bluuebunny/crossref_metadata_2025 huggingface dataset:
REFINEMENT METHOD FOR RESIDENTIAL AREA REVISION USING REMOTE SENSING IMAGE AND GIS DATA IN EARTHQUAKE RISK ASSESSMENT
Authors: Dou A. X. , Yuan X. X. , Wang X. Q. , Li Z. M.
Published: August 2016

This paper proposes an automatic approach for residential areas revision by means of analysing the correlation between the residential area and the topographic and geographical factors. The approach consists of four major steps: the extracting of missing residential area from the remote sensing images with high resolution; the statistic analysing on the size changes of missing residential area in each grade of the elevation, slope, distance from the road and other impact factors; modelling of residential area modification in the urban and rural region; testing the methods using 100 counties data which are located in the middle part of China North-South Seismic Belt and comparing the result to the Land Use in map scale 1:100000. The experimental results present the accuracy of urban residents by 70% increased to 89.4%, rural residents by 47% up to 81.9%, rural residents from 8% increased to 78.5%. Therefore, there is available risk exposure information in a sparsely populated area because the spatial grid distributions of population and buildings are based on the residential areas. The proposed approach in this paper will improve the accuracy of the seismic risk assessment if it is applied to the national or the whole world.
A NOVEL METHOD FOR ESTIMATION OF GLACIER SURFACE MOTION IN 1960s FROM ARGON KH-5 OPTICAL IMAGERY
Authors: Li R. , Ye W. , Kong F. , Qiao G. , Tong X. , Ma X. , Guo S. , Wang Z.
Published: August 2016

The Antarctic ice sheet response to the global climate change, specifically the ice flow speed change of the glaciers, has been investigated by many researchers. However, most research results cover the period since 1970s or after the operation of the LANDSAT series. The availability of the film-based ARGON KH-5 data makes it possible to quantify the changes of the Antarctic ice sheet in 1960s. To meet the challenges of processing the low quality film-based ARGON images, a novel method was developed to allow estimating the ice sheet surface motion and reconstructing the surface model simultaneously from ARGON stereo images by decomposing the total parallaxes to terrain and motion based components. A photogrammetric approach was developed to distinguish stable ice surface features from those on motion and use them for recovering the camera orientation information. Several existing Antarctic mapping products were used to establish the ground control. The ice flow speed field is reconstructed using a hierarchical image matching strategy. Firstly, epipolar images are generated via a fundamental matrix derived from correspondences used in the geometric modelling process, and then an image pyramid is built. Second, the normalized cross-correlation (NCC) technique is conducted on each layer of the pyramid to match the extracted features. Since the images were taken at different times, during which the glacier motion occurred, the measured total parallaxes are decomposed to terrain and motion parallaxes according to given ice flow directions which are derived from the iteratively produced DTM or images. Finally, a speed map and a DTM can be generated at each level of the image pyramid. This process repeats itself. At the bottom of the pyramid the final speed map and DTM are produced at a resolution of about 60m and represent the ice flow field of 1963. This approach was tested using two ARGON stereo-pairs in Rayner glacier in East Antarctica. Both the ice flow speed map and DTM were generated, and their difference with recent products is briefly discussed.
FUSION OF LASER ALTIMETRY DATA WITH DEMS DERIVED FROM STEREO IMAGING SYSTEMS
Authors: Schenk T. , Csatho B. M. , Duncan K.
Published: August 2016

During the last two decades surface elevation data have been gathered over the Greenland Ice Sheet (GrIS) from a variety of different sensors including spaceborne and airborne laser altimetry, such as NASA’s Ice Cloud and land Elevation Satellite (ICESat), Airborne Topographic Mapper (ATM) and Laser Vegetation Imaging Sensor (LVIS), as well as from stereo satellite imaging systems, most notably from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Worldview. The spatio-temporal resolution, the accuracy, and the spatial coverage of all these data differ widely. For example, laser altimetry systems are much more accurate than DEMs derived by correlation from imaging systems. On the other hand, DEMs usually have a superior spatial resolution and extended spatial coverage. We present in this paper an overview of the SERAC (Surface Elevation Reconstruction And Change detection) system, designed to cope with the data complexity and the computation of elevation change histories. SERAC simultaneously determines the ice sheet surface shape and the time-series of elevation changes for surface patches whose size depends on the ruggedness of the surface and the point distribution of the sensors involved. By incorporating different sensors, SERAC is a true fusion system that generates the best plausible result (time series of elevation changes) a result that is better than the sum of its individual parts. We follow this up with an example of the Helmheim gacier, involving ICESat, ATM and LVIS laser altimetry data, together with ASTER DEMs.
RECOGNITION OF DRAINAGE TUNNELS DURING GLACIER LAKE OUTBURST EVENTS FROM TERRESTRIAL IMAGE SEQUENCES
Authors: Schwalbe E. , Koschitzki R. , Maas H.-G.
Published: August 2016

In recent years, many glaciers all over the world have been distinctly retreating and thinning. One of the consequences of this is the increase of so called glacier lake outburst flood events (GLOFs). The mechanisms ruling such GLOF events are still not yet fully understood by glaciologists. Thus, there is a demand for data and measurements that can help to understand and model the phenomena. Thereby, a main issue is to obtain information about the location and formation of subglacial channels through which some lakes, dammed by a glacier, start to drain. The paper will show how photogrammetric image sequence analysis can be used to collect such data.

 For the purpose of detecting a subglacial tunnel, a camera has been installed in a pilot study to observe the area of the Colonia Glacier (Northern Patagonian Ice Field) where it dams the Lake Cachet II. To verify the hypothesis, that the course of the subglacial tunnel is indicated by irregular surface motion patterns during its collapse, the camera acquired image sequences of the glacier surface during several GLOF events. Applying tracking techniques to these image sequences, surface feature motion trajectories could be obtained for a dense raster of glacier points. Since only a single camera has been used for image sequence acquisition, depth information is required to scale the trajectories. Thus, for scaling and georeferencing of the measurements a GPS-supported photogrammetric network has been measured.

 The obtained motion fields of the Colonia Glacier deliver information about the glacier’s behaviour before during and after a GLOF event. If the daily vertical glacier motion of the glacier is integrated over a period of several days and projected into a satellite image, the location and shape of the drainage channel underneath the glacier becomes visible. The high temporal resolution of the motion fields may also allows for an analysis of the tunnels dynamic in comparison to the changing water level of the lake.
MULTI-TEMPORAL SAR INTERFEROMETRY FOR LANDSLIDE MONITORING
Authors: Dwivedi R. , Narayan A. B. , Tiwari A. , Dikshit O. , Singh A. K.
Published: August 2016

In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoring related applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Multi-temporal InSAR methods are developed in recent times to retrieve the deformation signal from pixels with different scattering characteristics. Presently, two classes of multi-temporal InSAR algorithms are available- Persistent Scatterer (PS) and Small Baseline (SB) methods. This paper discusses the Stanford Method for Persistent Scatterer (StaMPS) based PS-InSAR and the Small Baselines Subset (SBAS) techniques to estimate the surface deformation in Tehri dam reservoir region in Uttarkhand, India. Both PS-InSAR and SBAS approaches used sixteen ENVISAT ASAR C-Band images for generating single master and multiple master interferograms stack respectively and their StaMPS processing resulted in time series 1D-Line of Sight (LOS) mean velocity maps which are indicative of deformation in terms of movement towards and away from the satellites. From 1D LOS velocity maps, localization of landslide is evident along the reservoir rim area which was also investigated in the previous studies. Both PS-InSAR and SBAS effectively extract measurement pixels in the study region, and the general results provided by both approaches show a similar deformation pattern along the Tehri reservoir region. Further, we conclude that StaMPS based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean Line of Sight (LOS) velocity as compared to SBAS method. It is also proposed to take up a few major landslides area in Uttarakhand for slope stability assessment.
RESPONSE OF RIPARIAN VEGETATION IN AUSTRALIA"S LARGEST RIVER BASIN TO INTER AND INTRA-ANNUAL CLIMATE VARIABILITY AND FLOODING AS QUANTIFIED WITH LANDSAT AND MODIS
Authors: Broich M. , Tulbure M. G.
Published: August 2016

Australia is a continent subject to high rainfall variability, which has major influences on runoff and vegetation dynamics. However, the resulting spatial-temporal pattern of flooding and its influence on riparian vegetation has not been quantified in a spatially explicit way. Here we focused on the floodplains of the entire Murray-Darling Basin (MDB), an area that covers over 1M km2, as a case study. The MDB is the country’s primary agricultural area with scarce water resources subject to competing demands and impacted by climate change and more recently by the Millennium Drought (1999–2009). Riparian vegetation in the MDB floodplain suffered extensive decline providing a dramatic degradation of riparian vegetation.

 We quantified the spatial-temporal impact of rainfall, temperature and flooding patters on vegetation dynamics at the subcontinental to local scales and across inter to intra-annual time scales based on three decades of Landsat (25k images), Bureau of Meteorology data and one decade of MODIS data.

 Vegetation response varied in space and time and with vegetation types, densities and location relative to areas frequently flooded. Vegetation degradation trends were observed over riparian forests and woodlands in areas where flooding regimes have changed to less frequent and smaller inundation extents. Conversely, herbaceous vegetation phenology followed primarily a ‘boom’ and ‘bust’ cycle, related to inter-annual rainfall variability. Spatial patters of vegetation degradation changed along the N-S rainfall gradient but flooding regimes and vegetation degradation patterns also varied at finer scale, highlighting the importance of a spatially explicit, internally consistent analysis and setting the stage for investigating further cross-scale relationships.

 Results are of interest for land and water management decisions. The approach developed here can be applied to other areas globally such as the Nile river basin and Okavango River delta in Africa or the Mekong River Basin in Southeast Asia.
LIGHT DIFFUSION IN THE TROPICAL DRY FOREST OF COSTA RICA
Authors: Calvo-Rodriguez S. , Sanchez-Azofeifa G. A
Published: August 2016

Leaf Area Index (LAI) has been defined as the total leaf area (one-sided) in relation to the ground. LAI has an impact on tree growth and recruitment through the interception of light, which in turn affects primary productivity. Even though many instruments exist for estimating LAI from ground, they are often laborious and costly to run continuously. Measurements of LAI from the field using traditional sensors (e.g., LAI-2000) require multiple visits to the field under very specific sky conditions, making them unsuitable to operate in inaccessible areas and forests with dense vegetation, as well as areas where persistent sunny conditions are the norm like tropical dry forests. With this context, we proposed a methodology to characterize light diffusion based on NDVI and LAI measurements taken from the field in two successional stages in the tropical dry forest of Santa Rosa National Park in Costa Rica. We estimate a "K" coefficient to characterize light diffusion by the canopy, based on field NDVI measurements derived from optical phenology instruments and MODIS NDVI. From the coefficients determined, we estimated LAI values and compared them with ground measurements of LAI. In both successional stages ground measurements of LAI had no significant difference to the tower-derived LAI and the estimated LAI from MODIS NDVI.
DEFORESTATION ALONG THE MAYA MOUNTAIN MASSIF BELIZE-GUATEMALA BORDER
Authors: Chicas S. D. , Omine K. , Arevalo B. , Ford J. B. , Sugimura K.
Published: August 2016

In recent years trans-boundary incursions from Petén, Guatemala into Belize’s Maya Mountain Massif (MMM) have increased. The incursions are rapidly degrading cultural and natural resources in Belize’s protected areas. Given the local, regional and global importance of the MMM and the scarcity of deforestation data, our research team conducted a time series analysis 81 km by 12 km along the Belize-Guatemalan border adjacent to the protected areas of the MMM. Analysis drew on Landsat imagery from 1991 to 2014 to determine historic deforestation rates. The results indicate that the highest deforestation rates in the study area were −1.04% and −6.78% loss of forested area per year in 2012-2014 and 1995-1999 respectively. From 1991 to 2014, forested area decreased from 96.9 % to 85.72 % in Belize and 83.15 % to 31.52 % in Guatemala. During the study period, it was clear that deforestation rates fluctuated in Belize's MMM from one time-period to the next. This seems linked to either a decline in deforestation rates in Guatemala, the vertical expansion of deforestation in Guatemalan forested areas and monitoring. The results of this study urge action to reduce incursions and secure protected areas and remaining forest along the Belize-Guatemalan border.
TREE CANOPY COVER MAPPING USING LiDAR IN URBAN BARANGAYS OF CEBU CITY, CENTRAL PHILIPPINES
Authors: Ejares J. A. , Violanda R. R. , Diola A. G. , Dy D. T. , Otadoy J. B. , Otadoy R. E. S.
Published: August 2016

This paper investigates tree canopy cover mapping of urban barangays (smallest administrative division in the Philippines) in Cebu City using LiDAR (Light Detection and Ranging). Object-Based Image Analysis (OBIA) was used to extract tree canopy cover. Multi-resolution segmentation and a series of assign-class algorithm in eCognition software was also performed to extract different land features. Contextual features of tree canopies such as height, area, roundness, slope, length-width and elliptic fit were also evaluated. The results showed that at the time the LiDAR data was collected (June 24, 2014), the tree cover was around 25.11 % (or 15,674,341.8 m2) of the city’s urban barangays (or 62,426,064.6 m2). Among all urban barangays in Cebu City, Barangay Busay had the highest cover (55.79 %) while barangay Suba had the lowest (0.8 %). The 16 barangays with less than 10 % tree cover were generally located in the coastal area, presumably due to accelerated urbanization. Thirty-one barangays have tree cover ranging from 10.59–-27.3 %. Only 3 barangays (i.e., Lahug, Talamban, and Busay) have tree cover greater than 30 %. The overall accuracy of the analysis was 96.6 % with the Kappa Index of Agreement or KIA of 0.9. From the study, a grouping can be made of the city’s urban barangays with regards to tree cover. The grouping will be useful to urban planners not only in allocating budget to the tree planting program of the city but also in planning and creation of urban parks and playgrounds.
MANGROVE PLANTATION FOREST ASSESSMENT USING STRUCTURAL ATTRIBUTES DERIVED FROM LIGHT DETECTION AND RANGING (LiDAR) DATA
Authors: Faelga R. A. G. , Paringit E. C. , Perez G. J. P. , Ibanez C. A. G. , Argamosa R. A. L. , Posilero M. A. V. , Zaragosa G. P. , Tandoc F. A. M. , Malabanan M. V.
Published: August 2016

Estimating the structural and functional attributes of forests is integral in performing management strategies and for understanding forest ecosystem functions. Field sampling methods through plot level is one of the known strategies in forest studies; however, these methods have its limitations and are prone to subjected biases. Remote Sensing data, particularly that of Light Detection and Ranging (LiDAR) can be utilized to alleviate the limitations of extracting forest structure parameters. The study aims to characterize a Rhizophoraceae-dominated mangrove forest plantation. Point cloud distribution within a 1-hectare plot was processed by utilizing thirty (30) samples of 5x5 meter plots, which were analysed for the characterization and forest structure assessment. Point densities were grouped at intervals of 10% of the plot’s maximum height (Height at Bincentile or HBn) to determine where the clustering of points occur per plot. The result shows that most of the points are clustered at HBn with height values ranging from 2.98 to 4.15 meters for plots located at the middle part of the forest, with a standard deviation of 1.78 to 3.69, respectively. On the other hand, sample plots that are located at the periphery part of the forest shows that the point clustering occurs at different heights ranging from 1.71 meters to 4.43 meters, with standard deviation values ranging from 1.69 to 3.81.Plots that are located along the fringes of the forest reflect a stunted clustering of points, while plots that explicitly show mangrove trimmings and cuts reflect even distribution in terms of point density within each HBn. Both species present in the area (R. mucronata and R. apiculata) exhibits similar clustering, which could represent detection of Rhizophoraceae mangroves.
ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA
Authors: Hadaś E. , Borkowski A. , Estornell J.
Published: August 2016

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter R. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points m−2. We noticed, that there was a narrow range of the R parameter, from 0.48 m to 0.80 m, for which all trees were detected and for which we obtained a high correlation coefficient (> 0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8 m for the tree height, 0.5 m for the crown base height, 0.6 m and 0.4 m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.
ESTIMATING DBH OF TREES EMPLOYING MULTIPLE LINEAR REGRESSION OF THE BEST LIDAR-DERIVED PARAMETER COMBINATION AUTOMATED IN PYTHON IN A NATURAL BROADLEAF FOREST IN THE PHILIPPINES
Authors: Ibanez C. A. G. , Carcellar III B. G. , Paringit E. C. , Argamosa R. J. L. , Faelga R. A. G. , Posilero M. A. V. , Zaragosa G. P. , Dimayacyac N. A.
Published: August 2016

Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett’s Test for Spherecity (BTS).
UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION
Authors: Karpina M. , Jarząbek-Rychard M. , Tymków P. , Borkowski A.
Published: August 2016

Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV) imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.
MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY
Authors: Kemper G. , Weidauer A. , Coppack T.
Published: August 2016

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.
MODELLING FINE SCALE MOVEMENT CORRIDORS FOR THE TRICARINATE HILL TURTLE
Authors: Mondal I. , Kumar R. S. , Habib B. , Talukdar G.
Published: August 2016

Habitat loss and the destruction of habitat connectivity can lead to species extinction by isolation of population. Identifying important habitat corridors to enhance habitat connectivity is imperative for species conservation by preserving dispersal pattern to maintain genetic diversity. Circuit theory is a novel tool to model habitat connectivity as it considers habitat as an electronic circuit board and species movement as a certain amount of current moving around through different resistors in the circuit. Most studies involving circuit theory have been carried out at small scales on large ranging animals like wolves or pumas, and more recently on tigers. This calls for a study that tests circuit theory at a large scale to model micro-scale habitat connectivity. The present study on a small South-Asian geoemydid, the Tricarinate Hill-turtle (Melanochelys tricarinata), focuses on habitat connectivity at a very fine scale. The Tricarinate has a small body size (carapace length: 127–175 mm) and home range (8000–15000 m2), with very specific habitat requirements and movement patterns. We used very high resolution Worldview satellite data and extensive field observations to derive a model of landscape permeability at 1 : 2,000 scale to suit the target species. Circuit theory was applied to model potential corridors between core habitat patches for the Tricarinate Hill-turtle. The modelled corridors were validated by extensive ground tracking data collected using thread spool technique and found to be functional. Therefore, circuit theory is a promising tool for accurately identifying corridors, to aid in habitat studies of small species.
VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS
Authors: Stelling N. , Richter K.
Published: August 2016

The advantages of using airborne full-waveform laser scanner data in forest applications, e.g. for the description of the vertical vegetation structure or accurate biomass estimation, have been emphasized in many publications. To exploit the full potential offered by airborne full-waveform laser scanning data, the development of voxel based methods for data analysis is essential. In contrast to existing approaches based on the extraction of discrete 3D points by a Gaussian decomposition, it is very promising to derive the voxel attributes from the digitised waveform directly. For this purpose, the waveform data have to be transferred into a 3D voxel representation. This requires a series of radiometric and geometric transformations of the raw full-waveform laser scanner data. Thus, the paper deals with the geometric aspects and describes a processing chain from the raw waveform data to an attenuationcorrected volumetric forest stand reconstruction.

 The integration of attenuation-corrected waveform data into the voxel space is realised with an efficient parametric voxel traversal method operating on an octree data structure. The voxel attributes are derived from the amplitudes of the attenuation-corrected waveforms. Additionally, a new 3D filtering approach is presented to eliminate non-object voxel. Applying these methods to real full-waveform laser scanning data, a voxel based representation of a spruce was generated combining three flight strips from different viewing directions.
AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION
Authors: Zhou Guoqing , Zhou Xiang , Yue Tao , Liu Yilong
Published: August 2016

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.
THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY
Authors: Ai T. , Yang W.
Published: August 2016

This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points for the improved triangulation. Thirdly, extract the transport road by cutting short triangle edge and organizing the polygon topology. We have conducted the experiment of transport land-use change discovery using the data of taxi track in Beijing City. We extract not only the transport land-use area but also the semantic information such as the transformation speed, the traffic jam distribution, the main vehicle movement direction and others. Compared with the existed transport network data, such as OpenStreet Map, our method is proved to be quick and accurate.
URBAN LAND COVER/USE CHANGE DETECTION USING HIGH RESOLUTION SPOT 5 AND SPOT 6 IMAGES AND URBAN ATLAS NOMENCLATURE
Authors: Akay S. S. , Sertel E.
Published: August 2016

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time.

 This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.
GEOTECHNOLOGIES FOR THE CHARACTERIZATION OF SPECIALTY COFFEE ENVIRONMENTS OF MANTIQUEIRA DE MINAS IN BRAZIL
Authors: Alves H. M. R. , Vieira T. G. C. , Volpato M. M. L. , Lacerda M.P. C. , Borém F. M.
Published: August 2016

Land-use/land-cover change (LUCC) is a major cause of environmental transformation. Distant drivers, often associated with emerging markets for specific products, are now being considered one of the main factors of LUCC and are gaining attention in land change science. Consumers show an increasing interest in local and quality food, certified for its origin and its environmental production standards. A kind of agricultural product certification, Geographic Indication, identifies a product as originating from a specific region where a given quality, reputation or characteristic is attributed to its geographical origin. Sustainable land use is potentially an indirect effect of Geographic Indication, as it requires better land management in order to preserve the natural resources associated with the unique characteristics of the certified product. Located in the southern region of the state of Minas Gerais in Brazil is the region of Mantiqueira de Minas, considered one of the most important regions for the production of specialty coffees in Brazil. In 2011, the region’s tradition and reputation were recognized with a Geographic Indication, the second given for coffee regions in the country. To explore the full potential of this area for producing coffees with higher quality and meet the growing demand of this new international market, which aggregates value at specialty coffees, it became fundamental to understand the coffee environments of the region, the “terroirs” where these coffees are obtained. Geotechnology can give a significant contribution in filling this gap. This work is part of a research project that made a detailed characterization of the region’s coffee agroecosystems. Geotechnologies were employed to map the areas occupied by coffee plantations by using RapidEye satellite images and SPRING and ArcGIS software. All the segments of the environment were characterized and mapped in detail and the relations with coffee quality were evaluated. The results showed that coffee occupies approximately 8% of the region’s total area and is mostly distributed in the hilly areas with higher elevations. They also proved that specialty coffees are more likely to be obtained in altitudes above 1100 m, also being correlated to genotype and to the post-harvest methods employed. These results provide information that allows a better understanding of the factors involved in the expression of coffee quality. They also provide the scientific basis required for obtaining a new Geographic Indication for the Mantiqueira de Minas region, this time a Denomination of Origin (DO), which will certainly aggregate value to the specialty coffees produced in this unique region.
COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE
Authors: Bareth G. , Bolten A. , Gnyp M. L. , Reusch S. , Jasper J.
Published: August 2016

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.
USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL
Authors: Bendini H. , Sanches I. D. , Körting T. S. , Fonseca L. M. G. , Luiz A. J. B. , Formaggio A. R.
Published: August 2016

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.
MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS
Authors: Bernales A. M. , Antolihao J. A. , Samonte C. , Campomanes F. , Rojas R. J. , dela Serna A. M. , Silapan J.
Published: August 2016

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.
FORESTRY EXPANSION DURING THE LAST DECADES IN THE PARAIBA DO SUL BASIN – BRAZIL
Authors: Carriello F. , Rezende F. S. , Neves O. M. S. , Rodriguez D. A.
Published: August 2016

from 1986 to 2010. In this region is situated the most important and largest extension of reminiscent of Mata Atlântica Biome reminiscent. This biome has been one the most exploited Brazilian biome since 1500, when Brazilian colonization begun. To achieve this goal, we use the GIS “SPRING” and images from Landsat 5 Satellite, TM sensor from 1986, 1990, 1995, 2000, 2005 and 2010, distributed by the Brazilian National Institute for Space Research - INPE. The non-supervised-classification was applied to images in order to produce land use and land cover maps. After that, we intersect each classification for each date with the precedent date, so we can analyze the paths of each land use change, focusing forestry expansion in native’s Mata Atlântica areas. The results show that eucalyptus plantations in the region have expanded mostly over fragments of Mata Atlântica. About 99.389 hectares of Mata Atlântica were transformed into forestry in 25 years, an average rate of 4000 ha per year. Clear-cut was largest between 1990 and 1995, when 22810 hectares of rain forest were cut, and between 1995 and 2000, when 21430 hectares were cut.
REMOTE SENSING EFFICIENCY FOR URBAN ANALYSIS OF MECCA AND SURROUNDS
Authors: Imam Ayman , Alhaddad Bahaa , Roca Josep
Published: August 2016

Situated in the southwest of Saudi Arabia, Mecca is considered the spiritual capital of one and a half billion worldwide Muslims. The city is visited by millions of pilgrims every year. It has undergone significant changes in land cover (LC) since the government first embarked on a series of ambitious development projects 20 years ago to accommodate the growing number of pilgrims and citizens. The main objective of our study is to detect, identify, analyze and measure the evolving land cover and urban morphology composition from multi-temporal satellite images. To characterize the morphological change during a period of twenty years, four satellite images, acquired in 1998 by Landsat TM and in 2003, 2008 and 2013 by Landsat ETM+, were classified into five main categories: Urban, Street, Soil and Vegetation. In addition, DEM has been extracted and included as Mountain. Change detection (CD) analysis is applied using post-classification comparison and GIS. As part of the study, morphological index, such as, Entropy is included for better understanding of urban structures behaviour. Mecca and its surroundings show a noticeable increase in urban and vegetation cover. Urban cover (UC) changes were divided into five radial directions: Northeast, Southeast, Southwest, East, and Northwest. These changes are influenced by mountain ranges surrounding the city and the highways. These revelations can play a significant role towards future planning and development activities, which may further promote urban growth.
MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY
Authors: Irteza S. M. , Nichol J. E.
Published: August 2016

Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.
MAPPING SECONDARY FOREST SUCCESSION ON ABANDONED AGRICULTURAL LAND IN THE POLISH CARPATHIANS
Authors: Kolecka N. , Kozak J. , Kaim D. , Dobosz M. , Ginzler Ch. , Psomas A.
Published: August 2016

Land abandonment and secondary forest succession have played a significant role in land cover changes and forest cover increase in mountain areas in Europe over the past several decades. Land abandonment can be easily observed in the field over small areas, but it is difficult to map over the large areas, e.g., with remote sensing, due to its subtle and spatially dispersed character. Our previous paper presented how the LiDAR (Light Detection and Ranging) and topographic data were used to detect secondary forest succession on abandoned land in one commune located in the Polish Carpathians by means of object-based image analysis (OBIA) and GIS (Kolecka et al., 2015). This paper proposes how the method can be applied to efficiently map secondary forest succession over the entire Polish Carpathians, incorporating spatial sampling strategy supported by various ancillary data. Here we discuss the methods of spatial sampling, its limitations and results in the context of future secondary forest succession modelling.