The ice flow velocity is a critical variable in understanding the glacier dynamics. The Synthetic Aperture Radar Interferometry (InSAR) is a robust technique to monitor Earth's surface mainly to measure its topography and deformation. The phase information from two or more interferogram further helps to extract information about height and displacement of the surface. We used this technique to derive glacier velocity for Polar Record Glacier (PRG), East Antarctica using Sentinel-1 Single Look Complex images captured in Interferometric Wide mode. The PRG is located in the Prydz Bay area on the eastern side of the Amery Ice Shelf. It is about 50 km to the west of the India’s 3rd Antarctic research station Bharati. It is the largest outlet glacier along the Ingrid Christensen Coast, bounded by Meknattane Nunataks and Dodd Island. For velocity estimation, Persistent Scatterer interferometry (PS-InSAR) method has been applied. This method uses time coherent of permanent pixel of master images and correlates to same pixel of the slave image to get displacement by tracking the intensity of that pixels. C-band sensor of European Space Agency, Sentinel-1A and 1B data were used in this study. Estimated average velocity of the PRG is approximated to be ≈400 m/year which varies from ≈100 to ≈700 m/year. This study found that PRG moves with a velocity of ≈700 m/year in lower part whereas the upper inland area flowing with ≈200 m/year. The western part of the glacier is moving faster in comparison with the eastern part of the glacier.

The changes in the earth's surface significantly increase the natural disasters, resulting in severe damage to man-made objects, such as roads, buildings, bridges, and so on. Radar techniques have advantages such as lack of sensitivity to weather conditions, night and day, and cloud cover conditions which can be used to identify, alert, and mitigate these damages. Land use classification due to the importance of these areas and the need to care for them is one of the important applications of remote sensing. Therefore, using polarimetric synthetic aperture radar (PolSAR) images have many capabilities due having the scattering information on the four polarized of HH, HV, VH and VV, and consequently their dependence on the shape and structure of the environment. In this study, UAVSAR image is used. Meanwhile, the support vector machine (SVM) model is one of the well-known classification methods, in addition to being able to run on different types of features from different kinds and in large numbers, which can also distinguish classes those are not linearly separable. On the other hand, it is possible to use data mining method to facilitate data analysis like classification application. In this regards, it is recommended to use random forest (RF) technique. The RF is one of the useful methods for data classification which uses a tree structure for decision making. This method uses strategies to enhance the probability of reaching the goals with conditional probability. In this study, by incorporating a variety of target decomposition methods in PolSAR images, producing the land cover types are generated. Then, using the set of analysis and classification of characteristics, 70 features were obtained by applying SVM, RF, and KNN classification methods. In order to estimate accuracy, the output of these methods was evaluated by reference data.

The remote sensing as new technology provides data from earth with lowest cost and time. The Central Iranian Volcanic Belt is a volcano-plutonic complex which contains extrusive and intrusive rocks of Eocene to Quaternary age. In this area, the Meyduk porphyry copper deposit (55° 10′ 05″ E, 30° 25′ 10″ N) is located 45 km northeast of Shahr-e Babak city. The Cu-mineralization and associated hydrothermal alteration zones are focused on the Miocene dioritic Meyduk porphyry and Eocene andesitic rocks.

Today, remote sensing with having high spatial and spectral resolution, wide coverage and the lowest cost plays a key role in the field of earth sciences research (especially in the mineral ore explorations) (Melesse et al., 2007). In this case, hyperspectral images have a special status in remote sensing. Even though these images have a high spectral resolution, they have not a high spatial resolution. Because of the presence of different prospectives on a ground pixel, the amount of received energy by the receiver is the combination of multiple ground effects. Therefore, low spatial resolution of hyperspectral images can be a reason for spectral mixing in these images (Iordache et al., 2011). The purpose of spectral separation is recognition of observed surface components and calculation of abundance of the inside component of each pixel area. The purpose of applying unmixing algorithm is estimation and extraction of presence percentage of any considered mineral ores in each pixel of the image. We applied mentioned algorithm in three steps. In the first step, estimation of a number of the mineral ore types using Hysime algorithm is done. In the next step, we extracted spectral signature of each of these mineral ores using N-finder and in the final step, we calculated vector abundance of them using FLS algorithm.To evolution performance proposed methods a real hyperspectral were used in this research. The hyperspectral dataset related to Hyperion sensors related to Meyduk that lacoated in Iran. The Hyperion sensors carried by EO-1 satellite that it is first spaceborne hyperspectral instrument to acquire data in a wide of spectral bands.

Applying high resolution visible channels aerial scan of crop canopy to precision irrigation management

Chen, A., M. Meron and Valerie Orlov.

Abstract

Canopy cover (or vegetation cover) serves in irrigation management mainly to determine primary ET (evapotranspiration) coefficient, as radiation interception and evaporative surface area are directly related to canopy cover. Crop size and development with time depends on water supply, therefore crop canopy maps are tools for detection of irrigation systems spatial uniformity. Several aerial scan campaigns were deployed in the Upper Galilee of Israel in the 2017 growing season to follow up and evaluate irrigation uniformity and crop coefficients of peanuts, cotton and olives by RGB scans of a Phantom 4 multirotor unmanned aerial vehicle (UAV).  Foliage intensity and coverage were enhanced by a Green-Red Vegetation Index (GRVI), which is an NDVI like process where the green channel replaced the NIR. Results demonstrated that the GRVI index is suitable for the purpose of determining vegetation cover. Furthermore, the GRVI index yielded better results than the NDVI index, in recognizing phenological crop changes (especially senescence). Therefore, this research proves the applicability of a low cost system of digital camera mounted on a commodity UAV for crop cover and actual, in-field, ET coefficients determination, and irrigation uniformity evaluation.

Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g. DEM, DTM, canopy and forest structure, and urban infrastructure, to name a few). However, little research exists where Lidar has been used to derive coastal geomorphology. Therefore, the purpose for this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology and test this at several sites. The study areas were two developed and two undeveloped barrier islands in North Carolina. These sandy linear features protect the mainland from the open ocean. Various coastal processes, such as storms and longshore sediment transport, as well as human influences such as beach nourishment and urban development, shape barrier island geomorphology. This study used four dates of Lidar data from 1998 through 2014 and an automated model was developed in ArcGIS to process and classify Lidar data into ten geomorphic types: intertidal, supratidal, dune, hummock, overwash, channel, swale, upland, road and building. Model results were compared to compute change through time and derived the rate and direction of feature movement. When tropical storms occurred these were the dominant influence on the study areas. On the developed islands, there was less overall influence of storms due to the inability of features to move because of coastal infrastructure. For example, beach nourishment from 2005 to 2010 was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode and move landward. Understanding how these processes influence barrier island dynamics is critical to predicting an island’s future response to changing environmental factors such as sea-level rise. Policy makers and coastal managers rely on this type of information to make development and conservation decisions.

Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened as unsustainable agricultural practices reduce its capacity to provide life-supporting ecosystem services. Deforestation data is necessary for forest managers to efficiently allocate resources and make decisions for proper conservation and resource management. This study utilized satellite imagery to map and analyze current forest cover and recent forest loss in southern Belize in order to identify the areas that are the most susceptible to future deforestation. A forest cover change analysis was conducted using a supervised classification of Landsat imagery and ground-truthed land cover points in Google Earth Engine. Then, a proximity-based model was used to predict where deforestation could occur in the future based on the drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests. The results of this study will be used in spatial conservation planning in order to strategically focus conservation efforts in the most threatened areas in southern Belize. The sites that were found to be most vulnerable to future deforestation will be locations for implementing law enforcement and compliance, sustainable agriculture, and community outreach. This method could be applied to conservation planning in other regions to prioritize the protection of threatened areas.

A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia’s vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1 – 10 m²), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km², were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5 - 4 m tall. Exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced sub-canopy returns. Further development and application of vertical segmentation methods in multi-story vegetation communities should be evaluated due to the potential for targeted management and monitoring of vegetation communities and wildlife populations.

Glaciers exhibit a wide range of surface facies that can be analyzed as proxies for mass balance studies. Along with hydrological implications, these are in turn quintessential indicators of climate change. The use of moderate to high-resolution (MHR) data for mapping glacier facies has been performed previously; however, the use of very high-resolution (VHR) data for this purpose has not yet been fully exploited. This study uses WorldView-2 (WV-2) VHR data to classify available glacier surface facies on the Samudra Tapu glacier, located in the Himalayas. Traditional methods of facies classification using conventional multispectral data involve band ratioing and/or supervised classification. This study explores glacier surface facies classification by using the unique bands available in the multispectral range of WV-2 to develop customized spectral index ratios (SIRs) within an object-oriented domain. The results of this object-based classification (OBIA) is then compared with five popular supervised classification algorithms using error matrices to determine classification accuracies. The overall accuracy achieved by the OBIA approach is 97.14% (κ = 0.96) and the highest overall accuracy among the pixel-based classification methods is 74.28% (κ = 0.70). The present results show that the object-based approach is far more accurate than the pixel-based classification techniques. Further studies should test the robustness of the object-oriented domain for classification of glacier surface facies using customized sensor specific as well as transferable indices and the resultant accuracies.

Indian Space Research Organisation's SCATSAT-1 is a continuity mission for Oceansat-2 Scatterometer. The sensor works in Ku-band (13.515 GHz) similar to the one flown on-board Oceansat-2. It provides backscattering coefficient over the globe and wind vector data products over the oceans that are useful for weather forecasting, cyclone detection and tracking services. Besides backscattering coefficient (sigma nought), two other important parameters namely, Gamma nought (obtained from backscattering coefficient) and Brightness temperature (obtained from scatterometer noise measurement) are given as the Level-4 data products archived at the ISRO’s Meteorological & Oceanographic Satellite Data Archival Centre. We used these three parameters both in horizontal and vertical polarizations for the Antarctic region (South Polar) to perform, first, a principal component analysis. Then, we used the first three principal components explaining the largest variability in the data set to perform an unsupervised k-means classification to estimate the regions of sea ice in around Antarctica. The derived sea ice extent through this method is compared with other popular sea ice extent products available elsewhere.

Changes in the dynamics of the glacier must be assessed as they are important for the sea level changes. Glacier velocity is the most important parameter used in the glacier dynamics studies. Various image matching techniques based on different domains have been utilized to estimate the velocity of the glaciers since the first use of remote sensing technology. The present study has focused to derive precise velocity of the Polar Record Glacier, east Antarctica, in the recent years using optical remote sensing. The secondary objective of the study is to comparatively test the accurate geospatial tool for the velocity estimation. The study was first conducted on a single image pair and four different tools were used for estimation of glacier velocity, which are COSI-Corr tool in ENVI, IMGRAFT in Matlab, IMCORR feature tracking tool in SAGA-GIS and CIAS image correlation software. After evaluation of all the four feature tracking tools, COSI-Corr yielded pixel level velocity with both magnitude and directions, while IMGRAFT provided the speed of the glaciers without the directions. On the other hand, IMCORR provided good results with magnitude and directions of glacier velocity but pixel wise magnitude was not produced. CIAS also provided closely bundled velocity products but the pixel wise velocity was not obtained. COSI-Corr and IMGRAFT were found out to be the best of four tools in which COSI-Corr was preferred for further studies to estimate velocity of Polar Record Glacier.