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 Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals A spatial and temporal analysis on NDVI in Urad grassland during 2010-2019 over remote sensing

2021 ◽  
Vol 336 ◽  
pp. 06029
Author(s):  
Yueying Zhang ◽  
Tiantian Liu ◽  
Yuxi Wang ◽  
Ming Zhang ◽  
Yu Zheng
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Temporal Analysis ◽  
Vegetation Coverage ◽  
Analysis Method ◽  
Modis Ndvi ◽  
The Past ◽  
Spatial And Temporal Analysis ◽  
Change Trend ◽  
Temporal And Spatial

The temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland, Inner Mongolia has been investigated by analysing on MODIS NDVI remote sensing products. This paper applies pixel dichotomy approach and linear regression trend analysis method to analyze the temporal and spatial evolution trend of vegetation coverage over the past 10 years. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. The vegetation distribution and change trend analysis provide a thorough and scientific reference for policymaking in environmental protection.

The shifting of climate types: manifestation to phenology and ecosystems structure

2021 ◽  
Author(s):  
Gunta Kalvāne ◽  
Andis Kalvāns ◽  
Agrita Briede ◽  
Ilmārs Krampis ◽  
Dārta Kaupe ◽  
...  
Keyword(s):  
Climate Change ◽  
Breaking Point ◽  
Data Sets ◽  
Precipitation Regime ◽  
Climate Type ◽  
Data Set ◽  
Temperature And Precipitation ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
The Impact

<p>According to the Köppen climate classification, almost the entire area of Latvia belongs to the same climate type, Dfb, which is characterized by humid continental climates with warm (sometimes hot) summers and cold winters.  In the last decades whether conditions on the western coast of Latvia more characterized by temperate maritime climates. In this area there has been a transition (and still ongoing) to the climate type Cfb.</p><p>Temporal and spatial changes of temperature and precipitation regime have been examined in whole territory to identify the breaking point of climate type shifts. We used two type of climatological data sets: gridded daily temperature from the E-OBS data set version 21.0e (Cornes et al., 2018) and direct observations from meteorological stations (data source: Latvian Environment, Geology and Meteorology Centre). The temperature and precipitation regime have changed significantly in the last century - seasonal and regional differences can be observed in the territory of Latvia.</p><p>We have digitized and analysed more than 47 thousand phenological records, fixed by volunteers in period 1970-2018. Study has shown that significant seasonal changes have taken place across the Latvian landscape due to climate change (Kalvāne and Kalvāns, 2021). The largest changes have been recorded for the unfolding (BBCH11) and flowering (BBCH61) phase of plants – almost 90% of the data included in the database demonstrate a negative trend. The winter of 1988/1989 may be considered as breaking point, it has been common that many phases have begun sooner (particularly spring phases), while abiotic autumn phases have been characterized by late years.</p><p>Study gives an overview aboutclimate change (also climate type shift) impacts on ecosystems in Latvia, particularly to forest and semi-natural grasslands and temporal and spatial changes of vegetation structure and distribution areas.</p><p>This study was carried out within the framework of the Impact of Climate Change on Phytophenological Phases and Related Risks in the Baltic Region (No. 1.1.1.2/VIAA/2/18/265) ERDF project and the Climate change and sustainable use of natural resources institutional research grant of the University of Latvia (No. AAP2016/B041//ZD2016/AZ03).</p><p>Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res. Atmos., 123(17), 9391–9409, doi:10.1029/2017JD028200, 2018.</p><p>Kalvāne, G. and Kalvāns, A.(2021): Phenological trends of multi-taxonomic groups in Latvia, 1970-2018, Int. J. Biometeorol., doi:https://doi.org/10.1007/s00484-020-02068-8, 2021.</p>

scholarly journals Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Jingxian Yu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Collapsed Buildings ◽  
Improved Model ◽  
Detection Speed

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

Watermark-based Copyright Protection Using Invariant Features for Remote Sensing Images

2020 ◽  
Author(s):  
Na Ren ◽  
Changqing Zhu
Keyword(s):  
Remote Sensing ◽  
Copyright Protection ◽  
Low Frequency ◽  
Image Features ◽  
Remote Sensing Images ◽  
Processing Theory ◽  
Invariant Features ◽  
Original Host ◽  
Sensing Technology ◽  
Feature Processing

<p>With the development of remote sensing technology, the copyright protection of remote sensing images has become an urgent problem to be solved. In this paper, a blind watermarking scheme based on invariant features is applied. In the embedding process, the stable image features are firstly extracted from the original host using block DCT, and the embedding positions are constructed adaptively according to feature processing theory. Then, the watermark is embedded into the low-frequency coefficients by modifying the DC coefficients. For watermark extraction, according to the invariant image features in each region, the watermark location and the watermark information can be extracted without the original host. Experimental results show that the proposed watermarking is not only invisible and robust against common image processing, such as noise addition, image filtering, and JPEG compression, but also robust against cropping attack.</p>

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.

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