Spatio-Temporal Estimation of Surface Water Area in Dohuk Governorate Using Remote Sensing & GIS

2018 ◽  
Author(s):  
Jian Hassanpour ◽  
Yaseen T. Mustafa ◽  
Hendaf N. Habeeb
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Area ◽  
Temporal Estimation ◽  
Spatio Temporal

scholarly journals Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

2020 ◽  
Vol 12 (17) ◽  
pp. 2675
Author(s):  
Qianqian Han ◽  
Zhenguo Niu
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Temporal Parameter ◽  
Modis Ndvi ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Spatio Temporal ◽  
Global Surface ◽  
Global Water

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

scholarly journals STUDY OF THE TEMPERATURE CONDITION OF SURFACE WATERS OF NOVOTROITSKY RESERVOIR BASED ON REMOTE SENSING DATA AND EVALUATION OF ITS IMPACT ON FISH GROWTH AND DEVELOPMENT

2018 ◽  
Vol 13 (3) ◽  
pp. 150-158
Author(s):  
Evgeniy G. Mishvelov ◽  
Ivan A. Bakumenko ◽  
Anatoliy F. Shevkhuzhev ◽  
Vladimir A. Pogodaev
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Waters ◽  
Growth And Development ◽  
Water Surface ◽  
Water Reservoir ◽  
Remote Sensing Data ◽  
Water Area ◽  
Temperature Fields ◽  
Summer Period

Aim. The aim of the study is to analyze and reveal the features of the temperature regime of the Novotroitsky water reservoir surface based on remote sensing data; to determine the influence of the temperature regime of the surface waters of Novotroitsky reservoir on the ecological state of the reservoir, as well as to determine the areas of the surface water zones of the Novotroitsky reservoir with optimal and unfavorable temperature conditions for fish. Methods. The temperature values of the earth's surface (water area) were calculated according to the generally accepted methodology. Its essence lies in the fact that the calculation of the earth's surface temperature is performed after radiometric calibration of the images and compensation of the effect of the optical density of the atmosphere taking into account the emissivity of various objects on the earth's surface. The calculations were performed separately for the 10 and 11 channels of images from the Landsat 8 satellite, and then averaged. Results. Were established the quantitative characteristics of the inhomogeneity of the temperature fields of the water surface of Novotroitsky reservoir during the summer period due to discharges of the heated waters of the Stavropol GRES power plant. The peculiarities of the spatial variability of the temperature fields of the Novotroitsky water reservoir surface in summer season were revealed. It is shown that the use of the Novotroitsky water reservoir as a reservoir-cooler is potentially accompanied by the development of eutrophication processes and creation of risks for drinking purposes, as well as cultural, household and fishery use. The table shows the data demonstrating the temperature condition of the Novotroitsky reservoir water surface. The figure shows the temperature fields of the surface of the Novotroitsky water reservoir. Conclusions. In summer period, half of the water area of the Novotroitsky water reservoir can be attributed to the zones of optimum temperatures for the juvenile pikeperch. Were revealed the periods when the reservoir becomes practically unsuitable for growth and development of juveniles of oxyphilic fish. Excessive rise of water temperature in summer was established in accordance with SanPiN (Sanitary Rules and Regulations) 2.1.5.980-00.2.1.5. Such an increase in temperature is observed in 13-16% of the whole water area.

scholarly journals Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

2018 ◽  
Vol 10 (2) ◽  
pp. 252 ◽  
Author(s):  
Frederick Policelli ◽  
Alfred Hubbard ◽  
Hahn Jung ◽  
Ben Zaitchik ◽  
Charles Ichoku
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Water Area ◽  
Radar Remote Sensing ◽  
Lake Chad ◽  
Sensing Data

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

scholarly journals Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

2021 ◽  
Author(s):  
Al-Ekram Elahee Hridoy ◽  
Imrul Hasan Tipo ◽  
Md. Shamsudduha Sami ◽  
Md. Ripon Babu ◽  
Md. Sayem Ahmed ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Temporal Estimation ◽  
Spatio Temporal

scholarly journals Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 138
Author(s):  
Zijie Jiang ◽  
Weiguo Jiang ◽  
Ziyan Ling ◽  
Xiaoya Wang ◽  
Kaifeng Peng ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
Essential Element ◽  
Water Area ◽  
Google Earth ◽  
Water Extraction ◽  
Natural Ecosystem ◽  
The Sustainable Development ◽  
Baiyangdian Lake ◽  
Google Earth Engine

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

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