Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6124-1A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas. However, a classification system used for readability determines the final appearance of the map and may therefore influence the decision-making tasks adopted. The present paper addresses the spatial comparison and the accuracy assessment of some well-known classification methods applied to a susceptibility map that was based on a discriminant statistical model in an area in the Eastern Pyrenees. A number of statistical approaches (Spearman’s correlation, kappa index, factorial and cluster analyses and landslide density index) for map comparison were performed to quantify the information provided by the usual image analysis. The results showed the reliability and consistency of the kappa index against Spearman’s correlation as accuracy measures to assess the spatial agreement between maps. Inferential tests between unweighted and linear weighted kappa results showed that all the maps were more reliable in classifying areas of highest susceptibility and less reliable in classifying areas of low to moderate susceptibility. The spatial variability detected and quantified by factorial and cluster analyses showed that the maps classified by quantile and natural break methods were the closest whereas those classified by landslide percentage and equal interval methods displayed the greatest differences. The difference image analysis showed that the five classified maps only matched 9 % of the area. This area corresponded to the steeper slopes and the steeper watershed angle with forestless and sunny slopes at low altitudes. This means that the five maps coincide in identifying and classifying the most dangerous areas. The equal interval map overestimated the susceptibility of the study area, and the landslide percentage map was considered to be a very optimistic model. The spatial pattern of the quantile and natural break maps was very similar, but the latter was more consistent and predicted potential landslides more efficiently and reliably in the study area.Peer ReviewedPreprin

Solving the tasks of subsurface resources management in GIS RAPID environment

Purpose. Solving the tasks of subsurface resources management based on the created GIS RAPID geoinformation technology. Methods. Close spatial relationships of lineament network characteristics and earthquake epicenters were detected in 3 seismically active areas located in the mountainous regions of Central Europe. Digital elevation models (DEM) based on ASTER satellite surveys and earthquake epicenter data were used. The nature of spatial relationship of lineament network and vein ore objects was studied in the territory of Congo DR, in the Lake Kivu area using space imagery. Gold ore objects were searched and forecasted in Uzbekistan in the site of Jamansai Mountains. High- resolution imagery from QuickBird 2 satellite, geophysical field surveys, geological and geochemical data were used. Findings. It was found that a significant number of epicenters are located in areas of high concentration of “non-standard” azimuths lineaments – from 27 to 34% of the total number of lineaments. It was revealed that 59.6% of the epicenters are located within 10% of sites with the highest values of complex deformation maps; 50% of the areas with the highest values of these maps contain, on average, 89% of all earthquake epicenters. It was found that satellite image lineament concentration maps with “non-standard” azimuths reflect the spatial relationship with known deposits much better than the concentration map of all lineaments. It was detected that the total area of gold ore objects perspective sites is about 20 km2. Originality. The use of GIS RAPID in a number of earth’s crust areas has allowed to establish new regularities linking the networks of physical field and landscape lineament characteristics with ore bodies and earthquake epicenters localization. Practical implications. A new technology has been developed for solving geological forecasting and prospecting problems. The technology can be used to solve a wide range of practical problems, especially in difficult geological conditions when searching for deep objects weakly presented in external fields and landscape.Мета. Рішення задач надрокористування на базі створеної геоінформаційної технології ГІС РАПІД. Методика. Виявлення тісних просторових взаємозв’язків різноманітних характеристик мереж лінеаментів і епіцентрів землетрусів проводилося у 3 сейсмоактивних ділянках, розташованих в гірських районах Центральної Європи. Використовувалися цифрові моделі рельєфу (DEM), побудовані за зйомками зі супутника ASTER і дані по епіцентрах землетрусів. Дослідження характеру просторового взаємозв’язку мережі лінеаментів і жильних рудних об’єктів проводилися на території Демократичної Республіки Конго, в районі озера Ківу із використанням космічних зйомок. Дослідження пошуку та прогнозу золоторудних об’єктів виконувалися в Узбекистані на ділянці Джамансайскіх гір. Використовувалися високоточні космічні зйомки зі супутника QuickBird 2, зйомки геофізичних полів, геологічні та геохімічні дані. Результати. Виявлено, що значна частина епіцентрів приурочена саме до ділянок підвищеної концентрації лінеаментів “нестандартних” азимутів, складаючи від 27 до 34% загального числа лінеаментів. Встановлено, що 59.6% епіцентрів знаходяться всередині 10% території ділянок, що володіють найвищими значеннями комплексних карт деформацій; 50% території з найвищими значеннями цих карт вміщають, в середньому, 89% усіх епіцентрів землетрусів. Визначено, що карти концентрації лінеаментів космознімків з “нестанартними” азимутами значно краще відображають просторовий взаємозв’язок з відомими родовищами у порівнянні з картою концентрації всіх лінеаментів. Встановлено, що сумарна площа перспективних ділянок золоторудних об’єктів склала близько 20 км2. Наукова новизна. Застосування ГІС РАПІД на ряді ділянок земної кори дозволило встановити нові закономірності, що зв’язують характеристики мережі лінеаментів фізичних полів і ландшафту з локалізацією рудних тіл та епіцентрів землетрусів. Практична значимість. Розроблено нову технологію рішення прогнозних і пошукових геологічних завдань, яка може застосовуватися для вирішення широкого кола практичних задач, особливо у складних геологічних умовах при пошуках глибокозалягаючих об’єктів, що слабо виявляються в зовнішніх полях і ландшафті.Цель. Решения задач недропользования на базе созданной геоинформационной технологии ГИС РАПИД. Методика. Выявление тесных пространственных взаимосвязей разнообразных характеристик сетей линеаментов и эпицентров землетрясений проводилось в 3 сейсмоактивных участках, расположенных в горных районах Центральной Европы. Использовались цифровые модели рельефа (DEM), построенные по съемкам со спутника ASTER, и данные об эпицентрах землетрясений. Исследования характера пространственной взаимосвязи сети линеаментов и жильных рудных объектов проводились на территории Демократической Республики Конго, в районе озера Киву с использованием космических съемок. Исследования поиска и прогноза золоторудных объектов выполнялись в Узбекистане на участке Джамансайских гор. Использовались высокоточные космические съемки со спутника QuickBird 2, съемки геофизических полей, геологические и геохимические данные. Результаты. Выявлено, что значительная часть эпицентров приурочена именно к участкам повышенной концентрации линеаментов “нестандартных” азимутов, составляя от 27 до 34% общего числа линеаментов. Установлено, что 59.6% эпицентров находятся внутри 10% территории участков, обладающих наивысшими значениями комплексных карт деформаций; 50% территории с наивысшими значениями этих карт вмещают, в среднем, 89% всех эпицентров землетрясений. Определено, что карты концентрации линеаментов космоснимков с “нестанартными” азимутами значительно лучше отражают пространственную взаимосвязь с известными месторождениями по сравнению с картой концентрации всех линеаментов. Установлено, что суммарная площадь перспективных участков золоторудных объектов составила около 20 км2. Научная новизна. Применение ГИС РАПИД на ряде участков земной коры позволило установить новые закономерности, связывающие характеристики сети линеаментов физических полей и ландшафта с локализацией рудных тел и эпицентров землетрясений. Практическая значимость. Разработана новая технология решения прогнозных и поисковых геологических задач, которая может применяться для решения широкого круга практических задач, особенно в сложных геологических условиях при поисках глубокозалегающих объектов, слабо проявляющихся во внешних полях и ландшафте.The work is performed as a part of planned research of the geoinformation systems department of the Dnipro University of Technology. The results are obtained without any financial support of grants and research projects. The authors express appreciation to reviewers and editors for their valuable comments, recommendations, and attention to the work