Evapotranspiration (ET) is an important geo-biophysical parameter for water management in watersheds. The determination of ET generally involves the use of ground-based meteorological data, which does not adequate capture the spatial patterns of mass and energy fluxes, especially in mountainous areas. In this work we evaluate the daily spatial distribution of ET over mountainous watershed in southeastern Brazil, by coupling Surface Energy Balance Algorithms for Land (SEBAL), global solar radiation (GSR) model and a gridded weather dataset. We used OLI/Landsat-8 surface reflectance and TIRS thermal images, SRTMGL1 Digital Elevation Model and weather gridded data from Global Land Data Assimilation System (GLDAS). To estimate daily tilted GSR, an analytical parameterization was applied using the relation between terrain and sun angles over 24h integration time. Daily ET was estimated through SEBAL model, adapted for tilted surfaces, based on the estimated GSR and resampled GLDAS data. Tests were performed in summer/wet (01/12/2015) and winter/dry (09/25/2015) periods to evaluate the seasonal differences in ET over tilted surfaces. The results indicated different spatial patterns of daily ET on the watershed in each period. In summer, ET was 9.8% higher on slopes facing the South while in winter, ET was 10.6% higher on slopes facing North and East. High variability in daily ET was found on steeper slopes (above 45°), with mean coefficient of variation (CV) of 25% and 43.6% for summer and winter, respectively.The notable spatial heterogeneity of daily ET over this watershed indicate the complex partitioning of mass and energy fluxes from different terrain angles, which can influence hydro-ecological processes at local scale. The presented approach allowed a more detailed capture of the spatial variability of ET in a complex terrain, which can be useful in mountainous watersheds with scarcity ground-based data.

The study area has chosen along the N-S transect across the Himalaya, Gangetic plains and Chotanagpur plateau. We basically tried to observe the variation in the most important climatic variables i.e. Net Surface Radiation (Rn), Temperature, Rainfall, Evapotranspiration (ET) etc. during 2000-2016. Generally the meteorological parameters shows its variability over a large spatial domain and remains invariable for closer ground based observations. In contrast, the satellite data is capable to present a synoptic view in a single image. Therefore it is more reliable to use the satellite dataset for the study of such meteorological parameters. The TRMM monthly average precipitation (0.25° X 0.25°), MODIS-Terra 8 day average LST product (1km X 1km), MERRA-2 radiation (0.5° x 0.625°) and GLDAS reanalysis model data (0.25°X0.25°) has been used to study and analyse the spatial variability and distribution of rainfall, surface temperature, energy fluxes and evapotranspiration, respectively. The results have shown that the overall annual average rainfall has a gradual decreasing trend. It results after the correlation with temperature (>35°C) that the regions with low rainfall (<1000mm) have to witness warmer temperature conditions (>43°C). The difference in maximum and minimum temperature is increasing at the rate of 1°C per five years. The east-west central line of the Bihar, along the river Ganga is found to be the line of division i.e. almost 80% of the area which witness >35°C temperature lies below this line and few 10-20% lies above it. The results for Rn have shown an overall increasing trend over the period of time. The Nepal has a wider stretch of Rn due to its mountain topography followed by the Jharkhand (plateau) and Bihar (plain). The ET has also an increasing trend over the period of time and the results are noticeable for western Bihar-Jharkhand. There is an upward latitudinal shifting of the low rainfall bands in both the pre-monsoon and monsoon conditions. Due to the lack of availability of ground truth data, we have to restrict with the remotely sensed dataset. Since the dataset chosen are well accepted globally, the data inaccuracy is minimal. Hence, the study will help to understand the fluctuation in local weather phenomenon may useful for adapting sustainable strategies to mitigate the climate change effect on local scale.

Satellite earth observation enables the monitoring of different types of natural hazards, contributing to the mitigation of their fatal consequences. In this paper, satellite Synthetic Aperture Radar (SAR) images will be used to derive measurements of the deformation of the terrain. The images acquired with the ESA satellite Sentinel-1 are used. Sentinel-1 interferometric SAR data offer a set of unique characteristics, which confer them a great potential in terms of deformation monitoring. These characteristics include a wide area coverage of the Interferometric Wide Swath mode; the 12-day revisiting cycle of Sentinel-1A (6 days with the Sentinel-1B); the reduced orbital tube; the high image coherence; the acquisition in background mode; and the free of charge data availability. The paper describes the data processing and analysis procedures implemented by the authors to analyse Sentinel-1 interferometric data for deformation monitoring applications. Two different approaches to Persistent Scatterer Interferometry (PSI) are used depending on the characteristics of the study area and the available images. The main processing steps of the two methods, i.e. the simplified and the full PSI approach, are described and applied over an area of 7500 km² located in Catalonia (Spain). The deformation velocity map and deformation time series over this area of study are analysed, as well as the potential of the procedure.

Pan Sharpening is an important part of the Remote Sensing science. Obtaining high spatial resolution data can be crucial in some studies. Sentinel-2 provides data of 10, 20 and 60 meters, and it is a promising program for Earth observation studies. Although Sentinel-2 provides high range of multispectral bands, the lack of panchromatic band disables producing a set of fine-resolution (10 m) bands. However, few methods have been developed for increasing the spatial resolution of the 20 m bands up to 10 m. In this study, two different methods of producing panchromatic band have been compared. The first method uses the closest higher spatial resolution band to the lowest spatial resolution band as a panchromatic band, while the other method uses one single band as panchromatic band produced as an average value out of all fine resolution bands. The 60 m bands have not been taken into consideration in this study. In order to compare these methods, four image fusion techniques from different fusion subsections (Component substitution – Intensity Hue Saturation IHS; Numerical method – High Pass Filter HPF; Statistical Image Fusion – Principal Component Analysis PC; Hybrid Technique – Wavelet Principal Component WPC) have been applied on two Sentinel-2 images over the same study area, on different dates. For the accuracy assessment, both qualitative and quantitative analysis have been made.

InSAR technology is one of the powerful tool to measure deformation and/or deposition on the ground surface. In addition to that the mass movement can be monitored using Interferometric Synthetic Aperture Radar (InSAR) techniques. The earthquake that occurred on November 12, 2017 in South of Halabjah (Iraq) the magnitude of 7.2 caused 350 people to lose their lives and more than 2,500 people were injured. The aim of this study is to measure the deformation due to the earthquake using “Interferometric Wide Swath”, which is one of the four display types of Sentinel 1 data. In order to carry out this process, two type of data sets were used which are SRTM data and Sentinel 1 images acquired on November 7 and 19, 2017. In this study, VV polarization with C band were used generate interferogram. During the study, SNAP 5.0 free image analysis and processing software by ESA. According to obtained results, minimum and maximum surface displacement were acquired as -0.45 and 0.49 meters. When comparing the results with existing fault lines, these are appropriate for the tectonic structures. Using InSAR technologies with open source software and free data, it is possible to produce displacement maps just after the earthquake.

High resolution elevation data is a fundamental information for multiple applications in geomorphology, spanning from visual analyses (e.g., mapping) to modeling. For example, estimation of short-term erosion rates, quantitative geomorphic analysis, land-shaping processes modeling, landslide identification and mapping, river dynamics studies, among others, rely on good quality elevation data. Nowadays, gathering of high-quality elevation data relies on multiple sensors and technologies which can be mounted on terrestrial, aerial and satellite platforms. In the last years, the Structure from Motion (SfM) algorithms have made possible the acquisition of high and very-high resolution elevation data from optical images acquired with high overlapping rates at virtually no cost. Such a feature made it possible to exploit remote sensing archival imagery to build historical topographic information with unprecedented detail.

Despite the increasing number of applications of SfM algorithms in the scientific literature, however, still little has been done in terms of evaluation of the quality of the resulting elevation data, and of the best acquisition mode (i.e. scanning resolution and color depth) to get the most from such archival imagery. Moreover a large number of those application are based on proprietary commercial software and undisclosed algorithms which could make the experiments not reproducible and replicable.

We have applied the SfM algorithm developed in the photogrammetric open source software MicMac to six black and white aerial photographs taken in 1954 at 1:33.000 in in a mountainous and steep area in Central Italy, where, the 30th October 2016, a seismic sequence triggered, among the others, a large disrupted rock slide that partially dammed a river and blocked a road. The aim of the experiment consists in a quantitative evaluation of the digital surface models obtained for different scanning resolutions, along with the time needed for the computation. Elevation data were quantitatively compared to GPS RTK measurements, and results indicate planimetric and altimetric accuracies smaller than 1m at the calibration ground control points.  

Fire radiative power (FRP) method was adopted for evaluating of wildfires parameters under current conditions in Siberian forests. Long term Terra and Aqua/Modis data were used in spectral range 4, 10–12 μm. The study was carried out using the conjugate analysis of satellite data (1996–2017), ground survey data from the post-fire areas of different ages in the larch forests of Evenkia (Tura experimental station, Krasnoyarsk region, Russia) and in the pine forests of Near-Yenisey Siberia (Zotto experimental station, Krasnoyarsk region, Russia). The results of numerical simulation for burning characteristics were used also.

The main results are: 1) geospatial relationship of the forests burning (i.e. frequency, burned areas and fire intensity) and the anomalies of heat and moisture under current conditions in Siberia; 2) the first approximation of relation between the background temperature/ vegetation index (NDVI) variation and disturbance of the ground cover/ post-fire loads of forest fuels; 3) instrumental estimation for the area burnt under extreme intensity fire that is at least 8.5% of the annual average burned area; 4) the ratio of areas burnet by fire intensity/fire severity quintiles for calibrating the assessment of total biomass burned and emission calculating.

In the first approximation we expect to refine the amount of burned biomass in forests of Siberia by 7-20% of current assessment without the procedure of sub-classifying of area burnt.

Many works dealing with the problem of urban detection in large scale have been
published, but very little attention has been paid  to the investigation of the
features relative importance. Feature selection is known to be an NP-hard
problem, with many heuristics suggested to approximate the solution.
In this paper, a quick survey of the features used for large scale urban detection using Landsat
data is presented, then the question of finding the best subset of features is
investigated. Using Landsat scenes of five urban areas, all common features were
extracted to represent the full feature set. Employing mutual information based ranking methods,
Fisher score, SVM and Random Forest feature ranking, an importance score was
assigned to each feature by each method. To aggregate the individual rankings of features, a
two stage voting scheme was implemented to choose a subset of size $N$
 as the most relevant features.
To evaluate the chosen subset, a comparison to a baseline subset was
performed. The classification power of the two subsets was tested using four
classifiers in five urban regions. The results suggest better performance of the chosen subset
compared to the baseline.

Turkey is a country with a good climate and ecological properties for agricultural production, and the agriculture occupies 24.6% workforce of the whole country. Wheat production is important for Turkish economy and Turkey produced 17 million tons of wheat in 2016.

Traditionally, the monitoring of the wheat length is performed with field works. The breeders and agronomists measure the length of the wheat genotypes with random selection in predefined interval distances. But it is time consuming and not accurate since it is not possible to measure the all wheat genotypes tested on the field experiments. Thus, automated and accurate methods are needed.

High resolution imagery allows producing accurate 3D model of any object including agricultural fields. UAV technology gives an opportunity to acquire imagery from above and then photogrammetric workflow can produce high resolution orthoimage and also 3D model. UAV technology also allows repeating the process in predefined dates to monitor the growth of plant height of wheat genotypes periodically.

In this study, we have evaluated the use of UAV photogrammetry for monitoring of a wheat experiment under field condition, filtered DSM to derive the wheat heights, and compare the results with the field measurements. The images are acquired with use of low cost UAV Walkera QR350 and GoProHero3+ action camera in May 2015. 477 images were acquired. The calculated average wheat height is 77.962 cm and standard deviation is 16.525 cm. In a previous work (Karagöz et al 2017), barley heights are measured as 72.6 cm with 15.2 cm standard deviation with use of traditional methods explained below.

For quality assessment of the proposed method, a reference dataset is collected with terrestrial fieldwork.36 bread wheat genotypes were tested under triple latis design with three replications in Antalya, Turkey. Plot size were 5 m length and 1,2 m width A special circle with 1 m diameter is placed above the plots and the average wheat height in the plot which intersects with the circle was reported as wheat height for the selected plot. For comparison of field measurements with DSM-derived heights, the maximum calculated height in the plot is selected.. The mean, median, standard deviation are calculated as 4.66 cm, 3.75 cm., 13.78 cm. .Regarding statistical T-test between the field measurements and heights from DSM, T-Value is calculated as1,82 and P-Value is 0,071. Since T-value is larger than 0.50, the values between traditional method and our approach are highly correlated while p-value confirms this result.

References:

KARAGÖZ, A., ÖZBEK, K., AKAR, T., ERGÜN, N., AYDOĞAN, S., AYIM, İ, 2017. Agro-Morphological Variation Among an Ancient World Barley Collection, Journal of Agricultural Sciences, Volume 23(4), 444-452.

During the last decade remote sensing methods have significantly developed. The technological progress in development of new sensors and techniques opened up a large scope of new applications including near-field data collecting using Unmanned Aerial Vehicles (UAVs). State-of-the-art UAVs technologies provide such advantages as a cost-effectiveness and temporal flexibility. For our case study we acquired the high-resolution UAV data over the archaeological site near Černouček, the Czech Republic. This site has been discovered at the beginning of 1990’ as a result of low altitude aerial reconnaissance carried out by the Institute of Archaeology, Czech Academy of Sciences. Two ditched enclosures were identified due to vegetation marks in late spring and early summer – higher moisture and presence of some chemical constituents in the secondary infill of the ditches give better conditions for plants above them. In 2017, new UAV data (RGB and Near-infrared data: NIR) were acquired over the Černouček site at two different temporal windows (June, October) to find out whether there are some other objects hidden under ground. Using the RGB data digital elevation models were derived while the NIR data were used to compute vegetation indices (VI), further spatial filtering allowing enhancing the local anomalies in the VI values was employed. As a result, several small objects were detected and suggested for the further investigations.