scholarly journals Features of Visual Decoding of Water Erosion by Remote Sensing Data

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Water Erosion ◽  
Object Identification ◽  
Aerial Photographs ◽  
Linear Forms ◽  
Sensing Data ◽  
Erosion Processes ◽  
Sheet Erosion

Purpose: analysis of the features of visual decoding of eroded soils and erosion processes according to remote sensing data. Methods. Remote sensing, field, comparative geographical, historical, cartographic, GIS analysis. Results. The main attention in the article is paid to the features of visual decoding of linear forms of erosion. Comparative analysis of aerial photographs of 1943 and modern satellite imagery for the Kharkov region shown that in the second half of the 20th century the growth of gullies was almost stopped due to large-scale anti-erosion measures carried out at that time. Currently the main erosion losses occur in sheet erosion and small gully erosion. The article provides a list of decoding features that determine linear forms of erosion in the images. It is shown problems that can arise during automatic decoding. As an example of artifact formations requiring the participation of a human analyst in the decryption process, the so-called "Turkish Wall" is shown, the traces of which can be erroneously diagnosed as a manifestation of linear erosion Conclusions. Automatic decoding of water erosion processes and an inventory of erosion landforms requires the obligatory monitoring of a qualified analyst to eliminate object identification errors.

scholarly journals Assessment of soil erosion hazard and its mapping using GIS technologies

2021 ◽  
pp. 18-31
Author(s):  
Hleb S. Lazovik ◽  
Antonina A. Topaz
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing Data ◽  
Water Erosion ◽  
Integrated Map ◽  
Sensing Data ◽  
Erosion Hazard ◽  
Gis Technologies ◽  
Erosion Processes ◽  
Integral Indicator

The article presents a method for creating a territory erosion hazard integrated map using RUSLE integral model, Earth remote sensing data and GIS technologies. The studies carried out on this topic are presented, the analysis of which has shown a more active use of integral indicators of water-erosion processes in foreign scientific works. Urgency of updating methodology for studying erosion processes has been substantiated. Theoretical foundations of the application of integral models of soil erosion are given, the application of the RUSLE model is substantiated, and the optimal way of using this model is proposed. The research methodology has been developed, consisting of primary processing of remote sensing data, calculation of the factors of erosion development and creation of a territory erosion hazard integrated map. Based on the processing of aerial photography materials, a point cloud, a digital elevation model and an orthomosaic map of the study area were created. The results of the geoinformation analysis of the remote sensing data, which included calculation of the soil erodibility factor and the topographic factor, are presented. Based on the integral indicator of watererosion hazard, a complex map of the erosion hazard of the territory has been created. Main patterns of geographical distribution of the values of the integral indicator of the water-erosion hazard of the territory are revealed, devised methodology is assessed. It was found that the schematic map reflects the general pattern of water erosion processes: they are more active in places where more dissected relief is spread. Influence of the soil factor on the pattern of the schematic map is shown: the pattern in the territories occupied by sod-podzolic loamy soils qualitatively differs from the pattern on the lands where sod-podzolic sandy loam soils are widespread. Patterns on the schematic map of different parts of the developed linear forms of relief, formed by temporary streams, are described. It is shown that the proposed method can be used to assess the water-erosion hazard of the territory. The need to take into account a larger number of factors and to refine the assessment of existing ones is concluded.

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data

scholarly journals Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

2020 ◽  
Vol 12 (7) ◽  
pp. 2854 ◽  
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Human Interaction ◽  
Aerial Photographs ◽  
Sensing Data ◽  
Weather Patterns ◽  
Excess Water ◽  
Frequent Monitoring

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

scholarly journals Methods of calculating landslide volume using remote sensing data

2020 ◽  
Vol 149 ◽  
pp. 02009
Author(s):  
Maira Razakova ◽  
Alexandr Kuzmin ◽  
Igor Fedorov ◽  
Rustam Yergaliev ◽  
Zharas Ainakulov
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Remote Sensing Data ◽  
Field Studies ◽  
Aerial Photographs ◽  
Field Observations ◽  
Sensing Data ◽  
Quantitative Estimates ◽  
Landslide Volume ◽  
Engineering Protection

The paper considers the issues of calculating the volume of the landslide from remote sensing data. The main methods of obtaining information during research are field observations. The most important results of field studies are quantitative estimates, such as the volume of the embankment resulting from a landslide, morphometric indicators, etc. The study of a remote and remote object was carried out by remote methods using aerial photographs in the Ile Alatau foothills at 1,600 meters above sea level. The obtained materials from the mudflow survey will be useful in developing solutions to mitigate the effects of disasters and in the design of measures for engineering protection from landslides.

scholarly journals Quantifying large‐scale ecosystem stability with remote sensing data

2020 ◽  
Vol 6 (3) ◽  
pp. 354-365
Author(s):  
Hannah J. White ◽  
Willson Gaul ◽  
Dinara Sadykova ◽  
Lupe León‐Sánchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Ecosystem Stability ◽  
Sensing Data

Time-series processing of large scale remote sensing data with extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 199-206 ◽  
Author(s):  
Jiaoyan Chen ◽  
Guozhou Zheng ◽  
Cong Fang ◽  
Ningyu Zhang ◽  
Huajun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Machine

scholarly journals Remote sensing and the future of landscape ecology

2009 ◽  
Vol 33 (4) ◽  
pp. 528-546 ◽  
Author(s):  
Adrian C. Newton ◽  
Ross A. Hill ◽  
Cristian Echeverría ◽  
Duncan Golicher ◽  
José M. Rey Benayas ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landscape Ecology ◽  
Remote Sensing Data ◽  
Scientific Discipline ◽  
Aerial Photographs ◽  
Ecological Data ◽  
Ecological Processes ◽  
Sensing Data ◽  
Landsat Satellite ◽  
The Uk

Landscape ecology focuses on the analysis of spatial pattern and its relationship to ecological processes. As a scientific discipline, landscape ecology has grown rapidly in recent years, supported by developments in GIS and spatial analysis techniques. Although remote sensing data are widely employed in landscape ecology research, their current and potential roles have not been evaluated critically. To provide an overview of current practice, 438 research papers published in the journal Landscape Ecology for the years 2004—2008 were examined for information about use of remote sensing. Results indicated that only 36% of studies explicitly mentioned remote sensing. Of those that did so, aerial photographs and Landsat satellite sensor images were most commonly used, accounting for 46% and 42% of studies, respectively. The predominant application of remote sensing data across these studies was for thematic mapping purposes. This suggests that landscape ecologists have been relatively slow to recognize the potential value of recent developments in remote sensing technologies and methods. The review also provided evidence of a frequent lack of key detail in studies recently published in Landscape Ecology , with 75% failing to provide any assessment of uncertainty or error relating to image classification and mapping. It is suggested that the role of remote sensing in landscape ecology might be strengthened by closer collaboration between researchers in the two disciplines, by greater integration of diverse remote sensing data with ecological data, and by increased recognition of the value of remote sensing beyond land-cover mapping and pattern description. This is illustrated by case studies drawn from Latin America (focusing on forest loss and fragmentation) and the UK (focusing on habitat quality for woodland birds). Such approaches might improve the analytical and theoretical rigour of landscape ecology, and be applied usefully to issues of outstanding societal interest, such as the impacts of environmental change on biodiversity and ecosystem services.

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