bluuebunny/crossref_metadata_2025 huggingface dataset:
CHANGES IN SCATTERED GREENERY IN SELECTED AREA IN THE CZECH REPUBLIC FROM 1953 TO 2014
Authors: Ždímal V.
Published: August 2016

The Land Cover of the Czech landscape could change several times. Reconstructing Land Cover and especially greenery in the past is important for today’s use of the landscape. Two periods were chosen to track changes in scattered greenery, the years 1953 and 2014. The source of information was an historical orthophotomap supplemented with a Military Topographic Map and an orthophotomap supplemented by terrain mapping. The software ArcMap and GuidosToolbox were used. The greenery was highlighted and manually converted to a vector format.

 The total area of the monitored territory is 2,190 hectares. In 1953, greenery took up 102 ha and 90 individual green areas were identified here. In 2014, greenery took up 222 ha and it was divided into 113 individual areas. In 1953 the perimeter of all green areas was 41,537 meters; in 2014 the perimeter of all green areas was 89,974 meters. There are two apparent trends here. The first is the simplification of shapes as a result of large-scale management; the second is the formation of a large length of linear greenery and small width with a large perimeter.

 The shape of the surface is linked to the area and perimeter. In 1953 this parameter was on average 1.87, and in 2014 the average value was 2.58. Comparing the results of the distance analysis of Label Objects using the GuidosToolbox software found that virtually all green areas were classified differently in 1953 and 2014. The results are completely different and further analysis makes no sense.
MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES
Authors: Bakirman T. , Gumusay M. U. , Tuney I.
Published: August 2016

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5–10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.
AN OBJECT-BASED WORKFLOW DEVELOPED TO EXTRACT AQUACULTURE PONDS FROM AIRBORNE LIDAR DATA: A TEST CASE IN CENTRAL VISAYAS, PHILIPPINES
Authors: Loberternos R. A. , Porpetcho W. P. , Graciosa J. C. A. , Violanda R. R. , Diola A. G. , Dy D. T. , Otadoy R. E. S.
Published: August 2016

Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.
AN APPLICATION OF CLOSE-UP PHOTOGRAMMETRY IN VITICULTURE
Authors: D'Urso Maria Grazia , Marino Costantino Luis
Published: August 2016

Within the context of Geomatic-based Agriculture an application of close-up photogrammetry is illustrated with reference to precision farming. The application has been carried out in a particularly attractive archaeological site and concerns some viticulture species produced in situ by following a farming technique dated about two thousand years. Specifically we illustrate and comment the photogrammetric close-range acquisitions, carried out both outdoor and indoor, aimed at producing a digital botanic atlas that can adequately replace the traditional ones drawn by hand. High precision geometric measurements of vineyard leaves and grapes, are suitable indices of their growth rate and an objective estimate of the relevant repening degree.
PRECISE ORTHO IMAGERY AS THE SOURCE FOR AUTHORITATIVE AIRPORT MAPPING
Authors: Howard H. , Hummel P.
Published: August 2016

As the aviation industry moves from paper maps and charts to the digital cockpit and electronic flight bag, producers of these products need current and accurate data to ensure flight safety. FAA (Federal Aviation Administration) and ICAO (International Civil Aviation Organization) require certified suppliers to follow a defined protocol to produce authoritative map data for the aerodrome. Typical airport maps have been produced to meet 5 m accuracy requirements. The new digital aviation world is moving to 1 m accuracy maps to provide better situational awareness on the aerodrome. The commercial availability of 0.5 m satellite imagery combined with accurate ground control is enabling the production of avionics certified .85 m orthophotos of airports around the globe. CompassData maintains an archive of over 400+ airports as source data to support producers of 1 m certified Aerodrome Mapping Database (AMDB) critical to flight safety and automated situational awareness. CompassData is a DO200A certified supplier of authoritative orthoimagery and attendees will learn how to utilize current airport imagery to build digital aviation mapping products.
ASSESSING THE IMPACTS OF FLOODING CAUSED BY EXTREME RAINFALL EVENTS THROUGH A COMBINED GEOSPATIAL AND NUMERICAL MODELING APPROACH
Authors: Santillan J. R. , Amora A. M. , Makinano-Santillan M. , Marqueso J. T. , Cutamora L. C. , Serviano J. L. , Makinano R. M.
Published: August 2016

In this paper, we present a combined geospatial and two dimensional (2D) flood modeling approach to assess the impacts of flooding due to extreme rainfall events. We developed and implemented this approach to the Tago River Basin in the province of Surigao del Sur in Mindanao, Philippines, an area which suffered great damage due to flooding caused by Tropical Storms Lingling and Jangmi in the year 2014. The geospatial component of the approach involves extraction of several layers of information such as detailed topography/terrain, man-made features (buildings, roads, bridges) from 1-m spatial resolution LiDAR Digital Surface and Terrain Models (DTM/DSMs), and recent land-cover from Landsat 7 ETM+ and Landsat 8 OLI images. We then used these layers as inputs in developing a Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS)-based hydrologic model, and a hydraulic model based on the 2D module of the latest version of HEC River Analysis System (RAS) to dynamically simulate and map the depth and extent of flooding due to extreme rainfall events. The extreme rainfall events used in the simulation represent 6 hypothetical rainfall events with return periods of 2, 5, 10, 25, 50, and 100 years. For each event, maximum flood depth maps were generated from the simulations, and these maps were further transformed into hazard maps by categorizing the flood depth into low, medium and high hazard levels. Using both the flood hazard maps and the layers of information extracted from remotely-sensed datasets in spatial overlay analysis, we were then able to estimate and assess the impacts of these flooding events to buildings, roads, bridges and landcover. Results of the assessments revealed increase in number of buildings, roads and bridges; and increase in areas of land-cover exposed to various flood hazards as rainfall events become more extreme. The wealth of information generated from the flood impact assessment using the approach can be very useful to the local government units and the concerned communities within Tago River Basin as an aid in determining in an advance manner all those infrastructures (buildings, roads and bridges) and land-cover that can be affected by different extreme rainfall event flood scenarios.
NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES
Authors: Homainejad A. S.
Published: August 2016

This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.
MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA
Authors: Xu W. , Hays B. , Fayrer-Hosken R. , Presotto A.
Published: August 2016

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (Loxodonta africana) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.
TOWARDS CONSISTENT MAPPING OF URBAN STRUCTURES – GLOBAL HUMAN SETTLEMENT LAYER AND LOCAL CLIMATE ZONES
Authors: Bechtel B. , Pesaresi M. , See L. , Mills G. , Ching J. , Alexander P. J. , Feddema J. J. , Florczyk A. J. , Stewart I.
Published: August 2016

Although more than half of the Earth’s population live in urban areas, we know remarkably little about most cities and what we do know is incomplete (lack of coverage) and inconsistent (varying definitions and scale). While there have been considerable advances in the derivation of a global urban mask using satellite information, the complexity of urban structures, the heterogeneity of materials, and the multiplicity of spectral properties have impeded the derivation of universal urban structural types (UST). Further, the variety of UST typologies severely limits the comparability of such studies and although a common and generic description of urban structures is an essential requirement for the universal mapping of urban structures, such a standard scheme is still lacking. More recently, there have been two developments in urban mapping that have the potential for providing a standard approach: the Local Climate Zone (LCZ) scheme (used by the World Urban Database and Access Portal Tools project) and the Global Human Settlement Layer (GHSL) methodology by JRC. In this paper the LCZ scheme and the GHSL LABEL product were compared for selected cities. The comparison between both datasets revealed a good agreement at city and coarse scale, while the contingency at pixel scale was limited due to the mismatch in grid resolution and typology. At a 1 km scale, built-up as well as open and compact classes showed very good agreement in terms of correlation coefficient and mean absolute distance, spatial pattern, and radial distribution as a function of distance from town, which indicates that a decomposition relevant for modelling applications could be derived from both. On the other hand, specific problems were found for both datasets, which are discussed along with their general advantages and disadvantages as a standard for UST classification in urban remote sensing.
ASSESSMENT OF FLOODED AREAS PROJECTIONS AND FLOODS POTENTIAL IMPACTS APPLYING REMOTE SENSING IMAGERY AND DEMOGRAPHIC DATA
Authors: Rodriguez D. A. , Carriello F. , Fernandes P. J. F. , Garofolo Lopes L. , Siqueira Júnior J. L.
Published: August 2016

Assessing vulnerability and potential impacts associated with extreme discharges requires an accurate topographic description in order to estimate the extension of flooded areas. However, in most populated regions, topographic data obtained by in-situ measurements is not available. In this case, digital elevation models derived from remote sensing date are usually applied. Moreover, this digital elevation models have intrinsic errors that introduce bigger uncertainty in results than the associated to hydrological projections. On the other hand, estimations of flooded areas through remote sensing images provide accurate information, which could be used for the construction of river level-flooded area relationships regarding vulnerability assessment. In this work, this approach is applied for the city of Porto Velho in the Brazilian Amazonia to assess potential vulnerability to floods associated with climate change projections. The approach is validated using census data, provided by the Brazilian Institute of Geography and Statistics, and information about socio-economical injuries associated to historical floods, provided by the Brazilian Civil Defence. Hydrological projections under climate change are carried out using several downscaling of climate projections as inputs in a hydrological model. Results show more accurate estimation of flood impacts than the obtained using digital elevation models derivate from remote sensing data. This reduces uncertainties in the assessment of vulnerability to floods associated with climate change in the region.
DYKE MONITORIN BY THE MEANS OF PERSISTENT SCATTERING INTERFEROMETRY AT THE COAST OF NORTHERN GERMANY
Authors: Seidel M. , Marzahn P. , Ludwig R.
Published: August 2016

40 percent of the world's population are presently living in coastal areas or along the main rivers. Taking into account that the vulnerability of these areas is increasing due to sea-level rise and coastal hazards such as storm surges or extreme rainfall events accompanied with floods, the importance of safety structures such as dykes is increasing as well. Hence, a spatial distributed dyke monitoring should be part of a sustainable adaptation strategy.

 Due to increasing amount of SAR-data from various satellites with high spatial and temporal coverage, the means of SAR-interferometry could be an essential tool to ensure this kind of required monitoring. Given this prospect, Persistent Scattering Interferometry (PSI) will be a very suitable monitoring technique for dyke structures to identify dyke movement with the accuracy of few millimetres. This procedure focuses on pixels that show a stable scattering behaviour in a sequence of multiple SAR-scenes. In opposition to ground-measurements, the spatial coverage of this technique provides comparable results for different parts of the dyke; furthermore weak segments with particular high movements could be identified in advance. This could prevent future dyke crevasses and help to reduce risks in high-populated areas.

 This paper attempts to describe the potential of the PSI technique for a spatial distributed dyke monitoring at the coast in northern Germany. 21 ERS-2 scenes and 16 Envisat ASAR scenes were analysed. Those Scenes cover an area of a sea shore dyke including a flood regulation barrage and results point out the potential for this technique to monitor dyke structures. Even though the observed dyke doesn't show any significant deformation rates, the two datasets show the same signal for the whole dyke.
THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS
Authors: Bechtel B.
Published: August 2016

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.
Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey
Authors: Orhan O. , Yakar M.
Published: August 2016

The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.
DETERMINATION OF METHANE SOURCEX GLOBALLY BY SCIAMACHY
Authors: Park J. G. , Park S. Y.
Published: August 2016

Since the beginning of the industrial revolution, the atmospheric concentration of carbon dioxide (CO2) has increased by nearly 30%, and the Methane (CH4) concentration has more than doubled. CH4 is the second most important greenhouse gas, after CO2. Emissions, extrapolated from measurements of actual gas flux from wetlands, vary from place to place, even within the same wetland. This high variability makes large-scale estimates difficult and means that average emissions levels include a large degree of estimated uncertainty. The SCIAMACHY instrument on the European Space Agency satellite ENVISAT measured greenhouse gases in the troposphere and stratosphere. In this study, the CH4 source area is extracted by estimating the concentrations of methane emissions from time-series satellite data. Contamination of the data by cloud is interpolated both spatially and temporally. It is assumed that methane emission is negligible over ocean and that the concentration in the ocean area is due to advection from land. Background CH4 concentration on land was defined as the ocean CH4 concentration at the same latitude. Land CH4 emission concentrations show that areas of concentrated high CH4 emission are not in paddy fields only but also in broadleaf evergreen areas in South America and Central Africa.
UTILIZATION OF LANDSAT DATA FOR WATER QUALITY OBSERVATION IN SMALL INLAND WATER BODIES
Authors: Pásler M. , Komárková J.
Published: August 2016

Many studies deal with water quality evaluation using remotely sensed data. In the field of remote sensing, there have been proposed several procedures how to observe selected parameters of water quality and conditions. The majority of works use methods and procedures based on satellite data but they usually do not deal with suitability and practicability of the satellite data. This paper provides summary of determinants and limitations of satellite data utilization for water quality evaluation. Cloud cover and its influence on size of visible water surfaces is the most deeply evaluated determinants. Temporal resolution, spatial resolution and some other technical factors are discussed as next determinants. The case study demonstrates evaluation of the determinants for Landsat 7 and Landsat 8 data (level 1) and for area of small ponds in part of Pardubice region in the Czech Republic. It clearly demonstrates several limitations of Landsat data for evaluation of selected parameters of water quality and changes of small water bodies.
GOLD MINERAL PROSPECTING USING PHASED ARRAY TYPE L-BAND SYNTHETIC APERTURE RADAR (PALSAR) SATELLITE REMOTE SENSING DATA, CENTRAL GOLD BELT, MALAYSIA
Authors: Beiranvand Pour Amin , Hashim Mazlan
Published: August 2016

The Bentong-Raub Suture Zone (BRSZ) of Peninsular Malaysia is one of the significant structural zones in Sundaland, Southeast Asia. It forms the boundary between the Gondwana-derived Sibumasu terrane in the west and Sukhothai arc in the east. The BRSZ is also genetically related to the sediment-hosted/orogenic gold deposits associated with the major lineaments and form-lines in the central gold belt Central Gold Belt of Peninsular Malaysia. In tropical environments, heavy tropical rainforest and intense weathering makes it impossible to map geological structures over long distances. Advances in remote sensing technology allow the application of Synthetic Aperture Radar (SAR) data in geological structural analysis for tropical environments. In this investigation, the Phased Array type L-band Synthetic Aperture Radar (PALSAR) satellite remote sensing data were used to analyse major geological structures in Peninsular Malaysia and provide detailed characterization of lineaments and form-lines in the BRSZ, as well as its implication for sediment-hosted/orogenic gold exploration in tropical environments. The major geological structure directions of the BRSZ are N-S, NNE-SSW, NE-SW and NW-SE, which derived from directional filtering analysis to PALSAR data. The pervasive array of N-S faults in the study area and surrounding terrain is mainly linked to the N-S trending of the Suture Zone. N-S striking lineaments are often cut by younger NE-SW and NW-SE-trending lineaments. Gold mineralized trends lineaments are associated with the intersection of N-S, NE-SW, NNW-SSE and ESE-WNW faults and curvilinear features in shearing and alteration zones. Lineament analysis on PALSAR satellite remote sensing data is a useful tool for detecting the boundary between the Gondwana-derived terranes and major geological features associated with suture zone especially for large inaccessible regions in tropical environments.
APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA
Authors: Beiranvand Pour Amin , Hashim Mazlan
Published: August 2016

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.
GEOLOGICAL MAPPING BY COMBINING SPECTRAL UNMIXING AND CLUSTER ANALYSIS FOR HYPERSPECTRAL DATA
Authors: Ishidoshiro N. , Yamaguchi Y. , Noda S. , Asano Y. , Kondo T. , Kawakami Y. , Mitsuishi M. , Nakamura H.
Published: August 2016

Spectral unmixing of hyperspectral data often fails to select some minerals and rocks having flat spectra but no diagnostic absorption features as endmembers, even if they are actually important endmembers. To avoid this problem, we propose a novel approach that combined two methods: spectral unmixing and full-pixel classification. First, all pixels were divided into two categories, hydrothermally altered areas and unaltered rocks based on the absorption depth of 2.0 to 2.5 μm. For the hydrothermally altered areas, endmembers were extracted by the Improved Causal Random Pixel Purity Index (ICRPPI) method, which was improved from the existing Pixel Purity Index (PPI) and Causal Random Pixel Purity Index (CRPPI) methods. Endmember abundance in each pixel was calculated by linear spectral unmixing. In a separate operation, k-means clustering was applied to the unaltered rock areas. Finally, the results of these two methods were combined to generate a single distribution map of rocks and minerals. This approach was applied to the airborne hyperspectral HyMap data of Cuprite, Nevada, U.S.A. We confirmed that our mapping result was consistent with the existing geological map as well as our field survey result.
GLOBAL CHANGES IN THE SEA ICE COVER AND ASSOCIATED SURFACE TEMPERATURE CHANGES
Authors: Comiso Josefino C.
Published: August 2016

The trends in the sea ice cover in the two hemispheres have been observed to be asymmetric with the rate of change in the Arctic being negative at −3.8 % per decade while that of the Antarctic is positive at 1.7 % per decade. These observations are confirmed in this study through analyses of a more robust data set that has been enhanced for better consistency and updated for improved statistics. With reports of anthropogenic global warming such phenomenon appears physically counter intuitive but trend studies of surface temperature over the same time period show the occurrence of a similar asymmetry. Satellite surface temperature data show that while global warming is strong and dominant in the Arctic, it is relatively minor in the Antarctic with the trends in sea ice covered areas and surrounding ice free regions observed to be even negative. A strong correlation of ice extent with surface temperature is observed, especially during the growth season, and the observed trends in the sea ice cover are coherent with the trends in surface temperature. The trend of global averages of the ice cover is negative but modest and is consistent and compatible with the positive but modest trend in global surface temperature. A continuation of the trend would mean the disappearance of summer ice by the end of the century but modelling projections indicate that the summer ice could be salvaged if anthropogenic greenhouse gases in the atmosphere are kept constant at the current level.
OPTICAL FLOW APPLIED TO TIME-LAPSE IMAGE SERIES TO ESTIMATE GLACIER MOTION IN THE SOUTHERN PATAGONIA ICE FIELD
Authors: Lannutti E. , Lenzano M. G. , Toth C. , Lenzano L. , Rivera A.
Published: August 2016

In this work, we assessed the feasibility of using optical flow to obtain the motion estimation of a glacier. In general, former investigations used to detect glacier changes involve solutions that require repeated observations which are many times based on extensive field work. Taking into account glaciers are usually located in geographically complex and hard to access areas, deploying time-lapse imaging sensors, optical flow may provide an efficient solution at good spatial and temporal resolution to describe mass motion. Several studies in computer vision and image processing community have used this method to detect large displacements. Therefore, we carried out a test of the proposed Large Displacement Optical Flow method at the Viedma Glacier, located at South Patagonia Icefield, Argentina. We collected monoscopic terrestrial time-lapse imagery, acquired by a calibrated camera at every 24 hour from April 2014 until April 2015. A filter based on temporal correlation and RGB color discretization between the images was applied to minimize errors related to changes in lighting, shadows, clouds and snow. This selection allowed discarding images that do not follow a sequence of similarity. Our results show a flow field in the direction of the glacier movement with acceleration in the terminus. We analyzed the errors between image pairs, and the matching generally appears to be adequate, although some areas show random gross errors related to the presence of changes in lighting. The proposed technique allowed the determination of glacier motion during one year, providing accurate and reliable motion data for subsequent analysis.
ANTARCTIC ICE SHEET SURFACE MASS BALANCE ESTIMATES FROM 2003 TO 2015 USING ICESAT AND CRYOSAT-2 DATA
Authors: Xie Huan , Hai Gang , Chen Lei , Liu Shijie , Liu Jun , Tong Xiaohua , Li Rongxing
Published: August 2016

An assessment of Antarctic ice sheet surface mass balance from 2003 to 2015 has been carried out using a combination of ICESat data from 2003 to 2009 and CryoSat-2 data from 2010 to 2015. Both data sets are of L2 and are currently processed separately using different models. First, a repeat-track processing method that includes terms accounting for the trend and the first order fit of topography is applied to repeat-track measurements of all ICESat Campaigns. It uses the Least Squares fitting of the model to all observations in a box of 500 m x 500 m. The estimated trends in these boxes are then averaged inside a 30 km x 30 km cell. Similarly, the cells are used to estimate basin and ice sheet level surface elevation change trends. Mass balance calculating is performed at the cell level by multiplying the ice density by the volume change and then extended to the basin and the ice sheet level. Second, in CryoSat-2 data processing we applied a model within a cell of 5 km x 5 km considering that CryoSat-2 does not maintain repeated tracks. In this model the elevation trend, and a higher order topography are solved in an iterative way using the least squares technique. The mass change is computed at the cell level in the same way as the ICESat data. GIA correction is applied for both ICESat and CryoSat-2 estimates. Detailed information about the data processing, elevation and mass balance changes, and comparison with other studies will be introduced.
FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION
Authors: Argamosa R. J. L. , Paringit E. C. , Quinton K. R. , Tandoc F. A. M. , Faelga R. A. G. , Ibañez C. A. G. , Posilero M. A. V. , Zaragosa G. P.
Published: August 2016

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM.
EVALUATION OF FOREST CANOPY AND UNDERSTORY GAP FRACTION DERIVED FROM TERRESTRIAL LASER SCANNING
Authors: Chen K. C. , Wang C. K.
Published: August 2016

The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively.
FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA
Authors: Chiang Shou Hao , Valdez Miguel , Chen Chi-Farn
Published: August 2016

Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered.

 In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled with terrain variables produced better result, with the higher overall accuracy and kappa coefficient than first experiment. The results indicate that the Maximum Entropy method is an applicable, and to classify tree species using satellite imagery data coupled with terrain information can improve the classification of tree species in the study area.
IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING
Authors: Fang R.
Published: August 2016

Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in R2 and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.
VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA
Authors: Korets M. A. , Ryzhkova V. A. , Danilova I. V. , Prokushkin A. S.
Published: August 2016

An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m2). The proposed approach was applied for the test site area (~3600 km2), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.
ESTIMATING CARBON STOCK CHANGES OF MANGROVE FORESTS USING SATELLITE IMAGERY AND AIRBORNE LiDAR DATA IN THE SOUTH SUMATRA STATE, INDONESIA
Authors: Maeda Y. , Fukushima A. , Imai Y. , Tanahashi Y. , Nakama E. , Ohta S. , Kawazoe K. , Akune N.
Published: August 2016

The purposes of this study were 1) to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2) to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1) Classification of types of tree species using Satellite imagery in the study area, (2) Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3) Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.
ASSESING GEOGRAPHIC ISOLATION OF THE GALAPAGOS ISLANDS
Authors: Orellana D. , Smith F.
Published: August 2016

The Galapagos Archipelago is one of the most important ecological spots in the planet due its unique biodiversity, active geology, and relatively well-preserved ecosystems. These characteristics are strongly based on the geographical isolation of the islands. On the one hand this isolation allowed the evolution processes that gave the islands their international fame and on the other hand it kept them from major human impacts that affected the vast majority of the Earth’s surface. Galapagos’ geographical isolation is therefore of mayor value, but it is rapidly diminishing due to the increase of marine and air transportation among islands and with the rest of the world. This increased accessibility implies enhanced risks for the ecological dynamics on the archipelago (e.g. increased risk of biological invasions, uncontrolled tourism growth, more water and energy consumption). Here, we introduce a general accessibility model to assess geographical isolation of the Galapagos Islands. The model aims to characterize accessibility in terms of human mobility by evaluating travel time to each point of the archipelago using all available transportation modalities. Using a multi criteria cost surface for marine and land areas, we estimated travel time for each surface unit using the fastest route and mode of transportation available while considering several friction factors such as surface type, slope, infrastructure, transfer points, legal restrictions, and physical barriers. We created maps to evaluate the isolation of different islands and places, highlighting the potential risks for several habitats and ecosystems. The model can be used for research and decision-making regarding island conservation, such as estimating spreading paths for invasive species, informing decisions on tourism management, and monitoring isolation changes of sensitive ecosystems.
CHARACTERIZATION AND SPECTRAL MONITORING OF COFFEE LANDS IN BRAZIL
Authors: Alves H. M. R. , Volpato M. M. L. , Vieira T. G. C. , Maciel D. A. , Gonçalves T. G. , Dantas M. F.
Published: August 2016

In Brazil, coffee production has great economic and social importance. Despite this fact, there is still a shortage of information regarding its spatial distribution, crop management and environment. The aim of this study was to carry out spectral monitoring of coffee lands and to characterize their environments using geotechnologies. Coffee fields with contiguous areas over 0.01 km2 within a 488.5 km2 region in the south of Minas Gerais state were selected for the study. Spectral data from the sensors OLI/Landsat 8 and the Shuttle Radar Topography Mission from 2014 to 2015 were obtained, as well as information on production areas, surface temperature, vegetation indexes, altitude and slope, were gathered and analyzed. The results indicate that there is great variation in the NDVI and NDWI values, with means ranging from 0.21 to 0.91 (NDVI) and 0.108 to 0.543 (NDWI). The altitude ranged from 803 to 1150 m, and the surface temperature from 20.9°C to 27.6°C. The altitude and the surface temperature distribution patterns were correlated with the vegetation indexes. The slope classes were very homogeneous, predominantly with declivities between 8 to 20 %, characterized as wavy relief. This study made possible the characterization and monitoring of coffee lands and its results may be instrumental in decision-making processes related to coffee management.
ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES
Authors: Aslan N. , Koc-San D.
Published: August 2016

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively).