bluuebunny/crossref_metadata_2025 huggingface dataset:
Global atmospheric budget of simple monocyclic aromatic compounds
Authors: Cabrera-Perez David , Taraborrelli Domenico , Sander Rolf , Pozzer Andrea
Published: June 2016

Abstract. The global atmospheric budget and distribution of monocyclic aromatic compounds is estimated, using an atmospheric chemistry general circulation model. Simulation results are evaluated with an ensemble of surface and aircraft observations with the goal of understanding emission, production and removal of these compounds.Anthropogenic emissions provided by the RCP database represent the largest source of aromatics in the model (≃ 23 TgC year−1) and biomass burning from the GFAS inventory the second largest (≃ 5 TgC year−1). The simulated chemical production of aromatics accounts for  ≃ 5 TgC year−1. The atmospheric burden of aromatics sums up to 0.3 TgC. The main removal process of aromatics is photochemical decomposition (≃ 27 TgC  year−1), while wet and dry deposition are responsible for a removal of  ≃ 4 TgC year−1.Simulated mixing ratios at the surface and elsewhere in the troposphere show good spatial and temporal agreement with the observations for benzene, although the model generally underestimates mixing ratios. Toluene is generally well reproduced by the model at the surface, but mixing ratios in the free troposphere are underestimated. Finally, larger discrepancies are found for xylenes: surface mixing ratios are not only overestimated but also a low temporal correlation is found with respect to in situ observations.
Three-North Shelter Forest Program contribution to long-term increasing trends of biogenic isoprene emissions in northern China
Authors: Zhang Xiaodong , Huang Tao , Zhang Leiming , Shen Yanjie , Zhao Yuan , Gao Hong , Mao Xiaoxuan , Jia Chenhui , Ma Jianmin
Published: June 2016

Abstract. To assess the long-term trends of isoprene emissions in northern China and the impact of the Three-North Shelter Forest Program (TNRSF) on these trends, a database of historical biogenic isoprene emissions from 1982 to 2010 was developed for this region using a biogenic emission model for gases and aerosols. The total amount of the biogenic isoprene emissions during the 3 decades was 4.4 Tg in northern China and 1.6 Tg in the TNRSF, with annual emissions ranging from 132 000 to 176 000 t yr−1 and from 45 000 to 70 000 t yr−1, respectively, in the two regions. Isoprene emission fluxes have increased substantially in many areas of the TNRSF over the last 3 decades due to the growing trees and vegetation coverage, especially in the central north China region where the highest emission incline reached to 58 % from 1982 to 2010. Biogenic isoprene emissions produced from anthropogenic forests tended to surpass those produced from natural forests, such as boreal forests in northeastern China. The estimated isoprene emissions suggest that the TNRSF has altered the long-term emission trend in north China from a decreasing trend during 1982 to 2010 (slope  =  −0.533, R2 = 0.05) to an increasing trend for the same period of time (slope  =  0.347, R2 = 0.014), providing strong evidence for the change in the emissions of biogenic volatile organic compounds (BVOCs) induced by the human activities on decadal or longer timescales.
Frontiers in Geomorphometry and Earth Surface Dynamics: Possibilities, Limitations and Perspectives
Authors: Sofia Giulia , Hillier Jon K. , Conway Susan J.
Published: June 2016

Abstract. Geomorphometry, the science of quantitative land-surface analysis, has become a flourishing interdisciplinary subject, with applications in numerous fields. The interdisciplinarity of geomorphometry is its greatest strength and also one of its major challenges. Gaps are still present between the process focussed fields (e.g. soil science, glaciology, volcanology) and the technical domain (such as computer science, statistics…) where approaches and theories are developed. Thus, interesting geomorphometric applications struggle to jump between process-specific disciplines, but also struggle to take advantage of advances in computer science and technology. This special issue is therefore focused on facilitating cross-fertilization between disciplines, and highlighting novel technical developments and innovative applications of geomorphometry to various Earth-surface processes. The issue collects a variety of contributions which fall into two main categories: Perspectives and Research, further divided into "Research and innovation techniques" and "Research and innovative applications". It showcases potentially exciting developments and tools which are the building blocks for the next step-change in the field.
Potentiale und Grenzen umweltbasierter Konflikttransformation: Erkenntnisse aus dem Arava Institute for Environmental Studies in Israel
Authors: Fricke N. , Rodrian P. , Zademach H.-M.
Published: June 2016

Abstract. Der Beitrag untersucht am Beispiel des Arava Institute for Environmental Studies in Israel, wie Friedensbildungsmaßnahmen mit Bezug zu Umweltthemen die Sichtweisen auf einen Konflikt bei Akteuren auf Graswurzelebene verändern können. Ausgangsüberlegung ist dabei, Umwelt nicht nur als Teil des Konfliktes zu interpretieren, sondern als konfliktneutralen Katalysator einzusetzen. Die auf dieser Basis erreichte Veränderung der Wahrnehmung des Konflikts wird in Form von konstruierten Raumbildern konzeptualisiert. Die empirischen Einblicke zeigen zum einen die Bedeutung von physischen wie sozialen Orten und Räumen als Rahmen für die konstruktive Auseinandersetzung. Zum anderen lässt sich in Bezug auf die Konfliktwahrnehmung ein Bruch zwischen einer positiven Veränderung des Bildes der anderen Gruppe und der persistenten Konstruktion des Erbes ableiten.
A NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES
Authors: Liu Xinxin , Shen Huanfeng , Yuan Qiangqiang , Zhang Liangpei , Cheng Qing
Published: June 2016

Abstract. In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.
ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER
Authors: Srestasathiern P. , Lawawirojwong S. , Suwantong R. , Phuthong P
Published: June 2016

Abstract. This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.
A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION
Authors: Wang Ping , Wei Zheng , Cui Weihong , Lin Zhiyong
Published: June 2016

Abstract. This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.
CHANGE DETECTION WITH MULTI-SOURCE DEFECTIVE REMOTE SENSING IMAGES BASED ON EVIDENTIAL FUSION
Authors: Chen Xi , Li Jing , Zhang Yunfei , Tao Liangliang
Published: June 2016

Abstract. Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed.
INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES
Authors: Ru Hui , Yu Huai , Huang Pingping , Yang Wen
Published: June 2016

Abstract. Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.
ASSESSMENT OF CROPPING SYSTEM DIVERSITY IN THE FERGANA VALLEY THROUGH IMAGE FUSION OF LANDSAT 8 AND SENTINEL-1
Authors: Dimov D. , Kuhn J. , Conrad C.
Published: June 2016

Abstract. In the transitioning agricultural societies of the world, food security is an essential element of livelihood and economic development with the agricultural sector very often being the major employment factor and income source. Rapid population growth, urbanization, pollution, desertification, soil degradation and climate change pose a variety of threats to a sustainable agricultural development and can be expressed as agricultural vulnerability components. Diverse cropping patterns may help to adapt the agricultural systems to those hazards in terms of increasing the potential yield and resilience to water scarcity. Thus, the quantification of crop diversity using indices like the Simpson Index of Diversity (SID) e.g. through freely available remote sensing data becomes a very important issue. This however requires accurate land use classifications. In this study, the focus is set on the cropping system diversity of garden plots, summer crop fields and orchard plots which are the prevalent agricultural systems in the test area of the Fergana Valley in Uzbekistan. In order to improve the accuracy of land use classification algorithms with low or medium resolution data, a novel processing chain through the hitherto unique fusion of optical and SAR data from the Landsat 8 and Sentinel-1 platforms is proposed. The combination of both sensors is intended to enhance the object´s textural and spectral signature rather than just to enhance the spatial context through pansharpening. It could be concluded that the Ehlers fusion algorithm gave the most suitable results. Based on the derived image fusion different object-based image classification algorithms such as SVM, Naïve Bayesian and Random Forest were evaluated whereby the latter one achieved the highest classification accuracy. Subsequently, the SID was applied to measure the diversification of the three main cropping systems.
MAPPING URBAN TREE CANOPY COVER USING FUSED AIRBORNE LIDAR AND SATELLITE IMAGERY DATA
Authors: Parmehr Ebadat G. , Amati Marco , Fraser Clive S.
Published: June 2016

Abstract. Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.
GRAPH MATCHING FOR THE REGISTRATION OF PERSISTENT SCATTERERS TO OPTICAL OBLIQUE IMAGERY
Authors: Schack L. , Soergel U. , Heipke C.
Published: June 2016

Abstract. Matching Persistent Scatterers (PS) to airborne optical imagery is one possibility to augment applications and deepen the understanding of SAR processing and products. While recently this data registration task was done with PS and optical nadir images the alternatively available optical oblique imagery is mostly neglected. Yet, the sensing geometry of oblique images is very similar in terms of viewing direction with respect to SAR.We exploit the additional information coming with these optical sensors to assign individual PS to single parts of buildings. The key idea is to incorporate topology information which is derived by grouping regularly aligned PS at facades and use it together with a geometry based measure in order to establish a consistent and meaningful matching result. We formulate this task as an optimization problem and derive a graph matching based algorithm with guaranteed convergence in order to solve it. Two exemplary case studies show the plausibility of the presented approach.
A NEW SPATIAL AND TEMPORAL FUSION MODEL
Authors: Wang Jing , Huang Bo
Published: June 2016

Abstract. As Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) has a tradeoff between the high temporal resolution and high spatial resolution, this paper proposed a spatial and temporal model with auto-regression error correction (AREC) method to blend the two types of images in order to obtain the composed image with both high spatial and temporal resolution. Experiments and validation were conducted on a data set located in Shenzhen, China and compared with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in several objective indexes and visual analysis. It was found that AREC could effectively predict the land cover changes and the fusion results had better performances versus the ones of STARFM.
ALTERNATIVE TO FOUR-COMPONENT DECOMPOSITION FOR POLARIMETRIC SAR
Authors: Zhang J. X. , Huang G. M. , Wei J. J. , Zhao Z.
Published: June 2016

Abstract. There are more unknowns than equations to solve for previous four-component decomposition methods. In this case, the nonnegative power of each scattering mechanism has to be determined with some assumptions and physical power constraints. This paper presents a new decomposition scheme, which models the measured matrix after polarimetric orientation angle (POA) compensation as a linear sum of five scattering mechanisms (i.e., odd-bounce scattering, double-bounce scattering, diffuse scattering, volume scattering, and helix scattering). And the volume scattering power is calculated by a slight modified NNED method, owing to this method considering the external volume scattering model from oblique dihedral structure. After the helix and volume scattering powers have been determined sequentially, the other three scattering powers are estimated by combining the generalized similarity parameter (GSP) and the eigenvalue decomposition. Among them, due to POA compensation, the diffuse scattering induced from a dihedral with a relative orientation of 45º has negligible scattering power. Thus, the new method can be reduced as four-component decomposition automatically. And then the ALOS-2 PolSAR data covering Guiyang City, Guizhou Province, China were used to evaluate the performance of the new method in comparison with some classical decomposition methods (i.e. Y4R, S4R and G4U).
CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – A NEW METHOD OF MONITORING BUILDING CHANGES
Authors: Yang C. H. , Kenduiywo B. K. , Soergel U.
Published: June 2016

Abstract. Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.
SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY
Authors: Han Xiaobing , Zhong Yanfei , Zhang Liangpei
Published: June 2016

Abstract. Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.
SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES
Authors: Le Bris A. , Chehata N. , Briottet X. , Paparoditis N.
Published: June 2016

Abstract. In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown.
CIRRUS REMOVAL IN MULTISPECTRAL DATAWITHOUT 1.38μM SPECTRAL DATA
Authors: Makarau Aliaksei , Richter Rudolf , Zekoll Viktoria , Reinartz Peter
Published: June 2016

Abstract. Cirrus is one of the most common artifacts in the remotely sensed optical data. Contrary to the low altitude (1-3 km) cloud the cirrus cloud (8-20 km) is semitransparent and the extinction (cirrus influence) of the upward reflected solar radiance can be compensated. The widely employed and almost ’de-facto’ method for cirrus compensation is based on the 1.38μm spectral channel measuring the upwelling radiance reflected by the cirrus cloud. The knowledge on the cirrus spatial distribution allows to estimate the per spectral channel cirrus attenuation and to compensate the spectral channels. A wide range of existing and expected sensors have no 1.38μm spectral channel. These sensors data can be corrected by the recently developed haze/cirrus removal method. The additive model of the estimated cirrus thickness map (CTM) is applicable for cirrus-conditioned extinction compensation. Numeric and statistic evaluation of the CTM-based cirrus removal on more than 80 Landsat-8 OLI and 30 Sentinel-2 scenes demonstrates a close agreement with the 1.38μm channel based cirrus removal.
POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING
Authors: Babic Z. , Pilipovic R. , Risojevic V. , Mirjanic G.
Published: June 2016

Abstract. Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.
CROP TYPE MAPPING FROM A SEQUENCE OF TERRASAR-X IMAGES WITH DYNAMIC CONDITIONAL RANDOM FIELDS
Authors: Kenduiywo B. K. , Bargiel D. , Soergel U.
Published: June 2016

Abstract. Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It outperforms MLC and CRFs by 7.70% and 6.42% in overall accuracy, respectively.
A COMPARISON OF SUB-PIXEL MAPPING METHODS FOR COASTAL AREAS
Authors: Liu Qingxiang , Trinder John , Turner Ian
Published: June 2016

Abstract. This paper presents the comparisons of three soft classification methods and three sub-pixel mapping methods for the classification of coastal areas at sub-pixel level. Specifically, SPOT-7 multispectral images covering the coastal area of Perth are selected as the experiment dataset. For the soft classification, linear spectral unmixing model, supervised fully-fuzzy classification method and the support vector machine are applied to generate the fraction map. Then for the sub-pixel mapping, the sub-pixel/pixel attraction model, pixel swapping and wavelets method are compared. Besides, the influence of the correct fraction constraint is explored. Moreover, a post-processing step is implemented according to the known spatial knowledge of coastal areas. The accuracy assessment of the fraction values indicates that support vector machine generates the most accurate fraction result. For sub-pixel mapping, wavelets method outperforms the other two methods with overall classification accuracy of 91.79% and Kappa coefficient of 0.875 after the post-processing step and it also performs best for waterline extraction with mean distance of 0.71m to the reference waterline. In this experiment, the use of correct fraction constraint decreases the classification accuracy of sub-pixel mapping methods and waterline extraction. Finally, the post-processing step improves the accuracy of sub-pixel mapping methods, especially for those with correct coefficient constraint. The most significant improvement of overall accuracy is as much as 4% for the sub-pixel/pixel attraction model with correct coefficient constraint.
DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING
Authors: Maillard Philippe , Gomes Marília F.
Published: June 2016

Abstract. This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the “template matching” image processing approach, in which the template is based on a “geometricaloptical” model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications. The algorithm is tested on four images from different regions of the world and different crop types. These images all have < 1 meter spatial resolution and were downloaded from the GoogleEarth application. Results show that the algorithm is very efficient at detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required parameters is under development.
INVESTIGATION OF LATENT TRACES USING INFRARED REFLECTANCE HYPERSPECTRAL IMAGING
Authors: Schubert Till , Wenzel Susanne , Roscher Ribana , Stachniss Cyrill
Published: June 2016

Abstract. The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.
ANALYSIS OF THE INTRA-CITY VARIATION OF URBAN HEAT ISLAND AND ITS RELATION TO LAND SURFACE/COVER PARAMETERS
Authors: Gerçek D. , Güven İ. T. , Oktay İ. Ç.
Published: June 2016

Abstract. Along with urbanization, sealing of vegetated land and evaporation surfaces by impermeable materials, lead to changes in urban climate. This phenomenon is observed as temperatures several degrees higher in densely urbanized areas compared to the rural land at the urban fringe particularly at nights, so-called Urban Heat Island. Urban Heat Island (UHI) effect is related with urban form, pattern and building materials so far as it is associated with meteorological conditions, air pollution, excess heat from cooling. UHI effect has negative influences on human health, as well as other environmental problems such as higher energy demand, air pollution, and water shortage. Urban Heat Island (UHI) effect has long been studied by observations of air temperature from thermometers. However, with the advent and proliferation of remote sensing technology, synoptic coverage and better representations of spatial variation of surface temperature became possible. This has opened new avenues for the observation capabilities and research of UHIs. In this study, "UHI effect and its relation to factors that cause it" is explored for İzmit city which has been subject to excess urbanization and industrialization during the past decades. Spatial distribution and variation of UHI effect in İzmit is analysed using Landsat 8 and ASTER day & night images of 2015 summer. Surface temperature data derived from thermal bands of the images were analysed for UHI effect. Higher temperatures were classified into 4 grades of UHIs and mapped both for day and night. Inadequate urban form, pattern, density, high buildings and paved surfaces at the expanse of soil ground and vegetation cover are the main factors that cause microclimates giving rise to spatial variations in temperatures across cities. These factors quantified as land surface/cover parameters for the study include vegetation index (NDVI), imperviousness (NDISI), albedo, solar insolation, Sky View Factor (SVF), building envelope, distance to sea, and traffic space density. These parameters that cause variation in intra-city temperatures were evaluated for their relationship with different grades of UHIs. Zonal statistics of UHI classes and variations in average value of parameters were interpreted. The outcomes that highlight local temperature peaks are proposed to the attention of the decision makers for mitigation of Urban Heat Island effect in the city at local and neighbourhood scale.
DISASTER MANAGEMENT: AN INTEGRAL PART OF SCIENCE & TECHNOLOGY SYSTEM AND LAND ADMINISTRATION-MANAGEMENT SYSTEM
Authors: Ghawana T. , Zlatanova S.
Published: June 2016

Abstract. Disaster management is a multidisciplinary field, which requires a general coordination approach as well as specialist approaches. Science and Technology system of a country allows to create policies and execution of technical inputs required which provide services for the specific types of disasters management. Land administration and management agencies, as the administrative and management bodies, focus more on the coordination of designated tasks to various agencies responsible for their dedicated roles. They get help from Scientific and technical inputs & policies which require to be implemented in a professional manner. The paper provides an example of such integration from India where these two systems complement each other with their dedicated services. Delhi, the Capital of India, has such a disaster management system which has lot of technical departments of government which are mandated to provide their services as Emergency Service Functionaries. Thus, it is shown that disaster management is a job which is an integral part of Science & Technology system of a country while being implemented primarily with the help of land administration and management agencies. It is required that new policies or mandates for the Science and technology organizations of government should give a primary space to disaster management
MEASURE OF LANDSCAPE HETEROGENEITY BY AGENT-BASED METHODOLOGY
Authors: Wirth E. , Szabó Gy. , Czinkóczky A.
Published: June 2016

Abstract. With the rapid increase of the world's population, the efficient food production is one of the key factors of the human survival. Since biodiversity and heterogeneity is the basis of the sustainable agriculture, the authors tried to measure the heterogeneity of a chosen landscape. The EU farming and subsidizing policies (EEA, 2014) support landscape heterogeneity and diversity, nevertheless exact measurements and calculations apart from statistical parameters (standard deviation, mean), do not really exist. In the present paper the authors’ goal is to find an objective, dynamic method that measures landscape heterogeneity. It is achieved with the so called agent-based modelling, where randomly dispatched dynamic scouts record the observed land cover parameters and sum up the features of a new type of land. During the simulation the agents collect a Monte Carlo integral as a diversity landscape potential which can be considered as the unit of the ‘greening’ measure. As a final product of the ABM method, a landscape potential map is obtained that can serve as a tool for objective decision making to support agricultural diversity.
RAPID RISK EVALUATION (ER2) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK, CANADA)
Authors: McGrath H. , Stefanakis E. , Nastev M.
Published: June 2016

Abstract. Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA’s Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.
REMOTELY-SENSED GLACIER CHANGE ESTIMATION: A CASE STUDY AT LINDBLAD COVE, ANTARCTIC PENINSULA
Authors: Fieber K. D. , Mills J. P. , Miller P. E. , Fox A. J.
Published: June 2016

Abstract. This study builds on existing literature of glacier change estimation in polar regions and is a continuation of efforts aimed at unlocking the information encapsulated in archival aerial photography of Antarctic Peninsula glaciers. Historical aerial imagery acquired in 1957 over three marine-terminating glaciers at Lindblad Cove on the West Coast of Trinity Peninsula is processed to extract digital elevation models (DEMs) which are subsequently compared to DEMs generated from present day (2014) WorldView-2 satellite stereo-imagery. The new WorldView-2 images offer unprecedented sub-metre resolution of the Antarctic Peninsula and are explored here to facilitate improved registration and higher accuracy analysis of glacier changes. Unlike many studies, which focus on glacier fronts or only restricted regions of glaciers, this paper presents a complete coverage of elevation changes across the glacier surfaces for two of the studied glaciers. The study utilises a robust least squares matching technique to ensure precise registration of the archival and modern DEMs, which is applied due to lack of existing ground control in this remote region. This case study reveals that, while many glaciers in polar regions are reported as experiencing significant mass loss, some glaciers are stable or even demonstrate mass gain. All three glaciers reported here demonstrated overall mean increases in surface elevation, indicative of positive mass balance ranging from 0.6 to 5.8 metre water equivalent between 1957 and 2014.
EVALUATION OF VERTICAL LACUNARITY PROFILES IN FORESTED AREAS USING AIRBORNE LASER SCANNING POINT CLOUDS
Authors: Székely B. , Kania A. , Standovár T. , Heilmeier H.
Published: June 2016

Abstract. The horizontal variation and vertical layering of the vegetation are important properties of the canopy structure determining the habitat; three-dimensional (3D) distribution of objects (shrub layers, understory vegetation, etc.) is related to the environmental factors (e.g., illumination, visibility). It has been shown that gaps in forests, mosaic-like structures are essential to biodiversity; various methods have been introduced to quantify this property. As the distribution of gaps in the vegetation is a multi-scale phenomenon, in order to capture it in its entirety, scale-independent methods are preferred; one of these is the calculation of lacunarity. We used Airborne Laser Scanning point clouds measured over a forest plantation situated in a former floodplain. The flat topographic relief ensured that the tree growth is independent of the topographic effects. The tree pattern in the plantation crops provided various quasi-regular and irregular patterns, as well as various ages of the stands. The point clouds were voxelized and layers of voxels were considered as images for two-dimensional input. These images calculated for a certain vicinity of reference points were taken as images for the computation of lacunarity curves, providing a stack of lacunarity curves for each reference points. These sets of curves have been compared to reveal spatial changes of this property. As the dynamic range of the lacunarity values is very large, the natural logarithms of the values were considered. Logarithms of lacunarity functions show canopy-related variations, we analysed these variations along transects. The spatial variation can be related to forest properties and ecology-specific aspects.
3D GEO-INFORMATION REQUIREMENTS FOR DISASTER AND EMERGENCY MANAGEMENT
Authors: Demir Ozbek E. , Zlatanova S. , Ates Aydar S. , Yomralioglu T.
Published: June 2016

Abstract. A conceptual approach is proposed to define 3D geo-information requirement for different types of disasters. This approach includes components such as Disaster Type-Sector-Actor-Process-Activity-Task-Data. According to disaster types processes, activities, tasks, sectors, and responsible and operational actors are derived. Based on the tasks, the needed level of detail for 3D geo-information model is determined. The levels of detail are compliant with the 3D international standard CityGML. After a brief introduction on the disaster phases and geo-information requirement for actors to perform the tasks, the paper discusses the current situation of disaster and emergency management in Turkey and elaborates on components of conceptual approach. This paper discusses the 3D geo-information requirements for the tasks to be used in the framework of 3D geo-information model for Disaster and Emergency Management System in Turkey. The framework is demonstrated for an industrial fire case in Turkey.
TECHNICAL ASPECTS FOR THE CREATION OF A MULTI-DIMENSIONAL LAND INFORMATION SYSTEM
Authors: Ioannidis Charalabos , Potsiou Chryssy , Soile Sofia , Verykokou Styliani , Mourafetis George , Doulamis Nikolaos
Published: June 2016

Abstract. The complexity of modern urban environments and civil demands for fast, reliable and affordable decision-making requires not only a 3D Land Information System, which tends to replace traditional 2D LIS architectures, but also the need to address the time and scale parameters, that is, the 3D geometry of buildings in various time instances (4th dimension) at various levels of detail (LoDs - 5th dimension). This paper describes and proposes solutions for technical aspects that need to be addressed for the 5D modelling pipeline. Such solutions include the creation of a 3D model, the application of a selective modelling procedure between various time instances and at various LoDs, enriched with cadastral and other spatial data, and a procedural modelling approach for the representation of the inner parts of the buildings. The methodology is based on automatic change detection algorithms for spatial-temporal analysis of the changes that took place in subsequent time periods, using dense image matching and structure from motion algorithms. The selective modelling approach allows a detailed modelling only for the areas where spatial changes are detected. The procedural modelling techniques use programming languages for the textual semantic description of a building; they require the modeller to describe its part-to-whole relationships. Finally, a 5D viewer is developed, in order to tackle existing limitations that accompany the use of global systems, such as the Google Earth or the Google Maps, as visualization software. An application based on the proposed methodology in an urban area is presented and it provides satisfactory results.