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Special Issue "Geospatial Statistics and Spatiotemporal Analysis Based on Remote Sensing Imagery"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 6050

Special Issue Editors

Dr. Lluís Pesquer Mayos
E-Mail Website
Guest Editor
Grumets Research Group, CREAF, Edifici C, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
Interests: spatial analysis; geostatistics; remote sensing processing methods; GIS; interoperability; high-performance computing
Dr. Mariana Belgiu
E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Interests: satellite image time series analysis; machine learning; semantics in remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scientific community has become increasingly interested in using earth observation system (EOS) satellites. Remotely sensed data is also increasingly more accessible across multiple scientific domains. The Earth's surface and the corresponding variables captured by remotely sensed images have distinct spatial properties. Geospatial statistics provide methods for quantification and analysis of these spatial properties and their spatial dependencies. A long historical archive of remote sensing data is within the reach of scientists providing huge temporal datasets for monitoring, estimating, modeling, and understanding the dynamics of many of the Earth's surface phenomena. With the aim of increasing the knowledge of spatiotemporal properties and methodologies in remote sensing disciplines, the list of potential topics below is indicative of the research themes in which manuscripts are solicited:

  • Methods of scaling geospatial remote sensing data
  • Methods for coherent multisensor time series of remote sensing data
  • Uncertainty spatialization of remote sensing data
  • Analysis of geospatial properties: anisotropy, heterogeneity, fragmentation, autocorrelation, etc. of large remote sensing time series
  • Innovative analysis of cycle and phenology spatiotemporal patterns of remote sensing time series
  • Changes on autocorrelation patterns of large time series
  • Remote sensing imagery time series harmonization in geostatistical analysis
  • Statistical and spatial quality indicators for remote sensing imagery
  • Products composite (i.e., vegetation indexes) and multitemporal data fusion methods with preserving geospatial properties.
  • Geostatistical methodologies for filling time/spatial gaps or artifacts in remote sensing imagery
  • New approaches for spatial, statistical and spatiotemporal resolution issues on remote sensing imagery
  • Optimal sampling of in-situ measurements for calibration or validation of remote sensing variables

Dr. Lluís Pesquer Mayos
Dr. Mariana Belgiu
Guest Editors

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 submissions that pass pre-check are 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. Remote Sensing 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 2500 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

  • Spatiotemporal patterns 
  • Geostatistical remote sensing 
  • Spatialized uncertainty 
  • Multisource data fusion 
  • Time series coherence 
  • Optimal sampling 
  • Analysis of geospatial properties

Published Papers (3 papers)

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Research

Article
Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine
Remote Sens. 2021, 13(8), 1433; https://doi.org/10.3390/rs13081433 - 08 Apr 2021
Cited by 6 | Viewed by 1852
Abstract
Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect [...] Read more.
Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples. Full article
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Article
Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series
Remote Sens. 2021, 13(5), 974; https://doi.org/10.3390/rs13050974 - 04 Mar 2021
Cited by 5 | Viewed by 2422
Abstract
The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability [...] Read more.
The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types. Full article
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Article
Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning
Remote Sens. 2021, 13(5), 857; https://doi.org/10.3390/rs13050857 - 25 Feb 2021
Cited by 1 | Viewed by 1054
Abstract
We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × [...] Read more.
We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature. Full article
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