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Special Issue "Selected Papers from the “International Symposium on Remote Sensing 2018”"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2018)

Special Issue Editors

Guest Editor
Prof. Hyung-Sup Jung

Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
Website | E-Mail
Phone: +82-2-6490-2892
Interests: Remote Sensing; Geoinformatics; Satellite Data Processing; Environmental Sensing
Guest Editor
Dr. Joo-Hyung Ryu

Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology(KIOST), 385 Haeyang-ro, Yeongdo-gu, Busan Metropolitan City, 49111, Republic of Korea
E-Mail
Interests: Coastal Application; Marine Surveillance System; Ocean Color
Guest Editor
Prof. Sang-Eun Park

Department of Geoinformation Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Republic of Korea
Website | E-Mail
Interests: Radar Polarimetry; forward and inverse modeling of microwave scattering; data fusion
Guest Editor
Prof. Hoonyol Lee

Division of Geology and Geophysics, Kangwon National University, Hyoja-dong, Chuncheon, Kangwon-do 24341, Republic of Korea
Website | E-Mail
Interests: SAR Interferometry; Cryosphere; Geophysical Inversion
Guest Editor
Prof. No-Wook Park

Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea
Website | E-Mail
Interests: Remote Sensing Data Classification; Geostatistics; Machine Learning; Environmental Modeling

Special Issue Information

Dear Colleagues,

The International Symposium on Remote Sensing 2018 (ISRS 2018) is scheduled to be held in Pyeongchang, Korea, 9–11 May 2018 (http://isrs.or.kr/). This is the premier symposium that provides all participants with invaluable opportunities for catching up on state-of-the art techniques and the latest developments in remote sensing, but also serves for sharing new ideas and information with colleagues and young scholars engaged in similar studies, research or activity. This Special Issue in Remote Sensing is planned in conjunction with ISRS 2018 and will include peer-reviewed feature papers presented at ISRS 2018. Remote Sensing is an open access journal about the science and applications of remote sensing technology, and is published online. The ISRS 2018 conference papers must be fleshed out with, not only a more detailed presentation of the research, but also additional data sets and comparisons in an enhanced experimental section so that it can be published in Remote Sensing.

In the cover letter, authors should provide the corresponding paper number of ISRS 2018. If this information is not provided, the paper will not be considered as a Special Issue paper.

Prof. Hyung-Sup Jung
Dr. Joo-Hyung Ryu
Prof. Sang-Eun Park
Prof. Hoonyol Lee
Prof. 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. Remote Sensing is an international peer-reviewed open access monthly 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

  • International Symposium on Remote Sensing 2018;
  • Remote Sensing;
  • Geoinformatics;
  • Geoscience Information System (GIS);
  • Global Positioning System (GPS);
  • Image Processing

Published Papers (11 papers)

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Research

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Open AccessArticle Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations
Remote Sens. 2018, 10(12), 1971; https://doi.org/10.3390/rs10121971
Received: 26 October 2018 / Revised: 4 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
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Abstract
Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used
[...] Read more.
Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used frequently to estimate individual levels of exposures to ambient air pollution. This paper used remote sensing and geographical information system (GIS) tools to develop ten regression models for PM2.5-bound compound concentration based on measurements of a six-year period including NH 4 + ,   SO 4 2 ,   NO 3 , OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R2) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM2.5-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM2.5 sources like temples collected from GIS and remote sensing data were main variables. In particular, while they were much less used, this study showcased the necessity of remote sensing data of greenness in future LUR studies for reducing the exposure bias. In terms of local residents’ health outcome or health effect indicators, this study further offers much-needed support for future air epidemiological studies. The results provide important insights into expanding the application of GIS and remote sensing on exposure assessment for PM2.5-bound compounds. Full article
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Open AccessArticle Using TanDEM-X Pursuit Monostatic Observations with a Large Perpendicular Baseline to Extract Glacial Topography
Remote Sens. 2018, 10(11), 1851; https://doi.org/10.3390/rs10111851
Received: 3 October 2018 / Revised: 11 November 2018 / Accepted: 13 November 2018 / Published: 21 November 2018
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Abstract
Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Owing to temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. TanDEM-X observations can overcome the temporal decorrelation because of their simultaneous measurements
[...] Read more.
Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Owing to temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. TanDEM-X observations can overcome the temporal decorrelation because of their simultaneous measurements by two satellite constellations. In this study, we used the TanDEM-X pursuit monostatic mode with large baseline formation following a scientific phase timeline to develop highly precise topographic elevation models of the Petermann Glacier of Northwest Greenland. The large baseline provided the advantage of extracting topographic information over low relief areas, such as the surface of a glacier. As expected, coherent interferometric phases (>0.8) were well maintained over the glaciers, despite their fast movement, due to the nearly simultaneous TanDEM-X measurements. The height ambiguity, which was defined as the altitude difference corresponding to a 2π phase change of the flattened interferogram, of the dataset was 10.63 m, which was favorable for extracting topography in a low relief region. We validated the TanDEM-X derived glacial topography by comparing it to the SAR/Interferometric radar altimeter observations acquired by CryoSat-2 and the IceBridge Airborne Topographic Mapper laser altimeter measurements. Both observations showed very good correlation within a few meters of the offsets (−12.5~−3.1 m), with respect to the derived glacial topography. Routine TanDEM-X observations will be very useful to better understand the dynamics of glacial movements and topographic change. Full article
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Open AccessArticle Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks
Remote Sens. 2018, 10(11), 1827; https://doi.org/10.3390/rs10111827
Received: 12 October 2018 / Revised: 12 November 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such
[...] Read more.
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM. Full article
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Open AccessArticle Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas
Remote Sens. 2018, 10(11), 1804; https://doi.org/10.3390/rs10111804
Received: 12 October 2018 / Revised: 8 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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Abstract
Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by
[...] Read more.
Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data. Full article
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Open AccessArticle Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images
Remote Sens. 2018, 10(11), 1799; https://doi.org/10.3390/rs10111799
Received: 19 September 2018 / Revised: 10 November 2018 / Accepted: 11 November 2018 / Published: 13 November 2018
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Abstract
In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input
[...] Read more.
In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input neurons and (ii) selecting the correct training data. We focused on generating two optimal input data neurons that (i) strengthened ship targets and (ii) mitigated noise effects by image processing techniques, including median filtering, multi-looking, etc. The median filter and multi-look operations were used to reduce the background noise, and the median filter operation was also used to remove ships in an image in order to maximize the difference between the pixel values of ships and the sea. Through the root-mean-square difference calculation, most ship targets, even including small ships, were emphasized in the images. We tested the performance of the proposed method using X-band high-resolution SAR images including COSMO-SkyMed, KOMPSAT-5, and TerraSAR-X images. An intensity difference map and a texture difference map were extracted from the X-band SAR single-look complex (SLC) images, and then, the maps were used as input neurons for the ANN and SVM machine learning techniques. Finally, we created ship-probability maps through the machine learning techniques. To validate the ANN and SVM results, optimal threshold values were obtained by using the statistical approach and then used to identify ships from the ship-probability maps. Consequently, the level of recall achieved was greater than 90% in most cases. This means that the proposed method enables the detection of most ship targets from X-band SAR images with a reduced number of false detections from negative effects. Full article
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Open AccessArticle Improved Satellite Retrieval of Tropospheric NO2 Column Density via Updating of Air Mass Factor (AMF): Case Study of Southern China
Remote Sens. 2018, 10(11), 1789; https://doi.org/10.3390/rs10111789
Received: 9 October 2018 / Revised: 3 November 2018 / Accepted: 7 November 2018 / Published: 12 November 2018
PDF Full-text (5570 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters
[...] Read more.
Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR-HK) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. There are two major objectives and goals: (1) developing new BEHR-HK v3.0C product for retrieving tropospheric NO2 vertical column density (TVCD) within part of southern China, for four months of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI); and (2) evaluating BEHR-HK v3.0C retrieval result through validation, by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. Results show that all BEHR-HK retrieval algorithms (with R-value of 0.9839 for v3.0C) are of higher consistency with MAX-DOAS measurements than OMI-NASA retrieval (with R-value of 0.7644). This opens new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries. Full article
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Open AccessArticle Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data
Remote Sens. 2018, 10(11), 1702; https://doi.org/10.3390/rs10111702
Received: 18 September 2018 / Revised: 17 October 2018 / Accepted: 26 October 2018 / Published: 29 October 2018
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Abstract
This study presents the retrieval of rice paddy height using single polarization (single-pol) interferometric SAR (InSAR) data by means of model-based height inversion without an external DEM. A total of eight TanDEM-X (TDX) scenes were used and the TDX images were; acquired using
[...] Read more.
This study presents the retrieval of rice paddy height using single polarization (single-pol) interferometric SAR (InSAR) data by means of model-based height inversion without an external DEM. A total of eight TanDEM-X (TDX) scenes were used and the TDX images were; acquired using a large cross-track baseline configuration during the TDX Science Phase (June–August 2015). A single-pol inversion approach for a flooded rice field is proposed and evaluated over the Buan test site in South Korea. A novel approach is adopted for the estimation of the ground (i.e., water level) interferometric phase within a flooded rice paddy from TDX data acquired during a period of early rice growth. It is consequently possible to apply the model-based inversion algorithm to the single-pol InSAR data. Rice height maps during the rice growth cycle presented and validated by field measurements. The results demonstrated the high performance of the inversion with a correlation coefficient of 0.78 and an RMSE of 0.10 m. The proposed methodology will be useful to monitor rice plants and to predict a gross rice yield, along with dual and fully polarimetric interferometric SAR data. Full article
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Open AccessArticle Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea
Remote Sens. 2018, 10(10), 1545; https://doi.org/10.3390/rs10101545
Received: 21 August 2018 / Revised: 19 September 2018 / Accepted: 21 September 2018 / Published: 25 September 2018
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Abstract
We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the
[...] Read more.
We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results. Full article
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Open AccessArticle A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack
Remote Sens. 2018, 10(8), 1274; https://doi.org/10.3390/rs10081274
Received: 13 June 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 13 August 2018
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Abstract
Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing
[...] Read more.
Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS. Full article
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Open AccessArticle Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
Remote Sens. 2018, 10(8), 1252; https://doi.org/10.3390/rs10081252
Received: 21 June 2018 / Revised: 3 August 2018 / Accepted: 5 August 2018 / Published: 9 August 2018
Cited by 1 | PDF Full-text (5418 KB) | HTML Full-text | XML Full-text
Abstract
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762
[...] Read more.
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods. Full article
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Other

Jump to: Research

Open AccessLetter A Simple Method for the Parameterization of Surface Roughness from Microwave Remote Sensing
Remote Sens. 2018, 10(11), 1711; https://doi.org/10.3390/rs10111711
Received: 14 August 2018 / Revised: 11 October 2018 / Accepted: 26 October 2018 / Published: 30 October 2018
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Abstract
Generally, the characterization of land surface roughness is obtained from the analysis of height variations observed along transects (e.g., root mean square (RMS) height, correlation length, and autocorrelation function). These surface roughness measurements are then used as inputs for surface dynamics modeling, e.g.,
[...] Read more.
Generally, the characterization of land surface roughness is obtained from the analysis of height variations observed along transects (e.g., root mean square (RMS) height, correlation length, and autocorrelation function). These surface roughness measurements are then used as inputs for surface dynamics modeling, e.g., for soil erosion modeling, runoff estimation, and microwave remote sensing scattering modeling and calibration. In the past, researchers have suggested various methods for estimating roughness parameters based on ground measurements, e.g., using a pin profilometer, but these methods require physical contact with the land and can be time-consuming to conduct. The target of this research is to develop a technique for deriving surface roughness characteristics from digital camera images by applying photogrammetric and geographical information systems (GIS) analysis techniques. First, ground photos acquired by a digital camera in the field were used to create a point cloud and 3D digital terrain model (DTM). Then, the DTM was imported to a GIS environment to calculate the surface roughness parameter for each field site. The results of the roughness derivation can be integrated with soil moisture for backscattering simulation, e.g., for inversion modeling to retrieve the backscattering coefficient. The results show that the proposed method has a high potential for retrieving surface roughness parameters in a time- and cost-efficient manner. The selection of homogeneous fields and the increased spatial distribution of sites in the study area will show a better result for microwave backscattering modeling. Full article
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