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Special Issue "Remote Sensing, Spatial Analysis, and GIS for Natural and Cultural Heritage Documentation, Monitoring, and Preservation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 30 December 2019

Special Issue Editors

Guest Editor
Dr. Rosa Lasaponara

Consiglio Nazionale delle Ricerche, Rome, Italy
Website | E-Mail
Phone: 0039 0971 427214
Interests: remote sensing, satellite time series analysis, risk monitoring, archaeology, fire
Guest Editor
Dr. Xinyuan Wang

Chinese Academy of Sciences, Key Laboratory of Digital Earth Science, Beijing, China
Website | E-Mail
Interests: remote sensing archaeology, digital cultural and natural heritages, physical geography
Guest Editor
Dr. Eufemia Tarantino

Politecnico di Bari, Bari, Italy
Website | E-Mail
Interests: geomatic techniques for resource monitoring and control, remote sensing and GIS integration, optical satellite image processing and analysis
Guest Editor
Dr. Douglas C. Comer

Cultural Site Research and Management, INC., Baltimore, United States
Website | E-Mail
Interests: landscape and marine environmental analysis, archaeological investigations

Special Issue Information

Dear Colleagues,

In light of recent developments of both sensors and data availability, remote sensing for spatial analysis and GIS applications have attracted increasing attention, becoming fundamental elements in investigations of Earth observation for a wide spectra of application fields, for example, environmental monitoring, urban planning, civil infrastructures, built environment water resource management, marine ecosystems, agriculture, cultural heritage, geo-hazards and disaster management, and security.

However, innovative algorithms and methodologies, as well as new data exploitation strategies, are needed to serve these applications and exploit, as much as possible, the ever-growing quantity of geospatial data today available. A significant computation challenge is how to convert these datasets into accurate, meaningful information. There are still some open challenges, from the extraction of relevant information to the integration of diverse data sources to the efficient storage, management, and analysis of spatial and non-spatial data, which calls for innovative modelling, concepts, and interpretation on any scale, from local to global.

This Special Issue will report the latest advances and trends in the field of remote sensing for spatial analysis and GIS applications addressing both original developments, new applications. and practical solutions to open questions. Topics for this Special Issue include, but are not limited to, the following:

  • Remote and distributed sensing for heritage site analysis
  • Spatial modelling and GIS applications for cultural resources monitoring and enhancement
  • Earth science and social science for cultural resource management
  • Remote sensing and geoinformatics for the conservation and promotion of cultural heritage
  • Earth big data for monitoring and mapping of archaeolandscapes
  • New tools and methods for multi-temporal analysis of landscapes
  • Fusion and integration of data and information from multiple sources
  • Data integration for geo-hazards risk mitigation and disaster management
  • Integration of RS with climate and metereological data and forecasting
  • Earth observation for the sustainable development goals

Dr. Rosa Lasaponara
Dr. Xinyuan Wang
Dr. Eufemia Tarantino
Dr. Douglas C. Comer
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 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. 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 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 for spatial analysis and GIS applications
  • big Earth data 
  • data exploitation strategies 
  • spatial and non-spatial data integration 
  • Earth observation for sustainable development goals

Published Papers (5 papers)

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Research

Open AccessArticle
Monitoring Land Cover Change and Disturbance of the Mount Wutai World Cultural Landscape Heritage Protected Area, Based on Remote Sensing Time-Series Images from 1987 to 2018
Remote Sens. 2019, 11(11), 1332; https://doi.org/10.3390/rs11111332
Received: 16 April 2019 / Revised: 16 May 2019 / Accepted: 29 May 2019 / Published: 3 June 2019
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Abstract
The contextual-based multi-source time-series remote sensing and proposed Comprehensive Heritage Area Threats Index (CHATI) index are used to analyze the spatiotemporal land use/land cover (LULC) and threats to the Mount Wutai World Heritage Area. The results show disturbances, such as forest coverage, vegetation [...] Read more.
The contextual-based multi-source time-series remote sensing and proposed Comprehensive Heritage Area Threats Index (CHATI) index are used to analyze the spatiotemporal land use/land cover (LULC) and threats to the Mount Wutai World Heritage Area. The results show disturbances, such as forest coverage, vegetation conditions, mining area, and built-up area, in the research area changed dramatically. According to the CHATI, although different disturbances have positive or negative influences on environment, as an integrated system it kept stable from 1987 to 2018. Finally, this research uses linear regression and the F-test to mark the remarkable spatial-temporal variation. In consequence, the threats on Mount Wutai be addressed from the macro level and the micro level. Although there still have some drawbacks, the effectiveness of threat identification has been tested using field validation and the results are a reliable tool to raise the public awareness of WHA protection and governance. Full article
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Open AccessArticle
3D-Modelling of Charlemagne’s Summit Canal (Southern Germany)—Merging Remote Sensing and Geoarchaeological Subsurface Data
Remote Sens. 2019, 11(9), 1111; https://doi.org/10.3390/rs11091111
Received: 11 March 2019 / Revised: 30 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
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Abstract
The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this [...] Read more.
The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD. Full article
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Open AccessArticle
The Development of a GIS Methodology to Identify Oxbows and Former Stream Meanders from LiDAR-Derived Digital Elevation Models
Remote Sens. 2019, 11(1), 12; https://doi.org/10.3390/rs11010012
Received: 10 November 2018 / Revised: 8 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract
Anthropogenic development of floodplains and alteration to natural hydrological regimes have resulted in extensive loss of off-channel habitat. Interest has grown in restoring these habitats as an effective conservation strategy for numerous aquatic species. This study developed a process to reproducibly identify areas [...] Read more.
Anthropogenic development of floodplains and alteration to natural hydrological regimes have resulted in extensive loss of off-channel habitat. Interest has grown in restoring these habitats as an effective conservation strategy for numerous aquatic species. This study developed a process to reproducibly identify areas of former stream meanders to assist future off-channel restoration site selections. Three watersheds in Iowa and Minnesota where off-channel restorations are currently being conducted to aid the conservation of the Topeka Shiner (Notropis topeka) were selected as the study area. Floodplain depressions were identified with LiDAR-derived digital elevation models, and their morphologic and topographic characteristics were described. Classification tree models were developed to distinguish relic streams and oxbows from other landscape features. All models demonstrated a strong ability to distinguish between target and non-target features with area under the receiver operator curve (AUC) values ≥ 0.82 and correct classification rates ≥ 0.88. Solidity, concavity, and mean height above channel metrics were among the first splits in all trees. To compensate for the noise associated with the final model designation, features were ranked by their conditional probability. The results of this study will provide conservation managers with an improved process to identify candidate restoration sites. Full article
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Open AccessArticle
Partitioning of Terrain Features Based on Roughness
Remote Sens. 2018, 10(12), 1985; https://doi.org/10.3390/rs10121985
Received: 23 September 2018 / Revised: 5 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
Cited by 1 | PDF Full-text (4808 KB) | HTML Full-text | XML Full-text
Abstract
Surface roughness is a key parameter that reflects topographic characteristics and influences surface processes, and characterization of surface roughness is a fundamental problem in geoscience. In recent years, although there have been basic studies on roughness, few studies have compared the concept and [...] Read more.
Surface roughness is a key parameter that reflects topographic characteristics and influences surface processes, and characterization of surface roughness is a fundamental problem in geoscience. In recent years, although there have been basic studies on roughness, few studies have compared the concept and quantification of roughness, and there have been few studies that have evaluated the ability of partition terrain features. Based on 1″ resolution Shuttle Radar Topography Mission (SRTM) data and previous studies, we selected the Qinba Mountain region of China and its adjacent areas as our study area, and used 13 different roughness algorithms to extract roughness in this study. Using spatial patterns and statistical distributions, the results were analyzed, and the best algorithm suited to partitioning terrain features was selected. We then evaluated the ability of the algorithm to distinguish the terrain morphology. The results showed the following: (1) The 13 algorithms were able to be classified into four types, that is, gradient (SLOPE), relief (root mean squared height, RMSH), local vector (directional cosine eigenvalue, DCE) and power-spectral (two-dimensional continuous wavelet transform, 2D CWT). (2) The SLOPE and RMSH algorithms were better able to express and distinguish terrain, as they were able to macroscopically distinguish between four types of terrain in the study areas. Based on power-spectral methods, 2D CWT had the same discrimination ability as the first two methods following a normalization transform, whereas the DCE method had a general effect and could only distinguish two types of terrain. (3) Different roughness algorithms had their own applicability for different terrain areas and application directions. Full article
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Open AccessArticle
Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)
Remote Sens. 2018, 10(11), 1762; https://doi.org/10.3390/rs10111762
Received: 2 September 2018 / Revised: 26 October 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
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Abstract
Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores [...] Read more.
Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper. Full article
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