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

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 45285

Special Issue Editors

Prof. Fuan Tsai

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Chao-Hung Lin

E-Mail Website
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

E-Mail Website
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

E-Mail Website
Guest Editor
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

E-Mail Website
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

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Published Papers (9 papers)

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Editorial

Jump to: Research

4 pages, 182 KiB  
Editorial
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 1866
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 [...] Read more.
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  
Article
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 16 | Viewed by 3527
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 [...] Read more.
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sensors and configuration used in this study.</p> Full article ">Figure 2
<p>Illustration of image data collection.</p> Full article ">Figure 3
<p>The block diagram of the proposed scheme.</p> Full article ">Figure 4
<p>The geometry of a point triplet.</p> Full article ">Figure 5
<p>The geometry of a point triplet.</p> Full article ">Figure 6
<p>The principal component analysis (PCA) principal and second axes of a crack.</p> Full article ">Figure 7
<p>The estimation of the sectional area.</p> Full article ">Figure 8
<p>The demonstration of the acceleration behavior.</p> Full article ">Figure 9
<p>The floor plan of the testing field.</p> Full article ">Figure 10
<p>The on-site point cloud (<b>a</b>), and the estimated (red) and actual (yellow) moving trajectories (<b>b</b>).</p> Full article ">Figure 11
<p>The refined positions derived from the image simultaneous localization and mapping (SLAM).</p> Full article ">Figure 12
<p>The positioning deviation resulted from image and point cloud SLAM.</p> Full article ">Figure 13
<p>The tube with a small diameter.</p> Full article ">Figure 14
<p>The visual results of the identified obstacles.</p> Full article ">Figure 15
<p>The four results of tiny crack detection.</p> Full article ">
18 pages, 7613 KiB  
Article
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 4573
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 [...] Read more.
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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Graphical abstract
Full article ">Figure 1
<p>The geometry of VGI water level calculation.</p> Full article ">Figure 2
<p>Flowchart for the proposed VGI water level detection method.</p> Full article ">Figure 3
<p>Study area (red polygon) at Gongguan, Taipei City, Taiwan.</p> Full article ">Figure 4
<p>589 positioned unmanned aerial vehicle images generated a UAV orthophoto (<b>left</b>) and DSM with 0.03-m ground resolution (<b>right)</b>.</p> Full article ">Figure 5
<p>Image-based VGI illustrating photograph shooting locations and acquisition times of (<b>a</b>) 15:20, (<b>b</b>) 16:10, and (<b>c</b>) 16:20 through the point cloud.</p> Full article ">Figure 6
<p>VGI classified images (<b>upper</b>) and water line detection (<b>bottom</b>). Image-based VGIs were simplified into three classification categories, namely water in blue, vegetation in green, and buildings in black based on the classifications of RF (<b>left</b>), ML (<b>middle</b>), and SVM <b>(right</b>). The classified image-based VGIs were processed using HT-Canny to detect water lines (red lines) at the acquisition times of (<b>a</b>) 15:20, (<b>b</b>) 16:10, and (<b>c</b>) 16:20.</p> Full article ">Figure 6 Cont.
<p>VGI classified images (<b>upper</b>) and water line detection (<b>bottom</b>). Image-based VGIs were simplified into three classification categories, namely water in blue, vegetation in green, and buildings in black based on the classifications of RF (<b>left</b>), ML (<b>middle</b>), and SVM <b>(right</b>). The classified image-based VGIs were processed using HT-Canny to detect water lines (red lines) at the acquisition times of (<b>a</b>) 15:20, (<b>b</b>) 16:10, and (<b>c</b>) 16:20.</p> Full article ">Figure 6 Cont.
<p>VGI classified images (<b>upper</b>) and water line detection (<b>bottom</b>). Image-based VGIs were simplified into three classification categories, namely water in blue, vegetation in green, and buildings in black based on the classifications of RF (<b>left</b>), ML (<b>middle</b>), and SVM <b>(right</b>). The classified image-based VGIs were processed using HT-Canny to detect water lines (red lines) at the acquisition times of (<b>a</b>) 15:20, (<b>b</b>) 16:10, and (<b>c</b>) 16:20.</p> Full article ">Figure 7
<p>Rainfall hyetograph and corresponding water level distribution.</p> Full article ">Figure 8
<p>Serial illustration of the simulated flooding area from 14:30 to 17:40.</p> Full article ">
20 pages, 26285 KiB  
Article
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 137 | Viewed by 11858
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 [...] Read more.
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area with testing area enlarged on the right.</p> Full article ">Figure 2
<p>High-resolution orthomosaic images: (<b>a</b>) 2019 image, (<b>b</b>) 2019 image with histogram matching process, and (<b>c</b>) 2017 image.</p> Full article ">Figure 3
<p>Research flowchart.</p> Full article ">Figure 4
<p>Illustration of the labeled ground truth on UAV images.</p> Full article ">Figure 5
<p>Ground truth of rich lodging portion in white: (<b>a</b>) 2017 image and (<b>b</b>) 2019 image.</p> Full article ">Figure 6
<p>SegNet structure illustration (reproduced from Badrinarayanan et al. [<a href="#B32-remotesensing-12-00633" class="html-bibr">32</a>]).</p> Full article ">Figure 7
<p>FCN-AlexNet structure illustration (reproduced from Long et al. [<a href="#B33-remotesensing-12-00633" class="html-bibr">33</a>]).</p> Full article ">Figure 8
<p>Performance evaluated by Per-category F1-score for the validation dataset.</p> Full article ">Figure 9
<p>Rice lodging identification validation: (<b>a</b>) original image, (<b>b</b>) ground truth, (<b>c1–c4</b>) represents FCN-AlexNet (RGB), (RGB+ExG), (RGB+ExGR), (RGB+ExG+ExGR), respectively. (<b>d1–d4</b>) represents SegNet (RGB), (RGB+ExG), (RGB+ExGR), (RGB+ExG+ExGR), respectively.</p> Full article ">Figure 10
<p>F1-score and accuracy comparison on the 2017 testing dataset for rice lodging.</p> Full article ">Figure 11
<p>F1-score and accuracy comparison on the 2019 testing dataset for rice lodging.</p> Full article ">Figure 12
<p>Results of FCN-AlexNet identification for rice lodging on 2017 testing dataset using various spectrum information: (<b>a</b>) RGB, (<b>b</b>) RGB+ExG, (<b>c</b>) RGB+ExGR, and (<b>d</b>) RGB+ExG+ExGR.</p> Full article ">Figure 13
<p>Results of SegNet identification for rice lodging on 2017 testing dataset using various spectrum information: (<b>a</b>) RGB, (<b>b</b>) RGB+ExG, (<b>c</b>) RGB+ExGR, and (<b>d</b>) RGB+ExG+ExGR.</p> Full article ">Figure 14
<p>Results of MLC identification for rice lodging on 2017 testing dataset using various spectrum information: (<b>a</b>) RGB, (<b>b</b>) RGB+ExG, (<b>c</b>) RGB+ExGR, and (<b>d</b>) RGB+ExG+ExGR.</p> Full article ">Figure 15
<p>Results of rice lodging identification on 2019 testing dataset (white: rice lodging, black: others): (<b>a</b>) FCN-AlexNet using RGB+ExGR information, (<b>b</b>) SegNet using RGB+ExGR information, (<b>c</b>) ground truth. Results comparison with ground truth for (<b>d</b>) FCN-AlexNet using RGB+ExGR information and (<b>e</b>) SegNet using RGB+ExGR information.</p> Full article ">Figure 16
<p>Results of identification on the total 230 ha. area by the best performance models: (<b>a</b>) 2017 dataset orthoimage, (<b>b</b>) 2017 dataset prediction of FCN-AlexNet using RGB+ExGR information, (<b>c</b>) 2017 dataset prediction of SegNet using RGB+ExGR, (<b>d</b>) ground truth, (<b>e</b>) 2019 dataset orthoimage, (<b>f</b>) 2019 dataset prediction of FCN-AlexNet using RGB+ExGR information, <b>(g</b>) 2019 dataset prediction of SegNet using RGB+ExGR.</p> Full article ">
23 pages, 9130 KiB  
Article
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 - 4 Feb 2020
Cited by 10 | Viewed by 3888
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 [...] Read more.
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The study area is a 1063 km<sup>2</sup> (120°09′05″E–120°28′30″E, 23°17′45″N–23°35′45″N) plot in the Chiayi County, Taiwan, mostly covered by agricultural land. The black circles indicate locations of weather stations A (23°34′9.13″N, 120°17′13.2″E), B (23°33′12.6″N, 120°25′13.08″E), C (23°24′47.16″N, 120°18′0.72″E), D (23°20′57.12″N, 120°24′22.68″E), and D (23°18′44.64″N, 120°18′30.96″E).</p> Full article ">Figure 2
<p>Flowchart of STAEFM approach. NIR, near infrared; SWIR, shortwave infrared; TIR, thermal infrared; mNDVI, modified normalized difference vegetation index.</p> Full article ">Figure 3
<p>Digital number of Himawari-8 TIR images (<b>a</b>) without sharpening and (<b>b</b>) with sharpening, compared to (<b>c</b>) the digital number of the Landsat-8 TIR image at the same acquisition time (15 November 2018).</p> Full article ">Figure 4
<p>Results of fused TIR images from STARFM, ESTARFM, and STAEFM approaches.</p> Full article ">Figure 5
<p>LST (°C) estimated from (<b>a</b>) Landsat TIR, (<b>b</b>) STARFM, (<b>c</b>) ESTARFM, and (<b>d</b>) STAEFM on 18 January 2019.</p> Full article ">Figure 6
<p>Histograms of LST images from (<b>a</b>) Landsat, (<b>b</b>) STARFM, (<b>c</b>) ESTARFM, and (<b>d</b>) STAEFM on 18 January 2019.</p> Full article ">Figure 7
<p>Comparison of Landsat TIR images with fused images of (<b>a</b>) STARFM, (<b>b</b>) ESTARFM, and (<b>c</b>) STAEFM after being normalized from 0 to 10,000.</p> Full article ">Figure 8
<p>Hourly LST images (°C) estimated from STAEFM TIR on 18 January 2019 at (<b>a</b>) 09:00–10:00, (<b>b</b>) 11:00–12:00, and (<b>c</b>) 13:00–14:00, and (<b>d</b>–<b>f</b>) their histograms, respectively.</p> Full article ">Figure 9
<p>Regression of dry and wet edges estimated from STAEFM TIR on 18 January 2019 at (<b>a</b>) 09:00–10:00, (<b>b</b>) 11:00–12:00, and (<b>c</b>) 13:00–14:00.</p> Full article ">Figure 10
<p>Same as <a href="#remotesensing-12-00498-f008" class="html-fig">Figure 8</a>, but with the results of temperature vegetation dryness index (TVDI) on 18 January 2019.</p> Full article ">Figure 11
<p>Same as <a href="#remotesensing-12-00498-f008" class="html-fig">Figure 8</a>, but with the results of actual evapotranspiration (ET) (mm/hour) on 18 January 2019.</p> Full article ">Figure 12
<p>Correlation of hourly actual ET estimated from STAEFM TIR on 18 January 2019 with (<b>a</b>) albedo, (<b>b</b>) air temperature, (<b>c</b>) relative humidity, and (<b>d</b>) wind speed.</p> Full article ">
16 pages, 4135 KiB  
Article
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 - 1 Feb 2020
Cited by 35 | Viewed by 5779
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 [...] Read more.
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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Graphical abstract
Full article ">Figure 1
<p>Comparison of trends for each drought index for South Korea and North Korea.</p> Full article ">Figure 2
<p>Comparison of trends for each drought index for Taiwan.</p> Full article ">Figure 3
<p>Comparison of trends for each drought index for China.</p> Full article ">Figure 4
<p>Temporal changes in drought area ratios of total area in each district as determined by the (<b>a</b>) ESI, (<b>b</b>) VHI, (<b>c</b>) LAI, and (<b>d</b>) EVI values for the eight provinces in South Korea during the 2017 drought.</p> Full article ">Figure 5
<p>Temporal changes in drought area ratios of total area in each district as determined by the (<b>a</b>) ESI, (<b>b</b>) VHI, (<b>c</b>) LAI, and (<b>d</b>) EVI values for the nine provinces in North Korea during the 2017 drought event.</p> Full article ">Figure 6
<p>Temporal changes in drought area ratios of total area in each district as determined by the (<b>a</b>) ESI, (<b>b</b>) VHI, (<b>c</b>) LAI, and (<b>d</b>) EVI values for the four regions in Taiwan during the 2017 drought event.</p> Full article ">Figure 7
<p>Temporal changes in drought area ratios of total area in each district as determined by the (<b>a</b>) ESI, (<b>b</b>) VHI, (<b>c</b>) LAI, and (<b>d</b>) EVI values for the four regions in China during the 2017 drought event.</p> Full article ">
18 pages, 5521 KiB  
Article
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 2990
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 [...] Read more.
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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<p>The tendency of FS-2 RSI (remote sensing instrument) mean conversion factor (calibration coefficients) since preflight (before May 2004) [<a href="#B3-remotesensing-11-02634" class="html-bibr">3</a>].</p> Full article ">Figure 2
<p>The FS-5 RSI true images over the calibration sites: (<b>a</b>) Alkali Lake on September 10, 2018, and (<b>b</b>) Railroad Valley Playa on September 11, 2018. The red spots indicate the ground in situ measurement locations, and the cross-calibration area was selected within red squares.</p> Full article ">Figure 3
<p>The surface reflectance of FS-5 RSI spectral bands at the Alkali Lake site at zenith angles of (<b>a</b>) 10 degrees, (<b>b</b>) 30 degrees, and (<b>c</b>) 50 degrees including the azimuth angles when the solar zenith and azimuth angles were 60.84 and 171.09 degrees, respectively.</p> Full article ">Figure 4
<p>Same as <a href="#remotesensing-11-02634-f003" class="html-fig">Figure 3</a>, but over the Railroad Valley Playa site when the solar zenith and azimuth angles were 63.70 and 160.32 degrees, respectively.</p> Full article ">Figure 5
<p>The spectral radiations of FS-5 RSI varied with the atmospheric parameters. (<b>a</b>) Ozone content from 0 to 0.5 (cm-atm). (<b>b</b>) Water vapor content from 0 to 4 (g/ cm<sup>2</sup>) through the 6S model based on the vicarious calibration approach in Alkali Lake. The black, blue, green, red, and magenta lines stand for Pan, Blue, Green, Red, and NIR spectral bands, respectively.</p> Full article ">Figure 6
<p>Flow chart of the vicarious calibration.</p> Full article ">Figure 7
<p>The flow chart of cross-calibration.</p> Full article ">Figure 8
<p>Coefficient of variation of FS-5 RSI simulated at-sensor radiance under different atmospheric conditions in Alkali Lake and Railroad Valley Playa based on surface reflectance obtained from vicarious and cross-calibration methods. (<b>a</b>) Ozone content from 0 to 0.5 (cm-atm). (<b>b</b>) Water vapor from 0 to 4 (g/ cm<sup>2</sup>). (<b>c</b>) AOD (aerosol optical depth) content from 0 to 0.8.</p> Full article ">Figure 9
<p>Relative errors of FS-5 RSI simulated at-sensor radiance under the same atmospheric conditions in Alkali Lake and Railroad Valley Playa based on vicarious and cross-calibration methods. (<b>a</b>) Ozone content varied from 0 to 0.5 (cm-atm). (<b>b</b>) Water vapor varied from 0 to 4 (g/ cm<sup>2</sup>). (<b>c</b>) AOD content varied from 0 to 0.8.</p> Full article ">Figure 10
<p>The variation of at-sensor reflectance in different AOD550 values and different sizes of the calibration site. (<b>a</b>) The situation of ROC (reflectance of the calibration site) larger than ROE (reflectance of the environment). (<b>b</b>) The situation of ROC lower than ROE.</p> Full article ">Figure 11
<p>The FS-5 RSI calibration coefficient (K<sub>0</sub>) of vicarious calibration starting at preflight. The black, blue, green, red, and magenta lines stand for the Pan, Blue, Green, Red, and NIR (Near-infrared) spectral bands, respectively.</p> Full article ">Figure 12
<p>Same as <a href="#remotesensing-11-02634-f011" class="html-fig">Figure 11</a>, but for the cross-calibration.</p> Full article ">
21 pages, 3699 KiB  
Article
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 8 | Viewed by 5320
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 [...] Read more.
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 (LCOEPV) 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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<p>Flowchart of the methods used in this study.</p> Full article ">Figure 2
<p>Spatial distribution of high potential areas (HPA) for solar PV installation in Cambodia. HPA in red color are pixels with slope &lt;35° and &lt;10° for built-up areas and non-built-up areas which are deemed suitable for PV installation, respectively. Inset maps show zoomed-in views of areas that include the proposed location of a 100 MW solar park and an existing solar farm (Sunseap Asset) in Cambodia. The high density of pixels with high PV potential considering slope and land cover therefore can justify the installations of solar PV systems in these areas.</p> Full article ">Figure 3
<p>Spatial distribution of high potential areas (HPA) for solar PV installation in North Korea. HPA in red color are pixels with slope &lt;35° and &lt;10° for built-up areas and non-built-up areas which are deemed suitable for PV installation, respectively. Inset maps show zoomed-in views of areas with high density of pixels that have high potential for PV installation considering slope and land cover.</p> Full article ">Figure 4
<p>Spatial distribution of high potential areas (HPA) for solar PV installation in Myanmar. HPA in red color are pixels with slope &lt;35° and &lt;10° for built-up areas and non-built-up areas which are deemed suitable for PV installation, respectively. Inset maps show zoomed-in views of areas with high density of pixels that have high potential for PV installation considering slope and land cover.</p> Full article ">Figure 5
<p>Percentage of total area for each land cover suitable for PV installation for Bangladesh, Cambodia, Myanmar, North Korea and Timor-Leste.</p> Full article ">Figure 6
<p>Different types of electrification trend in highly populated areas in the Asia Pacific region: (<b>a</b>) Type A (Australia), (<b>b</b>) Type B (Lao PDR), and (<b>c</b>) Type C (North Korea). Many of the upper-middle to high income countries exhibit Type A but low to lower-middle income countries fall under Type B. Meanwhile, North Korea was the only country classified under Type C with a seemingly stagnant GDP per capita but increasing population living in unlit areas.</p> Full article ">Figure 7
<p>Percentage of household income allotted for PV electricity in the Asia Pacific Region. Bhutan and Russia are not shown due to very high PHIPVE values (&gt;100%).</p> Full article ">Figure 8
<p>Peak load demand and available supply of energy from solar PV in Myanmar. Excess energy can be saved or exported to the grid during window hours at ~10:15–15:45.</p> Full article ">Figure 9
<p>(<b>a</b>) Extracted transmission lines and (<b>b</b>) population in off-grid areas in Asia Pacific region that are potential service areas for solar PV installations.</p> Full article ">Figure 10
<p>Percentage of total population living in off-grid areas as defined by the extracted transmission lines.</p> Full article ">
29 pages, 6665 KiB  
Article
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 19 | Viewed by 4042
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 [...] Read more.
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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<p>Digital elevation model of the study area and stream network of the dry Afromontane forest landscape in northern Ethiopia.</p> Full article ">Figure 2
<p>Conceptual Framework.</p> Full article ">Figure 3
<p>(<b>a</b>) Digital elevation model (m) and (<b>b</b>) Slope (%) map of the catchment.</p> Full article ">Figure 4
<p>Land use and land cover dynamics in the year of 1986, 2001, and 2018 in Hugumburda Grat Kahisu State forest.</p> Full article ">Figure 5
<p>Soil textural classification based on Hydrological Soil Group (HSG) of Hugumburda Grat Kahisu state forest landscape.</p> Full article ">Figure 6
<p>Curve numbers (CN) based on digital elevation model (DEM) of study area.</p> Full article ">Figure 7
<p>Estimated mean of the top ten significant discharge (Q, in mm) of years from 1981 up to2018.</p> Full article ">Figure 8
<p>Annual rainfall dynamics in relationship to discharge and potential evapotranspiration (PET) (where Nprec is normalized precipitation, Qd is discharge, and NPET is normalized potential evapotranspiration).</p> Full article ">Figure 9
<p>Linear regression analysis between runoff and rainfall intensity.</p> Full article ">Figure 10
<p>Temporal scale factor increments of percentage rainfall and percentage runoff (Q).</p> Full article ">
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