Temporal and Spatial Distributions and Influencing Factors of HABs Outbreaks around the North of Shandong Peninsula during 2000–2019: Based on Remote Sensing Images and Field Monitoring Data

2021 ◽  
pp. 1-14
Author(s):  
Min Zhou ◽  
Mengquan Wu ◽  
Lianjie Zhao ◽  
Longxiao Zheng ◽  
Binyu Li
Keyword(s):  
Remote Sensing ◽  
Influencing Factors ◽  
Field Monitoring ◽  
Monitoring Data ◽  
Spatial Distributions ◽  
Remote Sensing Images ◽  
Shandong Peninsula ◽  
The North ◽  
Temporal And Spatial

The Study on Environmental Evolved Characteristic of Sandy Coast

2012 ◽  
Vol 226-228 ◽  
pp. 1170-1173
Author(s):  
Qi Peng Zhang ◽  
Xiao Qing Han ◽  
Jing Li ◽  
Jing Jing Zhao ◽  
Wei Biao Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bohai Sea ◽  
Hebei Province ◽  
Coastal Current ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
The North ◽  
Sensing Technology ◽  
Sandy Coast

In order to study the evolved characteristic of sandy coast in Hebei Province, the paper analyzed costal information by Remote Sensing technology from landform maps and remote sensing images from 1956 to 2007. It studied the evolvement characteristics and the reasons of sandy coast deeply. And it also analyzed the evolvement infections to the nearby coast of the sandy engineering. The results showed that the characteristic was erosion condition in sandy coast. There were several different evolved processing in different area from 1959 to 2007. In the region between Daihe River and Tazigou, the highest erosion speed was 3.45 m/a by the coastal current and wave between Daihe River and Yanghe River. The section was deposited into the ocean with the speed of 1.29 m/a by the cultivation ponds building in Bohai Sea farmland between the Yanghe River and Dapuhe River. In the region between Tazigou and Langwokou River, the beach had been eroded about 373 m with the speed of 13.32 m/a by 2007. And the section was eroded offshore more serious with the distance of 610 m and the speed of 21.79 m/a from the north of Luanhe River.In the region between Langwokou River and Daqinghe River, the average erosion distance was about 370 m with the speed of 13.21 m/a in Shegang sandbar. And it was eroded back to mainland about 164 m with the speed of 8.20 m/a. And it was about 504m with the speed of 18.00 m/a.

Mapping lake drainage and drained lake basins around Point Lay, Alaska using multi-source remote sensing data

2020 ◽  
Author(s):  
Helena Bergstedt ◽  
Benjamin Jones ◽  
Donald Walker ◽  
Louise Farquharson ◽  
Amy Breen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Source ◽  
Water Sources ◽  
The Arctic ◽  
North Slope ◽  
The North ◽  
Temporal And Spatial ◽  
Lake Drainage ◽  
The Village ◽  
Lake Basins

<p>The North Slope of Alaska is a permafrost affected landscape dominated by lakes and drained lake basins of different sizes, depths and ages. Local communities across the North Slope region rely on lakes as a fresh water source and as locations for subsistence fishing, while industry relies on lakes as a source of water for winter transportation. Lake drainage events are often disruptive to both communities and industry that rely on being in close proximity to surface water sources in a region underlain by continuous permafrost. Drained lake basins of different ages can provide information on the past effects of climate change in the region. Studying past drainage events gives insight about the causes and mechanisms of these complex systems and benefits our understanding of lake evolution on the Arctic Coastal Plain in Alaska and the circumpolar Arctic as a whole.</p><p>Lakes and drained lake basins can be identified using high to medium resolution multispectral imagery from a range of satellite-based sensors. We explore the history of lake drainage in the region around Point Lay, a community located on the northern Chukchi Coast of Alaska, using a multi-source remote sensing approach. We study the evolution of lake basins before and after drainage events, their transformation from fishing grounds and water sources to grazing grounds and the geomorphological changes in the surrounding permafrost-dominated landscapes associated with these transitions.  </p><p>We build a dense and long time series of satellite imagery of past lake drainage events by including a multitude of remote sensing acquisitions from different sources into our analysis. Incorporating imagery from different sensors that have different temporal and spatial resolutions allows us to assess past drainage events and current geomorphological states of lakes and drained lake basins at different temporal and spatial scales. Point Lay is known to be an area where drainage events occur frequently and are of high relevance to the community. In August of 2016, the village drinking water source drained during a period of intense rainfall causing the village to seek alternative sources for a freshwater supply. Our results from the analysis of the remotely sensed imagery were shared directly with the community as part of a public seminar series in the Spring of 2020. We hope that results from our study near Point Lay, Alaska can contribute towards the selection of a new freshwater source lake for the village.</p>

scholarly journals MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution

2021 ◽  
Vol 13 (18) ◽  
pp. 3724
Author(s):  
Weisheng Li ◽  
Dongwen Cao ◽  
Yidong Peng ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Temporal Correlation ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Spectral Angle Mapper ◽  
Convolution Operation ◽  
Input And Output ◽  
Temporal And Spatial ◽  
The Relationship ◽  
Spectral Angle

Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiotemporal fusion algorithms based on deep learning have gradually developed, but they also face some problems. For example, the amount of data affects the model’s ability to learn, and the robustness of the model is not high. The features extracted through the convolution operation alone are insufficient, and the complex fusion method also introduces noise. To solve these problems, we propose a multi-stream fusion network for remote sensing spatiotemporal fusion based on Transformer and convolution, called MSNet. We introduce the structure of the Transformer, which aims to learn the global temporal correlation of the image. At the same time, we also use a convolutional neural network to establish the relationship between input and output and to extract features. Finally, we adopt the fusion method of average weighting to avoid using complicated methods to introduce noise. To test the robustness of MSNet, we conducted experiments on three datasets and compared them with four representative spatiotemporal fusion algorithms to prove the superiority of MSNet (Spectral Angle Mapper (SAM) < 0.193 on the CIA dataset, erreur relative global adimensionnelle de synthese (ERGAS) < 1.687 on the LGC dataset, and root mean square error (RMSE) < 0.001 on the AHB dataset).

scholarly journals Exploring the Spatial–Temporal Analysis of Coastline Changes Using Place Name Information on Hainan Island, China

2021 ◽  
Vol 10 (9) ◽  
pp. 609
Author(s):  
Jisheng Xia ◽  
Guize Luan ◽  
Fei Zhao ◽  
Zhiyan Peng ◽  
Lu Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hainan Island ◽  
Temporal Analysis ◽  
Coastal Development ◽  
Remote Sensing Images ◽  
Coastline Change ◽  
Sensing Technology ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
The Impact

A coastline is the boundary zone between land and sea, an active zone of human social production activities and an area where the ecology is fragile and easy to change. The traditional method to analyze temporal and spatial changes in the coastline is to extract the coastline through remote sensing, LiDAR, and field sampling and analyze the temporal and spatial changes with statistical data. The coastline extracted by these methods has high spatial and temporal resolution, but it requires remote sensing images and data obtained by other sensors, so it is impossible to extract coastlines from before the emergence of remote sensing technology. This paper improves the coastline generation algorithm. Firstly, a triangulated irregular network is used to generate the preliminary rough coastline, and then, each line segment is optimized with Python language according to the influence range of the place names to further approach the real coastline. The accuracy of the coastline extracted by this method can reach 80% within 500 m, which is of great significance in the mapping and analysis of small- and medium-scale coastlines. This paper analyzes the changes in the coastline of Hainan Island before the founding of China (pre-founding) and in modern times and analyzes the impact of coastal development on coastline change. Through the analysis, it is found that, from before the founding of the People’s Republic of China to the present, the natural coastline of Hainan Island has become shorter, the artificial coastline has become longer, and the coastline generally presents a trend of advancing toward the ocean. This method realizes coastline construction under the condition of missing remote sensing images and puts forward a new way to study historical coastline changes.

scholarly journals Airborne Polarimetric Remote Sensing for Atmospheric Correction

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Tianquan Liang ◽  
Xiaobing Sun ◽  
Han Wang ◽  
Rufang Ti ◽  
Cunming Shu
Keyword(s):  
Remote Sensing ◽  
Information Entropy ◽  
Atmospheric Correction ◽  
Correction Method ◽  
North Coast ◽  
Remote Sensing Images ◽  
Airborne Remote Sensing ◽  
Image Information ◽  
Atmospheric Scattering ◽  
The North

The problem, whose targets can not be effectively identified for airborne remote sensing images, is mainly due to the atmospheric scattering effect. This problem is necessary to be overcome. According to the statistical evaluations method and the different characteristics of polarization between the objects radiance and atmospheric path radiation, a new atmospheric correction method for airborne remote sensing images was proposed. Using this new method on the airborne remote sensing images which acquired on the north coast areas of China during the haze weather, we achieved a high quality corrected atmosphere-free image. The results demonstrate the power of the method on the harbor area. The results show that the algorithm, improving image contrast and image information entropy, can effectively identify the targets after atmospheric correction. The image information entropy was enhanced from 5.59 to 6.62. The research provides a new and effective atmospheric correction technical approach for the airborne remote sensing images.

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