bluuebunny/crossref_metadata_2025 huggingface dataset:
TOWARDS A LOW-COST, REAL-TIME PHOTOGRAMMETRIC LANDSLIDE MONITORING SYSTEM UTILISING MOBILE AND CLOUD COMPUTING TECHNOLOGY
Authors: Chidburee P. , Mills J. P. , Miller P. E. , Fieber K. D.
Published: August 2016

Close-range photogrammetric techniques offer a potentially low-cost approach in terms of implementation and operation for initial assessment and monitoring of landslide processes over small areas. In particular, the Structure-from-Motion (SfM) pipeline is now extensively used to help overcome many constraints of traditional digital photogrammetry, offering increased user-friendliness to nonexperts, as well as lower costs. However, a landslide monitoring approach based on the SfM technique also presents some potential drawbacks due to the difficulty in managing and processing a large volume of data in real-time. This research addresses the aforementioned issues by attempting to combine a mobile device with cloud computing technology to develop a photogrammetric measurement solution as part of a monitoring system for landslide hazard analysis. The research presented here focusses on (i) the development of an Android mobile application; (ii) the implementation of SfM-based open-source software in the Amazon cloud computing web service, and (iii) performance assessment through a simulated environment using data collected at a recognized landslide test site in North Yorkshire, UK. Whilst the landslide monitoring mobile application is under development, this paper describes experiments carried out to ensure effective performance of the system in the future. Investigations presented here describe the initial assessment of a cloud-implemented approach, which is developed around the well-known VisualSFM algorithm. Results are compared to point clouds obtained from alternative SfM 3D reconstruction approaches considering a commercial software solution (Agisoft PhotoScan) and a web-based system (Autodesk 123D Catch). Investigations demonstrate that the cloud-based photogrammetric measurement system is capable of providing results of centimeter-level accuracy, evidencing its potential to provide an effective approach for quantifying and analyzing landslide hazard at a local-scale.
THREE DIMENTIONAL RECONSTRUCTION OF LARGE CULTURAL HERITAGE OBJECTS BASED ON UAV VIDEO AND TLS DATA
Authors: Xu Z. , Wu T. H. , Shen Y. , Wu L.
Published: August 2016

This paper investigates the synergetic use of unmanned aerial vehicle (UAV) and terrestrial laser scanner (TLS) in 3D reconstruction of cultural heritage objects. Rather than capturing still images, the UAV that equips a consumer digital camera is used to collect dynamic videos to overcome its limited endurance capacity. Then, a set of 3D point-cloud is generated from video image sequences using the automated structure-from-motion (SfM) and patch-based multi-view stereo (PMVS) methods. The TLS is used to collect the information that beyond the reachability of UAV imaging e.g., partial building facades. A coarse to fine method is introduced to integrate the two sets of point clouds UAV image-reconstruction and TLS scanning for completed 3D reconstruction. For increased reliability, a variant of ICP algorithm is introduced using local terrain invariant regions in the combined designation. The experimental study is conducted in the Tulou culture heritage building in Fujian province, China, which is focused on one of the TuLou clusters built several hundred years ago. Results show a digital 3D model of the Tulou cluster with complete coverage and textural information. This paper demonstrates the usability of the proposed method for efficient 3D reconstruction of heritage object based on UAV video and TLS data.
PREFACE
Authors: Gong Jianya , Woldai Tsehaie
Published: August 2016

No abstract available.
ANALYSIS OF JURE LANDSLIDE DAM, SINDHUPALCHOWK USING GIS AND REMOTE SENSING
Authors: Acharya T. D. , Mainali S. C. , Yang I. T. , Lee D. H.
Published: August 2016

On 2nd August 2014, a rainfall-induced massive landslide hit Jure village, Sindhupalchowk killing 156 people at a distance of 70 km North-East of Kathmandu, Nepal. The landslide was a typical slope failure with massive rock fragments, sand and soil. A total of estimated 6 million cubic meters debris raised more than 100 m from the water level and affected opposite side of the bank. The landslide blocked the Sunkoshi River completely forming an estimated 8 million cubic meter lake of 3km length and 300-350m width upstream. It took nearly 12 hour to fill the lake and overflow the debris dam. The lake affected five Village Development Committees (VDC) including highway, school, health post, postal service, police station, VDC office and temple upstream. The bottom of the dam was composed of highly cemented material and the derbies affected Sunkoshi hydropower downstream. Moreover, it caused the potential threat of Lake Outburst Flood. The lake was released by blasting off part of the landslide blockade and facilitated release of water from the lake. With the help of Remote Sensing (RS), series satellite images were used to identified, compared with previous state and quick estimation of potential treat was analysed. Using geographic information System (GIS) technology, estimation of volume, affected households, service centres, parcels etc. in the area was possible. In such hilly regions where disaster are very frequent, using GIS and RS technology comes very handy for immediate planning and response.
STATISTIC TESTS AIDED MULTI-SOURCE DEM FUSION
Authors: Fu C. Y. , Tsay J. R.
Published: August 2016

Since the land surface has been changing naturally or manually, DEMs have to be updated continually to satisfy applications using the latest DEM at present. However, the cost of wide-area DEM production is too high. DEMs, which cover the same area but have different quality, grid sizes, generation time or production methods, are called as multi-source DEMs. It provides a solution to fuse multi-source DEMs for low cost DEM updating. The coverage of DEM has to be classified according to slope and visibility in advance, because the precisions of DEM grid points in different areas with different slopes and visibilities are not the same. Next, difference DEM (dDEM) is computed by subtracting two DEMs. It is assumed that dDEM, which only contains random error, obeys normal distribution. Therefore, student test is implemented for blunder detection and three kinds of rejected grid points are generated. First kind of rejected grid points is blunder points and has to be eliminated. Another one is the ones in change areas, where the latest data are regarded as their fusion result. Moreover, the DEM grid points of type I error are correct data and have to be reserved for fusion. The experiment result shows that using DEMs with terrain classification can obtain better blunder detection result. A proper setting of significant levels (α) can detect real blunders without creating too many type I errors. Weighting averaging is chosen as DEM fusion algorithm. The priori precisions estimated by our national DEM production guideline are applied to define weights. Fisher’s test is implemented to prove that the priori precisions correspond to the RMSEs of blunder detection result.
PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY
Authors: Koc-San D. , Sonmez N. K.
Published: August 2016

Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.
OBJECT-ORIENTED CHANGE DETECTION FOR REMOTE SENSING IMAGES BASED ON MULTI-SCALE FUSION
Authors: Feng Wenqing , Sui Haigang , Tu Jihui
Published: August 2016

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.
COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS
Authors: Xie Zhenlei , Shi Ruoming , Zhu Ling , Peng Shu , Chen Xu
Published: August 2016

Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.
COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS
Authors: Landmann J. M. , Rutzinger M. , Bremer M. , chmidtner K.
Published: August 2016

Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (Larix decidua MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as “bias correction” for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.
PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2
Authors: Vaiopoulos A. D. , Karantzalos K.
Published: August 2016

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.
THE OPTIMIZED BLOCK-REGRESSION-BASED FUSION ALGORITHM FOR PANSHARPENING OF VERY HIGH RESOLUTION SATELLITE IMAGERY
Authors: Zhang J. X. , Yang J. H. , Reinartz P.
Published: August 2016

Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.
EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA
Authors: Homolová L. , Janoutová R. , Malenovský Z.
Published: August 2016

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm−2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.
TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION
Authors: Sawant S. A. , Chakraborty M. , Suradhaniwar S. , Adinarayana J. , Durbha S. S.
Published: August 2016

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.
INNOVATIV AIRBORNE SENSORS FOR DISASTER MANAGEMENT
Authors: Altan M. O. , Kemper G.
Published: August 2016

Modern Disaster Management Systems are based on 3 columns, crisis preparedness, early warning and the final crisis management. In all parts, special data are needed in order to analyze existing structures, assist in the early warning system and in the updating after a disaster happens to assist the crises management organizations. How can new and innovative sensors assist in these tasks?

 Aerial images have been frequently used in the past for generating spatial data, however in urban structures not all information can be extracted easily. Modern Oblique camera systems already assist in the evaluation of building structures to define rescue paths, analyze building structures and give also information of the stability of the urban fabric. For this application there is no need of a high geometric accurate sensor, also SLC Camera based Oblique Camera system as the OI X5, which uses Nikon Cameras, do a proper job. Such a camera also delivers worth full information after a Disaster happens to validate the degree of deformation in order to estimate stability and usability for the population.

 Thermal data in combination with RGB give further information of the building structure, damages and potential water intrusion. Under development is an oblique thermal sensor with 9 heads which enables nadir and oblique thermal data acquisition. Beside the application for searching people, thermal anomalies can be created out of humidity in constructions (transpiration effects), damaged power lines, burning gas tubes and many other dangerous facts.

 A big task is in the data analysis which should be made automatically and fast. This requires a good initial orientation and a proper relative adjustment of the single sensors. Like that, many modern software tools enable a rapid data extraction. Automated analysis of the data before and after a disaster can highlight areas of significant changes. Detecting anomalies are the way to get the focus on the prior area.

 Also Lidar supports Disaster management by analyzing changes in the DSM before and after the “event”. Advantage of Lidar is that beside rain and clouds, no other weather conditions limit their use. As an active sensor, missions in the nighttime are possible.

 The new mid-format cameras that make use CMOS sensors (e.g. Phase One IXU1000) can capture data also under poor and difficult light conditions and might will be the first choice for remotely sensed data acquisition in aircrafts and UAVs.

 UAVs will surely be more and more part of the disaster management on the detailed level. Today equipped with video live cams using RGB and Thermal IR, they assist in looking inside buildings and behind. Thus, they can continue with the aerial survey where airborne anomalies have been detected.
MODELLING LANDSCAPE MORPHODYNAMICS BY TERRESTRIAL PHOTOGRAMMETRY: AN APPLICATION TO BEACH AND FLUVIAL SYSTEMS
Authors: Sánchez-García E. , Balaguer-Beser A. , Taborda R. , Pardo-Pascual J. E.
Published: August 2016

Beach and fluvial systems are highly dynamic environments, being constantly modified by the action of different natural and anthropic phenomena. To understand their behaviour and to support a sustainable management of these fragile environments, it is very important to have access to cost-effective tools. These methods should be supported on cutting-edge technologies that allow monitoring the dynamics of the natural systems with high periodicity and repeatability at different temporal and spatial scales instead the tedious and expensive field-work that has been carried out up to date. The work herein presented analyses the potential of terrestrial photogrammetry to describe beach morphology. Data processing and generation of high resolution 3D point clouds and derived DEMs is supported by the commercial Agisoft PhotoScan. Model validation is done by comparison of the differences in the elevation among the photogrammetric point cloud and the GPS data along different beach profiles. Results obtained denote the potential that the photogrammetry 3D modelling has to monitor morphological changes and natural events getting differences between 6 and 25 cm. Furthermore, the usefulness of these techniques to control the layout of a fluvial system is tested by the performance of some modeling essays in a hydraulic pilot channel.
RANDOM FOREST CLASSIFICATION OF SEDIMENTS ON EXPOSED INTERTIDAL FLATS USING ALOS-2 QUAD-POLARIMETRIC SAR DATA
Authors: Wang W. , Yang X. , Liu G. , Zhou H. , Ma W. , Yu Y. , Li Z.
Published: August 2016

Coastal zones are one of the world’s most densely populated areas and it is necessary to propose an accurate, cost effective, frequent, and synoptic method of monitoring these complex ecosystems. However, misclassification of sediments on exposed intertidal flats restricts the development of coastal zones surveillance. With the advent of SAR (Synthetic Aperture Radar) satellites, polarimetric SAR satellite imagery plays an increasingly important role in monitoring changes in coastal wetland. This research investigated the necessity of combining SAR polarimetric features with optical data, and their contribution in accurately sediment classification. Three experimental groups were set to make assessment of the most appropriate descriptors. (i) Several SAR polarimetric descriptors were extracted from scattering matrix using Cloude-Pottier, Freeman-Durden and Yamaguchi methods; (ii) Optical remote sensing (RS) data with R, G and B channels formed the second feature combinations; (iii) The chosen SAR and optical RS indicators were both added into classifier. Classification was carried out using Random Forest (RF) classifiers and a general result mapping of intertidal flats was generated. Experiments were implemented using ALOS-2 L-band satellite imagery and GF-1 optical multi-spectral data acquired in the same period. The weights of descriptors were evaluated by VI (RF Variable Importance). Results suggested that optical data source has few advantages on sediment classification, and even reduce the effect of SAR indicators. Polarimetric SAR feature sets show great potentials in intertidal flats classification and are promising in classifying mud flats, sand flats, bare farmland and tidal water.
VISUAL TOOLS FOR CROWDSOURCING DATA VALIDATION WITHIN THE GLOBELAND30 GEOPORTAL
Authors: Chuprikova E. , Wu H. , Murphy C. E. , Meng L.
Published: August 2016

This research aims to investigate the role of visualization of the user generated data that can empower the geoportal of GlobeLand30 produced by NGCC (National Geomatics Center of China). The focus is set on the development of a concept of tools that can extend the Geo-tagging functionality and make use of it for different target groups. The anticipated tools should improve the continuous data validation, updating and efficient use of the remotely-sensed data distributed within GlobeLand30.
RAPID EXPOSURE ASSESSMENT OF NATIONWIDE RIVER FLOOD FOR DISASTER RISK REDUCTION
Authors: Kwak Y. , Park J. , Arifuzzaman B. , Iwami Y. , Amirul Md. , Kondoh A.
Published: August 2016

considerably increased. For flood disaster risk reduction, it is important to identify and characterize flood area, locations (particularly lowland along rivers), and durations. For this purpose, flood mapping and monitoring are an imperative process and the fundamental part of risk management as well as emergency response. Our ultimate goal is to detect flood inundation areas over a nationwide scale despite limitations of optical and multispectral images, and to estimate flood risk in terms of affected people. We propose a methodological possibility to be used as a standard approach for nationwide rapid flood exposure assessment with the use of the multi-temporal Moderate Resolution Imaging Spectrometer (MODIS), a big contributor to progress in near-real-time flood mapping. The preliminary results in Bangladesh show that a propensity of flood risk change strongly depends on the temporal and spatial dynamics of exposure such as distributed population.
GEOLOGICAL MAPPING USING MACHINE LEARNING ALGORITHMS
Authors: Harvey A. S. , Fotopoulos G.
Published: August 2016

Remotely sensed spectral imagery, geophysical (magnetic and gravity), and geodetic (elevation) data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA), which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines) are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.
COMPARISON OF DIGITAL SURFACE MODELS FOR SNOW DEPTH MAPPING WITH UAV AND AERIAL CAMERAS
Authors: Boesch R. , Bühler Y. , Marty M. , Ginzler C.
Published: August 2016

Photogrammetric workflows for aerial images have improved over the last years in a typically black-box fashion. Most parameters for building dense point cloud are either excessive or not explained and often the progress between software releases is poorly documented. On the other hand, development of better camera sensors and positional accuracy of image acquisition is significant by comparing product specifications. This study shows, that hardware evolutions over the last years have a much stronger impact on height measurements than photogrammetric software releases.

 Snow height measurements with airborne sensors like the ADS100 and UAV-based DSLR cameras can achieve accuracies close to GSD * 2 in comparison with ground-based GNSS reference measurements. Using a custom notch filter on the UAV camera sensor during image acquisition does not yield better height accuracies. UAV based digital surface models are very robust. Different workflow parameter variations for ADS100 and UAV camera workflows seem to have only random effects.
RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS
Authors: Chen C. F. , Son N. T. , Chen C. R. , Chang L. Y. , Chiang S. H.
Published: August 2016

Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R2 > 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.
URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING
Authors: Hernández H. J. , Gutiérrez M. A. , Acuña M. P.
Published: August 2016

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (end members): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All end members were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.
THE COMBINATION OF UAV SURVEY AND LANDSAT IMAGERY FOR MONITORING OF CROP VIGOR IN PRECISION AGRICULTURE
Authors: Lukas V. , Novák J. , Neudert L. , Svobodova I. , Rodriguez-Moreno F. , Edrees M. , Kren J.
Published: August 2016

Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 – 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.
Causes and consequences of the sinkhole at El Trébol of Quito, Ecuador –
implications for economic damage and risk assessment
Authors: Toulkeridis Theofilos , Rodríguez Fabián , Arias Jiménez Nelson , Baile Débora Simón , Martínez Rodolfo Salazar , Addison Aaron , Carreón Freyre Dora , Mato Fernando , Díaz Perez Carmen
Published: September 2016

Abstract. The so-called El Trébol is a critical road interchange in Quito connecting the north and south regions of the city. In addition, it connects Quito with the highly populated Los Chillos Valley, one of the most traveled zones in the Ecuadorian capital. El Trébol was constructed in the late 1960s in order to resolve the traffic jams of the capital city and for that purpose the Machángara River was rerouted through an underground concrete box tunnel. In March 2008, the tunnel contained a high amount of discarded furniture that had been impacting the top portion of the tunnel, compromising the structural integrity. On 31 March 2008 after a heavy rainfall a sinkhole of great proportions formed in the Trébol traffic hub. In the first few minutes, the sinkhole reached an initial diameter of 30 m. The collapse continued to grow in the following days until the final dimensions of 120 m in diameter and some 40 m of depth, revealing the Machángara River at the base of the sinkhole.A state of emergency was declared. The cause of the sinkhole was a result of the lack of monitoring of the older subterranean infrastructure where trash had accumulated and damaged the concrete tunnel that channelized the Machángara River until it was worn away for a length of some 20 m, leaving behind the sinkhole and the fear of recurrence in populated areas.With the intent to understand the causes and consequences of this sinkhole event, rainfall data are shown together with hydrogeological characteristics and a view back to the recent history of sinkhole lineation or arrangement of the city of Quito. The economic impact is also emphasized, where the direct costs of the damage and the reconstruction are presented and compared to indirect costs associated with this socio-natural disaster. These analyses suggest that the costs of indirect financial damage, like time loss or delay, and subsequent higher expenses for different types of vehicles, are equivalent to many times the costs of the reconstruction of El Trébol.
Inverting Rayleigh surface wave velocities for eastern Tibet and western
Yangtze craton crustal thickness based on deep learning neural networks
Authors: Cheng Xian-Qiong , Liu Qi-He , Li Ping Ping
Published: September 2016

Abstract. Crustal thickness is an important factor affecting lithosphere structure and therefore deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities simultaneously as input and theoretical crustal thickness as output, we construct twelve deep neural networks trained by 70,000 and tested by 30,000 theoretical models. We then invert observed phase and group velocities by these twelve neural networks. Based on test errors and misfits with other crustal thickness models, we select the optimized one as crustal thickness for study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we conclude that deep learning neural network is a promising, believable and inexpensive tool for geophysical inversion.
3-D surface properties of glacier penitentes over an ablation season,
measured using a Microsoft Xbox Kinect
Authors: Nicholson Lindsey I. , Pętlicki Michał , Partan Ben , MacDonell Shelley
Published: September 2016

Abstract. In this study, the first small-scale digital surface models (DSMs) of natural penitentes on a glacier surface were produced using a Microsoft Xbox Kinect sensor on Tapado Glacier, Chile (30°08′ S, 69°55′ W). The surfaces produced by the complete processing chain were within the error of standard terrestrial laser scanning techniques, but insufficient overlap between scanned sections that were mosaicked to cover the sampled areas can result in three-dimensional (3-D) positional errors of up to 0.3 m. Between November 2013 and January 2014 penitentes become fewer, wider and deeper, and the distribution of surface slope angles becomes more skewed to steep faces. Although these morphological changes cannot be captured by manual point measurements, mean surface lowering of the scanned areas was comparable to that derived from manual measurements of penitente surface height at a minimum density of 5 m−1 over a 5 m transverse profile. Roughness was computed on the 3-D surfaces by applying two previously published geometrical formulae: one for a 3-D surface and one for single profiles sampled from the surface. Morphometric analysis shows that skimming flow is persistent over penitentes, providing conditions conducive for the development of a distinct microclimate within the penitente troughs. For each method a range of ways of defining the representative roughness element height was used, and the calculations were done both with and without application of a zero displacement height offset to account for the likelihood of skimming air flow over the closely spaced penitentes. The computed roughness values are on the order of 0.01–0.10 m during the early part of the ablation season, increasing to 0.10–0.50 m after the end of December, in line with the roughest values previously published for glacier ice. Both the 3-D surface and profile methods of computing roughness are strongly dependent on wind direction. However, the two methods contradict each other in that the maximum roughness computed for the 3-D surface coincides with airflow across the penitente lineation, while maximum roughness computed for sampled profiles coincides with airflow along the penitente lineation. These findings highlight the importance of determining directional roughness and wind direction for strongly aligned surface features and also suggest more work is required to determine appropriate geometrical roughness formulae for linearized features.
Interrogative Slifting: More syntactic, less parenthetical
Authors: Stepanov Arthur , Stateva Penka
Published: August 2016

In this article we re-assess the recent analysis of interrogative Slifting (e.g., Who is a Martian, do you think?) proposed in Haddican et al. (2014). In this analysis, the two component clauses have an indirect syntactic relation to each other, and the semantic and pragmatic relationship between the “slift” question and the main clause is conceived around the notion of evidentiality. We advance an alternative proposal whereby interrogative Slifting can be construed more on a par with wh-scope marking questions attested in languages like German or Hindi. Placing interrogative Slifting alongside wh-scope marking, a more familiar and better-studied construction type, avoids certain empirical difficulties of the original analysis and paves a way toward a uniform treatment of its syntactic, semantic and interface properties.
Effectiveness of the Group Play Therapy on the Insecure Attachment and Social Skills of Orphans in Ahvaz City
Authors: Mousavi Bahareh , Safarzadeh Sahar
Published: August 2016

This study aimed to determine the effectiveness of the group play therapy on the insecure attachment and social skills of orphans in Ahvaz city. Statistical population included all orphans in Ahvaz city, of whom 30 students were selected whose scores in insecure attachment and in social skills were one standard deviation higher and one standard deviation lower than the mean, respectively and they were randomly divided into two treatment (15 persons) and control (15 persons) groups. The research tools included Randolph Attachment Disorder Questionnaire (2000) (RADQ) and Social Skills Rating System (SSRS) questionnaire (Gresham and Elliot, 1990). This is an experimental study with pretest, posttest, and follow-up by the control group. Firstly, pretest was implemented for both groups, and then experimental intervention (play therapy) was carried out for the treatment group during 10 sessions. After the therapeutic program, the posttest and two months later follow-up were implemented. The results obtained using the statistical method of multivariate covariance analysis showed that group play therapy reduces the insecure attachment and increases the social skills at P < 0.001 during the stages of posttest and follow-up in the treatment group compared to the control group. Results also indicated that there is a significant difference between posttest and follow-up of the treatment and control group in terms of the components of social skills (collaboration, assertiveness, and self-control).