bluuebunny/crossref_metadata_2025 huggingface dataset:
SPATIAL DISTRUBITON OF CHILDREN TREATED BY CANCER IN ZONGULDAK, TURKEY
Authors: Topan Aysel , Bayram Dilek , Özendi Mustafa , Cam Ali , Öztürk Cam , Kuzlu Ayyıldız Tülay , Kulakçı Hülya , Veren Funda
Published: August 2016

This research is focused on the examination of child cancer cases in Zonguldak (Turkey) descriptively in epidemiological aspect thanks to GIS. Universe of the study is composed of 60 children between 0-19 years old, treated in Children Oncology Clinic of Health Application and Research Center in BEU. Whole universe was reached without selecting a sample in the study. Data were collected by using a form prepared by obtaining expert advice and they were applied to children and their parents at study dates. Results were expressed as percentages. Chi-Square test was used in intergroup comparisons, results were assessed within 95% confidence interval and p<0.05 was considered as statistically significant. Variables that were used in the study were assessed, recorded in prepared data collection form and distribution maps were produced. When disease diagnosis of the children participated in the study were evaluated, it was observed that 33.3% (n=20) were being treated for ALL, 13.3% (n=8) for Medullablastoma and 11.7% (n=7) for Hodgkin-nonHodgkin Lymphoma. It was detected that 31.7% (n=19) were in Ereğli, 31.7% (n=19) were in Central district and 18.3% (n=11) were in Çaycuma, when the places where children were living were evaluated. Statistically significant difference was found (p=0.016) comparing disease diagnosis with living place, and overall distribution map of the number of cancer cases was produced in this context. This is the first research subjecting the distribution of cancer cases for Zonguldak province.
DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI
Authors: Bektas Balcik F. , Ergene E. M.
Published: August 2016

Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.
DETECTION OF DRY INTRUSION ON WATER VAPOR IMAGES OVER CENTRAL EUROPE – JUNE 2010 TO SEPTEMBER 2011
Authors: Novotny J. , Dejmal K. , Hudec F. , Kolar P.
Published: August 2016

The knowledge of evaluation of the intensity of cyclogenesis which could be connected with the weather having a significant impact on Earth’s surface is quite useful. If, as one of the basic assumptions, the existence of connection between dry intrusions, dry bands, tropopause height and warm dark areas distribution on water vapor images (WV images) is considered, it is possible to set up a method of detecting dry intrusions on searching and tracking areas with higher brightness temperature compared with the surrounding environment. This paper covers the period between June 2010 and September 2011 over Central Europe. The ISIS method (Instrument de Suivi dans I’Imagerie satellitaire), originally developed for detection of cold cloud tops, was used as an initial ideological point. Subsequently, this method was modified by Michel and Bouttier for usage on WV images. Some of the applied criteria and parameters were chosen with reference to the results published by Michel and Bouttier as well as by Novotny. The procedure can be divided into two steps: detection of warm areas and their tracking. Cases of detection of areas not evidently connected with dry intrusions can be solved by filtering off based on the connection between detected warm areas to the cyclonic side of jet streams and significant lowering of the tropopause.
VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST
Authors: Ballari D. , Castro E. , Campozano L.
Published: August 2016

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.
SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS
Authors: Chang K. T. , Dou J. , Chang Y. , Kuo C. P. , Xu K. M. , Liu J. K.
Published: August 2016

The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.
CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA
Authors: Kaczynski R. , Rylko A.
Published: August 2016

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.
IMPROVED CORRECTION METHOD FOR WATER-REFRACTED TERRESTRIAL LASER SCANNING DATA ACQUIRED IN THE MOUNTAIN CHANNEL
Authors: Miura N. , Asano Y. , Moribe Y.
Published: August 2016

Detailed information of underwater topography is required for better understanding and prediction of water and sediment transport in a mountain channel. Recent research showed promising utility of green-wavelength Terrestrial Laser Scanning (TLS) for measuring submerged stream-bed structure in fluvial environment. However, difficulty in acquiring reliable underwater data has been remained in the part of mountain channel where water surface has some gradient. Since horizontal water surface was a major premise for the existing water refraction correction method, significant error was resulted in such area. Therefore, this paper presents a modified method to correct water-refracted TLS data acquired over mountain channel with complex water-surface slope. Applicability of the modified method was validated using the field data and compared with the existing correction method and non-corrected data. The results showed that the modified method has much smaller error with RMSE value of 3 mm than the existing method (RMSE = 10 mm) and non-corrected data (RMSE = 23 mm). Presented method successfully corrected water-refracted TLS data acquired over sloped channel. This would enable us to quantitatively measure whole units of complex mountain channels, and help us to understand water dynamics better in the area.
OPTIMAL BAND RATIO ANALYSIS OF WORLDVIEW-3 IMAGERY FOR BATHYMETRY OF SHALLOW RIVERS (CASE STUDY: SARCA RIVER, ITALY)
Authors: Niroumand-Jadidi M. , Vitti A.
Published: August 2016

The Optimal Band Ratio Analysis (OBRA) could be considered as an efficient technique for bathymetry from optical imagery due to its robustness on substrate variability. This point receives more attention for very shallow rivers where different substrate types can contribute remarkably into total at-sensor radiance. The OBRA examines the total possible pairs of spectral bands in order to identify the optimal two-band ratio that its log transformation yields a strong linear relation with field measured water depths. This paper aims at investigating the effectiveness of additional spectral bands of newly launched WorldView-3 (WV-3) imagery in the visible and NIR spectrum through OBRA for retrieving water depths in shallow rivers. In this regard, the OBRA is performed on a WV-3 image as well as a GeoEye image of a small Alpine river in Italy. In-situ depths are gathered in two river reaches using a precise GPS device. In each testing scenario, 50% of the field data is used for calibration of the model and the remained as independent check points for accuracy assessment. In general, the effect of changes in water depth is highly pronounced in longer wavelengths (i.e. NIR) due to high and rapid absorption of light in this spectrum as long as it is not saturated. As the studied river is shallow, NIR portion of the spectrum has not been reduced so much not to reach the riverbed; making use of the observed radiance over this spectral range as denominator has shown a strong correlation through OBRA. More specifically, tightly focused channels of red-edge, NIR-1 and NIR-2 provide a wealth of choices for OBRA rather than a single NIR band of conventional 4-band images (e.g. GeoEye). This advantage of WV-3 images is outstanding as well for choosing the optimal numerator of the ratio model. Coastal-blue and yellow bands of WV-3 are identified as proper numerators while only green band of the GeoEye image contributed to a reliable correlation of image derived values and field measured depths. According to the results, the additional and narrow spectral bands of WV-3 image lead to an average determination coefficient of 67% in two river segments, which is 10% higher than that of obtained from the 4-band GeoEye image. In addition, RMSEs of depth estimations are calculated as 4 cm and 6 cm respectively for WV-3 and GeoEye images, considering the optimal band ratio.
REMOTE SENSING APPLICATION OF THE GEOPHYSICAL CHANGES IN THE COASTLINES AND RIVERS OF ZAMBALES, PHILIPPINES
Authors: Paz-Alberto Annie Melinda , Sison Melissa Joy M. , Bulaong Edmark Pablo , Pakaigue Marietta A.
Published: August 2016

Geophysical changes in river outlet, river upstream and coastlines near the rivers of Bucao and Santo Tomas in Zambales, Philippines were analyzed using the Google Earth’s historical satellite imageries from 2004 to 2013. Data in 2015 were gathered from in situ field measurements ground validation. The study aimed to measure and determine changes in the width of river outlet, width of river bank upstream and shifting of coastline.

 Results revealed that there was a decrease and increase in the width size of the Bucao and Santo Tomas river outlets, respectively during the study period. Geophysical changes occurred in the two rivers due to the continuous supply of lahar as an after effect of the Mount Pinatubo eruption in 1991. Coastline positions near the two rivers also changed. The highest rate of erosion along the coastal area was prevalently observed near the river outlet of both rivers. Moreover, accretion was observed in the coastline of Santo Tomas and erosion phenomenon was observed in the North and South coastlines of Bucao River. The shifting was caused by natural processes such as erosion, sedimentation and natural calamities as well as anthropogenic processes such as reclamation/quarrying. Occurrence of erosion and sedimentation played active roles in the changes of coastlines during the study period.

 Furthermore, the upstream of the Bucao river changed physically due to deposits of lahar present in the upstream which are being discharged directly and continuously going down to the river. Generally, the width of the Bucao River upstream decreased its size because of the accumulated sediment in the riverbank. On the other hand, the observed erosion is caused by high velocity of river during heavy rains and typhoons. The width of the Santo Tomas river bank upstream did not change due to the construction of concrete dikes which prevent the lahar-filled river from breaching the embankment and flooding the agricultural, residential and commercial areas near the river.
A COMBINED APPROACH WITH SMOS AND MODIS TO MONITOR AGRICULTURAL DROUGHT
Authors: Sánchez N. , Martínez-Fernández J. , González-Zamora A.
Published: August 2016

A synergistic fusion of the Soil Moisture and Ocean Salinity (SMOS) L2 soil moisture with the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and several water/vegetation indices for agricultural drought monitoring was tested. The rationale of the calculation is based on the inverse relationship between LST and vegetation condition, related in turn with the soil moisture content. All the products were time-integrated, including the lagged response of vegetation. The product aims to detect and characterize soil moisture drought conditions and, particularly, to identify potential short-term agricultural droughts among them. The new index, so-called the Soil Moisture Agricultural Drought Index (SMADI), was retrieved at 500 m spatial resolution at the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) area from 2010 to 2014 at 8-days temporal scale. SMADI was compared with other agricultural indices in REMEDHUS through statistical correlation, affording a good agreement with them, and depicting a suitable description of the drought conditions in this area during the study period.
QUANTIFICATION OF GLACIER DEPLETION IN THE CENTRAL TIBETAN PLATEAU BY USING INTEGRATED SATELLITE REMOTE SENSING AND GRAVIMETRY
Authors: Tseng K.-H. , Liu K. T. , Shum C. K. , Jia Y. , Shang K. , Dai C.
Published: August 2016

Glaciers over the Tibetan Plateau have experienced accelerated depletion in the last few decades due primarily to the global warming. The freshwater drained into brackish lakes is also observed by optical remote sensing and altimetry satellites. However, the actual water storage change is difficult to be quantified since the altimetry or remote sensing only provide data in limited dimensions. The altimetry data give an elevation change of surface while the remote sensing images provide an extent variation in horizontal plane. Hence a data set used to describe the volume change is needed to measure the exact mass transition in a time span. In this study, we utilize GRACE gravimetry mission to quantify the total column mass change in the central Tibetan Plateau, especially focused on the lakes near Tanggula Mountains. By removing these factors, the freshwater storage change of glacier system at study area can be potentially isolated.
A SIMPLE AND EFFECTIVE RETRIEVAL OF LAND SURFACE TEMPERATURE USING A NEW REFLECTANCE BASED EMISSIVITY ESTIMATION TECHNIQUE
Authors: Nithiyanandam Y. , Nichol J. E.
Published: August 2016

Emissivity is a significant factor in determining land surface temperature (LST) retrieved from the thermal infrared (TIR) satellite images. A new simplified method (reflectance method) for emissivity correction was developed in this study while estimating emissivity values at a spatial resolution of 30 m from the radiance values of the SWIR image. This in turn enables mapping surface temperatures at a much finer spatial resolution (30 m). Temperatures so estimated are validated against surface temperatures measured in the ground by thermocouple data loggers recorded during satellite overpass time. In this study, surface emissivity values are derived directly from the AST_ L1B images. The reflectance method estimates temperature at higher spatial resolution of 30 m when compared to the 90 m spatial resolution of TES and reference channel methods. Temperature determined for the daytime image of 30th November 2007 using different emissivity techniques was compared with the temperatures measured on the field using thermocouple data loggers. It is observed that the estimates from the reflectance method are much closer to the field measurements than the TES and reference channel methods. The temperature difference values range from 0.2 to 2.3 °C, 0.15 to 5.6 °C, and 2.6 to 8.6 °C for the reflectance method, normalization method and reference channel method, respectively. The new reflectance emissivity techniques i.e. reflectance method exhibits the least deviation from the field measured temperature values. While considering the accuracy of data logger (1 °C) the reflectance method enables one to map surface temperature precisely than other two methods.
ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION
Authors: Zhao S. S. , Wang N. , Hui J. , Ye X. , Qin Q.
Published: August 2016

Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.
FORECASTING ANTARCTIC SEA ICE CONCENTRATIONS USING RESULTS OF TEMPORAL MIXTURE ANALYSIS
Authors: Chi Junhwa , Kim Hyun-Cheol
Published: August 2016

Sea ice concentration (SIC) data acquired by passive microwave sensors at daily temporal frequencies over extended areas provide seasonal characteristics of sea ice dynamics and play a key role as an indicator of global climate trends; however, it is typically challenging to study long-term time series. Of the various advanced remote sensing techniques that address this issue, temporal mixture analysis (TMA) methods are often used to investigate the temporal characteristics of environmental factors, including SICs in the case of the present study. This study aims to forecast daily SICs for one year using a combination of TMA and time series modeling in two stages. First, we identify temporally meaningful sea ice signatures, referred to as temporal endmembers, using machine learning algorithms, and then we decompose each pixel into a linear combination of temporal endmembers. Using these corresponding fractional abundances of endmembers, we apply a autoregressive model that generally fits all Antarctic SIC data for 1979 to 2013 to forecast SIC values for 2014. We compare our results using the proposed approach based on daily SIC data reconstructed from real fractional abundances derived from a pixel unmixing method and temporal endmember signatures. The proposed method successfully forecasts new fractional abundance values, and the resulting images are qualitatively and quantitatively similar to the reference data.
LONG-TERM MONITORING OF GLACIER CHANGE AT GÖSSNITZKEES (AUSTRIA) USING TERRESTRIAL PHOTOGRAMMETRY
Authors: Kaufmann V. , Seier G.
Published: August 2016

Gössnitzkees is a small heavily debris-covered cirque glacier (49.8 ha) located in the Schober Mountains, Hohe Tauern Range, Austrian Alps. Glacier nourishment is mainly due to avalanches descending from its surrounding headwalls. Gössnitzkees is the largest glacier in the Schober Mountains and is highly representative of the other 25 glaciers of this mountain group. All glaciers of this mountain group have receded continuously since 1850. Ongoing atmospheric warming sustains excessive glacier melt. In 1988 a long-term monitoring program was started at Gössnitzkees using terrestrial photogrammetry in order to document and quantify glacier change. The surveys have been repeated from time to time using different types of cameras. Recent surveys date from 2009, 2012, and 2015. The aim of this paper is twofold: firstly, to investigate whether or not the rather complex photogrammetric evaluation process using a conventional photogrammetric workstation (mostly with a limited degree of automation for terrestrial applications) can be replaced by modern fully automated Structure-from-Motion (SfM) based approaches, and secondly, to document and quantify the glacier change at Gössnitzkees based on available information augmented by results obtained from the most recent surveys mentioned. Over the last 27 years (1988-2015) the terminus of Gössnitzkees has receded by 179 m and the glacier ice has melted at a mean annual rate of about 1.5 m/year. The Schober Mountains are in the process of deglaciation and the glaciers will likely disappear within the next two decades. Based on our practical investigations we found out that SfM-based software is in general capable of handling terrestrial photographs in a fully automatic mode supporting challenging glacier studies.
A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA
Authors: Lee Hwa-Seon , Lee Kyu-Sung
Published: August 2016

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.
STUDY ON THE RETRIEVAL OF SNOW DEPTH FROM FY3B/MWRI IN THE ATCTIC
Authors: Li Lele , Chen Haihua , Guan Lei
Published: August 2016

temperatures. Given the high albedo and low thermal conductivity, snow is regarded as one of the key reasons for the amplification of the warming in polar regions. The distributions of sea ice and snow depth are essential to the whole thermal conduction in the Arctic. This study focused on the retrieval of snow depth on sea ice from brightness temperatures of the MicroWave Radiometer Imager (MWRI) onboard the FengYun (FY)-3B satellite during the period from December 1, 2010 to April 30, 2011. After cross calibrated to the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) Level 2A data, the MWRI brightness temperatures were applied to calculate the sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. According to the proportional relationship between the snow depth and the surface scattering in 18.7 and 36.5 GHz, the snow depths were derived. In order to eliminate the influence of uncertainties in grain sizes of snow as well as sporadic weather effects, the seven-day averaged snow depths were calculated. Then the results were compared with the snow depths from the AMSR-E Level 3 Sea Ice products. The bias of differences between the MWRI and the AMSR-E Level 3 products are ranged between −1.09 and −0.32 cm，while the standard deviations and the correlation coefficients are ranged from 2.47 to 2.88 cm and from 0.78 to 0.90 for different months. As a result, it could be summarized that FY3B/MWRI showed a promising prospect in retrieving snow depth on sea ice.
AUTOMATIC EXTRACTION OF MANGROVE VEGETATION FROM OPTICAL SATELLITE DATA
Authors: Agrawal Mayank , Reddy Devireddy Sushma , Prasad Ram Chandra
Published: August 2016

Mangrove, the intertidal halophytic vegetation, are one of the most significant and diverse ecosystem in the world. They protect the coast from sea erosion and other natural disasters like tsunami and cyclone. In view of their increased destruction and degradation in the current scenario, mapping of this vegetation is at priority. Globally researchers mapped mangrove vegetation using visual interpretation method or digital classification approaches or a combination of both (hybrid) approaches using varied spatial and spectral data sets. In the recent past techniques have been developed to extract these coastal vegetation automatically using varied algorithms. In the current study we tried to delineate mangrove vegetation using LISS III and Landsat 8 data sets for selected locations of Andaman and Nicobar islands. Towards this we made an attempt to use segmentation method, that characterize the mangrove vegetation based on their tone and the texture and the pixel based classification method, where the mangroves are identified based on their pixel values. The results obtained from the both approaches are validated using maps available for the region selected and obtained better accuracy with respect to their delineation. The main focus of this paper is simplicity of the methods and the availability of the data on which these methods are applied as these data (Landsat) are readily available for many regions. Our methods are very flexible and can be applied on any region.
A NEW METHOD TO DETECT REGIONS ENDANGERED BY HIGH WIND SPEEDS
Authors: Fischer P. , Ehrensperger S. , Krauß T.
Published: August 2016

In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landform classification). Furthermore land cover data from the CORINE dataset is added. The response variable is maximum wind speed, measurements are provided by a network of weather stations. The area of interest is Switzerland, a country which suits perfectly for this study because of its highly dynamic orography and various landforms.
ANALYSIS OF THE SIDE-LAP EFFECT ON FULL-WAVEFORM LIDAR DATA ACQUISITION FOR THE ESTIMATION OF FOREST STRUCTURE VARIABLES
Authors: Crespo-Peremarch P. , Ruiz L. A. , Balaguer-Beser A. , Estornell J.
Published: August 2016

LiDAR full-waveform provides a better description of the physical and forest vertical structure properties than discrete LiDAR since it registers the full wave that interacts with the canopy. In this paper, the effect of flight line side-lap is analysed on forest structure and canopy fuel variables estimations. Differences are related to pulse density changes between flight stripe side-lap areas, varying the point density between 2.65 m−2 and 33.77 m−2 in our study area. These differences modify metrics extracted from data and therefore variable values estimated from these metrics such as forest stand variables. In order to assess this effect, 64 pairwise samples were selected in adjacent areas with similar canopy structure, but having different point densities. Two parameters were tested and evaluated to minimise this effect: voxel size and voxel value assignation testing maximum, mean, median, mode, percentiles 90 and 95.

 Student’s t-test or Wilcoxon test were used for the comparison of paired samples. Moreover, the absolute value of standardised paired samples was calculated to quantify dissimilarities. It was concluded that optimizing voxel size and voxel value assignation minimised the effect of point density variations and homogenised full-waveform metrics. Height/median ratio (HTMR) and Vertical distribution ratio (VDR) had the lowest variability between different densities, and Return waveform energy (RWE) reached the best improvement with respect to initial data, being the difference between standardised paired samples 1.28 before and 0.69 after modification.
TLS FIELD DATA BASED INTENSITY CORRECTION FOR FOREST ENVIRONMENTS
Authors: Heinzel J. , Huber M. O.
Published: August 2016

Terrestrial laser scanning (TLS) is increasingly used for forestry applications. Besides the three dimensional point coordinates, the 'intensity' of the reflected signal plays an important role in forestry and vegetation studies. The benefit of the signal intensity is caused by the wavelength of the laser that is within the near infrared (NIR) for most scanners. The NIR is highly indicative for various vegetation characteristics.

 However, the intensity as recorded by most terrestrial scanners is distorted by both external and scanner specific factors. Since details about system internal alteration of the signal are often unknown to the user, model driven approaches are impractical. On the other hand, existing data driven calibration procedures require laborious acquisition of separate reference datasets or areas of homogenous reflection characteristics from the field data.

 In order to fill this gap, the present study introduces an approach to correct unwanted intensity variations directly from the point cloud of the field data. The focus is on the variation over range and sensor specific distortions. Instead of an absolute calibration of the values, a relative correction within the dataset is sufficient for most forestry applications. Finally, a method similar to time series detrending is presented with the only pre-condition of a relative equal distribution of forest objects and materials over range. Our test data covers 50 terrestrial scans captured with a FARO Focus 3D S120 scanner using a laser wavelength of 905 nm. Practical tests demonstrate that our correction method removes range and scanner based alterations of the intensity.
ASSESSMENT OF THE UTILITY OF THE ADVANCED HIMAWARI IMAGER TO DETECT ACTIVE FIRE OVER AUSTRALIA
Authors: Hally B. , Wallace L. , Reinke K. , Jones S.
Published: August 2016

Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.
EXPLORING THE CONNECTIVITY OF ECOLOGICAL CORRIDORS BETWEEN LOW ELEVATION MOUNTAINS AND PINGTUNG LINHOUSILIN FOREST PARK OF TAIWAN BY LEAST-COST PATH METHOD
Authors: Huang Y. L. , Liu H. F. , Chen J. C. , Chen C. T.
Published: August 2016

The primary purpose of this study was explored the variation of landscape process and its impact on the possibility of ecological corridors on Pingtung Linhousilin Forest Park. Developing the landscape change process in year 2002, 2005, 2012 and 2014 via the land-use definition of IPCC (forest land, cropland, grassland, wetlands, settlements and other land). In the landscape structure analysis, the cropland was gradually changed to forest land in this area. Moreover, the variation of gravity model showed that the interaction between Linhousilin Forest Park and low elevation mountains were gradually increased which means the function of ecological corridors has increased.
ESTIMATION OF STAND HEIGHT AND FOREST VOLUME USING HIGH RESOLUTION STEREO PHOTOGRAPHY AND FOREST TYPE MAP
Authors: Kim K. M.
Published: August 2016

Traditional field methods for measuring tree heights are often too costly and time consuming. An alternative remote sensing approach is to measure tree heights from digital stereo photographs which is more practical for forest managers and less expensive than LiDAR or synthetic aperture radar. This work proposes an estimation of stand height and forest volume(m3/ha) using normalized digital surface model (nDSM) from high resolution stereo photography (25cm resolution) and forest type map. The study area was located in Mt. Maehwa model forest in Hong Chun-Gun, South Korea. The forest type map has four attributes such as major species, age class, DBH class and crown density class by stand. Overlapping aerial photos were taken in September 2013 and digital surface model (DSM) was created by photogrammetric methods(aerial triangulation, digital image matching). Then, digital terrain model (DTM) was created by filtering DSM and subtracted DTM from DSM pixel by pixel, resulting in nDSM which represents object heights (buildings, trees, etc.). Two independent variables from nDSM were used to estimate forest stand volume: crown density (%) and stand height (m). First, crown density was calculated using canopy segmentation method considering live crown ratio. Next, stand height was produced by averaging individual tree heights in a stand using Esri’s ArcGIS and the USDA Forest Service’s FUSION software. Finally, stand volume was estimated and mapped using aerial photo stand volume equations by species which have two independent variables, crown density and stand height. South Korea has a historical imagery archive which can show forest change in 40 years of successful forest rehabilitation. For a future study, forest volume change map (1970s–present) will be produced using this stand volume estimation method and a historical imagery archive.
ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE
Authors: Han Ge , Gong Wei , Cui Xiaohui , Zhang Miao , Chen Jun
Published: August 2016

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.
SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA
Authors: Pang Yong , Li Zengyuan
Published: August 2016

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.
ABILITY OF LANDSAT-8 OLI DERIVED TEXTURE METRICS IN ESTIMATING ABOVEGROUND CARBON STOCKS OF COPPICE OAK FORESTS
Authors: Safari A. , Sohrabi H.
Published: August 2016

The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics were able to explain about half of the variation in aboveground carbon stocks. These results demonstrated that Landsat 8 derived texture metrics can be applied for mapping aboveground carbon stocks of coppice Oak Forests in large areas.
USING MULTI-CRITERIA ANALYSIS FOR THE STUDY OF HUMAN IMPACT ON AGRO-FORESTRY-PASTORAL ECOSYSTEM IN THE REGION OF KHENCHELA (ALGERIA)
Authors: Bouzekri A. , Benmessaoud H.
Published: August 2016

The objective of this work is to study and analyze the human impact on agro-forestry-pastoral ecosystem of Khenchela region through the application of multi-criteria analysis methods to integrate geographic information systems, our methodology is based on a weighted linear combination of information on four criteria chosen in our analysis representative in the vicinity of variables in relation to roads, urban areas, water resources and agricultural space, the results shows the effect of urbanization and socio-economic activity on the degradation of the physical environment and found that 32% of the total area are very sensitive to human impact.
LANDSCAPES IMPACTED BY LIGHT
Authors: Arellano B. , Roca J.
Published: August 2016

The gradual spread of urbanization, the phenomenon known under the term urban sprawl, has become one of the paradigms that have characterized the urban development since the second half of the twentieth century and early twenty-first century. However, there is no unanimous consensus about what means "urbanization". The plurality of forms of human settlement on the planet difficult to identify the urbanization processes. The arrival of electrification to nearly every corner of the planet is certainly the first and more meaningful indicator of artificialization of land. In this sense, the paper proposes a new methodology based on the analysis of the satellite image of nighttime lights designed to identify the highly impacted landscapes worldwide and to build an index of Land Impacted by Light per capita (LILpc) as an indicator of the level of urbanization. The used methodology allows the identification of different typologies of urbanized areas (villages, cities or metropolitan areas), as well as “rural”, “rurban”, “periurban” and “central” landscapes.

 The study identifies 186,134 illuminated contours (urbanized areas). In one hand, 404 of these contours could be consider as real “metropolitan areas”; and in the other hand, there are 161,821 contours with less than 5,000 inhabitants, which could be identify as “villages”. Finally, the paper shows that 44.5 % live in rural areas, 15.5 % in rurban spaces, 26.2 % in suburban areas and only 18.4 % in central areas.