Special Issue "Remote Sensing and Geoscience Information Systems in Applied Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental and Sustainable Science and Technology".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Saro Lee
Website
Guest Editor
1. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
2. Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
Interests: GIS application in Geological Hazard and Geological Resources
Special Issues and Collections in MDPI journals
Prof. Dr. Kyung-Soo Han
SciProfiles
Guest Editor
Department of Spatial Information Engineering, Punkyong National University,45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea
Interests: Remote sensing for meteorology and land surface, Eco-climatological monitoring
Prof. Dr. No-Wook Park
Website
Guest Editor
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: Remote Sensing Data Classification; Geostatistics; Machine Learning; Environmental Modeling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing and geoscience information systems in applied sciences, which cover all aspects of applied biology, applied chemistry, applied physics, and applied engineering, have become more essential in measuring, analyzing, understanding and applying the physical, ecological, geological, hydrological, and environmental characteristics of Earth surfaces. Original research articles and literature review papers addressing the advanced technologies of remote sensing and geoscience information systems in applied sciences will be considered for the publication in this Special Issue. The objectives of this Special Issue are to create a multidisciplinary forum of discussion on recent advances in the fields of remote sensing and geoscience information system for applied sciences and to find new applications to applied geology, applied biology, applied ecology, applied hydrology, applied environmentology, and so on.

Potential topics include but are not limited to the following:

  • Sensor design and platforms development;
  • Multisensor system design and onboard processing;
  • Advances in sensors for applications of remote sensing and geoscience information systems;
  • Multisensor integration in applied sciences;
  • Geospatial data models in applied sciences;
  • Spatial big data analysis in applied sciences;
  • Position and localization systems, algorithms, and techniques;
  • Innovative of remote sensor techniques;
  • Hyperspectral remote sensing in applied sciences;
  • Laser scanning sensors in applied sciences;
  • Image processing algorithm and systems;
  • Spatiotemporal analysis in remote sensing and geoscience information systems.

Prof. Dr. Hyung-Sup Jung
Prof. Dr. Saro Lee
Prof. Dr. Kyung-Soo Han
Prof. Dr. No-Wook Park
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. Applied Sciences 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
  • Geoscience information system (GIS)
  • Global positioning system (GPS)
  • Satellite application
  • Image processing
  • Machine learning

Published Papers (20 papers)

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Research

Open AccessArticle
Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
Appl. Sci. 2020, 10(16), 5628; https://doi.org/10.3390/app10165628 - 13 Aug 2020
Abstract
Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as [...] Read more.
Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Agricultural Evolution: Process, Pattern and Water Resource Effect
Appl. Sci. 2020, 10(15), 5065; https://doi.org/10.3390/app10155065 - 23 Jul 2020
Abstract
Assessing historical landscape change and its related land–use changes is necessary for understanding agricultural evolution processes and their ecological effects. In our study, the landscape patterns of paddy fields and dry farmland were studied using information obtained from remote-sensing data. Land-use changes related [...] Read more.
Assessing historical landscape change and its related land–use changes is necessary for understanding agricultural evolution processes and their ecological effects. In our study, the landscape patterns of paddy fields and dry farmland were studied using information obtained from remote-sensing data. Land-use changes related to cultivated land were analyzed based on transition probability index and trajectory computing method. Furthermore, the possible driving force and water resource effect of cultivated land changes were discussed. The results indicated that paddy field and dry farmland expanded by 56.99% and 10.92% in the West Songnen Plain, respectively, compared with their own area in 1990. Trajectory analyses showed that dry farmland was usually more stable than paddy field. Climate warming, wind speed reduction, population growth, technological development, as well as land use policies all drove cultivated land changes. The net water consumption of cultivated land showed an increased trend. To achieve the sustainable development of land-system, optimizing land-use structure as well as configuration between water and soil resources should be given more attention in the future. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning
Appl. Sci. 2020, 10(13), 4667; https://doi.org/10.3390/app10134667 - 06 Jul 2020
Abstract
The agriculture sector is currently facing the problems of aging and decreasing skilled labor, meaning that the future direction of agriculture will be a transition to automation and mechanization that can maximize efficiency and decrease costs. Moreover, interest in the development of autonomous [...] Read more.
The agriculture sector is currently facing the problems of aging and decreasing skilled labor, meaning that the future direction of agriculture will be a transition to automation and mechanization that can maximize efficiency and decrease costs. Moreover, interest in the development of autonomous agricultural vehicles is increasing due to advances in sensor technology and information and communication technology (ICT). Therefore, an autonomous driving control algorithm using a low-cost global navigation satellite system (GNSS)-real-time kinematic (RTK) module and a low-cost motion sensor module was developed to commercialize an autonomous driving system for a crawler-type agricultural vehicle. Moreover, an autonomous driving control algorithm, including the GNSS-RTK/motion sensor integration algorithm and the path-tracking control algorithm, was proposed. Then, the performance of the proposed algorithm was evaluated based on three trajectories. The Root Mean Square Errors (RMSEs) of the path-following of each trajectory are calculated to be 9, 7, and 7 cm, respectively, and the maximum error is smaller than 30 cm. Thus, it is expected that the proposed algorithm could be used to conduct autonomous driving with about a 10 cm-level of accuracy. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network
Appl. Sci. 2020, 10(11), 3773; https://doi.org/10.3390/app10113773 - 29 May 2020
Abstract
As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate [...] Read more.
As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
Appl. Sci. 2020, 10(11), 3730; https://doi.org/10.3390/app10113730 - 28 May 2020
Abstract
Every year more interest is focused on high frequencies (HF) communications for remote sensing platforms due to their capacity to establish links of more than 250 km without a line of sight and due to them being a low-cost alternative to satellite communications. [...] Read more.
Every year more interest is focused on high frequencies (HF) communications for remote sensing platforms due to their capacity to establish links of more than 250 km without a line of sight and due to them being a low-cost alternative to satellite communications. In this article, we study the ionospheric ordinary and extraordinary waves to improve the applications of near vertical incidence skywave (NVIS) on a single input multiple output (SIMO) configuration. To obtain the results, we established a link of 95 km to test the diversity combining of ordinary and extraordinary waves by using selection combining (SC) and equal-gain combining (EGC) on a remote sensing platform. The testbench is based on digital modulation transmissions with power transmission between 3 and 100 W. The results show us the main energy per bit to noise spectral density ratio (Eb/N0) and the bit error rate (BER) differences between ordinary and extraordinary waves, SC, and EGC. To conclude, diversity techniques show us a decrease of the power transmission need, allowing for the use of compact antennas and increasing battery autonomy. Furthermore, we present three different improvement options for NVIS SIMO remote sensing platforms depending on the requirements of bitrate, power consumption, and efficiency of communication. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Episode-Based Analysis of Size-Resolved Carbonaceous Aerosol Compositions in Wintertime of Xinxiang: Implication for the Haze Formation Processes in Central China
Appl. Sci. 2020, 10(10), 3498; https://doi.org/10.3390/app10103498 - 19 May 2020
Cited by 1
Abstract
To provide a comprehensive understanding of carbonaceous aerosol and its role in the haze formation in the Central Plains Urban Agglomeration of China, size-segregated particulate matter samples (PM1, PM2.5 and PM10) were continually collected from 20 December 2017, [...] Read more.
To provide a comprehensive understanding of carbonaceous aerosol and its role in the haze formation in the Central Plains Urban Agglomeration of China, size-segregated particulate matter samples (PM1, PM2.5 and PM10) were continually collected from 20 December 2017, to 17 January 2018, in Xinxiang, the third largest city of Henan province. The results showed that the mean mass concentrations of PM1, PM2.5 and PM10 were 63.20, 119.63 and 211.95 μg·m−3, respectively, and the organic carbon (OC) and elemental carbon (EC) were 11.37 (5.87), 19.24 (7.36), and 27.04 (10.27) μg·m−3, respectively. Four pollution episodes that were categorized by short evolution patterns (PE1 and PE3) and long evolution patterns (PE2 and PE4) were observed. Meteorological condition was attributed to haze episodes evolution pattern. Carbonaceous components contributed to PE1 and PE2 under drier condition through transportation and local combustion emission, while they were not main species in PE3 and PE4 for haze explosive growth under suitable RH, whatever for the short or long evolution pattern. The atmospheric self-cleaning processes were analyzed by a case study, which showed the wet scavenging effectively reduced the coarse particles with a removal rate of 73%, while it was not for the carbonaceous components in fine particles that is hydrophobic in nature. These results highlight that local primary emissions such as biomass combustion were the important sources for haze formation in Central China, especially in dry conditions. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area
Appl. Sci. 2020, 10(8), 2928; https://doi.org/10.3390/app10082928 - 23 Apr 2020
Cited by 1
Abstract
Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of [...] Read more.
Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks
Appl. Sci. 2020, 10(5), 1666; https://doi.org/10.3390/app10051666 - 01 Mar 2020
Cited by 1
Abstract
As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is [...] Read more.
As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of ~65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
Appl. Sci. 2020, 10(3), 806; https://doi.org/10.3390/app10030806 - 23 Jan 2020
Abstract
The Gaofen-3 satellite is the first SAR satellite independently developed in China that achieves interferometric imaging and measurement, which improves upon Chinese civil SAR satellite data. To verify the ability of the Gaofen-3 satellite’s InSAR technology, we acquired data from Dengfeng, China, to [...] Read more.
The Gaofen-3 satellite is the first SAR satellite independently developed in China that achieves interferometric imaging and measurement, which improves upon Chinese civil SAR satellite data. To verify the ability of the Gaofen-3 satellite’s InSAR technology, we acquired data from Dengfeng, China, to evaluate the application and accuracy of an InSAR terrain survey. To reduce the effects introduced by data processing of Gaofen-3 data, high-accuracy InSAR image pair co-registration and phase filtering methods were adopted. Six GCPs data and 1:2000-scale DEM data were used to evaluate the elevation accuracy. In addition, for comparison with other satellites, we processed the dataset of the same area acquired by the non-civilian Yaogan-29 satellite with the same methods and evaluated the results. The experimental results indicated that the interferometric data of the Gaofen-3 satellite can achieve an accuracy of higher than 4 m of interferometric height measurement. Therefore, it will have broad prospects in the domestic InSAR application. Our research provides a certain value for the reference of the development of InSAR sensors in China. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Time–Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
Appl. Sci. 2020, 10(2), 535; https://doi.org/10.3390/app10020535 - 10 Jan 2020
Abstract
The Lunar Penetrating Radar (LPR) carried by the Chang’E-3 (CE-3) and Chang’E-4 (CE-4) mission plays a very important role in lunar exploration. The dual-frequency radar on the rover (DFR) provides a meaningful opportunity to detect the underground structure of the CE-3 landing site. [...] Read more.
The Lunar Penetrating Radar (LPR) carried by the Chang’E-3 (CE-3) and Chang’E-4 (CE-4) mission plays a very important role in lunar exploration. The dual-frequency radar on the rover (DFR) provides a meaningful opportunity to detect the underground structure of the CE-3 landing site. The low-frequency channel (channel 1) maps the underground structure to a depth of several hundred meters, while the high-frequency channel (channel 2) can observe the stratigraphic structure of gravel near the surface. As the low-frequency radar image is troubled by unknown noise, time–frequency analysis of a single trace is applied. Then, a method named complete ensemble empirical mode decomposition (CEEMD) is conducted to decompose the channel 1 data, and the Hilbert transform gives us the chance for further data analysis. Finally, combined with regional geology, previous studies, and channel 2 data, a usability analysis of LPR channel 1 data provides a reference for the availability of the CE-4 LPR data. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea
Appl. Sci. 2020, 10(1), 23; https://doi.org/10.3390/app10010023 - 18 Dec 2019
Abstract
The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine [...] Read more.
The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bpcpj, bbp and bpBp, and j and Bpnp. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
The Potential of Multi- and HyperSpectral Air- and Spaceborne Sensors to Detect Crude Oil Hydrocarbon in Soils Long after a Contamination Event
Appl. Sci. 2019, 9(23), 5151; https://doi.org/10.3390/app9235151 - 28 Nov 2019
Cited by 1
Abstract
Crude oil contamination is hazardous to health, negatively impacts vital life sources, and causes land and ecological degradation. The basic premise of the prevalent spectroscopic analyses for detecting such contamination is that crude oil spectral features are observable in the spectrum. Such analyses, [...] Read more.
Crude oil contamination is hazardous to health, negatively impacts vital life sources, and causes land and ecological degradation. The basic premise of the prevalent spectroscopic analyses for detecting such contamination is that crude oil spectral features are observable in the spectrum. Such analyses, however, have failed to address instances where the expected spectral features are not visible in the spectrum. Hence, a more refined method was recently published, which accounts for such cases. This method was successfully applied to a hyperspectral image over an arid area long after a contamination event. This study aimed to determine whether that same method could be successfully applied using a variety of other operational and future instruments, both air- and spaceborne, with different spatial and spectral characteristics. To that end, a series of simulation experiments was performed, including various spectral and spatial resolutions. Quantitative and qualitative evaluations of the classification are reported. The results indicate that the hyperspectral information can be reduced to one-third of its original size, while maintaining high accuracy and a quality classification map. A ground sampling distance of 7.5 m seems to be the boundary of an acceptable classification outcome. The overall conclusion of this study was that the method is robust enough to perform under various spectral and spatial configurations. Therefore, it could be a promising tool to be integrated into environmental protection and resource management programs. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand
Appl. Sci. 2019, 9(22), 4846; https://doi.org/10.3390/app9224846 - 12 Nov 2019
Abstract
The brown planthopper Nilaparvata lugens (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would [...] Read more.
The brown planthopper Nilaparvata lugens (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation. In this study, artificial neural network (ANN), random forest (RF) and classic linear multiple regression (MLR) analyses were applied and compared to forecast the BPH population using weather and host-plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand. Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density. An ANN model with integrated ground-based meteorological variables and satellite-derived host plant variables was more accurate for short-term forecasting of the peak abundance of BPH when compared with RF and MLR, according to a reasonably validating dataset (RMSE of natural log-transformed (ln) BPH light trap catches = 1.686, 1.737, and 2.015, respectively). This finding indicates that the utilization of ground meteorological observations, satellite-derived NDVI time series, and ANN have the potential to predict BPH population density in support of integrated pest management programs. We expect the results from this study can be applied in conjunction with the satellite-based rice monitoring system developed by the Geo-Informatic and Space Technology Development Agency of Thailand (GISTDA) to support an effective pest early warning system. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
Appl. Sci. 2019, 9(22), 4825; https://doi.org/10.3390/app9224825 - 11 Nov 2019
Abstract
Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements [...] Read more.
Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements in these regards. One of these techniques is the use of deep convolution neural networks (DCNNs). This study presents a road segmentation model consisting of a skip connection of U-net and residual blocks (ResBlocks) in the encoding part and convolution layers (Conv. layer) in the decoding part. Although the model uses fewer residual blocks in the encoding part and fewer convolution layers in the decoding part, it produces better image predictions in comparison with other state-of-the-art models. This model automatically and efficiently extracts road networks from high-resolution aerial imagery in an unexpansive manner using a small training dataset. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images
Appl. Sci. 2019, 9(19), 4043; https://doi.org/10.3390/app9194043 - 27 Sep 2019
Cited by 1
Abstract
Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, [...] Read more.
Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
A New Method for Positional Accuracy Control for Non-Normal Errors Applied to Airborne Laser Scanner Data
Appl. Sci. 2019, 9(18), 3887; https://doi.org/10.3390/app9183887 - 16 Sep 2019
Cited by 5
Abstract
A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on [...] Read more.
A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on the use of a multinomial distribution that categorizes cases of errors according to metric tolerances. The use of the multinomial distribution is a very novel and powerful approach to the problem of evaluating positional accuracy, since it allows for eliminating the need for a parametric model for positional errors. Three different study cases based on ALS data (infrastructure, urban, and natural cases) that contain non-normal errors were used. Three positional accuracy controls with different tolerances were developed. In two of the control cases, the tolerances were defined by a Gaussian model, and in the third control case, the tolerances were defined from the quantiles of the observed error distribution. The analysis of the test results based on the type I and type II errors show that the method is able to control the positional accuracy of freely distributed data. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Synthetic Aperture Radar Interferometry (InSAR) Ionospheric Correction Based on Faraday Rotation: Two Case Studies
Appl. Sci. 2019, 9(18), 3871; https://doi.org/10.3390/app9183871 - 15 Sep 2019
Cited by 1
Abstract
Spaceborne synthetic aperture radar (SAR) imagery is affected by the ionosphere, resulting in distortions of the SAR intensity, phase, and polarization. Although several methods have been proposed to mitigate the ionospheric phase delay of SAR interferometry, the application of them with full-polarimetric SAR [...] Read more.
Spaceborne synthetic aperture radar (SAR) imagery is affected by the ionosphere, resulting in distortions of the SAR intensity, phase, and polarization. Although several methods have been proposed to mitigate the ionospheric phase delay of SAR interferometry, the application of them with full-polarimetric SAR interferometry is limited. Based on this background, Faraday rotation (FR)-based methods are used in this study to mitigate the ionospheric phase errors on full-polarimetric SAR interferometry. For a performance test of the selected method, L-band Advanced Land Observation Satellite (ALOS) Phase Array L-band SAR (PALSAR) full-polarimetric SAR images over high-latitude and low-latitude regions are processed. The result shows that most long-wavelength ionospheric phase errors are removed from the original phase after using the FR-based method, where standard deviations of the corrected result have decreased by almost a factor of eight times for the high-latitude region and 28 times for low-latitude region, compared to those of the original phase, demonstrating the efficiency of the method. This result proves that the FR-based method not only can mitigate the ionospheric effect on SAR interferometry, but also can map the high-spatial-resolution vertical total electronic content (VTEC) distribution. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
Detection and 3D Visualization of Deformations for High-Rise Buildings in Shenzhen, China from High-Resolution TerraSAR-X Datasets
Appl. Sci. 2019, 9(18), 3818; https://doi.org/10.3390/app9183818 - 11 Sep 2019
Cited by 1
Abstract
Shenzhen, a coastal city, has changed from a small village to a supercity since the late 1980s. With the rapid development of its population and economy, ground disasters also occur frequently. These disasters bring great harm to human life and surface architecture. However, [...] Read more.
Shenzhen, a coastal city, has changed from a small village to a supercity since the late 1980s. With the rapid development of its population and economy, ground disasters also occur frequently. These disasters bring great harm to human life and surface architecture. However, there is a lack of regular ground measurement data in this area. Permanent scatterer interferometry (PSI) technology can detect millimeter deformation of urban surface. In this paper, the building height and deformation from 2008 to 2010 in the Futian District of Shenzhen are obtained by using this technique alongside high-resolution TerraSAR-X data. For a visual expression of the result, we export the permanent scatterer (PS) points on buildings to Google Earth for 3D visualization after ortho-rectification of the PS height. Based on the Google Earth 3D model, the temporal and spatial characteristics of the building deformation became obvious. The InSAR measurements show that during the study period, the deformation rates of the Futian area are between −10 and 10 mm/year, and deformation is mainly distributed in a few buildings. These unstable activities can be attributed to human activities and the natural climate, which provides a reference for the local government to carry out a survey of surface deformation, as well as the monitoring and management of urban buildings, in the future. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
A Novel in Situ Stress Monitoring Technique for Fracture Rock Mass and Its Application in Deep Coal Mines
Appl. Sci. 2019, 9(18), 3742; https://doi.org/10.3390/app9183742 - 07 Sep 2019
Cited by 2
Abstract
A novel in situ stress monitoring method, based on rheological stress recovery (RSR) theory, was proposed to monitor the stress of rock mass in deep underground engineering. The RSR theory indicates that the tiny hole in the rock can close gradually after it [...] Read more.
A novel in situ stress monitoring method, based on rheological stress recovery (RSR) theory, was proposed to monitor the stress of rock mass in deep underground engineering. The RSR theory indicates that the tiny hole in the rock can close gradually after it was drilled due to the rheology characteristic, during which process the stress that existed in the rock can be monitored in real-time. Then, a three-dimensional stress monitoring sensor, based on the vibrating wire technique, was developed for in field measurement. Furthermore, the in-field monitoring procedures for the proposed technique are introduced, including hole drilling, sensor installation, grouting, and data acquisition. Finally, two in situ tests were carried out on deep roadways at the Pingdingshan (PDS) No. 1 and No. 11 coal mines to verify the feasibility and reliability of the proposed technique. The relationship between the recovery stress and the time for the six sensor faces are discussed and the final stable values are calculated. The in situ stress components of rock masses under geodetic coordinates were calculated via the coordinate transformation equation and the results are consistent with the in situ stress data by different methods, which verified the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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Open AccessArticle
A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface
Appl. Sci. 2019, 9(18), 3693; https://doi.org/10.3390/app9183693 - 05 Sep 2019
Cited by 2
Abstract
Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial [...] Read more.
Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat–MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (RI). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface. Full article
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
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