bluuebunny/crossref_metadata_2025 huggingface dataset:
ESTIMATION OF PHOSPHORUS EMISSIONS IN THE UPPER IGUAZU BASIN (BRAZIL) USING GIS AND THE MORE MODEL
Authors: Acosta Porras E. A. , Kishi R. T. , Fuchs S. , Hilgert S.
Published: June 2016

Abstract. Pollution emissions into the drainage basin have direct impact on surface water quality. These emissions result from human activities that turn into pollution loads when they reach the water bodies, as point or diffuse sources. Their pollution potential depends on the characteristics and quantity of the transported materials. The estimation of pollution loads can assist decision-making in basin management. Knowledge about the potential pollution sources allows for a prioritization of pollution control policies to achieve the desired water quality. Consequently, it helps avoiding problems such as eutrophication of water bodies. The focus of the research described in this study is related to phosphorus emissions into river basins. The study area is the upper Iguazu basin that lies in the northeast region of the State of Paraná, Brazil, covering about 2,965 km2 and around 4 million inhabitants live concentrated on just 16% of its area. The MoRE (Modeling of Regionalized Emissions) model was used to estimate phosphorus emissions. MoRE is a model that uses empirical approaches to model processes in analytical units, capable of using spatially distributed parameters, covering both, emissions from point sources as well as non-point sources. In order to model the processes, the basin was divided into 152 analytical units with an average size of 20 km2. Available data was organized in a GIS environment. Using e.g. layers of precipitation, the Digital Terrain Model from a 1:10000 scale map as well as soils and land cover, which were derived from remote sensing imagery. Further data is used, such as point pollution discharges and statistical socio-economic data. The model shows that one of the main pollution sources in the upper Iguazu basin is the domestic sewage that enters the river as point source (effluents of treatment stations) and/or as diffuse pollution, caused by failures of sanitary sewer systems or clandestine sewer discharges, accounting for about 56% of the emissions. Second significant shares of emissions come from direct runoff or groundwater, being responsible for 32% of the total emissions. Finally, agricultural erosion and industry pathways represent 12% of emissions. This study shows that MoRE is capable of producing valid emission calculation on a relatively reduced input data basis.
CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA
Authors: Kaczynski R. , Rylko A.
Published: June 2016

Abstract. 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.
UTILIZATION OF LANDSAT DATA FOR WATER QUALITY OBSERVATION IN SMALL INLAND WATER BODIES
Authors: Pásler M. , Komárková J.
Published: June 2016

Abstract. 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.
A COMBINED APPROACH WITH SMOS AND MODIS TO MONITOR AGRICULTURAL DROUGHT
Authors: Sánchez N. , Martínez-Fernández J. , González-Zamora A.
Published: June 2016

Abstract. 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.
RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA
Authors: Huang J. , Zhou Q.
Published: June 2016

Abstract. Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.
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: June 2016

Abstract. 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.
DETECTION AND DISCRIMINATION OF THE THICK OIL PATCHES ON THE SEA SURFACE
Authors: Dubucq Dominique , Sicot Guillaume , Lennon Marc , Miegebielle Véronique
Published: June 2016

Abstract. Detection of natural or accidental oil slick at sea surface is important both for exploration purposes and for environment protection. Radar imagery, either satellite or airborne is the prime tool to detect those slicks. Radar is widely used by the national agencies to monitor their maritime areas for accidental pollutions or boat discharges. Radar images can detect oil slick even in the presence of clouds. However the sea surface back scattered energy is rather insensitive to oil thickness. On the contrary several studies tend to prove that optical data may be used to estimate the oil thickness. These data may be in the form of hyperspectral data or thermal infrared data. The objective of this study is to show that SWIR satellite data which are more widely available than hyperspectral data, better resolved than thermal data and available at a very limited cost, can be used to detect and qualitatively assess the thickness of oil slicks. This is important to assess volumes of naturally release oil in the oceans and in case of a crisis to send intervention teams where oil is thickest.
A SIMPLE AND EFFECTIVE RETRIEVAL OF LAND SURFACE TEMPERATURE USING A NEW REFLECTANCE BASED EMISSIVITY ESTIMATION TECHNIQUE
Authors: Nithiyanandam Y. , Nichol J. E.
Published: June 2016

Abstract. 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: June 2016

Abstract. 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.
GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS
Authors: Girod Luc , Nuth Christopher , Kääb Andreas
Published: June 2016

Abstract. Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.
LONG-TERM MONITORING OF GLACIER CHANGE AT GÖSSNITZKEES (AUSTRIA) USING TERRESTRIAL PHOTOGRAMMETRY
Authors: Kaufmann V. , Seier G.
Published: June 2016

Abstract. 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.
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: June 2016

Abstract. 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.
STUDY ON THE RETRIEVAL OF SNOW DEPTH FROM FY3B/MWRI IN THE ATCTIC
Authors: Li Lele , Chen Haihua , Guan Lei
Published: June 2016

Abstract. 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.
A NOVEL METHOD FOR ESTIMATION OF GLACIER SURFACE MOTION IN 1960s FROM ARGON KH-5 OPTICAL IMAGERY
Authors: Li R. , Ye W. , Kong F. , Qiao G. , Tong X. , Ma X. , Guo S. , Wang Z.
Published: June 2016

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

Abstract. 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.
LIGHT DIFFUSION IN THE TROPICAL DRY FOREST OF COSTA RICA
Authors: Calvo-Rodriguez S. , Sanchez-Azofeifa G. A
Published: June 2016

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

Abstract. 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.
TREE CANOPY COVER MAPPING USING LiDAR IN URBAN BARANGAYS OF CEBU CITY, CENTRAL PHILIPPINES
Authors: Ejares J. A. , Violanda R. R. , Diola A. G. , Dy D. T. , Otadoy J. B. , Otadoy R. E. S.
Published: June 2016

Abstract. This paper investigates tree canopy cover mapping of urban barangays (smallest administrative division in the Philippines) in Cebu City using LiDAR (Light Detection and Ranging). Object-Based Image Analysis (OBIA) was used to extract tree canopy cover. Multi-resolution segmentation and a series of assign-class algorithm in eCognition software was also performed to extract different land features. Contextual features of tree canopies such as height, area, roundness, slope, length-width and elliptic fit were also evaluated. The results showed that at the time the LiDAR data was collected (June 24, 2014), the tree cover was around 25.11 % (or 15,674,341.8 m2) of the city’s urban barangays (or 62,426,064.6 m2). Among all urban barangays in Cebu City, Barangay Busay had the highest cover (55.79 %) while barangay Suba had the lowest (0.8 %). The 16 barangays with less than 10 % tree cover were generally located in the coastal area, presumably due to accelerated urbanization. Thirty-one barangays have tree cover ranging from 10.59–-27.3 %. Only 3 barangays (i.e., Lahug, Talamban, and Busay) have tree cover greater than 30 %. The overall accuracy of the analysis was 96.6 % with the Kappa Index of Agreement or KIA of 0.9. From the study, a grouping can be made of the city’s urban barangays with regards to tree cover. The grouping will be useful to urban planners not only in allocating budget to the tree planting program of the city but also in planning and creation of urban parks and playgrounds.
ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA
Authors: Hadaś E. , Borkowski A. , Estornell J.
Published: June 2016

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

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

Abstract. Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R2 = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.
AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION
Authors: Zhou Guoqing , Zhou Xiang , Yue Tao , Liu Yilong
Published: June 2016

Abstract. This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.
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: June 2016

Abstract. 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: June 2016

Abstract. 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.
A SPATIAL ANALYSIS ON GIS-HEDONIC PRICING MODEL ON THE INFLUENCE OF PUBLIC OPEN SPACE AND HOUSE PRICE IN KLANG VALLEY, MALAYSIA
Authors: Zainora A. M. , Norzailawati M. N. , Tuminah P.
Published: June 2016

Abstract. Presently, it is noticeable that there is a significant influence of public open space about house price, especially in many developed nations. Literature suggests the relationship between the two aspects give impact on the housing market, however not many studies undertaken in Malaysia. Thus, this research was initiated to analyse the relationship of open space and house price via the techniques of GIS-Hedonic Pricing Model. In this regards, the GIS tool indicates the pattern of the relationship between open space and house price spatially. Meanwhile, Hedonic Pricing Model demonstrates the index of the selected criteria in determining the housing price. This research is a perceptual study of 200 respondents who were the house owners of double-storey terrace houses in four townships, namely Bandar Baru Bangi, Taman Melawati, Subang Jaya and Shah Alam, in Klang Valley. The key research question is whether the relationship between open space and house price exists and the nature of its pattern and intensity. The findings indicate that there is a positive correlation between open space and house price. Correlation analysis reveals that a weak relationship (rs < 0.1) established between the variable of open space and house price (rs = 0.91, N = 200, p = 0.2). Consequently, the rate of house price change is rather small. In overall, this research has achieved its research aims and thus, offers the value added in applying the GIS-Hedonic pricing model in analysing the influence of open space to the house price in the form of spatially and textually.
USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL
Authors: Bendini H. , Sanches I. D. , Körting T. S. , Fonseca L. M. G. , Luiz A. J. B. , Formaggio A. R.
Published: June 2016

Abstract. The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.
URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING
Authors: Hernández H. J. , Gutiérrez M. A. , Acuña M. P.
Published: June 2016

Abstract. Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (end members): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All end members were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.