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Selected Papers from the "2019 International Symposium on Remote Sensing"

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 41200

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Prof. Fuan Tsai

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Guest Editor

Center for Space and Remtoe Sensing Research, National Central University, 300 Zhongda Road, Zhongli Taoyuan 32001, Taiwan
Interests: remote sensing; spatial analysis; image analysis; 3D metrology and reconstruction, geovisualization

Prof. Chao-Hung Lin

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Guest Editor

Department of Geomatics, National Chung-Kung University, No.1, Daxue Rd., East Dist., Tainan City 701, Taiwan
Interests: remote sensing; GIS; point cloud modelling; information visualization; computer graphics

Prof. Dr. Walter Chen

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Guest Editor

Department of Civil Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan
Interests: soil erosion; machine learning; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Jen-Jer Jaw

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Department of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
Interests: photogrammetry; errors theory; geo-spatial data acquisition and analysis

Dr. Kuo-Hsin Tseng

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Guest Editor

Center for Space and Remtoe Sensing Research, National Central University, 300 Zhongda Road, Zhongli Taoyuan 32001, Taiwan
Interests: satellite altimetry; satellite geodesy; environmental monitoring; multispectral analysis; InSAR applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2019 International Symposium on Remote Sensing (ISRS2019) will take place in Taipei, Taiwan, April 17–19th, 2019 (http://isrs.csrsr.ncu.edu.tw/). In recent years, various disaster events and environmental issues have occurred and emerged worldwide, raising not only public awareness of the situations but also demands for effective solutions. With their powerful data acquisition capacity and analytical capability, remote sensing and spatial information sciences are indispensable for providing viable solutions to address these issues. In addition, emerging sensing technologies and innovative artificial intelligence (AI) in conjunction with information and communication technology (ICT) have also provided us with a great opportunity to tackle challenges in related fields. To this end, ISRS2019 will be a scientific forum and an effective platform for participants to exchange and share their experiences with state-of-the-art techniques and the latest developments in remote sensing and spatial information sciences.

This Special Issue of Remote Sensing is planned in conjunction with ISRS2019 and will include peer-reviewed feature papers presented at the symposium. The ISRS2019 conference papers must be revised significantly, with not only a more detailed presentation of the research (methodology, results, and discussions) but also additional data sets and comparisons to qualify for publication in Remote Sensing. When submitting the manuscript, authors should provide the corresponding ISRS2019 abstract number. If this information is not provided, the submission will not be considered in this Special Issue.

Prof. Fuan Tsai
Prof. Chao-Hung Lin
Prof. Walter Chen
Prof. Jen-Jer Jaw
Prof. Kuo-Hsin Tseng
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

International symposium on remote sensing
ISRS2019
Remote sensing
Spatial information sciences
Global navigation satellite systems
Photogrammetry
Geoinformatics

Published Papers (9 papers)

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Editorial

Jump to: Research

4 pages, 182 KiB

Open AccessEditorial

Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”

by Fuan Tsai, Chao-Hung Lin, Walter W. Chen, Jen-Jer Jaw and Kuo-Hsin Tseng

Remote Sens. 2020, 12(12), 1947; https://doi.org/10.3390/rs12121947 - 17 Jun 2020

Viewed by 1683

Abstract

The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

Research

Jump to: Editorial

18 pages, 28740 KiB

Open AccessArticle

Learning and SLAM Based Decision Support Platform for Sewer Inspection

by Tzu-Yi Chuang and Cheng-Che Sung

Remote Sens. 2020, 12(6), 968; https://doi.org/10.3390/rs12060968 - 17 Mar 2020

Cited by 10 | Viewed by 3099

Abstract

Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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18 pages, 7613 KiB

Open AccessArticle

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

by Yan-Ting Lin, Ming-Der Yang, Jen-Yu Han, Yuan-Fong Su and Jiun-Huei Jang

Remote Sens. 2020, 12(4), 706; https://doi.org/10.3390/rs12040706 - 21 Feb 2020

Cited by 23 | Viewed by 4105

Abstract

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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20 pages, 26285 KiB

Open AccessArticle

Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images

by Ming-Der Yang, Hsin-Hung Tseng, Yu-Chun Hsu and Hui Ping Tsai

Remote Sens. 2020, 12(4), 633; https://doi.org/10.3390/rs12040633 - 14 Feb 2020

Cited by 123 | Viewed by 11075

Abstract

A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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23 pages, 9130 KiB

Open AccessArticle

Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations

by Tri Wandi Januar, Tang-Huang Lin, Chih-Yuan Huang and Kuo-En Chang

Remote Sens. 2020, 12(3), 498; https://doi.org/10.3390/rs12030498 - 04 Feb 2020

Cited by 8 | Viewed by 3508

Abstract

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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16 pages, 4135 KiB

Open AccessArticle

Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

by Dong-Hyun Yoon, Won-Ho Nam, Hee-Jin Lee, Eun-Mi Hong, Song Feng, Brian D. Wardlow, Tsegaye Tadesse, Mark D. Svoboda, Michael J. Hayes and Dae-Eui Kim

Remote Sens. 2020, 12(3), 444; https://doi.org/10.3390/rs12030444 - 01 Feb 2020

Cited by 31 | Viewed by 5158

Abstract

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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18 pages, 5521 KiB

Open AccessArticle

Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements

by Tang-Huang Lin, Jui-Chung Chang, Kuo-Hsien Hsu, Yun-Shan Lee, Sheng-Kai Zeng, Gin-Rong Liu, Fu-An Tsai and Hai-Po Chan

Remote Sens. 2019, 11(22), 2634; https://doi.org/10.3390/rs11222634 - 11 Nov 2019

Cited by 5 | Viewed by 2721

Abstract

A new Taiwanese satellite, FORMOSAT-5 (FS-5), with a payload remote sensing instrument (RSI) was launched in August 2017 to continue the mission of its predecessor FORMOSAT-2 (FS-2). Similar to FS-2, the RSI provides 2-m resolution panchromatic and 4-m resolution multi-spectral images as the primary payload on FS-5. However, the radiometric properties of the optical sensor may vary, based on the environment and time after being launched into the space. Thus, maintaining the radiometric quality of FS-5 RSI imagery is essential and significant to scientific research and further applications. Therefore, the objective of this study aimed at the on-orbit absolute radiometric assessment and calibration of on-orbit FS-5 RSI observations. Two renowned approaches, vicarious calibrations and cross-calibrations, were conducted at two calibration sites that employ a stable atmosphere and high surface reflectance, namely, Alkali Lake and Railroad Valley Playa in North America. For cross-calibrations, the Landsat-8 Operational Land Imager (LS-8 OLI) was selected as the reference. A second simulation of the satellite signal in the solar spectrum (6S) radiative transfer model was performed to compute the surface reflectance, atmospheric effects, and path radiance for the radiometric intensity at the top of the atmosphere. Results of vicarious calibrations from 11 field experiments demonstrated high consistency with those of seven case examinations of cross-calibration in terms of physical gain in spectra, implying that the practicality of the proposed approaches is high. Moreover, the multi-temporal results illustrated that RSI decay in optical sensitivity was evident after launch. The variation in the calibration coefficient of each band showed no obvious consistency (6%–24%) in 2017, but it tended to be stable at the order of 3%–5% of variation in most spectral bands during 2018. The results strongly suggest that periodical calibration is required and essential for further scientific applications. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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21 pages, 3699 KiB

Open AccessArticle

Supply and Demand Assessment of Solar PV as Off-Grid Option in Asia Pacific Region with Remotely Sensed Data

by Jeark Principe and Wataru Takeuchi

Remote Sens. 2019, 11(19), 2255; https://doi.org/10.3390/rs11192255 - 27 Sep 2019

Cited by 7 | Viewed by 4867

Abstract

The introduction of solar photovoltaic (PV) systems in isolated areas which are far from the main grid has provided energy to non-electrified households. Such off-grid technology is very promising in the Asia Pacific region where increase in population and regional development has brought an increase in energy demand. This paper presents a methodology to assess the available supply of energy from solar PV systems and the corresponding demand from non-electrified areas. Non-electrified high population density areas were extracted using global population distribution and nightlight data, while the suitability of installing solar PV systems in those areas were identified based on slope, land cover and estimated solar PV power output. Moreover, the cost and benefits of installation were estimated based on the levelized cost of electricity generation from PV (LCOE_PV) and the percentage in the total household budget that can shoulder the said expense. Lastly, this study also proposed a novel and simple method to extract the power transmission lines (TLs) based on global road network and nightlight data used for defining off-grid areas. Results show that there are three general types of electrification trend in the region with only 11 out 28 countries exhibiting the ideal trend of decreasing population living in unlit areas with increasing GDP. This study also generated maps showing the spatial distribution of high potential areas for solar PV installation in Cambodia, North Korea and Myanmar as case studies. To date, the high estimated household income allotted for PV electricity is still experienced in most countries in the region, but these countries also have high initial generated electricity from PV systems. Outputs from this study can provide stakeholders with relevant information on the suitable areas for installations in the region and the expected socio-economic benefits. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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29 pages, 6665 KiB

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Hydrological Response of Dry Afromontane Forest to Changes in Land Use and Land Cover in Northern Ethiopia

by Belay Manjur Gebru, Woo-Kyun Lee, Asia Khamzina, Sle-gee Lee and Emnet Negash

Remote Sens. 2019, 11(16), 1905; https://doi.org/10.3390/rs11161905 - 15 Aug 2019

Cited by 18 | Viewed by 3722

Abstract

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R² = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R² = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems. Full article

(This article belongs to the Special Issue Selected Papers from the "2019 International Symposium on Remote Sensing")

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