Proceedings, 2019, ECRS-3

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

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

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

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

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

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

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

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

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

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

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

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

Urban areas are rapidly changing all over the world and, therefore, continuous mapping of the changes is essential for urban planners and decision makers. Urban changes can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The urban scene is characterized by very high complexity, containing objects formed from different types of man-made materials as well as natural objects. The aim of this study is to detect urban growth which can be further utilized for urban planning. Although high-resolution optical data can be used to determine classes more precisely, it is still difficult to distinguish classes, such as residential regions with different building type, due to spectral similarities. Synthetic aperture radar (SAR) data provide valuable information about the type of scattering backscatter from an object in the scene as well as its geometry and its dielectric properties. Therefore, the information obtained using SAR processing is complementary to that obtained using optical data. This proposed algorithm has been applied on a multi-sensor dataset consisting of optical QuickBird images (RGB) and full polarimetric L-band UAVSAR (Unmanned Aerial Vehicle Synthetic Aperture Radar) image data. After preprocessing the data, the coherency matrix (T), and Pauli decomposition are extracted from multi-temporal UAVSAR images. Next, the SVM (support vector machine) classification method is applied to the multi-temporal features in order to generate two classified maps. In the next step, a post-classification-based algorithm is used to generate the change map. Finally, the results of the change maps are fused by the majority voting algorithm to improve the detection of urban changes. In order to clarify the importance of using both optical and polarimetric images, the majority voting algorithm was also separately applied to change maps of optical and polarimetric images. In order to analyze the accuracy of the change maps, the ground truth change and no-change area that were gathered by visual interpretation of Google earth images were used. After correcting for the noise generated by the post-classification method, the final change map was obtained with an overall accuracy of 89.81% and kappa of 0.8049. Full article