scholarly journals Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2802
Author(s):  
Soraya Kaiser ◽  
Guido Grosse ◽  
Julia Boike ◽  
Moritz Langer
Keyword(s):  
High Resolution ◽  
Shoreline Change ◽  
Weather Conditions ◽  
Water Bodies ◽  
Additional Information ◽  
High Resolution Imagery ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery ◽  
Image Metadata ◽  
Very High

Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40–0.56 m.yr−1 for lake sizes of 0.10 ha–23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.

Photogrammetric processing and application of multi-temporal satellite imagery stereopairs

2021 ◽  
Vol 974 (8) ◽  
pp. 36-44
Author(s):  
R.V. Permyakov
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
3D Models ◽  
High Resolution Imagery ◽  
Multi Temporal ◽  
Unfavourable Weather ◽  
High Resolution Satellite Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

scholarly journals Adaptive digital technique for discriminating between shadow and water bodies in the high resolution satellite imagery

2019 ◽  
Vol 14 (31) ◽  
pp. 81-88
Author(s):  
Anaam Kadhim Hadi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Google Earth ◽  
Experimental Results ◽  
Water Bodies ◽  
Digital Technique ◽  
Matlab Program ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery

This research presents a new algorithm for classification theshadow and water bodies for high-resolution satellite images (4-meter) of Baghdad city, have been modulated the equations of thecolor space components C1-C2-C3. Have been using the color spacecomponent C3 (blue) for discriminating the shadow, and has beenused C1 (red) to detect the water bodies (river). The new techniquewas successfully tested on many images of the Google earth andIkonos. Experimental results show that this algorithm effective todetect all the types of the shadows with color, and also detects thewater bodies in another color. The benefit of this new technique todiscriminate between the shadows and water in fast Matlab program.

scholarly journals Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2508
Author(s):  
Loredana Oreti ◽  
Diego Giuliarelli ◽  
Antonio Tomao ◽  
Anna Barbati
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Parametric Method ◽  
Mixed Forest ◽  
Mixed Stands ◽  
Mapping Unit ◽  
Mixed Forests ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High
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