bluuebunny/crossref_metadata_2025 huggingface dataset:
LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS
Authors: Symeonakis E. , Petroulaki K. , Higginbottom T.
Published: August 2016

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km2 covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.
EXTRACTION OF ICE SHEET LAYERS FROM TWO INTERSECTED RADAR ECHOGRAMS NEAR NEEM ICE CORE IN GREENLAND
Authors: Xiong S. , Muller J.-P.
Published: August 2016

Accumulation of snow and ice over time result in ice sheet layers. These can be remotely sensed where there is a contrast in electromagnetic properties, which reflect variations of the ice density, acidity and fabric orientation. Internal ice layers are assumed to be isochronous, deep beneath the ice surface, and parallel to the direction of ice flow. The distribution of internal layers is related to ice sheet dynamics, such as the basal melt rate, basal elevation variation and changes in ice flow mode, which are important parameters to model the ice sheet. Radar echo sounder is an effective instrument used to study the sedimentology of the Earth and planets. Ice Penetrating Radar (IPR) is specific kind of radar echo sounder, which extends studies of ice sheets from surface to subsurface to deep internal ice sheets depending on the frequency utilised. In this study, we examine a study site where folded ice occurs in the internal ice sheet south of the North Greenland Eemian ice drilling (NEEM) station, where two intersected radar echograms acquired by the Multi-channel Coherent Radar Depth Sounder (MCoRDS) employed in the NASA’s Operation IceBridge (OIB) mission imaged this folded ice. We propose a slice processing flow based on a Radon Transform to trace and extract these two sets of curved ice sheet layers, which can then be viewed in 3-D, demonstrating the 3-D structure of the ice folds.
AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL
Authors: Xu C. , Sui H. G. , Li D. R. , Sun K. M. , Liu J. Y.
Published: August 2016

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.
BATHYMETRY MAPPING USING HYPERSPECTRAL DATA: A CASE STUDY OF YAMADA BAY, NORTHEAST JAPAN
Authors: Ariyasu E. , Kakuta S. , Takeda T.
Published: August 2016

This study aims to examine if the inversion method using hyperspectral data is applicable in Japan. Nowadays, overseas researchers are mainly applied an inversion method for accurately estimating water depth. It is able to gain not only water depth, but also benthic spectral reflection and inherent optical properties (IOPs) at the same time, based on physics-based radiative transfer theory for hyperspectral data. It is highly significant to understand the possibility to develop the application in future for coastal zone of main island, which is a common water quality in Japan, but there is not any case study applied this method in Japan. The study site of Yamada bay in Iwate Prefecture is located in northeast of Japan. An existed analytical model was optimized for mapping water depth in Yamada bay using airborne hyperspectral image and ground survey data which were simultaneously acquired in December, 2015. The retrieved remote-sensing reflectance (Rrs) is basically qualitatively appropriate result. However, when compared with all ground survey points, the retrieved water depth showed low correlation, even though ground points which are selected sand bottom indicates high relationship. Overall, we could understand the inversion method is applicable in Japan. However, it needs to challenge to improve solving error-caused problems.
THE EFFECT OF PANSHARPENING ALGORITHMS ON THE RESULTING ORTHOIMAGERY
Authors: Agrafiotis P. , Georgopoulos A. , Karantzalos K.
Published: August 2016

This paper evaluates the geometric effects of pansharpening algorithms on automatically generated DSMs and thus on the resulting orthoimagery through a quantitative assessment of the accuracy on the end products. The main motivation was based on the fact that for automatically generated Digital Surface Models, an image correlation step is employed for extracting correspondences between the overlapping images. Thus their accuracy and reliability is strictly related to image quality, while pansharpening may result into lower image quality which may affect the DSM generation and the resulting orthoimage accuracy. To this direction, an iterative methodology was applied in order to combine the process described by Agrafiotis and Georgopoulos (2015) with different pansharpening algorithms and check the accuracy of orthoimagery resulting from pansharpened data. Results are thoroughly examined and statistically analysed. The overall evaluation indicated that the pansharpening process didn’t affect the geometric accuracy of the resulting DSM with a 10m interval, as well as the resulting orthoimagery. Although some residuals in the orthoimages were observed, their magnitude cannot adversely affect the accuracy of the final orthoimagery.
METHODOLOGY FOR ORIENTATION AND FUSION OF PHOTOGRAMMETRIC AND LIDARDATAS FOR MULTITEMPORAL STUDIES
Authors: Colomo-Jiménez C. , Pérez-García J. L. , Fernández-del Castillo T. , Gómez-López J. M. , Mozas-Calvache A. T.
Published: August 2016

Nowadays, data fusion is one of the trends in geomatics sciences, due to the necessity of merging data from different kind of sensors and periods of time. Also, to extrract the maximum information from data and useful multitemporal analysis, an exact geoconnection of all datasets in a common and stable reference system is essential. The results of the application of a methodology for an integrated orientation into a common reference system using data obtained by LiDAR systems, digital and historical photogrammetric flights dataset, used for proper analysis in multitemporal studies, are presented in this paper. In order to analyse the results of the presented methodology, several photogrammetric datasets have been used. This data corresponds with digital and analogic data. The most current flight (2010) combines data obtained with digital photogrammetric camera and LiDAR sensor which will be used as reference model for all subsequent photogrammetry flights. The philosophy of the methodology consists of orientating all photogrammetric flights to the DEM obtained by LiDAR data. All the models obtained from every photogrammetric block are comparable in terms of the geometric resolution of each one. For that reason, altimetric stable points are extracted automatically from the LiDAR points cloud to use these points such as altimetric control point in the different flights that must be oriented. Using LiDAR control points, we demonstrate the improvement in the results between initial orientation and final results. Also it is possible to improve the planimetric correspondence between different photogrammetric blocks using only altimetric control points iteratively.
A STUDY OF THE IMPACT OF INSOLATION ON REMOTE SENSING-BASED LANDCOVER AND LANDUSE DATA EXTRACTION
Authors: Becek K. , Borkowski A. , Mekik Ç.
Published: August 2016

We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee.
REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM
Authors: Hnatushenko V. V. , Vasyliev V. V.
Published: August 2016

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.
RESEARCH ON THE CONSTRUCTION METHOD OF COMPREHENSIVE EVALUATION INDEX OF GEOGRAPHIC CONDITIONS
Authors: Lv S. H. , Zhang J. X. , Zhang Z. H. , Jiao J. C.
Published: August 2016

This article proposes a construction method for a comprehensive geographic conditions evaluation index based on geographic conditions survey data combined with thematic data on society, economy, ecology and population. This article constructs a three-level evaluation framework composed of index level, object level and factor level from the perspectives of ecological coordination, urban development, regional economic potential and basic public services, studies a method of acquiring all-level factor data on geographic conditions and discusses the comprehensive evaluation factor system of geographic conditions. The components of the all-level index are selected through principal component analysis, and iterative analysis is performed by innovatively setting conditions to ensure the independence of the factors and establish an evaluation factor set for geographic conditions. The weighting for the all-level index is obtained through the analytic hierarchy process resulting in the index of geographic conditions. From the perspective of geographic conditions, this article makes a dynamic and quantitative evaluation of national conditions and strengths to provide a reference basis for regional sustainable development and governmental management decisions. By using the method, this article first obtains the index of geographic conditions of Q city with comprehensive evaluation and analysis to verify the objectivity and scientific nature of the method and expand and deepen the application of survey data on geographic conditions.
ENHANCEMENT OF SPATIAL RESOLUTION OF THE LROC WIDE ANGLE CAMERA IMAGES
Authors: Mahanti P. , Robinson M. S. , Sato H. , Awumah A. , Henriksen M.
Published: August 2016

Image fusion, a popular method for resolution enhancement in Earth-based remote sensing studies involves the integration of geometric (sharpness) detail of a high-resolution panchromatic (Pan) image and the spectral information of a lower resolution multi-spectral (MS) image. Image fusion with planetary images is not as widespread as with terrestrial studies, although successful application of image fusion can lead to the generation of higher resolution MS image data. A comprehensive comparison of six image fusion algorithms in the context of lunar images is presented in this work. Performance of these algorithms is compared by visual inspection of the high-resolution multi-spectral products, derived products such as band-to-band ratio and composite images, and performance metrics with an emphasis on spectral content preservation. Enhanced MS images of the lunar surface can enable new science and maximize the science return for current and future missions.
PAN-SHARPENING APPROACHES BASED ON UNMIXING OF MULTISPECTRAL REMOTE SENSING IMAGERY
Authors: Palubinskas G.
Published: August 2016

Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.
CLASSIFICATION OF CROPLANDS THROUGH FUSION OF OPTICAL AND SAR TIME SERIES DATA
Authors: Park S. , Im J.
Published: August 2016

Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of single data (e.g., such as cloud contamination) through multi-sensor data fusion and the use of time series data. This study investigated two areas with different environment and climate conditions: one in South Korea and the other in US. Cropland classification was conducted by using multi-temporal Landsat 5, Radarsat-1 and digital elevation models (DEM) based on two machine learning approaches (i.e., random forest and support vector machines). Seven classification scenarios were examined and evaluated through accuracy assessment. Results show that SVM produced the best performance (overall accuracy of 93.87%) when using all temporal and spectral data as input variables. Normalized Difference Water Index (NDWI), SAR backscattering, and Normalized Difference Vegetation Index (NDVI) were identified as more contributing variables than the others for cropland classification.
ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES
Authors: Xing Yanqiu , Qiu Sai , Ding Jianhua , Tian Jing
Published: August 2016

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70 m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. MNF1, MNF2 and MNF3), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R2 and RMSE of SVR-AGB models were 0.75 and 4.68 t hm−2 for broadleaf forests, 0.73 and 5.39 t hm−2 for coniferous forests and 0.71 and 6.15 t hm−2 for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11 t hm−2 of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.
SENTINEL-1 INSAR PROCESSING OF CORNER REFLECTOR INFORMATION IN THE NORTHERN-BOHEMIAN COAL BASIN
Authors: Hlaváčová I. , Halounová L. , Stanislav P.
Published: August 2016

The mining area previously monitored by TerraSAR-X InSAR is now monitored by Sentinel-1 InSAR. Although the processing of the IWS (TOPS) mode requires additional processing steps and the coregistration has to be performed with the precision of 0.001 pixel (in the azimuth direction), if an area within one burst is processed, such a precise coregistration is not necessary. Information from 11 corner reflectors is evaluated, and significant movements at one of them were detected. Although it seems to be uplift, it is more probable that the movement is in down-the-slope direction, which has a negative sensitivity with regard to the satellite line of sight. The movement is similar to the one detected by TerraSAR-X satellite in the past. At the end of the monitoring period, the movement seems to settle down; future monitoring will show more about the dynamicity of the movement.
POTENTIAL OF SENTINEL-1A FOR NATION-WIDE ROUTINE UPDATES OF ACTIVE LANDSLIDE MAPS
Authors: Lazecky M. , Canaslan Comut F. , Nikolaeva E. , Bakon M. , Papco J. , Ruiz-Armenteros A. M. , Qin Y. , de Sousa J. J. M. , Ondrejka P.
Published: August 2016

Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.
COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES
Authors: Roychowdhury K.
Published: August 2016

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.
POTENTIAL OF MULTITEMPORAL TANDEM-X DERIVED CROP SURFACE MODELS FOR MAIZE GROWTH MONITORING
Authors: Hütt C. , Tilly N. , Schiedung H. , Bareth G.
Published: August 2016

In this study, first results of retrieving plant heights of maize fields from multitemporal TanDEM-X images are shown. Three TanDEM-X dual polarization spotlight acquisitions were taken over a rural area in Germany in the growing season 2014. By interferometric processing, digital terrain models (DTM) were derived for each date with 5m resolution. From the data of the first acquisition (June 1st) taken before planting, a DTM of the bare ground is generated. The data of the following acquisition dates (July 15th, July 26th) are used to establish crop surface models (CSM). A CSM represents the crop surface of a whole field in a high resolution. By subtracting the DTM of the ground from each CSM, the actual plant height is calculated. Within these data sets 30 maize fields in the area of interest could be detected and verified by external land use data. Besides the spaceborne measurements, one of the maize fields was intensively investigated using terrestrial laser scanning (TLS), which was carried out at the same dates as the predicted TanDEM-X acquisitions. Visual inspection of the derived plant heights, and accordance of the individually processed polarisations over the maize fields, demonstrate the feasibility of the proposed method. Unfortunately, the infield variability of the intensively monitored field could not be successfully captured in the TanDEM-X derived plant heights and merely the general trend is visible. Nevertheless, the study shows the potential of the TanDEM-X constellation for maize height monitoring on field level.
WETLAND MAPPING WITH SAR/QUAD-POL DATA ACQUIRED DURING TANDEM-X SCIENCE PHASE
Authors: Mleczko M. , Mroz M. , Fitrzyk M.
Published: August 2016

The aim of this study was to exploit fully polarimetric SAR data acquired during TanDEM-X – Science Phase (2014/2015) over herbaceous wetlands of the Biebrza National Park (BbNP) in North-Eastern Poland for mapping seasonally flooded grasslands and permanent natural vegetation associations. The main goal of this work was to estimate the advantage of fully polarimetric radar images (QuadPol) versus alternative polarization (AltPol) modes. The methodology consisted in processing of several data subsets through polarimetric decompositions of complex quad-pol datasets, classification of multitemporal backscattering images, complementing backscattering images with Shannon Entropy, exploitation of interferometric coherence from tandem operations. In each case the multidimensional stack of images has been classified using ISODATA unsupervised clustering algorithm. With 6 QUAD-POL TSX/TDX acquisitions it was possible to distinguish correctly 5 thematic classes related to their water regime: permanent water bodies, temporarily flooded areas, wet grasslands, dry grasslands and common reed. This last category was possible to distinguish from deciduous forest only with Yamaguchi 4 component decomposition. The interferometric coherence calculated for tandem pairs turned out not so efficient as expected for this wetland mapping.
CENTIMETER COSMO-SKYMED RANGE MEASUREMENTS FOR MONITORING GROUND DISPLACEMENTS
Authors: Fratarcangeli F. , Nascetti A. , Capaldo P. , Mazzoni A. , Crespi M.
Published: August 2016

The SAR (Synthetic Aperture Radar) imagery are widely used in order to monitor displacements impacting the Earth surface and infrastructures. The main remote sensing technique to extract sub-centimeter information from SAR imagery is the Differential SAR Interferometry (DInSAR), based on the phase information only. However, it is well known that DInSAR technique may suffer for lack of coherence among the considered stack of images. New Earth observation SAR satellite sensors, as COSMO-SkyMed, TerraSAR-X, and the coming PAZ, can acquire imagery with high amplitude resolutions too, up to few decimeters. Thanks to this feature, and to the on board dual frequency GPS receivers, allowing orbits determination with an accuracy at few centimetres level, the it was proven by different groups that TerraSAR-X imagery offer the capability to achieve, in a global reference frame, 3D positioning accuracies in the decimeter range and even better just exploiting the slant-range measurements coming from the amplitude information, provided proper corrections of all the involved geophysical phenomena are carefully applied. The core of this work is to test this methodology on COSMO-SkyMed data acquired over the Corvara area (Bolzano – Northern Italy), where, currently, a landslide with relevant yearly displacements, up to decimeters, is monitored, using GPS survey and DInSAR technique. The leading idea is to measure the distance between the satellite and a well identifiable natural or artificial Persistent Scatterer (PS), taking in account the signal propagation delays through the troposphere and ionosphere and filtering out the known geophysical effects that induce periodic and secular ground displacements. The preliminary results here presented and discussed indicate that COSMO-SkyMed Himage imagery appear able to guarantee a displacements monitoring with an accuracy of few centimetres using only the amplitude data, provided few (at least one) stable PS’s are available around the monitored area, in order to correct residual biases, likely due to orbit errors.
STANDWISE CHANGE DETECTION FOR GROWING STOCK USING REPEAT-PASS ALOS PALSAR / PALSAR-2 DATA
Authors: Hong M.–G. , Kim C.
Published: August 2016

This study demonstrates the possibility of detecting the changes of growing stocks in mountainous forest stands derived from ALOS PALSAR and PALSAR-2 images. The ALOS PALSAR were obtained over the Kwangneung Experiment Forest (KEF, Korea) during the period of nineteen and a half months from the April 26, 2009 to December 12, 2010, whereas the PALSAR-2 data were acquired on the April 7, 2015. The KEF test site comprises 58 stands, which cover approximately 1,000ha and have steep slope topography. Owing to topographic effects of SAR data in mountainous areas, the DEM-assisted topographic normalized backscattering coefficient γ0 was applied to the evaluation of the relationships between the ALOS PALSAR / PALSAR-2 HV backscatter and the field inventory–based stand stock volume. The results indicate that: 1) the γ0 values for the volume obtained from ALOS PALSAR data on December 12, 2010 show a gradual increase higher than those computed from the data on April 26, 2009, here the γ0 value increases in accordance with an increase in the volume: 2) the γ0 values determined from the PALSAR-2 data increase with the same inventory-based volume, when compared with those computed from both ALOS PALSAR data. They also increase substantially as the values of the volume rise, with the exception of the volume interval from 130 m3 ha−1 to 160 m3 ha−1. This is understandable because the volume of the aforementioned interval has been reduced through clearing. Consequently, the γ0–based relationship between PALSAR-2 HV backscatter and growing stock can lead to detecting the stand growth changes in the KEF of Korea.
A RAIL CENTRAL DISPLACEMENT METHOD ABOUT GB-SAR
Authors: Peng J. , Cai J. , Yang H.
Published: August 2016

This paper presents a new method to correct rail errors of Ground Based Synthetic Aperture Radar (GB-SAR) in the discontinue mode. Generally, “light positioning” is performed to mark the GB-SAR position in the dis-continuous observation mode. Usually we assume there is no difference between the marked position and the real installation position. But in fact, it is hard to keep the GB-SAR positions of two campaigns the same, so repositioning errors can’t be neglected. In order to solve this problem, we propose an algorithm to correct the rail error after analyzing the GB-SAR rail error geometry. Results of the simulation experiment and the real experiment of a landslide in Lvliang, Shanxi, China, show the proposed method achieves an mm-level precision, enabling the D-GBSAR mode to be used in engineering projects.
Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo
Authors: Kranz Olaf , Schoepfer Elisabeth , Spröhnle Kristin , Lang Stefan
Published: August 2016

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.
OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS
Authors: Maboudi M. , Amini J. , Hahn M.
Published: August 2016

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods.

 Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.
AN AUTOMATED GRASS-BASED PROCEDURE TO ASSESS THE GEOMETRICAL ACCURACY OF THE OPENSTREETMAP PARIS ROAD NETWORK
Authors: Brovelli M. A. , Minghini M. , Molinari M. E.
Published: August 2016

OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.
New implementation of OGC Web Processing Service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS
Authors: Čepický Jáchym , de Sousa Luís Moreira
Published: August 2016

The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server.

 PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch.

 The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.
PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS
Authors: Petras V. , Petrasova A. , Jeziorska J. , Mitasova H.
Published: August 2016

Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
OPEN SOURCE APPROACH TO URBAN GROWTH SIMULATION
Authors: Petrasova A. , Petras V. , Van Berkel D. , Harmon B. A. , Mitasova H. , Meentemeyer R. K.
Published: August 2016

Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We present FUTURES (FUTure Urban – Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling framework as a case showing how what was once a closed and inaccessible model benefited from integration with open source GIS.We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre and open source GIS and research platform for the geospatial domain. GRASS GIS provides efficient libraries for FUTURES model development as well as standard GIS tools and graphical user interface for model users. Releasing FUTURES as a GRASS GIS add-on simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future. We will describe FUTURES integration into GRASS GIS and demonstrate its usage on a case study in Asheville, North Carolina. The developed dataset and tutorial for this case study enable researchers to experiment with the model, explore its potential or even modify the model for their applications.
NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES
Authors: Villar Ricardo G. , Pelayo Jigg L. , Mozo Ray Mari N. , Salig Jr. James B. , Bantugan Jojemar
Published: August 2016

Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.
LAND USE AND LAND COVER CHANGES AND URBAN SPRAWL IN RIYADH, SAUDI ARABIA: AN ANALYSIS USING MULTI-TEMPORAL LANDSAT DATA AND SHANNON'S ENTROPY INDEX
Authors: Rahman M. T.
Published: August 2016

The city of Riyadh, Saudi Arabia has experienced rapid population growth and urban expansion over the past several decades. Due to such growth, the capital city faces many short and long-term social and environmental consequences. In order to monitor and mitigate some of these consequences, it is essential to examine the past changes and historical growth of the city. It is also essential to measure its urban sprawl over the past few decades. The objective of this study is to fulfil these goals. It does so by first examining the historical growth of the city of Riyadh. To do so, Landsat data over the past two and half decades are classified using a combination of supervised and unsupervised classification techniques. Based on the classification results, the study then uses Shannon’s Entropy to measure the urban sprawl in the city. The results show that from 1990-2009, the urban built-up area of the city has increased by 90% in the western, south-eastern, and northern parts. The Shannon’s entropy values show that the city is dispersing towards the outskirts of the city. The results from this study will assist city planners and government officials to plan, reduce, and perhaps mitigate some of the social and environmental consequences and enable the growth of the city in a sustainable manner in the near future.