Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Proceedings, 2019, ECRS-3 2019

3rd International Electronic Conference on Remote Sensing

Online | 22 May–5 June 2018

Volume Editor: Dr. Qi Wang


  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story (view full-size image) Today, an increasing number of remote sensing platforms and sensors can provide more detailed [...] Read more.
View options order results:
result details:
Displaying articles 1-12
Export citation of selected articles as:

Research

Open AccessProceedings
Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)
Proceedings 2019, 18(1), 1; https://doi.org/10.3390/ECRS-3-06187
Published: 23 May 2019
Viewed by 156 | PDF Full-text (1291 KB)
Abstract
Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the [...] Read more.
Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event. Full article
Open AccessProceedings
Land Subsidence Monitoring in Azar Oil Field Based on Time Series Analysis
Proceedings 2019, 18(1), 2; https://doi.org/10.3390/ECRS-3-06190
Published: 23 May 2019
Viewed by 105
Abstract
Azar oil field is located to the east of the city of Mehran, Ilam. The tank of this oil field is shared by Iraq’s oil field whose name is Badra where oil extraction started in 2014, and they have maximized its oil exploration [...] Read more.
Azar oil field is located to the east of the city of Mehran, Ilam. The tank of this oil field is shared by Iraq’s oil field whose name is Badra where oil extraction started in 2014, and they have maximized its oil exploration since 2017. Iran started oil exploration in 2017. In this study, we estimated the land surface deformation in Azar oil field using persistent scattering interferometry (PSI) in order to determine the corresponding subsidence source. PSI is a method of time series analysis used to measure various surface deformations. The Stanford Method for Persistent Scatterers (StaMPS) package was employed to process 50 ascending Sentinel-1A satellite images collected between 2016 and 2019, and 50 descending Sentinel-1A satellite images were collected between 2014 and 2019 to extract horizontal and vertical displacement components from the Interferometric Synthetic Aperture Radar (InSAR) LOS (line-of-sight) displacement. The results showed that the maximum displacement rate in the Iran-Iraq joint oil field between 2016 and 2019 was 15 mm in the vertical direction. Moreover, the maximum displacement rate measured in the horizontal direction was 30 mm. The vertical deformation confirms typical patterns of subsidence caused by oil extraction, and the horizontal deformation occurred due to considerable precipitation events after a drought period, as well as the presence of sand layers at different depths of the earth. Full article
Open AccessProceedings
RISAT-1 Compact Polarimetric Calibration and Decomposition
Proceedings 2019, 18(1), 3; https://doi.org/10.3390/ECRS-3-06189
Published: 23 May 2019
Viewed by 87 | PDF Full-text (834 KB)
Abstract
Indian Space Research Organisation’s Radar Imaging Satellite (RISAT) -1 was the first Synthetic Aperture Radar (SAR) satellite equipped with the compact polarimetric (CP) mode for data acquisition. To exploit the advantages offered by the CP mode, the datasets need to be polarimetrically calibrated. [...] Read more.
Indian Space Research Organisation’s Radar Imaging Satellite (RISAT) -1 was the first Synthetic Aperture Radar (SAR) satellite equipped with the compact polarimetric (CP) mode for data acquisition. To exploit the advantages offered by the CP mode, the datasets need to be polarimetrically calibrated. The polarimetric calibration procedure estimates the polarimetric distortions in the datasets caused due to channel imbalance, crosstalk, and Faraday rotation. These polarimetric distortions cause the misinterpretation of the ground targets in the polarimetric decomposition techniques. The Freeman compact-pol polarimetric calibration algorithm is the most commonly used algorithm. In this study, the RISAT-1 Circular Transmit Linear Receive (CTLR) dataset of the RISAT Cal Val site was used to estimate the polarimetric distortion parameters and these distortion parameters were used to polarimetrically calibrate the RISAT-1 CTLR dataset of the Doon Valley region, Uttarakhand, India. The Cloude compact-pol decomposition algorithm was used to evaluate the ground target characterization accuracy before and after polarimetric calibration using the Freeman compact-pol polarimetric calibration algorithm. Before polarimetric calibration, urban targets were showing surface scattering behavior and river channels were showing increased double-bounce scattering behavior. After polarimetric calibration, the urban targets showed dominance in double-bounce scattering and river channels showed dominance in surface scattering as per the theory. Full article
Open AccessProceedings
Spaceborne Nitrogen Dioxide Observations from the Sentinel-5P TROPOMI over Turkey
Proceedings 2019, 18(1), 4; https://doi.org/10.3390/ECRS-3-06181
Published: 23 May 2019
Viewed by 97 | PDF Full-text (2639 KB)
Abstract
With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the [...] Read more.
With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutants, nitrogen dioxide (NO2), is mainly caused by the combustion of fossil fuels, especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relationship of the tropospheric NO2 retrieved from the recently launched Sentinel-5 Precursor, a low-earth-orbit atmosphere mission dedicated to monitoring air pollution equipped with the spectrometer Tropomoi (Tropospheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of the NO2 collected from July 2018 to January 2019 and the statistic population data from 2017. The results showed a significant correlation of higher than 0.72 between the population density and the maximum NO2 values. For future studies, we recommend investigating the correlation of different air pollutants with population and other factors contributing to air and environmental pollution. Full article
Open AccessProceedings
Effect of Atmospheric propagation of Electromagnetic Wave on DInSAR Phase
Proceedings 2019, 18(1), 5; https://doi.org/10.3390/ECRS-3-06188
Published: 22 May 2019
Viewed by 93 | PDF Full-text (762 KB)
Abstract
Earth’s topography and deformation mapping have become easier by the use of a geodetic technique popularly known as repeat-pass Synthetic Aperture Radio Detection and Ranging (SAR/RADAR) Interferometry (InSAR). However, the measurements obtained through InSAR are liable to atmospheric errors. Water vapor and clouds [...] Read more.
Earth’s topography and deformation mapping have become easier by the use of a geodetic technique popularly known as repeat-pass Synthetic Aperture Radio Detection and Ranging (SAR/RADAR) Interferometry (InSAR). However, the measurements obtained through InSAR are liable to atmospheric errors. Water vapor and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the wave. An increase is induced in the observed range due to tropospheric refractivity and path shortenings are observed due to ionospheric electron density. The quality of phase measurement is affected by these atmospheric induced propagation delays and hence errors are introduced in the topography and deformation fields. A three-pass differential synthetic aperture radar interferometry (DInSAR) is performed from two interferograms and the effect of this atmospheric delay is studied on the same study area. The interferograms are generated from three single look complex (SLC) phased array type L-band synthetic aperture radar (PALSAR) data of advanced land observing satellite (ALOS). Atmospheric phase correction is done on the generated DInSAR and it is found that atmospheric error correction is essential in order to avoid inaccurate erratic height and deformation measurements. Full article
Open AccessProceedings
Post-Fire Effect Modeling for the Permafrost Zone in Central Siberia on the Basis of Remote Sensing Data
Proceedings 2019, 18(1), 6; https://doi.org/10.3390/ECRS-3-06202
Published: 4 June 2019
Viewed by 61 | PDF Full-text (327 KB)
Abstract
The increasing trend of larch forests burning in the permafrost zone (60–65° N, 95–105° E) is observed in Siberia. Up to 10–15% of entire larch forests were damaged by wildfire during the last two decades. Current research analysed the reflectance and thermal anomalies [...] Read more.
The increasing trend of larch forests burning in the permafrost zone (60–65° N, 95–105° E) is observed in Siberia. Up to 10–15% of entire larch forests were damaged by wildfire during the last two decades. Current research analysed the reflectance and thermal anomalies of the post-pyrogenic sites under the conditions of permafrost. Studies are based on a long-term Terra and Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) survey for 2006–2018. We used IR thermal range data of 10.780–11.280 microns (MOD11A1 product) and we evaluated the Normalized Difference Vegetation Index (NDVI) from MOD09GQ product as well. The averaged temperature and NDVI dynamics were investigated in total for 50 post-fire plots under different stages of succession (1, 2, 5 and 10 years after burning) in comparison with non-disturbed vegetation cover sites under the same conditions. We recorded higher temperatures (20–47% higher than average background value) and lower NDVI values (9–63% lower than non-disturbed vegetation cover) persisting for the first 10 years after the fire. Under conditions of natural restoration, thermal anomalies of the ground cover remained significant for more than 15 years, which was reflected in long-term satellite data and confirmed by ground-based measurements. To estimate the impact of thermal anomalies on soil temperature and thawed layer depth we used the Stefan’s solution for the thermal conductivity equation. According to the results of numerical simulation, depth of the seasonal thawed layer could increase more than 20% in comparison with the average statistical norm under the conditions of excessive heating of the underlying layers. This is a significant factor in the stability of Siberian permafrost ecosystems requiring long-term monitoring. Full article
Figures

Graphical abstract

Open AccessProceedings
Estimation of Sunflower Yields at a Decametric Spatial Scale—A Statistical Approach Based on Multi-Temporal Satellite Images
Proceedings 2019, 18(1), 7; https://doi.org/10.3390/ECRS-3-06203
Published: 4 June 2019
Viewed by 73 | PDF Full-text (246 KB)
Abstract
Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. The aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal images [...] Read more.
Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. The aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal images provided by the Landsat-8 and Sentinel-2 missions. The proposed approach is based on a statistical algorithm, testing different sampling strategies to partition the dataset into independent training and testing sets: A random selection (testing different ratio), a systematic selection (focusing on different plots) and a forecast procedure (using an increasing number of images). Emphasis is put on the use of high spatial and temporal resolution satellite data acquired throughout two agricultural seasons, on a study site located in southwestern France. Ground measurements consist in intra-plot yields collected by a surveying harvesting machine with GPS system on track mode. The forecast of yield throughout the agricultural season provides early accurate estimation two months before the harvest, with R2 equal to 0.59 or 0.66 and root mean square error (RMSE) of 4.7 or 3.4 q ha−1, for the agricultural seasons 2016 and 2017 respectively. Results obtained with the random selection or the systematic selection will be developed later, in a longer paper. Full article
Open AccessProceedings
Probability Estimation of Change Maps Using Spectral Similarity
Proceedings 2019, 18(1), 8; https://doi.org/10.3390/ECRS-3-06183
Published: 4 June 2019
Viewed by 83 | PDF Full-text (1321 KB)
Abstract
Change Detection (CD), which is a process of identifying changes that have occurred in a geographical area over the time, plays a key role in many applications, including assessing natural disasters, monitoring crops, and managing water resources. In the past decades, many CD [...] Read more.
Change Detection (CD), which is a process of identifying changes that have occurred in a geographical area over the time, plays a key role in many applications, including assessing natural disasters, monitoring crops, and managing water resources. In the past decades, many CD techniques have been proposed. Hence, evaluating and analyzing of the probability of changes and interpreting them is an essential task which leads to better management of natural resources and the prevention of disasters. For this purpose, we adopted an approach to estimate the probability of changes that have occurred in the image scene. Based on this approach, changed pixels are categorized and labeled as probabilities (in percentage format). In this paper, the proposed framework consists of the following four steps. Firstly, this research produces a final binary change map (BCM) through combining the results of some of popular binary CD methods that have been proposed in the literature. Then an unmixing process is adopted and in the next step the spectral similarity of pixels is calculated in the abundance maps of endmembers. A measurement of spectral similarity identifies the finer spectral differences between the two hyperspectral images (HSIs). Finally, spectral similarity values are masked by the final BCM resulting in a probability map of changes. The experimental results verified that the method is able to obtain good results and may be well suited for hyperspectral CD applications. Full article
Open AccessProceedings
Comparison of Proximal Remote Sensing Devices for Estimating Physiological Responses of Eggplants to Root-Knot Nematodes
Proceedings 2019, 18(1), 9; https://doi.org/10.3390/ECRS-3-06182
Published: 23 May 2019
Viewed by 87 | PDF Full-text (258 KB)
Abstract
Proximal remote sensing devices are becoming widely applied in field plant research to estimate biochemical (e.g., pigments or nitrogen) or agronomical (e.g., leaf area, biomass, or yield) parameters as indicators of stress. Non-invasive characterization of plant responses allows for the screening of larger [...] Read more.
Proximal remote sensing devices are becoming widely applied in field plant research to estimate biochemical (e.g., pigments or nitrogen) or agronomical (e.g., leaf area, biomass, or yield) parameters as indicators of stress. Non-invasive characterization of plant responses allows for the screening of larger populations faster than conventional procedures which, in addition to requiring more time, either imply the destruction of material or are subjective (e.g., visual ranking). This study explores the comparison of a set of remote sensing devices at single-leaf and whole-canopy levels based on their performance in assessing how the eggplant and its yield responds to grafting as a way to tolerate root-knot nematodes. The results showed that whole-canopy measurements, such as the Green Area (GA) derived from the Red-Green-Blue (RGB) images (r = 0.706 and p-value = 0.001**) or the canopy temperature (r = −0.619 and p-value = 0.005**), outperformed single-leaf measurements, such as the leaf chlorophyll content measured by the Dualex (r = 0.422 and p-value = 0.059) assessing yield. Moreover, other parameters, such as the time required to measure each sample or the cost of the sensors, were taken into account in the discussion. In sum, indices derived from the RGB images demonstrated their robust potential for the assessment of crop status as a low-cost alternative to other more expensive and time-consuming devices. Full article
Open AccessProceedings
Observing Post-Fire Vegetation Regeneration Dynamics Exploiting High-Resolution Sentinel-2 Data
Proceedings 2019, 18(1), 10; https://doi.org/10.3390/ECRS-3-06200
Published: 4 June 2019
Viewed by 40 | PDF Full-text (621 KB) | Supplementary Files
Abstract
Information related to the impact of wildfire disturbances on ecosystems is of paramount interest to account for environmental loss, to plan strategies for facilitating ecosystem restoration, and to monitor the dynamics of vegetation restoration. Phenological metrics can represent a good candidate to monitor [...] Read more.
Information related to the impact of wildfire disturbances on ecosystems is of paramount interest to account for environmental loss, to plan strategies for facilitating ecosystem restoration, and to monitor the dynamics of vegetation restoration. Phenological metrics can represent a good candidate to monitor and quantify vegetation recovery after natural hazards like wildfire disturbances. Satellite observations have been demonstrated to be a suitable tool for wildfire disturbed areas monitoring, allowing both the identification of burned areas and the monitoring of vegetation recovery. This research study aims to identify post-fire vegetation restoration dynamics for the area surrounding Naples (Italy), affected by severe wildfires events in 2017. Sentinel-2 satellite data were used to extract phenological metrics from the estimated Leaf Area Index (LAI) and to relate such metrics to environmental variables in order to evaluate the vegetation restoration and landslide susceptibility for different land use classes. Full article
Open AccessProceedings
Sentinel-1 GRD Preprocessing Workflow
Proceedings 2019, 18(1), 11; https://doi.org/10.3390/ECRS-3-06201
Published: 4 June 2019
Viewed by 79 | PDF Full-text (252 KB) | Supplementary Files
Abstract
The Copernicus Programme has become the world’s largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. Sentinel-1 Synthetic Aperture Radar (SAR) data have improved spatial resolution and high revisit frequency, making them useful for [...] Read more.
The Copernicus Programme has become the world’s largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. Sentinel-1 Synthetic Aperture Radar (SAR) data have improved spatial resolution and high revisit frequency, making them useful for a wide range of applications. While few research applications need Sentinel-1 Ground Range Detected (GRD) data with few corrections applied, a wider range of users needs products with a standard set of corrections applied. In order to facilitate the exploitation of Sentinel-1 GRD products, there is the need to standardise procedures to preprocess SAR data to a higher processing level. A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data is presented here. The workflow aims to apply a series of standard corrections, and to apply a precise orbit of acquisition, remove thermal and image border noise, perform radiometric calibration, and apply range Doppler and terrain correction. Additionally, the workflow allows spatially snapping of Sentinel-1 GRD products to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual constellations by means of data fusion techniques. The presented workflow allows the production of a set of preprocessed Sentinel-1 GRD data, offering a benchmark for the development of new products and operational down-streaming services based on consistent Copernicus Sentinel-1 GRD datasets, with the aim of providing reliable information of interest to a wide range of communities. Full article
Open AccessProceedings
Evaluating Sentinel-2 Red-Edge Bands for Wetland Classification
Proceedings 2019, 18(1), 12; https://doi.org/10.3390/ECRS-3-06184
Published: 9 August 2019
Viewed by 182 | PDF Full-text (1182 KB)
Abstract
Due to the high spatial heterogeneity and temporal variability, wetlands are one of the most difficult ecosystems to observe using remote sensing data. With the additional Sentinel-2 vegetation red-edge bands, an improvement of the vegetated classes classification is expected. In order to investigate [...] Read more.
Due to the high spatial heterogeneity and temporal variability, wetlands are one of the most difficult ecosystems to observe using remote sensing data. With the additional Sentinel-2 vegetation red-edge bands, an improvement of the vegetated classes classification is expected. In order to investigate the influence of the Sentinel-2 red-edge bands, in this paper we evaluate two classification scenarios over wetland classes. The first scenario excludes the red-edge bands, while in the second scenario all red-edge bands are included in the classification dataset where two different wetland classes—intensive vegetated wetland classes such as swamps and partially decayed vegetated wetland areas such as bogs—are classified using a support vector machine (SVM) learning classifier. The classes are defined using high-resolution images from an Unmanned Aerial Vehicle (UAV) obtained on the same date with the passing of the Sentinel-2 satellite over the study area. As expected, the results show a significant improvement of the intensive vegetated wetlands, with more than 30% in both user and producer accuracy, while no significant changes are noted in the partially decayed vegetated wetlands. For future studies, we recommend evaluating the influence of the Sentinel radar data over wetland areas. Full article
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top