scholarly journals Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

2019 ◽  
Vol 11 (6) ◽  
pp. 699
Author(s):  
Remzi Eker ◽  
Yves Bühler ◽  
Sebastian Schlögl ◽  
Andreas Stoffel ◽  
Abdurrahim Aydın

This study tested the potential of a short time series of very high spatial resolution (cm to dm) remote sensing datasets obtained from unmanned aerial system (UAS)-based photogrammetry and terrestrial laser scanning (TLS) to monitor snow cover ablation in the upper Dischma valley (Davos, Switzerland). Five flight missions (for UAS) and five scans (for TLS) were carried out simultaneously: Four during the snow-covered period (9, 10, 11, and 27 May 2016) and one during the snow-free period (24 June 2016 for UAS and 31 May 2016 for TLS). The changes in both the areal extent of the snow cover and the snow depth (HS) were assessed together in the same case study. The areal extent of the snow cover was estimated from both UAS- and TLS-based orthophotos by classifying pixels as snow-covered and snow-free based on a threshold value applied to the blue band information of the orthophotos. Also, the usage possibility of TLS-based orthophotos for mapping snow cover was investigated in this study. The UAS-based orthophotos provided higher overall classification accuracy (97%) than the TLS-based orthophotos (86%) and allowed for mapping snow cover in larger areas than the ones from TLS scans by preventing the occurrence of gaps in the orthophotos. The UAS-based HS were evaluated and compared to TLS-based HS. Initially, the CANUPO (CAractérisation de NUages de POints) binary classification method, a proposed approach for improving the quality of models to obtain more accurate HS values, was applied to the TLS 3D raw point clouds. In this study, the use of additional artificial ground control points (GCPs) was also proposed to improve the quality of UAS-based digital elevation models (DEMs). The UAS-based HS values were mapped with an error of around 0.1 m during the time series. Most pixels representing change in the HS derived from the UAS data were consistent with the TLS data. The time series used in this study allowed for testing of the significance of the data acquisition interval in the monitoring of snow ablation. Accordingly, this study concluded that both the UAS- and TLS-based high-resolution DSMs were biased in detecting change in HS, particularly for short time spans, such as a few days, where only a few centimeters in HS change occur. On the other hand, UAS proved to be a valuable tool for monitoring snow ablation if longer time intervals are chosen.

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.


Author(s):  
Linmei Wu ◽  
Li Shen ◽  
Zhipeng Li

A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as <i>K</i>&thinsp;=&thinsp;<i>u<sub>1</sub></i><i>K</i><sup>spec</sup>&thinsp;+&thinsp;<i>u<sub>2</sub></i><i>K</i><sup>spat</sup>&thinsp;+&thinsp;<i>u<sub>3</sub></i><i>K</i><sup>stru</sup>, in which <i>K</i><sup>spec</sup>, <i>K</i><sup>spat</sup>, <i>K</i><sup>stru</sup> are radial basis function (RBF) and <i>u<sub>1</sub></i>&thinsp;+&thinsp;<i>u<sub>2</sub></i>&thinsp;+&thinsp;<i>u<sub>3</sub></i>&thinsp;=&thinsp;1. In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900&thinsp;×&thinsp;900 pixels and spatial resolution of 0.6&thinsp;m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80&thinsp;%, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67&thinsp;% and 74&thinsp;%. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83&thinsp;%. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.


2013 ◽  
Vol 5 (10) ◽  
pp. 5064-5088 ◽  
Author(s):  
Roberto Chávez ◽  
Jan Clevers ◽  
Martin Herold ◽  
Edmundo Acevedo ◽  
Mauricio Ortiz

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