bluuebunny/crossref_metadata_2025 huggingface dataset:
DEVELOPING A CLOUD-BASED ONLINE GEOSPATIAL INFORMATION SHARING AND GEOPROCESSING PLATFORM TO FACILITATE COLLABORATIVE EDUCATION AND RESEARCH
Authors: Yang Z. L. , Cao J. , Hu K. , Gui Z. P. , Wu H. Y. , You L.
Published: August 2016

Efficient online discovering and applying geospatial information resources (GIRs) is critical in Earth Science domain as while for cross-disciplinary applications. However, to achieve it is challenging due to the heterogeneity, complexity and privacy of online GIRs. In this article, GeoSquare, a collaborative online geospatial information sharing and geoprocessing platform, was developed to tackle this problem. Specifically, (1) GIRs registration and multi-view query functions allow users to publish and discover GIRs more effectively. (2) Online geoprocessing and real-time execution status checking help users process data and conduct analysis without pre-installation of cumbersome professional tools on their own machines. (3) A service chain orchestration function enables domain experts to contribute and share their domain knowledge with community members through workflow modeling. (4) User inventory management allows registered users to collect and manage their own GIRs, monitor their execution status, and track their own geoprocessing histories. Besides, to enhance the flexibility and capacity of GeoSquare, distributed storage and cloud computing technologies are employed. To support interactive teaching and training, GeoSquare adopts the rich internet application (RIA) technology to create user-friendly graphical user interface (GUI). Results show that GeoSquare can integrate and foster collaboration between dispersed GIRs, computing resources and people. Subsequently, educators and researchers can share and exchange resources in an efficient and harmonious way.
DEVELOPMENT OF AN ALL-PURPOSE FREE PHOTOGRAMMETRIC TOOL
Authors: González-Aguilera D. , López-Fernández L. , Rodriguez-Gonzalvez P. , Guerrero D. , Hernandez-Lopez D. , Remondino F. , Menna F. , Nocerino E. , Toschi I. , Ballabeni A. , Gaiani M.
Published: August 2016

Photogrammetry is currently facing some challenges and changes mainly related to automation, ubiquitous processing and variety of applications. Within an ISPRS Scientific Initiative a team of researchers from USAL, UCLM, FBK and UNIBO have developed an open photogrammetric tool, called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS allows to obtain dense and metric 3D point clouds from terrestrial and UAV images. It encloses robust photogrammetric and computer vision algorithms with the following aims: (i) increase automation, allowing to get dense 3D point clouds through a friendly and easy-to-use interface; (ii) increase flexibility, working with any type of images, scenarios and cameras; (iii) improve quality, guaranteeing high accuracy and resolution; (iv) preserve photogrammetric reliability and repeatability. Last but not least, GRAPHOS has also an educational component reinforced with some didactical explanations about algorithms and their performance. The developments were carried out at different levels: GUI realization, image pre-processing, photogrammetric processing with weight parameters, dataset creation and system evaluation.

 The paper will present in detail the developments of GRAPHOS with all its photogrammetric components and the evaluation analyses based on various image datasets. GRAPHOS is distributed for free for research and educational needs.
THE ‘MOON MAPPING’ PROJECT TO PROMOTE COOPERATION BETWEEN STUDENTS OF ITALY AND CHINA
Authors: Scaioni M. , Giommi P. , Brunetti M. T. , Carli C. , Cerroni P. , Cremonese G. , Forlani G. , Gamba P. , Lavagna M. , Melis M. T. , Massironi M. , Ori G. , Salese F. , Zinzi A. , Xie G. , Kang Z. , Shi R. , Sun Y. , Wu Y.
Published: August 2016

The research project ‘Moon Mapping’ has been established in 2014 between the Italian and Chinese Governments to promote cooperation and exchange between undergraduate students from both countries. The operational phase of the project started in early 2015, and will end in 2017, for a total length of three years. The main aim is to train new scholars to be able to work on different kinds of remotely-sensed data collected over the Moon surface by the Chinese space missions Chang’E-1/2. The project coordination has been assigned to the Italian Space Agency for the Italian side and to the Center of Space Exploration, China Ministry of Education, for the Chinese side. Several Chinese universities and Italian national research institutes and universities have been officially involved in this project. Six main research topics have been identified: (1) map of the solar wind ion; (2) geomorphological map of the Moon; (3) data preprocessing of Chang’E-1 mission; (4) map of element distribution; (5) establishment of 3D digital visualization system; and (6) compilation and publication of a tutorial on joint lunar mapping.
TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA
Authors: Bakuła K. , Kupidura P. , Jełowicki Ł.
Published: August 2016

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.
KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS
Authors: Pirotti F. , Travaglini D. , Giannetti F. , Kutchartt E. , Bottalico F. , Chirici G.
Published: August 2016

In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone.

 In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13x13 pixels kernel with a simplified linear-feature representation of a cylinder is applied at different rotation angles (from 0° to 170° at 10° steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (svm) with linear kernel and random forests. The first two methods gave the best results.

 The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from ~1.8 x102 m3 to ~1.2x104 m3. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow.
GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS
Authors: Fukue Kiyonari , Shimoda Haruhisa
Published: August 2016

The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir BRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.
APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES
Authors: Kamiya K. , Fuse T. , Takahashi M.
Published: August 2016

Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.
MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS
Authors: Lang F. , Yang J. , Wu L. , Li D.
Published: August 2016

Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.
GROUND DEFORMATION MONITORING IN QINGDAO COASTAL AREAS BY TIME-SERIES TERRASAR-X IMAGES
Authors: Hou A. Y. , Qiao X. , Li D.
Published: August 2016

As a new generation of high resolution and short revisit period of radar satellite, TerraSAR-X is not only able to meet the requirements of monitoring large scale surface subsidence, but also make it possible to monitor the small deformation of the short period. This articles takes the coastal areas of the west coast of Qingdao as the research object. With Small baselines subsets interferometry synthetic aperture radar (SBASI), this paper obtained the period the average annual rate of change from the time series analysis of TerraSAR-X data from April 2015 to October 2014.In order to enrich the historical deformation data of the study area, it analyse the time series of ALOS images from December 2010 to October 2008 with the same method. Finally,it analyse and demonstrate the experimental results.
MAPPING TROPICAL FOREST FOR SUSTAINABLE MANAGEMENT USING SPOT 5 SATELLITE IMAGE
Authors: Nguyen Huong Thi Thanh
Published: August 2016

This paper describes the combination of multi-data in stratifying the natural evergreen broadleaved tropical forest of the Central Highlands of Vietnam. The forests were stratified using both unsupervised and supervised classification methods based on SPOT5 and field data. The forests were classified into 3 and 4 strata separably. Correlation between stratified forest classes and forest variables was analyzed in order to find out 1) how many classes is suitable to stratify for the forest in this area and 2) how closely the forest variables are related with forest classes. The correlation coefficient shows although all forest variables did have a significant correlation with the forest classes, stand volume appeared to have the strongest correlation with forest classes. These are 0.64 and 0.59 for four and three strata respectively. The results of supervised classification also show the four strata of heavily degraded forest, moderate disturbance, insignificant disturbance, and dense forest were discriminated more clearly comparing to the forest stratified into three classes. The proof is that overall accuracy of supervised classification was 86% with Kappa of 0.8 for four classes, meanwhile, these are 77% and 0.62 respectively for forest area classified into 3 classes.
RELATIONSHIP BETWEEN HIMAWARI-8-DERIVED OVERSHOOTING TOPS AND EXTREME WEATHER EVENTS IN SOUTHEAST ASIA
Authors: Park H. M. , Kim M. A. , Im J.
Published: August 2016

Severe weathers such as heavy rainfall, floods, strong wind, and lightning are closely related with the strong convection activities of atmosphere. Overshooting tops sometimes occur by deep convection above tropopause, penetrating into the lower stratosphere. Due to its high potential energy, the detection of OT is crucial to understand the climatic phenomena. Satellite images are useful to detect the dynamics of atmospheric conditions using cloud observation. This study used machine learning methods for extracting OTs. The reference cases were built using CloudSat, CALIPSO, and Numerical Weather Prediction (NWP) data with Himawari-8 imagery. As reference cases, 11 OT events were detected. The aim of this study is the investigation of relationship between OTs cases and the occurrences of heavy rainfall. For investigation of OT effects, TRMM daily rain rate data (mm/hr) were collected and averaged at 25 km intervals until 250km from the center of OT cases. As the result, precipitation rate clearly coincides with the distance from the center of OT occurrence.
BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE
Authors: Pirotti F. , Sunar F. , Piragnolo M.
Published: August 2016

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations.

 In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands.

 Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.
COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN
Authors: Shao Zhenfeng , Zhang Yuan , Zhang Lei , Song Yang , Peng Minjun
Published: August 2016

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.
TOPIC MODELLING FOR OBJECT-BASED CLASSIFICATION OF VHR SATELLITE IMAGES BASED ON MULTISCALE SEGMENTATIONS
Authors: Shen Li , Wu Linmei , Li Zhipeng
Published: August 2016

Multiscale segmentation is a key prerequisite step for object-based classification methods. However, it is often not possible to determine a sole optimal scale for the image to be classified because in many cases different geo-objects and even an identical geo-object may appear at different scales in one image. In this paper, an object-based classification method based on mutliscale segmentation results in the framework of topic modelling is proposed to classify VHR satellite images in an entirely unsupervised fashion. In the stage of topic modelling, grayscale histogram distributions for each geo-object class and each segment are learned in an unsupervised manner from multiscale segments. In the stage of classification, each segment is allocated a geo-object class label by the similarity comparison between the grayscale histogram distributions of each segment and each geo-object class. Experimental results show that the proposed method can perform better than the traditional methods based on topic modelling.
A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: Zhong P. , Gong Z. Q. , Schönlieb C.
Published: August 2016

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.
AN APPROACH TO ALLEVIATE THE FALSE ALARM IN BUILDING CHANGE DETECTION FROM URBAN VHR IMAGE
Authors: Chen J. , Hou J. L. , Deng M.
Published: August 2016

Building change detection from very-high-resolution (VHR) urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.
ACCELERATION OF TOPOGRAPHIC MAP PRODUCTION USING SEMI-AUTOMATIC DTM FROM DSM RADAR DATA
Authors: Rizaldy Aldino , Mayasari Ratna
Published: August 2016

Badan Informasi Geospasial (BIG) is government institution in Indonesia which is responsible to provide Topographic Map at several map scale. For medium map scale, e.g. 1:25.000 or 1:50.000, DSM from Radar data is very good solution since Radar is able to penetrate cloud that usually covering tropical area in Indonesia. DSM Radar is produced using Radargrammetry and Interferrometry technique. The conventional method of DTM production is using “stereo-mate”, the stereo image created from DSM Radar and ORRI (Ortho Rectified Radar Image), and human operator will digitizing masspoint and breakline manually using digital stereoplotter workstation. This technique is accurate but very costly and time consuming, also needs large resource of human operator. Since DSMs are already generated, it is possible to filter DSM to DTM using several techniques. This paper will study the possibility of DSM to DTM filtering using technique that usually used in point cloud LIDAR filtering. Accuracy of this method will also be calculated using enough numbers of check points. If the accuracy meets the requirement, this method is very potential to accelerate the production of Topographic Map in Indonesia.
MONITORING TREE POPULATION DYNAMICS IN ARID ZONE THROUGH MULTIPLE TEMPORAL SCALES: INTEGRATION OF SPATIAL ANALYSIS, CHANGE DETECTION AND FIELD LONG TERM MONITORING
Authors: Isaacson S. , Rachmilevitch S. , Ephrath J. E. , Maman S. , Blumberg D. G.
Published: August 2016

High mortality rates and lack of recruitment in the acacia populations throughout the Negev Desert and the Arava rift valley of Israel have been reported in previous studies. However, it is difficult to determine whether these reports can be evidence to a significant decline trend of the trees populations. This is because of the slow dynamic processes of acaia tree populations and the lack of long term continuous monitoring data. We suggest a new data analysis technique that expands the time scope of the field long term monitoring of trees in arid environments. This will enables us to improve our understanding of the spatial and temporal changes of these populations.

 We implemented two different approaches in order to expand the time scope of the acacia population field survey: (1) individual based tree change detection using Corona satellite images and (2) spatial analysis of trees population, converting spatial data into temporal data. The next step was to integrate the results of the two analysis techniques (change detection and spatial analysis) with field monitoring. This technique can be implemented to other tree populations in arid environments to help assess the vegetation conditions and dynamics of those ecosystems.
UPDATING NATIONAL TOPOGRAPHIC DATA BASE USING CHANGE DETECTION METHODS
Authors: Keinan E. , Felus Y. A. , Tal Y. , Zilberstien O. , Elihai Y.
Published: August 2016

The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed.

 Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.
ANALYSIS OF LANDSLIDES BASED ON DISPLACEMENTS OF LINES
Authors: Mozas-Calvache A. T. , Pérez-García J. L. , Fernández-del Castillo T. , Gómez-López J. M. , Colomo-Jiménez C.
Published: August 2016

Nowadays, the development of UAS has allowed the obtaining of high resolution and accurate cartographic products, such as DSMs and orthoimages. These products can be used in studies of the evolution of landslides. The stability of slopes is a main issue because, among others, it can suppose a serious risk to infrastructures. Until this moment, some studies for analysing slope movements have been carried out using the comparison of positions of well-defined points or comparing two surfaces obtained from two DSMs. In this paper we present a methodology for analysing landslides based on some linear elements located on the terrain. More specifically, we analyse some lines of road sections which are located on an unstable slope, checking the movements of the landslide and their effect on the infrastructure. The methodology includes the obtaining of high resolution orthoimages and DSMs which correspond to two or more epochs of the same landslide, the 3D digitizing of common linear elements, and the computing of the displacements of matched lines (from two epochs) using positional control methods based on lines. The proposed methodology has been tested using two DSMs and two orthoimages (corresponding to two epochs) obtained from two photogrammetric projects developed with an UAS. This real case was applied to an unstable slope with landslides which affected several sections of a road. The results have demonstrated the viability of the proposed methodology in analysing the behaviour of the landslide and more specifically, the effects on these infrastructures.
MONITORING PHENOLOGY OF FLOODPLAIN GRASSLAND AND HERBACEOUS VEGETATION WITH UAV IMAGERY
Authors: van Iersel W. K. , Straatsma M. W. , Addink E. A. , Middelkoop H.
Published: August 2016

River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.
TRAJECTORY-BASED ANALYSIS OF URBAN LAND-COVER CHANGE DETECTION
Authors: Zhang Y. H. , Liu H. P.
Published: August 2016

China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China’s urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.
DIFFERENTIAL SEARCH ALGORITHM BASED EDGE DETECTION
Authors: Gunen M. A. , Civicioglu P. , Beşdok E.
Published: August 2016

In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.
A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY
Authors: Roncat A. , Briese C. , Pfeifer N.
Published: August 2016

In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.
SEGMENTATION AND CLASSIFICATION OF NEPAL EARTHQUAKE INDUCED LANDSLIDES USING SENTINEL-1 PRODUCT
Authors: Kunwar Saket
Published: August 2016

On April 26, 2015, an earthquake of magnitude 7.8 on the Richter scale occurred, with epicentre at Barpak (28°12'20''N,84°44'19''E), Nepal. Landslides induced due to the earthquake and its aftershock added to the natural disaster claiming more than 9000 lives. Landslides represented as lines that extend from the head scarp to the toe of the deposit were mapped by the staff of the British Geological Survey and is available freely under Open Data Commons Open Database License(ODC-ODbL) license at the Humanitarian Data Exchange Program. This collection of 5578 landslides is used as preliminary ground truth in this study with the aim of producing polygonal delineation of the landslides from the polylines via object oriented segmentation. Texture measures from Sentinel-1a Ground Range Detected (GRD) Amplitude data and eigenvalue-decomposed Single Look Complex (SLC) polarimetry product are stacked for this purpose. This has also enabled the investigation of landslide properties in the H-Alpha plane, while developing a classification mechanism for identifying the occurrence of landslides.
GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS
Authors: Honkavaara E. , Hakala T. , Nevalainen O. , Viljanen N. , Rosnell T. , Khoramshahi E. , Näsi R. , Oliveira R. , Tommaselli A.
Published: August 2016

Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
EXPLOITING SENTINEL-1 AMPLITUDE DATA FOR GLACIER SURFACE VELOCITY FIELD MEASUREMENTS: FEASIBILITY DEMONSTRATION ON BALTORO GLACIER
Authors: Nascetti A. , Nocchi F. , Camplani A. , Di Rico C. , Crespi M.
Published: August 2016

The leading idea of this work is to continuously retrieve glaciers surface velocity through SAR imagery, in particular using the amplitude data from the new ESA satellite sensor Sentinel-1 imagery. These imagery key aspects are the free access policy, the very short revisit time (down to 6 days with the launch of the Sentinel-1B satellite) and the high amplitude resolution (up to 5 m). In order to verify the reliability of the proposed approach, a first experiment has been performed using Sentinel-1 imagery acquired over the Karakoram mountain range (North Pakistan) and Baltoro and other three glaciers have been investigated. During this study, a stack of 11 images acquired in the period from October 2014 to September 2015 has been used in order to investigate the potentialities of the Sentinel-1 SAR sensor to retrieve the glacier surface velocity every month. The aim of this test was to measure the glacier surface velocity between each subsequent pair, in order to produce a time series of the surface velocity fields along the investigated period. The necessary coregistration procedure between the images has been performed and subsequently the glaciers areas have been sampled using a regular grid with a 250 × 250 meters posting. Finally the surface velocity field has been estimated, for each image pair, using a template matching procedure, and an outlier filtering procedure based on the signal to noise ratio values has been applied, in order to exclude from the analysis unreliable points. The achieved velocity values range from 10 to 25 meters/month and they are coherent to those obtained in previous studies carried out on the same glaciers and the results highlight that it is possible to have a continuous update of the glacier surface velocity field through free Sentinel-1 imagery, that could be very useful to investigate the seasonal effects on the glaciers fluid-dynamics.
EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: Hsu Pai-Hui
Published: August 2016

The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.
LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA
Authors: Neinavaz Elnaz , Skidmore Andrew K. , Darvishzadeh Roshanak , Groen Thomas A.
Published: August 2016

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R2 < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
OBJECT-BASED CLASSIFICATION AND CHANGE DETECTION OF HOKKAIDO, JAPAN
Authors: Park J. G. , Harada I. , Kwak Y.
Published: August 2016

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.
ANALYSING RELATIONSHIPS BETWEEN URBAN LAND USE FRAGMENTATION METRICS AND SOCIO-ECONOMIC VARIABLES
Authors: Sapena M. , Ruiz L. A. , Goerlich F. J.
Published: August 2016

Analysing urban regions is essential for their correct monitoring and planning. This is mainly accounted for the sharp increase of people living in urban areas, and consequently, the need to manage them. At the same time there has been a rise in the use of spatial and statistical datasets, such as the Urban Atlas, which offers high-resolution urban land use maps obtained from satellite imagery, and the Urban Audit, which provides statistics of European cities and their surroundings. In this study, we analyse the relations between urban fragmentation metrics derived from Land Use and Land Cover (LULC) data from the Urban Atlas dataset, and socio-economic data from the Urban Audit for the reference years 2006 and 2012. We conducted the analysis on a sample of sixty-eight Functional Urban Areas (FUAs). One-date and two-date based fragmentation indices were computed for each FUA, land use class and date. Correlation tests and principal component analysis were then applied to select the most representative indices. Finally, multiple regression models were tested to explore the prediction of socio-economic variables, using different combinations of land use metrics as explanatory variables, both at a given date and in a dynamic context. The outcomes show that demography, living conditions, labour, and transportation variables have a clear relation with the morphology of the FUAs. This methodology allows us to compare European FUAs in terms of the spatial distribution of the land use classes, their complexity, and their structural changes, as well as to preview and model different growth patterns and socio-economic indicators.
CROP SPECIES RECOGNITION AND DISCRIMINATION PADDY-RICE-GROWINGFIELDS FROM REAPED-FIELDS BY THE RADAR VEGETATION INDEX (RVI) OF ALOS-2/PALSAR2
Authors: Yamada Y.
Published: August 2016

The Japanese ALOS-2 satellite was launched on May 24th, 2014. It has the L-band SAR, PALSAR-2. Kim,Y. and van Zyl, J.J. proposed a kind of Radar Vegetation Index (RVI) as RVI = 8 * σ0hv / (σ0hh + σ0vv + 2* σ0hv) by L-band full-polarimetric radar data. Kim, Y. and Jackson, T.J., et al. applied the equation into rice and soybean by multi-frequency polarimetric scatterometer above 4.16 meters from the ground. Their report showed the L-band was the most promising wave length for estimating LAI and NDVI from RVI. The author tried to apply the analysis to the actual paddy field areas, both Inashiki region and Miyagi region in the eastern main island, “Honshu”, areas of Japan by ALOS-2/PALSAR-2 full-polarimetry data in the summer season, the main crop growing time, of 2015. Judging from conventional methods, it will be possible to discriminate paddy rice growing fields from reaped fields or the other crops growing fields by the PALSAR-2 data. But the RVI value is vaguely related to such land use or biomass at the present preliminary experiment. The continuous research by the additional PALSAR-2 full-polarimetry data should be desired.