Photogrammetry Education for Multidisciplinary Geomatics in China

IntroductionThe word photogrammetry comes from Greekwords photos ( meaning “light”) , gramma( meaning that which is drawn or written) andmetron ( meaning“to measure”) . It originallysignifies measuring graphically by means oflight[1].The development o…     

Evolutionary stages of the karst-anthropogenic system of the Yucatán Peninsula

An analysis is made of the karst geomorphosystems of the Yucatán Peninsula. It is found that the main factors influencing its formation were climate and ocean level oscillations, tectonic movements, and impact effects. It is established that as a result of an active influence of the Maya civilization on the karst landscape of the peninsula, the geomorphosystems was transformed to the karst-anthropogenic system. The karstic landforms, and also the underground caverns experienced the anthropogenic impact, and the subsurface waters were particularly heavily influenced, which, on the one hand, determined the places of concentration of settlements and were actively consumed by the population, and, on the other, their reserves were replenished at regular intervals as a result of the creation of artificial subsurface water storages. In a further transformation of the karst-anthropogenic system, two stages were additionally identified: dominated by natural karstic processes (after the decline of the Maya civilization), and with a renewed intensification of anthropogenic pressure (1980s–current period). The last transformation stage is distinguished by an extension of the impact to the surface karst as well as to the underground caverns: they are subjected to mechanical destruction with an enhancement in the chemical impact as a result of inputs of polluted runoffs to encompass ever deeper horizons of subsurface waters. It is established that the rates of transformation processes of anthropogenic objects created of limestone are, in general, comparable with the mean rates of natural karstic processes on the neighboring territories: the formation of karrens on Maya steles is generally proceeding at the rate of 0.04 mm/year, while the growth rates of flowstones within urban structures of limestone vary from 0.03 to 0.6 mm/year.     

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico

Three-dimensional urban cartography is needed for city changes’ assessment. The variety of studies using 3D calculations of urban elements grows each year. Building and vegetation volumes are necessary to assess and understand spatio-temporal urban changeable environments. However, there are technical questions as to which method can improve 3D urban cartographic accuracy. The innovative part of this current study is the creation of a six-band hybrid obtained from LIDAR and WorldView2 synergy. Two different enhancement algorithms demonstrated the most important spectral features for the urban development and vegetation classes. Results indicated an improvement in accuracy by up to 21.3%, according to the Kappa coefficient. Both infra-red band and intensity band were the most significant, according to the principal components analysis. The synergy delimited classes and polygons, as well as the direct display of information regarding heights of elements and improving the extraction of roads, buildings and vegetation classes.     

Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.     

Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution

In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM). It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty.     

Accuracy improvement of ASTER stereo satellite generated DEM using texture filter

The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images.Currently,apart from the deployment of point-clouds from LiDAR data acquisition,the generally favoured approach refers to applications of digital photogrammetry.One of the most important steps in such deployment i...     

Land suitability studies for major crops in Pavagada taluk,Karnataka using remote sensing and GIS techniques

Pavagada taluk of Tumkur district in Karnataka is one of the most backward taluks receiving less than 500 mm annual rainfall. The maximum area of the taluk is under monocropping, reasons for the same were not documented well. The present study was carried out using remote sensing data along with field survey and laboratory analysis for assessing the potentials and limitations of soil. Using the basic information on soil, climate and topography based on the matching exercise between the growth and production requirements of the crop, suitability of soils for groundnut, paddy and finger millet was assessed as per FAO land evaluation. The soil suitability maps were prepared using Arc GIS software. About 48 per cent of the total area was moderate to marginally suitable and 13 per cent of the area was not suitable for both groundnut and finger millet. Lowland areas covering 12 per cent of the area was highly suitable, 15 per cent was moderate to marginally suitable and 20 per cent was not suitable for paddy cultivation.     

Safety factor nomograms for homogeneous earth dams less than ten meters high

Homogeneous earth dams that are waterproofed with geomembranes are a suitable option for storing water and other sorts of liquids, like leachates from landfills. Such dams do not require complicated engineering technical calculations, their cost is usually low and they are not difficult to construct. To ensure the geotechnical safety of the dam, the slopes of the embankment must be correctly designed and constructed. This paper provides a set of nomograms which allow the user to get the safety factor of the slopes immediately. In some cases, it is only necessary to know previously the material classification according to the Unified Soil Classifications System. From this information it can be determined whether the material is appropriate or not. If the material classification is not available, geotechnical data of the material used in the construction of the embankment are needed. Examples of the application of nomograms are presented. Secondly, the paper includes a set of equations to calculate quickly the safety factor of a slope of earth upper than 7.5 m height.     

Despeckling of TerraSAR-X Data Using Second-Generation Wavelets

This letter presents the despeckling of synthetic aperture radar (SAR) images within the bandelet and contourlet domains. A model-based approach is presented for the despeckling of SAR images. The speckle-reduced estimate is found using the first-order Bayesian inference, and the best model's parameters are estimated using the second-order Bayesian inference. Synthetic and real images are used for evaluating the qualities of the despeckling methods. The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and objective measurements.