The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of Earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image-space and ground-space. The automatically generated DEM initially represents the digital surface model (DSM) which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km². DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, specially in areas like Dehradun which is having highly undulating terrain with a large number of natural drainages. The difference image of reference i.e. edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e. DSM has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at 10m posting interval (referred to as Carto10 DEM), results in a mean difference of 8.74 m.

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and sentinel-1 data, and sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

Several studies have demonstrated the usefulness of SAR satellite images for monitoring surface parameters, in particular regular estimates of surface soil moisture (SSM) at regional (i.e., swath of several km²) or plot (i.e., area of interest of several hectares) spatial scales. These scales are rarely suitable for precision farming which requires mapping SSM at intra-plot spatial scale. In this context, the objective of this study is to analyze the capabilities of multi-temporal TerraSAR-X images to estimate the fine-scale SSM variability over bare agricultural plots (at a spatial scale ranging from 80 to 2800 m²). Time series of X-band satellite images were collected over a study site located in southwestern France, together with intra-field measurements of key soil descriptors (i.e., SSM, surface roughness, soil texture). The large dataset allows independent training and validating steps of a statistical algorithm (random forest), SSM being estimated using images acquired at low at high incidence angles. The level of performances obtained at the plot spatial scale, with R² ranging from 0.64 to 0.67 (depending on the considered incidence angle) and a RMSE close to 5.0 m^-3.m^-3, are exceeded by those obtained at a finer scale (700 m², corresponding to buffers with a 15 m radius). At this intra-plot spatial scale, the estimates based on the low incidence angles images are associated to a R² of 0.69 and a RMSE of 4.89 m^-3.m^-3, results slightly lower than performance obtained using high incidence angles images, R² of 0.72 and a RMSE of 4.55 m^-3.m^-3. Such magnitude of performance slightly increases over larger intra-plot spatial scales, the values of R² being superior to 0.75 with RMSE lower than 4.20 m^-3.m^-3 over areas of 2800 m² (corresponding to buffers with a 30 m radius).

In order to prevent serious damages from a possible earthquake and to determine the possible losses, in settlements under earthquake risk, it is very important to extract building inventory information for further determination of the performance of existing buildings. As conventional methods, such as field investigations, can be time-consuming and costly on an urban scale, approaches that are able to speed up these processes and reduce the costs are required. Determining at least some of the data required to determine the seismic performance of an existing building using alternative methods instead of conventional methods will provide a significant advantage. The study aims to investigate the potential of PlanetScope satellite imagery for extracting building inventory information. Thus, the main objective of the study are; to extract building using deep learning methods, to determine the height and the construction period of the buildings, and to extract building area. For this purpose, two 3-m PlanetScope satellite images were used over the study area located in Eskisehir, Turkey. Over forty buildings were located in the study area. The results showed that with PlanetScope Imagery detached buildings can be detected with high accuracy using deep learning methods, their height and area can be calculated, and the construction period can be determined. For future studies, the obtained information are planned to further be processed in a Geographical Information System (GIS) for building inventory and to be used for seismic vulnerability assessment studies of existing buildings.

Rapid economic development and population growth lead to fast urban expansion in both urban and rural landscapes. Accurate and updated mapping of urban expansions is curtail in urban and territorial planning for sustainable and strategic urban development. Using Earth Observation (EO) technologies, classification of urban areas in a rural landscape is more challenging than big cities. In this regard, in this paper, we aim at assessing the integration of Sentinel-1 and Sentinel-2 satellite data for classifying small urban areas in rural landscape in Google Earth Engine (GEE). Images of close dates from Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated to develop a machine learning classification through a Support Vector Classification (SVM) classifier. We have also added vegetation indices to the investigated dataset. As a study area, two rural areas in the Republic of North Macedonia has been selected. The results showed that the integration of Sentinel-1 and Sentinel-2 performed better than Sentinel-2 alone, with accuracy higher than 90%. For future studies, we recommend testing the dataset to different study areas and adding different EO data for obtaining even higher accuracy.

Numerous studies based on synthetic aperture radar (SAR) imagery have demonstrated the usefulness of microwave remote sensing data for surface soil moisture (SSM) estimation. Among the parameters that can be derived from these images, backscatter coefficients have been the subject of most studies, unlike polarimetric approaches whose performance and limitations remain to be established. In this context, this paper aims at addressing the potential of polarimetric indices derived from C-band Radarsat-2 images to estimate the surface soil moisture over bare agricultural soils (at the plot spatial scale). Images have been acquired during the Multispectral Crop Monitoring (MCM) experiment throughout an agricultural season over a study site located in southwestern France. Synchronously with the acquisitions of the 22 SAR images, field measurements of soil descriptors were collected on surface states with contrasting conditions, with SSM levels ranging from 2.4 to 35.3% m³·m⁻³, surface roughness characterized by standard deviation of roughness heights ranging from 0.5 to 7.9 cm, and soil texture showing fractions of clay, silt and sand between 9-58%, 22-77%, and 4-53%, respectively. The dataset was used to independently train and validate a statistical algorithm (random forest), SSM being estimated using the polarimetric indices and backscatter coefficients with co and cross-polarization states derived from the SAR images. Among the SAR signals tested, the performance levels are very uneven, as evidenced by magnitude of correlation (R²) ranging from 0.35 to 0.67. The following polarimetric indices derived from the SAR images present the best estimates of SSM: the first, second and third elements of the diagonal (T11, T22 and T33), eigenvalues (λ₁, λ₂, λ₃ from Cloude–Pottier decomposition), Shannon entropy, Freeman double-bounce and volume scattering mechanisms, the total scattered power (SPAN), and the backscattering coefficients whatever the polarization state, with correlations greater than 0.6 and with RMSE ranged between 4.8 and 5.3% m³·m⁻³. These performances remain limited although similar to those obtained using other approaches (empirical, physical based, or model inversion).

Land monitoring is a dynamic process that is subject to permanent change and transformation over time under the influence of various natural and human factors. Since solving problems related to change detection manually is a time-consuming operation, for this reason, in this paper two methods of change detection based on deep learning algorithms are presented to generate a map of changes. The purpose of using DL algorithms and especially CNN's is monitoring environmental change in to change and no change classes. In order to evaluate the capability of the first proposed method, hyperspectral images were used. This method divided into two phases: generating training data and accuracy assessment by CNN parameters. The OSCD datasets were used to evaluate the second proposed method. In this method, the networks based on Unet. The proposed frameworks are automatic and capable of extracting information with high precision. In each method, the overall accuracy is over 95% and the kappa coefficient is close to one.

One of the numerous fundamental tasks to perform rescue operations after the earthquake, check the status of buildings has been destroyed. The methods to obtain the damage map are two categories Shared. The first group of methods uses data before and after the earthquake, and the second group that only uses the data after the earthquakes that we want to offer a flexible and according to information that we are available to achieve the damage map. In this paper, we work on VHR satellite images of Haiti, and by UNet that is a convolution network. The learning algorithms profound changes to improve the results were intended to identify the damage of the buildings caused by the earthquake. The deep learning algorithms require very training data that it's one of the problems that we want to solve it. As well as Unlike previous studies by examining pixel by pixel degradation, ultimate precision to increase that show the success of this approach felt and has been able to reach the overall accuracy of 78.61%. The proposed method for other natural disasters such as rockets, explosions, tsunamis, and floods also destroyed buildings in urban areas is to be used.

The increased frequency of floods, landslides and avalanches recorded across Peru in the last decades suggests that the country is one of the most affected by El Niño-Southern Oscillation and its cascading hazards. Catastrophic floods that happened in Lima in 1997–1998 and 2017–2018 caused hundreds of fatalities and significant economic damage. In this paper, we test the hypothesis that information mined from satellite synthetic aperture radar (SAR) images can provide valuable input into the common workflow of flooding hazard assessment, and thus improve current methods for risk assessment in urban areas. The complete SAR image archives collected over the Rímac River basin by the European Space Agency (ESA)’s European Remote-Sensing (ERS-1/2) missions and the European Commission’s Copernicus Sentinel-1 constellation were screened. SAR backscatter color composites and ratio maps were created to identify change patterns occurred prior, during and after the catastrophic flooding events mentioned above. A total of 409 areas (58.50 km2) revealing change were mapped, including 197 changes (32.10 km2) due to flooding-related backscatter variations (flooded areas, increased water flow in the riverbed, and riverbank collapses and damage), and 212 (26.40 km2) due to other processes (e.g., new urban developments, construction of river embankments, other engineering works, vegetation changes). The areas inundated during the flooding events in 1997–1998 and 2017–2018 mostly concentrate along the riverbanks and plain, where low-lying topography and gentle slopes (≤5°), together with the presence of alluvial deposits, also indicate greater susceptibility to flooding. The accuracy in flood area delineation achieved with the proposed change detection method was assessed by comparison with the potential maximum flood extent map that was produced by Copernicus Emergency Management Service (EMS) in the framework of the Risk and Recovery Activation EMSN-038 during the March 2017 flood. All the observed spatial and temporal backscatter change patterns were interpreted through geospatial integration with ancillary data (topography, geology, permanent and seasonal water bodies, urban footprint, new urban development, roads and infrastructures, and population at the district level) and a risk classification map of Lima was produced. The map highlights the sectors of potential concern along the Rímac River, should flooding events of equal severity as those captured by SAR images occur in the future. Compared to published hazard maps made solely based on geological factors, this product has the advantage to embed event-based information and knowledge of the impacts of urbanization.

Reference

Alvan Romero, N.; Cigna, F.; Tapete, D. ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru. Appl. Sci. 2020, 10, 6598, doi:10.3390/app10186598

The Methana peninsula in Greece is the westernmost dormant but geodynamically and hydrothermally active volcanic system of the Hellenic Volcanic Arc, which formed from the subduction of the African tectonic plate beneath the Eurasian plate. Volcanic hazard in Methana is considered “low” as the last historic eruption was registered in approximately 230 BC and no alarming signs were observed in recent times. Nevertheless, several aspects (including the proximity to a densely populated region and the city of Athens) provide sufficient motivation for a dedicated investigation into present-day deformation patterns at the volcano. This study exploits a long stack of 99 C-band Synthetic Aperture Radar (SAR) images acquired by the Copernicus Sentinel-1 satellite constellation along ascending track 102 in the period from March 2015 to August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using both Persistent (PS) and Distributed (DS) scatterers. Satellite geodetic data from permanent GNSS stations and 2006-2019 GPS benchmark surveying are used as reference to calibrate the MT-InSAR data analysis, as well as to validate MT-InSAR results accuracy. MT-InSAR and geodetic results are combined with geological, seismological and geomorphological data, to better understand the observed potential ground deformation patterns and trends. Line-of-sight displacement velocities within the peninsula reach −18.1 mm/year. The results suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes, such as settlement in the suburban zones, mass movements and some seasonal fluctuation overlapping with the long-term trend. The retrieved geoinformation provide a first account of deformation patterns, that can feed into the volcano baseline hazard assessment, as well as the monitoring system that is being built in these recent years.

Reference:

GATSIOS, T.; CIGNA, F.; TAPETE, D.; SAKKAS, V.; PAVLOU, K.; PARCHARIDIS, I. Copernicus Sentinel-1 MT-InSAR, GNSS and seismic monitoring of deformation patterns and trends at the Methana volcano, Greece. Applied Sciences 2020, 10, 6445, doi:10.3390/app10186445