommend this (or a similar) quality driven protocol.

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Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

Remote Sensing ◽

10.3390/rs13163065 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3065

Author(s):

Libo Wang ◽

Rui Li ◽

Dongzhi Wang ◽

Chenxi Duan ◽

Teng Wang ◽

...

Keyword(s):

Large Scale ◽

Texture Features ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Research Field ◽

Learning Approaches ◽

Fine Grained ◽

Urban Scene ◽

Fine Resolution ◽

With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

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