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Special Issue "Spatial Analysis and Remote Sensing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: closed (30 April 2018).

Special Issue Editor

Prof. Alexis Comber
E-Mail Website
Guest Editor
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Interests: spatial analysis; geocomputation; GIS; land cover; land use; spatial data analytics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will collate papers that describe the application of explicitly spatial analyses to remote sensing data, either in the creation of information from remotely sensed data, or in the downstream analysis of that information.

The analysis of spatial data is not in itself spatial analysis. Explicitly spatial analyses employ a range of tools and techniques that typically acknowledge and address spatial autocorrelation, spatial heterogeneity and spatial non-stationarity. They seek to uncover, describe and quantify spatial variations in data, relationships and processes. The spatial analysis mindset is one that does not expect processes to be the same everywhere (the world may not be uniformly, normally or randomly distributed) rather there is an expectation that processes, relationships vary in space. Thus, the call for papers reflects a move away from ‘whole map’ statistics [1] towards ones that reflect in Goodchild’s [2] proposal for a second law of geography, the principle of spatial heterogeneity or non-stationarity, in which he noted the lack of a “concept of an average place on the Earth's surface comparable, for example, to the concept of an average human”.

The Special Issues seeks submissions on any topic within “Spatial Analysis and Remote Sensing”. It is anticipated that most submissions will fall into one of two categories:

1. Research that applies Spatial Analysis techniques to data derived from remote sensing. Many of the outputs of remote sensing support spatial analyses, generating spatially distributed information that describe different processes and phenomena. They provide information to support spatial analysis and there are many examples of such research in the literature.

2. Research that applies Spatial Analysis techniques in the processing of remote sensing data. Less common are research describing the application of spatial analyses to process remote sensing data and generate spatial information. Some recent research has included explicitly spatial analyses—i.e., it has not applied some global measure—in order to process remote sensing data, with impressive results [3].

These are just suggested groupings and papers are welcome from any area of remote sensing where some spatial analyses have been employed.

Of course, you are welcome to contact me if you would like to discuss your idea or submission.  

[1] Openshaw S (1996). Developing GIS-relevant zone-based spatial analysis methods. In: Longley P and Batty M (eds). Spatial analysis: modelling in a GIS environment. New York: John Wiley and Sons, pp 55-73.

[2] Goodchild MF (2004). The Validity and Usefulness of Laws in Geographic Information Science and Geography. Annals of the Association of American Geographers, 94(2), 300-303.

[3] Comber A, Harris P and Tsutsumida N (2016). Improving land cover classification using input variables derived from a geographically weighted principal components analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 347–360.

Prof. Dr. Alexis Comber
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Remote sensing
  • Autocorrelation
  • Spatial heterogeneity
  • Spatial non-stationarity
  • Land Cover / Land Use
  • Ecosystem Service
  • Spatiotemporal analyses
  • Local statistical models
  • Error analysis
  • Accuracy analysis
  • Change analysis

Published Papers (7 papers)

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Research

Open AccessArticle
Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China
Sensors 2018, 18(8), 2558; https://doi.org/10.3390/s18082558 - 04 Aug 2018
Cited by 3
Abstract
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and [...] Read more.
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE
Sensors 2018, 18(7), 2082; https://doi.org/10.3390/s18072082 - 28 Jun 2018
Cited by 3
Abstract
Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies [...] Read more.
Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies of various sizes without much financial hassle. Landsat 7 and 8 images were used in this study to estimate area changes around Lake Chad. The Automated Water Extraction Index (AWEI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) were compared for the remote sensing retrieval process of surface water. Otsu threshold method was used to separate water from non-water features. With an overall accuracy of ~96% and an inter-rater agreement (kappa coefficient) of 0.91, the MNDWI was a better indicator for mapping recent area changes in Lake Chad and was used to estimate the lake’s area changes from 2003–2016. Extracted monthly areas showed an increasing trend and ranged between ~1242 km2 and 2231 km2 indicating high variability within the 13-year period, 2003–2016. In addition, we combined Landsat measurements with Total Water Storage Anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) satellites. This combination is well matched with our estimated surface area trends. This work not only demonstrates the importance of remote sensing in sparsely gauged developing countries, it also suggests the use of freely available high-quality imagery data to address existing lake crisis. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
Sensors 2018, 18(6), 1695; https://doi.org/10.3390/s18061695 - 24 May 2018
Cited by 4
Abstract
Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial [...] Read more.
Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery
Sensors 2018, 18(5), 1599; https://doi.org/10.3390/s18051599 - 17 May 2018
Cited by 4
Abstract
For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal [...] Read more.
For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor’s off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification
Sensors 2018, 18(2), 373; https://doi.org/10.3390/s18020373 - 27 Jan 2018
Cited by 6
Abstract
This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and [...] Read more.
This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images
Sensors 2017, 17(10), 2427; https://doi.org/10.3390/s17102427 - 24 Oct 2017
Cited by 13
Abstract
The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic [...] Read more.
The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessArticle
Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods
Sensors 2017, 17(10), 2357; https://doi.org/10.3390/s17102357 - 16 Oct 2017
Cited by 2
Abstract
Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, [...] Read more.
Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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