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Geographic Data Analysis and Modeling in Remote Sensing

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 43812

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Special Issue Editors

Dr. Joanne N. Halls

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Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S College Rd., Wilmington, NC 28403, USA
Interests: geographic information science; spatial statistics; coastal ecosystems; applications of remote sensing and UAS; population dynamics; ecosystem health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chuanrong Zhang

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Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographical information science and systems; cyberinfrastructure; land use and land cover; spatial data analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Weidong Li

E-Mail Website
Guest Editor

Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Road, U-4148, Storrs, CT 06269, USA
Interests: geostatistics; geographical information science; environmental informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic data analysis and modeling are becoming more important than ever to extract meaningful information or specific objects of interest from all types of remote sensing imagery. Geographic data analysis and modeling tools enable users to extract meaningful information, compute spatial metrics and statistics, or identify objects such as buildings, roads, coastlines, lakes, rivers, trees, power lines, and other features. Geographic data analysis and modeling techniques also assist with image preparation, database integration and data fusion.

The aim of this Special Issue is to assemble papers that explore spatial data analysis and modeling methods and approaches for remote sensing imagery processing, classification, analysis and inference across multiple disciplines, including, but not limited to, atmospheric and environmental sciences, ecology, earth sciences, health, energy, agriculture, hydrology, population, and socio-economic studies. The topics covered by this Special Issue include, but are not limited to:

The latest developments of image classification methods
Machine learning or deep learning for spatial data analysis
Development of new spatial analysis algorithms for deriving information from imagery such as spatial patterns and landscape modelling
Application of geographic data analysis and modeling for change detection including spatial statistical significance of change and methods of measuring spatial and attribute accuracy
New tools and approaches for geographic data acquisition, integration or fusion such as innovative cyberinfrastructure, data mining, machine learning/deep learning techniques for data acquisition and fusion from multi-source remote sensing
Methods for deriving spatial objects/features including assessing object accuracy

This Special Issue seeks submissions on any topic that 1) applies geographic data analysis and modeling to data derived from remote sensing or 2) applies geographic data analysis and modeling in the processing of remote sensing imagery.

You may choose our Joint Special Issue in Geomatics.

Prof. Dr. Joanne Halls
Prof. Dr. Chuanrong Zhang
Prof. Dr. Weidong Li
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 2700 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
Imagery classification
Spatial modelling
Spatial analysis
Spatial statistics
Spatial data processing
GIS
Spatial patterns extraction/modelling

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Published Papers (13 papers)

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22 pages, 18976 KiB

Open AccessArticle

Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China

by Kun Qin, Zhanpeng Wang, Shaoqing Dai, Yuchen Li, Manyao Li, Chen Li, Ge Qiu, Yuanyuan Shi, Chun Yin, Shujuan Yang and Peng Jia

Remote Sens. 2024, 16(7), 1298; https://doi.org/10.3390/rs16071298 - 7 Apr 2024

Cited by 1 | Viewed by 1930

Abstract

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R², demonstrated high accuracy with values of 0.92 for PM_2.5, 0.95 for PM₁₀, 0.95 for O₃, 0.90 for NO₂, 0.79 for SO₂, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM_2.5, although PM₁₀ exhibited a rebound in northern regions. The concentrations of SO₂ and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O₃ concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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22 pages, 18845 KiB

Open AccessArticle

Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China

by Weiwei Zhang, Zixi Liu, Kun Qin, Shaoqing Dai, Huiyuan Lu, Miao Lu, Jianwan Ji, Zhaohui Yang, Chao Chen and Peng Jia

Remote Sens. 2024, 16(6), 1028; https://doi.org/10.3390/rs16061028 - 14 Mar 2024

Cited by 3 | Viewed by 1564

Abstract

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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17 pages, 4357 KiB

Open AccessArticle

Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery

by Alan J. Stern, Craig S. T. Daughtry, E. Raymond Hunt, Jr. and Feng Gao

Remote Sens. 2023, 15(18), 4596; https://doi.org/10.3390/rs15184596 - 19 Sep 2023

Cited by 1 | Viewed by 1642

Abstract

Determining residue cover on agricultural land is an important task. Residue cover helps reduce soil erosion and helps sequester carbon. Many studies have used either spectral indices or classification techniques to map residue cover using satellite imagery. Unfortunately, most of these studies use only a few spectral indices or classification techniques and generally only study an area for a single year with a certain level of success. This manuscript presents an investigation of five spectral indices and six classification techniques over four years to determine if a single spectral index or classification technique performs consistently better than the others. A second objective is to determine whether using the coefficient of determination (R²) from the relationship between residue cover and a spectral index is a reasonable substitute for calculating accuracy. Field visits were conducted for each of the years studied and used to create the correlations with the spectral indices and as ground truth for the classification techniques. It was found that no spectral index/classification technique is consistently better than all the others. Classification techniques tended to be more accurate in 2011 and 2013, while spectral indices tended to be more accurate in 2015 and 2018. The combination of spectral indices/classification techniques outperformed the individual approach. For the second objective, it was found that R² is not a great indicator of accuracy. Root mean square error (RMSE) is a better indicator of accuracy than R². However, simply calculating the accuracy would be the best of all. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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20 pages, 6538 KiB

Open AccessArticle

A General Paradigm for Retrieving Soil Moisture and Surface Temperature from Passive Microwave Remote Sensing Data Based on Artificial Intelligence

by Kebiao Mao, Han Wang, Jiancheng Shi, Essam Heggy, Shengli Wu, Sayed M. Bateni and Guoming Du

Remote Sens. 2023, 15(7), 1793; https://doi.org/10.3390/rs15071793 - 27 Mar 2023

Cited by 11 | Viewed by 3248

Abstract

Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrieval accuracy needs to be improved. In this study, a novel fully-coupled paradigm is developed to robustly retrieve SM and LST from passive microwave data, which integrates deep learning, physical methods, and statistical methods. The key condition of the general paradigm proposed by us is that the output parameters of deep learning can be uniquely determined by the input parameters theoretically through a certain mathematical equation. Firstly, the physical method is deduced based on the energy radiation balance equation. The nine unknowns require the brightness temperatures of nine channels to construct nine equations, and the solutions of the physical method equations are obtained by model simulation. Based on the derivation of the physical method, the solution of the statistical method is constructed using multi-source data. Secondly, the solutions of physical and statistical methods constitute the training and test data of deep learning, which is used to obtain the solution curve of physical and statistical methods. The retrieval accuracy of LST and SM is greatly improved by smartly utilizing the mutual prior knowledge of SM and LST and cross iterative optimization calculations. Finally, validation indicates that the mean absolute error of the retrieved SM and LST data are 0.027 m³/m³ and 1.38 K, respectively, at an incidence angle of 0–65°. A model-data-knowledge-driven and deep learning method can overcome the shortcomings of traditional methods and provide a paradigm for retrieval of other geophysical variables. The proposed paradigm not only has physical meaning, but also makes deep learning physically interpretable, which is a milestone in the retrieval of geophysical remote sensing parameters based on artificial intelligence technology. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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16 pages, 10805 KiB

Open AccessArticle

Persistent Scatterer Interferometry (PSI) Technique for the Identification and Monitoring of Critical Landslide Areas in a Regional and Mountainous Road Network

by Constantinos Nefros, Stavroula Alatza, Constantinos Loupasakis and Charalampos Kontoes

Remote Sens. 2023, 15(6), 1550; https://doi.org/10.3390/rs15061550 - 12 Mar 2023

Cited by 13 | Viewed by 2981

Abstract

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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22 pages, 6990 KiB

Open AccessArticle

Analysis of Post-Mining Vegetation Development Using Remote Sensing and Spatial Regression Approach: A Case Study of Former Babina Mine (Western Poland)

by Anna Buczyńska, Jan Blachowski and Natalia Bugajska-Jędraszek

Remote Sens. 2023, 15(3), 719; https://doi.org/10.3390/rs15030719 - 26 Jan 2023

Cited by 15 | Viewed by 2728

Abstract

The vegetation of the post-mining areas is subject to constant and significant changes. Reclamation works, carried out after the cessation of mineral extraction, contribute to the intensive development of new plant species. However, secondary deformations, occurring even many years after the end of exploitation, may cause the degradation of the vegetation cover. It is, therefore, an important issue to identify changes in flora conditions and to determine whether and to what extent past mining has a negative impact on the plant cover state. The objectives of this research have been as follows: (1) analysis of the flora condition in the post-mining area in the 1989–2019 period, (2) identification of sites with significant changes in vegetation state, and (3) modeling of the relationship between the identified changes in vegetation and former mining activities. The research was carried out in the area of the former opencast and underground lignite mine “Friendship of Nations—Babina Shaft,” which is located in the present-day Geopark (Western Poland), using Landsat TM/ETM+/OLI derived vegetation indices (NDVI, NDII, MTVI2) and GIS-based spatial regression. The results indicate a general improvement in flora condition, especially in the vicinity of post-mining waste heaps and former opencast excavations, with the exception of the northwestern part of the former mining field where the values of all of the analyzed vegetation indices have decreased. Also, four zones of statistically significant changes in the flora condition were identified. Finally, the developed GWR models demonstrate that former mining activities had a significant influence on changes in the plant cover state of the analyzed region. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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16 pages, 5581 KiB

Open AccessArticle

First Implementation of a Normalized Hotspot Index on Himawari-8 and GOES-R Data for the Active Volcanoes Monitoring: Results and Future Developments

by Alfredo Falconieri, Nicola Genzano, Giuseppe Mazzeo, Nicola Pergola and Francesco Marchese

Remote Sens. 2022, 14(21), 5481; https://doi.org/10.3390/rs14215481 - 31 Oct 2022

Cited by 3 | Viewed by 2202

Abstract

The Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI), respectively aboard Himawari-8 and GOES-R geostationary satellites, are two important instruments for the near-real time monitoring of active volcanoes in the Eastern Asia/Western Pacific region and the Pacific Ring of Fire. In this work, we use for the first time AHI and ABI data, at 10 min temporal resolution, to assess the behavior of a Normalized Hotspot Index (NHI) in presence of active lava flows/lakes, at Krakatau (Indonesia), Ambrym (Vanuatu) and Kilauea (HI, USA) volcanoes. Results show that the index, which is used operationally to map hot targets through the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), is sensitive to high-temperature features even when short-wave infrared (SWIR) data at 2 km spatial resolution are analyzed. On the other hand, thresholds should be tailored to those data to better discriminate thermal anomalies from the background in daylight conditions. In this context, the multi-temporal analysis of NHI may enable an efficient identification of high-temperature targets without using fixed thresholds. This approach could be exported to SWIR data from the Flexible Combined Imager (FCI) instrument aboard the next Meteosat Third Generation (MTG) satellites. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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24 pages, 8714 KiB

Open AccessArticle

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

by Wenhuan Wu, Qiang Zhang, Vijay P. Singh, Gang Wang, Jiaqi Zhao, Zexi Shen and Shuai Sun

Remote Sens. 2022, 14(18), 4662; https://doi.org/10.3390/rs14184662 - 19 Sep 2022

Cited by 19 | Viewed by 5267

Abstract

Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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27 pages, 11808 KiB

Open AccessArticle

Remotely Sensed Ecological Protection Redline and Security Pattern Construction: A Comparative Analysis of Pingtan (China) and Durban (South Africa)

by Qixin Lin, Ahmed Eladawy, Jinming Sha, Xiaomei Li, Jinliang Wang, Eldar Kurbanov and Abraham Thomas

Remote Sens. 2021, 13(15), 2865; https://doi.org/10.3390/rs13152865 - 22 Jul 2021

Cited by 29 | Viewed by 4217

Abstract

The unprecedented regional urbanization has brought great pressure on the ecological environment. Building an ecological security pattern and guide regional land and space development is an important technique to ensure regional ecological security and stability to achieve sustainable development. In this study, the Pingtan Island of China and the Durban city of South Africa were chosen as case study area for a comparative study of different scales. The importance of ecosystem services and ecological sensitivity were evaluated, respectively. The core area of landscape which is vital for ecological function maintenance was extracted by morphological spatial pattern analysis (MSPA) and landscape connectivity analysis. Furthermore, the ecological sources were determined by combining the results of ecological protection redline delimitation and core area landscape extraction. The potential ecological corridors were identified based on the minimum cumulative resistance model, and the ecological security pattern of study areas was constructed. The results showed that the ecological protection redline areas of Pingtan and Durban were 42.78 km² and 389.07 km², respectively, which were mainly distributed in mountainous areas with good habitat quality. Pingtan ecological security pattern is composed of 15 ecological sources, 16 ecological corridors, 10 stepping stone patches and 15 ecological obstacle points. The total length of corridors is 112.23 km, which is radially distributed in the form of “one ring, three belts”. The ecological security pattern of Durban is composed of 15 ecological sources, 17 ecological corridors, 11 stepping stone patches and 18 ecological obstacle points. The total length of corridors is 274.25 km, which is radially distributed in the form of “two rings and three belts”. The research results can provide an important reference for the land space construction planning and ecological restoration projects in Pingtan and Durban. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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23 pages, 4724 KiB

Open AccessArticle

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

by Peng Zhang, Shougeng Hu, Weidong Li, Chuanrong Zhang and Peikun Cheng

Remote Sens. 2021, 13(11), 2146; https://doi.org/10.3390/rs13112146 - 29 May 2021

Cited by 11 | Viewed by 3268

Abstract

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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26 pages, 9638 KiB

Open AccessArticle

Towards Effective BIM/GIS Data Integration for Smart City by Integrating Computer Graphics Technique

by Junxiang Zhu and Peng Wu

Remote Sens. 2021, 13(10), 1889; https://doi.org/10.3390/rs13101889 - 12 May 2021

Cited by 61 | Viewed by 8701

Abstract

The development of a smart city and digital twin requires the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), where BIM models are to be integrated into GIS for visualization and/or analysis. However, the intrinsic differences between BIM and GIS have led to enormous problems in BIM-to-GIS data conversion, and the use of City Geography Markup Language (CityGML) has further escalated this issue. This study aims to facilitate the use of BIM models in GIS by proposing using the shapefile format, and a creative approach for converting Industry Foundation Classes (IFC) to shapefile was developed by integrating a computer graphics technique. Thirteen building models were used to validate the proposed method. The result shows that: (1) the IFC-to-shapefile conversion is easier and more flexible to realize than the IFC-to-CityGML conversion, and (2) the computer graphics technique can improve the efficiency and reliability of BIM-to-GIS data conversion. This study can facilitate the use of BIM information in GIS and benefit studies working on digital twins and smart cities where building models are to be processed and integrated in GIS, or any other studies that need to manipulate IFC geometry in depth. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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13 pages, 7679 KiB

Open AccessTechnical Note

Crustal Imaging across the Princess Elizabeth Land, East Antarctica from 2D Gravity and Magnetic Inversions

by Lin Li, Enzhao Xiao, Xiaolong Wei, Ning Qiu, Khalid Latif, Jingxue Guo and Bo Sun

Remote Sens. 2023, 15(23), 5523; https://doi.org/10.3390/rs15235523 - 27 Nov 2023

Viewed by 1135

Abstract

The Princess Elizabeth Land landscape in East Antarctica was shaped by a complex process, involving the supercontinent’s breakup and convergence cycle. However, the lack of geological knowledge about the subglacial bedrock has made it challenging to understand this process. Our study aimed to investigate the structural characteristics of the subglacial bedrock in the Mount Brown region, utilizing airborne geophysical data collected from the China Antarctic Scientific Expedition in 2015–2017. We reconstructed bedrock density contrast and magnetic susceptibility models by leveraging Tikhonov regularized gravity and magnetic inversions. The deep bedrock in the inland direction exhibited different physical properties, indicating the presence of distinct basement sources. The east–west discontinuity of bedrock changed in the inland areas, suggesting the possibility of large fault structures or amalgamation belts. We also identified several normal faults in the western sedimentary basin, intersected by the southwest section of these survey lines. Furthermore, lithologic separators and sinistral strike-slip faults may exist in the northeast section, demarcating the boundary between Princess Elizabeth Land and Knox Valley. Our study provides new insights into the subglacial geological structure in this region, highlighting the violent impact of the I-A-A-S (Indo-Australo-Antarctic Suture) on the subglacial basement composition. Additionally, by identifying and describing different bedrock types, our study redefines the potential contribution of this region to the paleocontinent splicing process and East Antarctic basement remodeling. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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16 pages, 7844 KiB

Open AccessTechnical Note

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

by Lokmen Farhat, Ioannis Manakos, Georgios Sylaios and Chariton Kalaitzidis

Remote Sens. 2023, 15(21), 5122; https://doi.org/10.3390/rs15215122 - 26 Oct 2023

Cited by 1 | Viewed by 1562

Abstract

Earth Observation (EO) data, such as Landsat 7 (L7) and Sentinel 2 (S2) imagery, are often used to monitor the state of natural resources all over the world. However, this type of data tends to suffer from high cloud cover percentages during rainfall/snow seasons. This has led researchers to focus on developing algorithms for filling gaps in optical satellite imagery. The present work proposes two modifications to an existing gap-filling approach known as the Direct Sampling (DS) method. These modifications refer to ensuring the algorithm starts filling unknown pixels (UPs) that have a specified minimum number of known neighbors (Nx) and to reducing the search area to pixels that share similar reflectance as the Nx of the selected UP. Experiments were performed on images acquired from coastal water bodies in France. The validation of the modified gap-filling approach was performed by imposing artificial gaps on originally gap-free images and comparing the simulated images with the real ones. Results indicate that satisfactory performance can be achieved for most spectral bands. Moreover, it appears that the bi-layer (BL) version of the algorithm tends to outperform the uni-layer (UL) version in terms of overall accuracy. For instance, in the case of B04 of an L7 image with a cloud percentage of 27.26%, accuracy values for UL and BL simulations are, respectively, 64.05 and 79.61%. Furthermore, it has been confirmed that the introduced modifications have indeed helped in improving the overall accuracy and in reducing the processing time. As a matter of fact, the implementation of a conditional filling path (minNx = 4) and a targeted search (n2 = 200) when filling cloud gaps in L7 imagery has contributed to an average increase in accuracy of around 35.06% and an average gain in processing time by around 78.18%, respectively. Full article

(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)

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