scholarly journals Method for Environmental Flows Regulation and Early Warning with Remote Sensing and Land Cover Data

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1216
Author(s):  
Yuming Lu ◽  
Bingfang Wu ◽  
Nana Yan ◽  
Weiwei Zhu ◽  
Hongwei Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Water Consumption ◽  
Early Warning ◽  
Water Resource Management ◽  
Environmental Flows ◽  
Early Warning Systems ◽  
Land Cover Data ◽  
The Mean ◽  
Arid And Semiarid

Environmental flows play a vital role in ecosystem and water resource management. The regulation and management of environmental flows can improve the function and stability of river and lake ecosystems. However, current methods for assessing environmental flows mainly emphasize water management, and there is no complete set of regulations or early warning systems, especially in arid and semiarid basins. In this study, we proposed a method for environmental flows regulation and early warning with remote sensing and land cover data and carried out a case study in the Yongding River Basin, which is a basin typical of arid and semiarid areas. The results show that from 2001 to 2014 the mean precipitation was 17.90 × 109 m3, and the mean water consumption was 19.42 × 109 m3, indicating that the basin water budget was clearly unbalanced and that there was an overall deficiency. Notably, from 2005 to 2014 and in 2014, the available consumable water was less than the water consumption required for human activities, which both showed a trend of further reduction; therefore, long-term and annual early warnings should have been issued. The methods applied in this study and the study outcomes could help in the development of comprehensive management and ecological restoration plans, further improving the ecological environments of river basins.

Dengue early warning systems using environmental remote sensing data

2020 ◽  
Author(s):  
Zahidur Rahman ◽  
Leonid Roytman ◽  
Abdelhamid Kadik ◽  
Dilara A. Rosy ◽  
Pradipta Nandi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Remote Sensing Data ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Sensing Data ◽  
Environmental Remote Sensing

scholarly journals A Remote Sensing–based Intensity-Duration Curve, Faifa Mountains, Saudi Arabia

2018 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Saleh A. Al-Sefry ◽  
Hassan M. Alharbi ◽  
Mustafa Kemal Emil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Early Warning ◽  
Field Data ◽  
Early Warning Systems ◽  
Google Earth ◽  
Archival Data ◽  
Warning Systems ◽  
Temporal And Spatial ◽  
Red Sea Hills

Abstract. Construction of intensity-duration (ID) curves and early warning systems for landslides (EWSL) are hampered by the paucity of temporal and spatial archival data. We developed methodologies that could be used for the construction of an ID curve that could be used for the construction of an EWSL over the Faifa Mountains in the Red Sea Hills. The developed methodologies relies on temporal, readily available, archival Google Earth and Sentinel-1 imagery, precipitation measurements, and limited field data. These methodologies accurately distinguished landslide-producing storms from non–landslide producing ones and identified the locations of these landslides with an accuracy of 60 %.

scholarly journals A Regression-Based Prediction Model of Suspended Sediment Yield in the Cuyahoga River in Ohio Using Historical Satellite Images and Precipitation Data

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 881 ◽  
Author(s):  
Richard Ampomah ◽  
Hossein Hosseiny ◽  
Lan Zhang ◽  
Virginia Smith ◽  
Kristin Sample-Lord
Keyword(s):  
Land Cover ◽  
Suspended Sediment ◽  
Sediment Yield ◽  
Flood Management ◽  
Mean Annual Precipitation ◽  
Ecological Processes ◽  
Yield Data ◽  
Suspended Sediment Yield ◽  
Land Cover Data ◽  
The Mean

Urbanization typically results in increased imperviousness which alters suspended sediment yield and impacts geomorphic and ecological processes within urban streams. Therefore, there is an increasing interest in the ability to predict suspended sediment yield. This study assesses the combined impact of urban development and increased precipitation on suspended sediment yield in the Cuyahoga River using statistical modeling. Historical satellite-based land-cover data was combined with precipitation and suspended sediment yield data to create a Multiple Linear Regression (MLR) model for the Cuyahoga watershed. An R2 value of 0.71 was obtained for the comparison between the observed and predicted results based on limited land-use and land-cover data. The model also shows that every 1 mm increase in the mean annual precipitation has the potential to increase the mean annual suspended sediment yield by 860 tons/day. Further, a 1 km2 increase in developed land area has the potential to increase mean annual suspended sediment yield by 0.9 tons/day. The framework proposed in this study provides decision makers with a measure for assessing the potential impacts of future development and climate alteration on water quality in the watershed and implications for stream stability, dam and flood management, and in-stream and near-stream infrastructure life.

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 817-820
Author(s):  
B. Liu ◽  
J. Chen ◽  
H. Xing ◽  
H. Wu ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Image Data ◽  
Resolution Time ◽  
Simulated Experiment ◽  
Land Cover Data ◽  
Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

scholarly journals Brief communication: The role of geophysical imaging in local landslide early warning systems

2021 ◽  
Author(s):  
Jim Scott Whiteley ◽  
Arnaud Watlet ◽  
Jonathan Michael Kendall ◽  
Jonathan Edward Chambers
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Slope Failure ◽  
Time Lapse ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Geophysical Monitoring ◽  
Landslide Early Warning ◽  
Geophysical Imaging

Abstract. We summarise the contribution of geophysical imaging to local landslide early warning systems (LoLEWS), highlighting how LoLEWS design and monitoring components benefit from the enhanced spatial and temporal resolutions of time-lapse geophysical imaging. In addition, we discuss how with appropriate laboratory-based petrophysical transforms, these geophysical data can be crucial for future slope failure forecasting and modelling, linking other methods of remote sensing and intrusive monitoring across different scales. We conclude that in light of ever increasing spatiotemporal resolutions of data acquisition, geophysical monitoring should be a more widely considered technology in the toolbox of methods available to stakeholders operating LoLEWS.

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