The ground control points (GCPs) are used in the process of indirect georeferencing the Unmanned Aerial Systems (UAS) images. A minimum of 3 ground control points is required, but increasing the number of GCPs will lead to higher accuracy of the final results i.e. point cloud, 3D mesh, orthomosaic or DSM, if the GCPs are introduced as constraints in the bundle block adjustment process. Moreover, exceeding the number of ground control points is a time-consuming process, both in the field and computationally. The aim of the study is to provide the answer to the question of how many ground control points are necessary in order to derive high precision results. To obtain the results, an area of about 1.5 ha has been photographed with a low-cost UAS, namely DJI Phantom 3 Standard at two different heights: 28 m and 35 m above ground and a number of 50 ground control points, uniformly distributed over the study area, were measured using a total station. The flight planning was made using the Pix4D software, choo sing the longitudinal and transversal overlap of 80% and 40% respective ly, the camera being oriented in nadiral pos ition. The first flight was made in double grid, 122 images with the GSD of 1.2 cm being acquired and after the second flight, 51 images were acquired in a single grid, with the GSD of 1 cm. First, the UAS images were process using the Pix4D Mapper Pro software, using a minimum number of ground control points while the 47 remaining control points served as check points (CP) for accuracy assessment. So, the CPs were manually measured on each image, the coordinates being compared with the ones determined with high precision. Then, the number of GCP was gradually increased up to 40, the accuracy being checked on the remaining 10 CPs. The accuracy assessment, both in horizontal and vertical direction, with dense and well distributed GCP showed a RMS of 23.3 cm for a minimum of 3 GCPs while compared with the other cases, in which the RMS decreases under 6 cm after 10 GCPs. The second test was made with 3DF Zephyr software using a free-network approach in the bundle adjustment and only at the end of the bundle adjustment the GCPs are used to perform a similarity (Helmert) transformation in order to bring the image network results into the desired reference coordinate system. As already demonstrated in literature, if no constraint is introduced in the process of bundle adjustment, increasing the GCPs number will not improve the 3D shape of the surveyed scene, therefore, the accuracy of the georeferencing process by using different number of GCP must be evaluated in this situation too. Also, the point clouds and the mesh surfaces derived automatically after using the minimum and the optimum number of GCPs respectivel y, were compared with a TLS point cloud. The results expressed a clear overview on the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results.

    Clouds and cloud-shadow are a persistent problem in all optical
    satellite imagery. Plenty of methods have been suggested in the literature
    to address this problem, and reconstruct the missing part of the optical signal.
    In this work, three methods representative
    of different approaches to the cloud removal problem are
    compared. The first method
    is temporal fitting using Fourier series, which benefits from the temporal continuity of the
    signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs
    radiometric consistency as a tool to determine the missing part of the
    optical signal. These three methods are first presented and their theoretical background described,
    followed by a discussion of their implied assumptions,
    general performance, and failure modes. A set of experiments using Landsat 8 time series with
    diverse land cover types were conducted. The quantitative results of the
    three methods using simulated clouds as well as real ones are presented.
    Finally, some concluding remarks about the relative advantages of the three
    approaches are listed, in addition to some recommendations about their use.

Development of remote sensing techniques has made a significant contribution to assess climatology phenomena and determine which predictors have a noticeable influence on the intensity of the surface urban heat island (SUHI) effects. The aim of this study is to analyse the effectiveness of the geostatistical modelling of thermal properties of land surface in an expanding city, Poznan in west Poland. The applied models – Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were used to explore the strength of the correlation between explanatory variables (e.g. porosity index, ISA, road density) and dependent variables defined as mean SUHI intensity (MSUHIⁱ_intensity ) and difference mean SUHI intensity between 2001 and 2011 ( ΔMSUHIⁱ_intensity) for each district within the city . In the research we employed two Landsat images (2001 and 2011) on the basis of which SUHI intensity and land-cover maps were generated. Classification results ( overall accuracy of 97,5% ) obtained through Artificial Neural Network (ANN) algorithm were used as explanatory variables to identify the impact of land-cover and its derived products on the SUHI effect within the city. On the grounds of the combination of the chosen predictors it was possible to examine whether land-cover types and their derivatives determined mean SUHI values (MSUHIⁱ_intensity) and if changes in land cover effected ΔMSUHIⁱ_intensity. On the basis of statistical indicators ( I-Moran index, AIC_c, VIF, R²) it turned out that the most suitable predictors were ISA, ΔISA and road density. The results for the cross-sectional GWR model (R² = 0,730) were better than for the OLS (R² = 0,470). In contrast to the cross-sectional analyses, the goodness of fit for the longitundinal OLS model (R² = 0,501) was similar to the GWR results (R² = 0,500). However, the GWR revealed that local regression residuals were differentiated – values for some regions in the city centre were overestimated and for the outskirts of Poznan R² underestimation values were noted . This situation indicated that unlike other cities for which the longitundinal GWR modelling gave better results, for Poznan the GWR did not improve the modelling effectiveness (ZHOU, WANG, 2011; DEILAMI, KAMRUZZAMAN, 2017). This means that associations between dependent and explanatory variables are stationary and as a result ΔMSUHIⁱ_intensity is not spatially variable.

This study has identified associations between SUHI effects and remotely-sensed land-cover parameters in Poznan. Results demonstrated that the GWR methods have proved effective in modelling using cross-sectional analysis (R² = 0,730). In the case of estimating thermal conditions variability between 2001 and 2011 applying the GWR did not improve the modelling results (R² = 0,500), what could be explained by the different spatial structure of the city and a moderate climate with both maritime and continental elements.

NASA RapidScat is the first satellite scatterometer that flew in non-Sun-synchronous orbit. It’s unique orbit enabled co-located measurements with multiple satellite remote-sensing instruments that mostly fly in Sun-synchronous orbits. RapidScat primary mission was the retrieval of global ocean wind vectors from normalized radar backscatter measurements. Instrument operated onboard the International Space Station between September 2014 and November 2016 covering a latitude range between ±51.6^o. This paper describes process that combines RapidScat’s active/passive mode, simultaneously measuring both the radar surface backscatter and microwave emission from the system noise temperature. This work presents the radiometric (passive mode) cross-calibration using the GPM Microwave Imager (GMI), to eliminate brightness temperature measurement biases between a pair of radiometer channels operating at slightly different frequencies and incidence angles. The GPM Microwave Imager (GMI) on the GPM Core is a non-sun-synchronous orbital, dual-polarization, conical-scanning, multi-channel (ranging from 10 to 183 GHz). Since the RapidScat operates at 13.4 GHz and the closest GMI channel is 10.65 GHz, GMI Tb’s were required to be normalized before the calibration. The GMI brightness temperatures were translated using the radiative transfer model (RTM) to yield an equivalent Tb prior to direct comparison with RapidScat. Seasonal biases between two radiometers have been calculated for both polarizations as a function of atmospheric and ocean brightness temperature models. Calculated biases may be used for measurement correction and reprocessing. Trends from observations during a 20-month period have been described, and indicate that RapidScat instrument in both active and passive modes can be used to connect the sun-synchronous sensors that observe the oceans at different local times to remove inter sensor biases. Both L2A and L2B RapidScat data sets were provided by the NASA Physical Oceanography Distributed Active Archive Center (PODAAC) at the Jet Propulsion Laboratory.

Information about the land cover and land use of a region are fundamental in studies such as mapping of deforestation and forest degradation. Quantifying and monitoring woody cover distribution in semiarid regions is challenging, due to their scattered distribution. Data mining has been widely used in remote sensing data for information extraction of spectral and temporal data in the analysis of change detection. The main objective of this study was to characterize the land cover and land use over 2000-2010 time period for the Brazilian Caatinga seasonal biome using a temporal NDVI series and Geographic Object-Based Image Analysis. For each of the target years was obtained NDVI images derived from MODIS (MOD13Q1, at 250 m spatial and 16 day temporal scale) sensor during the dry season to predict wood cover in the municipality of Buriti dos Montes, in the state of Piauí, Northeast region of Brazil (H13V09 tile). The images were automatically pre-processed and in the GEOBIA approach was performed image segmentation, spatial and spectral attribute extraction and labelled according to the following legend: Tree Cover (TC) and Cropland/Grass (CG), to obtain a classification using the decision tree supervised algorithm. Our results showed that approach using GEOBIA presented Kappa Index of 0.58 and Global Accuracy (GA) of 0.81% and showed better accuracy for the Tree Cover. Finally, we recommend new studies using a higher spatial resolution data, as well as the addition of other parameters strongly related to vegetation of semiarid regions.

Arable soils are subjected to altering influence of agricultural and natural processes determining surface feedback patterns (spatial arrangement, distribution and manifestation of surface structure elements) therefore affecting their ability to reflect light. However remote soil mapping and monitoring usually ignore information on surface state at the time of spectral data acquisition. Conducted research demonstrates the contribution of seasonal surface feedback dynamics to soil reflectance and its relationship with soil properties.

Research area is comprised of 4 test sites located in Saratovskaya and Tulskaya oblasts of Russia.

Analysis of variance showed that even at a sample level surface patterns significantly affect soil spectral features accounting for 71 % of their variation (Pillai’s trace=0.71, F=2.37, p=0.03, partial eta squared=0.71). The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In case of organic matter and medium and coarse sand particles correlation decreases with the destruction of surface patterns. For particles of fine sand and coarse silt, homogenization changes the spectral areas of high correlation. Partial least squares regression models also demonstrated the variations in complexity, R²cv and RMSEPcv.

Two-year field research showed that on arable lands seasonal surface feedback dynamics causes 22-46 % of soil spectral variations depending on the growing season and soil types represented on the test plots. Tillage interference adds up to the impact of seasonality. The directions and areas of spectral changes seem to be soil-specific.

Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of remotely measured spectral data to ensure stable, reliable and reproducible results.

An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to human activities. The urban area of the city of Skopje has been rising rapidly in the past decade. In this study, the effect of UHI is analyzed using Landsat 8 data in the summer period of 2014 – 2017 as a case study in Skopje. An algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data. In addition, the correlation between land surface temperature and the normalized difference vegetation index (NDVI) and the normalized difference build-up index (NDBI) were analyzed to explore the impacts of the green land and the build-up land on the urban heat island. The results indicate that the effect of urban heat island in Skopje is located in many sub-urban areas. The negative correlation between LST and NDVI indicates that the green land can weaken the effect on urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of urban heat island in the study area.

Natural disaster and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process.

Field damage assessment is time consuming and requires trained personnel, whilst remote sensing techniques can be used to provide rescue teams, reconstruction and rehabilitation authorities with damage related information and features in a timely manner.

In this work, a novel autonomous building damage detection technique that relies on both pre- and post-war aerial images is proposed. To the best of our knowledge, building damage detection due to war activities has not been discussed in the literature. Pre- and post-war images may be captured in different conditions, camera type, angle, and capturing conditions. Thus applying affine transformation as a pre-processing step is firstly used to correct for geometric distortions or deformations that occur with non-ideal camera angles.

Next, our novel building detection algorithm is applied on the pre-war image. Detected buildings positions will be projected on the post-image, resulting in a set of damaged buildings candidates.  Then, thorough damage analysis is done using three main features: (i) Shadow, (ii) Correlation, and (iii) Uniformity.

Shadow can play an important role in analyzing buildings state (damaged or not). Following airstrikes, bombing explosions or other military activities, building’s structure will be partially ruined and/or totally demolished, thus, changing the shadow area and orientation. Uniformity, a statistical metric from the Gray-Level Co-Occurrence Matrix (GLCM), is used to measure a Region of Interest (ROI) homogeneity. Uniformity is a useful feature in this scope since damaged buildings are expected to show lower homogeneity in post-war Images. Correlation, lastly, measures the linear dependency of grey levels of neighboring pixels within ROI. Correlation is affected by the destruction and thus relevant towards damage estimation.

Those three features are compared in both pre- and post-war images. The validity of all three hypotheses is needed to label a specific building under study as damaged. Otherwise, the building is classified as a non-damaged residential unit.

The literature does not provide for the best of our knowledge a relevant dataset to benchmark against, and thus we focused in this work on images taken between 2014 and 2016 during the Syrian civil war. Accuracy assessment applied over several regions including different affected war zones reveals the high performance of our novel approach.

Future work includes the extension of the proposed approach to classify damaged buildings into several states based on destruction severity. In addition, we plan to assemble and publish a dataset to be used by researchers in the field.

Urban areas are important for city planning, security, traffic purposes, an decision makers etc. Remotely sensed data are useful to detect the urban areas either with active or passive systems. Each system has advantages and disadvantages. Passive images are mainly multispectral images, and they have rich information with their rich spectral resolution. On the other hand, they are effected with atmospherical conditions, so there should not be clouds over the sensed region during the data acquisition. On the other hand, SAR systems are not affected by the atmospheric conditions, but their spectral resolution is low, mainly with one channel. On the other hand, the structure of the passive images is completely different than the multispectral images. Secondly, the geometrical and electrical properties of objects play an important role in the pixel values. In this study, multispectral GOKTURK-2 MS image and SENTINEL 1A SAR image have been used to detect the urban buildings to use the advantages of the both datasets. Firstly, SVM method is applied to detect the buildings in GOKTURK image. The buildings are detected from SAR image with fuzzy logic approach. Finally, the buildings have been detected with intersection of the both results. The results from SAR image could eliminate false negative results from GOKTURK-2 image. Study area is selected from Antalya, Kepez distinct. The detected urban area is 288.353 m² at the selected study area.

Post-earthquake analysis using radar interferometry has become a standard procedure for assessing earthquakes with significant damages. Sentinel-1 satellite provides 6-day revisiting time, Sentinel-2 data has 5-day revisiting time with the same viewing angle which can enable detecting changes in surface/land-cover after major seismic event. Using Sentinel-2 alongside with Sentinel-1 could bring new benefits when gathering spatial information of post seismic event. In our study we focused on analyzing major earthquake, which occurred on 14 November 2016 with 7,8 magnitude near city of Kaikoura, New Zealand, using both Sentinel-1 radar images and Sentinel-2 optical data. Hundreds of landslides were reported as a result of this earthquake. In addition, substantial land uplift was detected in some parts of the sea shore. Differential interferometry allowed us to estimate earthquake strength by analyzing the distribution of absolute vertical displacement values. Sentinel-2 pre- and post-earthquake images were used in order to assess land-cover changes and automatically detect landslides, which occurred after the earthquake. Linking DInSAR results with Sentinel-2 change detection analysis helped us to get a more complex perspective on the earthquake impact, to create landslide susceptibility maps and subsequently develop workflows for quick post-event analysis.