bluuebunny/crossref_metadata_2025 huggingface dataset:
RADIOACTIVE POLLUTION ESTIMATE FOR FUKUSHIMA NUCLEAR POWER PLANT BY A PARTICLE MODEL
Authors: Saito Keisuke , Ogawa Susumu
Published: August 2016

On Mar 12, 2011, very wide radioactive pollution occurred by a hydrogen explosion in Fukushima Nuclear Power Plant. A large amount of radioisotopes started with four times of explosions. With traditional atmospheric diffusion models could not reconstruct radioactive pollution in Fukushima. Then, with a particle model, this accident was reconstructed from meteorological archive and Radar- AMeDAS. Calculations with the particle model were carried out for Mar 12, 15, 18 and 20 when east southeast winds blew for five hours continuously. Meteorological archive is expressed by wind speeds and directions in five-km grid every hour with eight classes of height till 3000 m. Radar- AMeDAS is precipitation data in one-km grid every thirty minutes. Particles are ten scales of 0.01 to 0.1 mm in diameter with specific weight of 2.65 and vertical speeds given by Stokes equation. But, on Mar 15, it rained from 16:30 and then the particles fell down at a moment as wet deposit in calculation. On the other hand, the altitudes on the ground were given by DEM with 1 km-grid. The spatial dose by emitted radioisotopes was referred to the observation data at monitoring posts of Tokyo Electric Power Company. The falling points of radioisotopes were expressed on the map using the particle model. As a result, the same distributions were obtained as the surface spatial dose of radioisotopes in aero-monitoring by Ministry of Education, Culture, Sports, Science and Technology. Especially, on Mar 15, the simulated pollution fitted to the observation, which extended to the northwest of Fukushima Daiichi Nuclear Power Plant and caused mainly sever pollution. By the particle model, the falling positions on the ground were estimated each particle size. Particles with more than 0.05 mm of size were affected by the topography and blocked by the mountains with the altitudes of more than 700 m. The particle model does not include the atmospheric stability, the source height, and deposit speeds. The present assignment is how to express the difference of deposition each nucleus.
INFLUENCE OF TOPOGRAPHIC AND HYDROGRAPHIC FACTORS ON THE SPATIAL DISTRIBUTION OF LEPTOSPIROSIS DISEASE IN SÃO PAULO COUNTY, BRAZIL: AN APPROACH USING GEOSPATIAL TECHNIQUES AND GIS ANALYSIS
Authors: Ferreira M. C. , Ferreira M. F. M.
Published: August 2016

Leptospirosis is a zoonosis caused by Leptospira genus bacteria. Rodents, especially Rattus norvegicus, are the most frequent hosts of this microorganism in the cities. The human transmission occurs by contact with urine, blood or tissues of the rodent and contacting water or mud contaminated by rodent urine. Spatial patterns of concentration of leptospirosis are related to the multiple environmental and socioeconomic factors, like housing near flooding areas, domestic garbage disposal sites and high-density of peoples living in slums located near river channels. We used geospatial techniques and geographical information system (GIS) to analysing spatial relationship between the distribution of leptospirosis cases and distance from rivers, river density in the census sector and terrain slope factors, in Sao Paulo County, Brazil. To test this methodology we used a sample of 183 geocoded leptospirosis cases confirmed in 2007, ASTER GDEM2 data, hydrography and census sectors shapefiles. Our results showed that GIS and geospatial analysis techniques improved the mapping of the disease and permitted identify the spatial pattern of association between location of cases and spatial distribution of the environmental variables analyzed. This study showed also that leptospirosis cases might be more related to the census sectors located on higher river density areas and households situated at shorter distances from rivers. In the other hand, it was not possible to assert that slope terrain contributes significantly to the location of leptospirosis cases.
COMBINING REMOTELY SENSED ENVIRONMENTAL CHARACTERISTICS WITH SOCIAL AND BEHAVIORAL CONDITIONS THAT AFFECT SURFACE WATER USE IN SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA
Authors: Kulinkina A. V. , Walz Y. , Liss A. , Kosinski K. C. , Biritwum N. K. , Naumova E. N.
Published: August 2016

Schistosoma haematobium transmission is influenced by environmental conditions that determine the suitability of the parasite and intermediate host snail habitats, as well as by socioeconomic conditions, access to water and sanitation infrastructure, and human behaviors. Remote sensing is a demonstrated valuable tool to characterize environmental conditions that support schistosomiasis transmission. Socioeconomic and behavioral conditions that propagate repeated domestic and recreational surface water contact are more difficult to quantify at large spatial scales. We present a mixed-methods approach that builds on the remotely sensed ecological variables by exploring water and sanitation related community characteristics as independent risk factors of schistosomiasis transmission.
RE-EMERGING MALARIA VECTORS IN RURAL SAHEL (NOUNA, BURKINA FASO): THE PALUCLIM PROJECT
Authors: Vignolles Cécile , Sauerborn Rainer , Dambach Peter , Viel Christian , Soubeyroux Jean-Michel , Sié Ali , Rogier Christophe , Tourre Yves M.
Published: August 2016

The Paluclim project applied the tele-epidemiology approach, linking climate, environment and public health (CNES, 2008), to rural malaria in Nouna (Burkina Faso). It was to analyze the climate impact on vectorial risks, and its consequences on entomological risks forecast. The objectives were to: 1) produce entomological risks maps in the Nouna region, 2) produce dynamic maps on larvae sites and their productivity, 3) study the climate impact on malaria risks, and 4) evaluate the feasibility of strategic larviciding approach.
LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME
Authors: Cao W. , Tong X. H. , Liu S. C. , Wang D.
Published: August 2016

Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should consider primarily the semantic aspects of the data. In this paper, we propose a scheme for recognizing landslides by using an object-oriented analysis technique and a semantic reasoning model on high spatial resolution optical imagery. Three case regions with different data sources are presented to evaluate its practicality. The procedure is designed as follows: first, the Gray Level Co-occurrence Matrix (GLCM) is used to extract texture features after the image explanation. Spectral features, shape features and thematic features are derived for semiautomatic landslide recognition. A semantic reasoning model is used afterwards to refine the classification results, by representing expert knowledge as first-order logic (FOL) rules. The experimental results are essentially consistent with the experts’ field interpretation, which demonstrate the feasibility and accuracy of the proposed approach. The results also show that the scheme has a good generality on diverse data sources.
IMPACT OF LEVEL OF DETAILS IN THE 3D RECONSTRUCTION OF TREES FOR MICROCLIMATE MODELING
Authors: Bournez E. , Landes T. , Saudreau M. , Kastendeuch P. , Najjar G.
Published: August 2016

In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented.
SPATIOTEMPORAL EVALUATION OF NOCTURNAL COLD AIR DRAINAGE OVER A SIMPLE SLOPE USING THERMAL INFRARED IMAGERY
Authors: Ikani V. , Chokmani K. , Fathollahi L. , Granberg H. , Fournier R.
Published: August 2016

Measurements of climatic processes such as cold air drainage flows are problematic over mountainous areas. Observation of cold air drainage is not available in the existing observation network and it requires a special methodology. The main objective of this study was to characterize the cold air drainage over regions with a slope. A high resolution infrared camera, a meteorological station and Digital Elevation Model (DEM) were used. The specific objective was to derive nocturnal cold air drainage velocity over the slope. To address these objectives, a number of infrared measurement campaigns were conducted during calm and clear sky conditions over an agricultural zone (blackcurrant farm) in Canada. Using thermal infrared images, the nocturnal surface temperature gradient were computed in hourly basis. The largest gradient magnitudes were found between 17h -20h. The cooling rates at basin area were two times higher in comparison to the magnitudes observed within slope area. The image analysis illustrated this considerable temperature gradient of the basin may be partly due to transport of cold air drainage into the basin from the slope. The results show that thermal imagery can be used to characterize and understand the microclimate related to the occurrence of radiation frost in the agricultural field. This study provided the opportunity to track the cold air drainage flow and pooling of cold air in low lying areas. The infrared analysis demonstrated that nocturnal drainage flow displayed continuous variation in terms of space and time in response to microscale slope heterogeneities. In addition, the analysis highlighted the periodic aspect for cold air drainage flow.
INFLUENCE OF DEM IN WATERSHED MANAGEMENT AS FLOOD ZONATION MAPPING
Authors: Alrajhi Muhamad , Khan Mudasir , Khan Mohammad Afroz , Alobeid Abdalla
Published: August 2016

Despite of valuable efforts from working groups and research organizations towards flood hazard reduction through its program, still minimal diminution from these hazards has been realized. This is mainly due to the fact that with rapid increase in population and urbanization coupled with climate change, flood hazards are becoming increasingly catastrophic. Therefore there is a need to understand and access flood hazards and develop means to deal with it through proper preparations, and preventive measures. To achieve this aim, Geographical Information System (GIS), geospatial and hydrological models were used as tools to tackle with influence of flash floods in the Kingdom of Saudi Arabia due to existence of large valleys (Wadis) which is a matter of great concern. In this research paper, Digital Elevation Models (DEMs) of different resolution (30m, 20m,10m and 5m) have been used, which have proven to be valuable tool for the topographic parameterization of hydrological models which are the basis for any flood modelling process. The DEM was used as input for performing spatial analysis and obtaining derivative products and delineate watershed characteristics of the study area using ArcGIS desktop and its Arc Hydro extension tools to check comparability of different elevation models for flood Zonation mapping. The derived drainage patterns have been overlaid over aerial imagery of study area, to check influence of greater amount of precipitation which can turn into massive destructions. The flow accumulation maps derived provide zones of highest accumulation and possible flow directions. This approach provide simplified means of predicting extent of inundation during flood events for emergency action especially for large areas because of large coverage area of the remotely sensed data.
LONG-TERM MONITORING OF WATER DYNAMICS IN THE SAHEL REGION USING THE MULTI-SAR-SYSTEM
Authors: Bertram A. , Wendleder A. , Schmitt A. , Huber M.
Published: August 2016

Fresh water is a scarce resource in the West-African Sahel region, seasonally influenced by droughts and floods. Particularly in terms of climate change, the importance of wetlands increases for flora, fauna, human population, agriculture, livestock and fishery. Hence, access to open water is a key factor. Long-term monitoring of water dynamics is of great importance, especially with regard to the spatio-temporal extend of wetlands and drylands. It can predict future trends and facilitate the development of adequate management strategies. Lake Tabalak, a Ramsar wetland of international importance, is one of the most significant ponds in Niger and a refuge for waterbirds. Nevertheless, human population growth increased the pressure on this ecosystem, which is now degrading for all uses. The main objective of the study is a long-term monitoring of the Lake Tabalak’s water dynamics to delineate permanent and seasonal water bodies, using weather- and daytime-independent multi-sensor and multi-temporal Synthetic Aperture Radar (SAR) data available for the study area. Data of the following sensors from 1993 until 2016 are used: Sentinel-1A, TerraSARX, ALOS PALSAR-1/2, Envisat ASAR, RADARSAT-1/2, and ERS-1/2. All SAR data are processed with the Multi-SAR-System, unifying the different characteristics of all above mentioned sensors in terms of geometric, radiometric and polarimetric resolution to a consistent format. The polarimetric representation in Kennaugh elements allows fusing single-polarized data acquired by older sensors with multi-polarized data acquired by current sensors. The TANH-normalization guarantees a consistent and therefore comparable description in a closed data range in terms of radiometry. The geometric aspect is solved by projecting all images to an earth-fixed coordinate system correcting the brightness by the help of the incidence angle. The elevation model used in the geocoding step is the novel global model produced by the TanDEM-X satellite mission. The advantage of the Multi-SAR-System is that it comprises ortho-rectification, radiometric enhancement, normalization and Kennaugh decomposition, independent from sensors, modes, polarizations or acquisition date of SAR data. In addition, optical satellite data can be included as well, to fill gaps where SAR data are missing due to the special normalization scheme. This kind of pre-processing is exclusively implemented at the Earth Observation Center of the German Aerospace Center in Oberpfaffenhofen, Germany. Therefore, the dynamic change of the open water of the Lake Tabalak could be classified over dry and rainy seasons and years, using different SAR data. The study provides a unique database and contributes to a better understanding of wetland systems in the Sahel region influenced by human pressure and climate change.
EVALUATION OF RAINFALL-RUNOFF MODELS FOR MEDITERRANEAN SUBCATCHMENTS
Authors: Cilek A. , Berberoglu S. , Donmez C.
Published: August 2016

The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations.

 The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA.

 This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km2. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.
AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL
Authors: Costa Paulo Henrique , Oliveira Pereira Eric , Maillard Philippe
Published: August 2016

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of in situ gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby in situ gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.
STUDY OF WATER POLLUTION EARLY WARNING FRAMEWORK BASED ON INTERNET OF THINGS
Authors: Chengfang H. , Xiao X. , Dingtao S. , Bo C. , Xiongfei W.
Published: August 2016

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.
AUTOMATIC RIVER NETWORK EXTRACTION FROM LIDAR DATA
Authors: Maderal E. N. , Valcarcel N. , Delgado J. , Sevilla C. , Ojeda J. C.
Published: August 2016

National Geographic Institute of Spain (IGN-ES) has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI) within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network) and hydrological criteria (flow accumulation river network), and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files), and process; using local virtualization and the Amazon Web Service (AWS), which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri) and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector.

 This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.
SENTINEL-1/2 DATA FOR SHIP TRAFFIC MONITORING ON THE DANUBE RIVER
Authors: Dana Negula I. , Poenaru V. D. , Olteanu V. G. , Badea A.
Published: August 2016

After a long period of drought, the water level of the Danube River has significantly dropped especially on the Romanian sector, in July-August 2015. Danube reached the lowest water level recorded in the last 12 years, causing the blockage of the ships in the sector located close to Zimnicea Harbour. The rising sand banks in the navigable channel congested the commercial traffic for a few days with more than 100 ships involved. The monitoring of the decreasing water level and the traffic jam was performed based on Sentinel-1 and Sentinel-2 free data provided by the European Space Agency and the European Commission within the Copernicus Programme. Specific processing methods (calibration, speckle filtering, geocoding, change detection, image classification, principal component analysis, etc.) were applied in order to generate useful products that the responsible authorities could benefit from. The Sentinel data yielded good results for water mask extraction and ships detection. The analysis continued after the closure of the crisis situation when the water reached the nominal level again. The results indicate that Sentinel data can be successfully used for ship traffic monitoring, building the foundation of future endeavours for a durable monitoring of the Danube River.
ASSESSMENT OF SURFACE WATER AT THE SOBRADINHO RESERVOIR UNDER THE EFFECTS OF DROUGHT USING MULTI-TEMPORAL LANDSAT IMAGES
Authors: Silva E. A. , Pedrosa M. M. , Azevedo S. C. , Cardim G. P. , Carvalho F. P. S.
Published: August 2016

The matrix of energy generation in Brazil is predominantly hydroelectric power. Consequently, the reservoirs need constant monitoring due to the large volume of artificially dammed water. Images from remote sensing can provide reliable information concerning water bodies. In this paper, we use remote sensing imagery to monitor the Sobradinho dam in three different epochs. The objective was to verify quantitatively the area of the dam’s surface reduced due to the drought of 2015, which was considered the worst in history. The approach used water surface area estimations from bands of Landsat5 and Landsat8 satellites which highlight water bodies better from other features present on surface of the Earth. Through the techniques of growth region and normalized difference water index (NDWI), we determined the surface area of the reservoir in 2011 and calculated the decrease caused by the drought. By analyzing the numbers provided by the results it is possible to estimate how the Sobradinho reservoir has been affected by the drastic drought. The results show that the Landsat images enable the monitoring of large reservoirs. Bearing in mind that monitoring is a primary and indispensable tool, not only for technical study, but also for economic and environmental research, it can help establish planning projects and water administration strategies for future decisions about the hydrical resource priority.
MASS BALANCE CHANGES AND ICE DYNAMICS OF GREENLAND AND ANTARCTIC ICE SHEETS FROM LASER ALTIMETRY
Authors: Babonis G. S. , Csatho B. , Schenk T.
Published: August 2016

During the past few decades the Greenland and Antarctic ice sheets have lost ice at accelerating rates, caused by increasing surface temperature. The melting of the two big ice sheets has a big impact on global sea level rise. If the ice sheets would melt down entirely, the sea level would rise more than 60 m. Even a much smaller rise would cause dramatic damage along coastal regions. In this paper we report about a major upgrade of surface elevation changes derived from laser altimetry data, acquired by NASA’s Ice, Cloud and land Elevation Satellite mission (ICESat) and airborne laser campaigns, such as Airborne Topographic Mapper (ATM) and Land, Vegetation and Ice Sensor (LVIS). For detecting changes in ice sheet elevations we have developed the Surface Elevation Reconstruction And Change detection (SERAC) method. It computes elevation changes of small surface patches by keeping the surface shape constant and considering the absolute values as surface elevations. We report about important upgrades of earlier results, for example the inclusion of local ice caps and the temporal extension from 1993 to 2014 for the Greenland Ice Sheet and for a comprehensive reconstruction of ice thickness and mass changes for the Antarctic Ice Sheets.
GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS
Authors: Girod Luc , Nuth Christopher , Kääb Andreas
Published: August 2016

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.
NOVEL SNOW DEPTH RETRIEVAL METHOD USING TIME SERIES SSMI PASSIVE MICROWAVE IMAGERY
Authors: Nikraftar Z. , Hasanlou M. , Esmaeilzadeh M.
Published: August 2016

The Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSM/IS) are satellites that work in passive microwave range. The SSM/I has capability to measure geophysical parameters which these parameters are key to investigate the climate and hydrology condition in the world. In this research the SSMI passive microwave data is used to study the feasibility of monitoring snow depth during snowfall month from 2010 to 2015 using an algorithm in conjunction with ground depth measured at meteorological stations of the National Centre for Environmental Information (NCEI). The previous procedures for snow depth retrieval algorithms uses only one or two passive bands for modelling snow depth. This study enable us to use of a nonlinear multidimensional regression algorithm which incorporates all channels and their related weighting coefficients for each band. Higher value of these coefficients are indicator of the importance of each band in the regression model. All channels and their combination were used in support of the vector algorithm combined with genetic algorithm (GA) for feature selection to estimate snow depth. The results were compared with those algorithms developed by recent researchers and the results clearly shows the superiority of proposed method (R2 = 0.82 and RMSE = 6.3 cm).
CHANGE ANALYSIS OF ANTARCTIC ICE SHELVES BASED ON MULTIPLE REMOTE SENSING PRODUCTS
Authors: Tian Yixiang , Weng Hexia , Lv Da , Tong Xiaohua , Li Rongxing
Published: August 2016

The Antarctic ice sheet is well known as the most sensitive and key issue in the global climate change research and is playing a more and more important role for the global sea level change. Measurement of changes in area and mass of the Antarctic ice sheet is critically important and has been made by using different remote sensing technologies and ground exploration data. Sequential mapping of Antarctic boundaries provides a simple and direct method for measuring the area and volume if ice sheet or ice shelves advances or retreats in the Antarctic coasts. Our results show that the total ice shelf area is retreated between 1963 and 2009. However, the trend for each ice shelf is quite different.
VEGETATION DISTURBANCE AND RECOVERY FOLLOWING A RARE WINDTHROW EVENT IN THE GREAT SMOKY MOUNTAINS NATIONAL PARK
Authors: Bernardes S. , Madden M.
Published: August 2016

The tornado outbreak of April 2011 in the Southeastern United States caused major damage to property and natural ecosystems. During the outbreak, the Great Smoky Mountains National Park (GRSM) was hit by an EF4 tornado, resulting in a long strip of broken branches and toppled old-growth forest trees. Little is known of the consequences of extreme windthrow events, partly due to limitations in characterizing and monitoring wind-driven vegetation disturbance and recovery over large areas and over time. This work analyzed vegetation damage in the GRSM resulting from the 2011 tornado outbreak and monitored vegetation recovery in the region over a four-year period. Anomalies of the Enhanced Vegetation Index (EVI) calculated using Landsat scenes showed that the 2011 tornado affected 21.38 km2 of forest, including submesic to mesic oak/hardwoods, Southern Appalachian cove hardwood forests and montane alluvial forests. Tornado damage severity was mapped and investigated by using anomalies of EVI over space and time and showed track discontinuity and significant variation in damage intensity along the tornado track, suggesting vortex-topography interactions. Temporal profiles and spatial representations of EVI anomalies for the period 2011-2015 indicated that EVI in 2015 was above pre-event values, indicating homogeneous canopy and lack of vertical structure during regrowth.
ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA
Authors: Chang K. T , Lin C. , Lin Y. C. , Liu J. K.
Published: August 2016

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.
MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT
Authors: Haywood A. , Verbesselt J. , Baker P. J.
Published: August 2016

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9 %, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2 % to 64.3 %. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2 %, and individual agents were successfully attributed with overall accuracies ranging from 25.5 % to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4 %, and individual agents were successfully attributed with overall accuracies ranging from 94.2 % to 95.2 %. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.
UAV AND GIS BASED TOOL FOR COLLECTION AND PROPAGATION OF SEEDS MATERIAL – FIRST RESULTS
Authors: Stereńczak K. , Mroczek P. , Jastrzębowski S. , Krok G. , Lisańczuk M. , Klisz M. , Kantorowicz W.
Published: August 2016

Seed management carried out by The State Forests National Forest Holding is an integral part of rational forest management. Seed collection takes place mainly from stands belonging to first category of forest reproductive material, which is the largest seed base in Poland. In smaller amount, seeds are collected in selective objects of highest forest reproductive material category (selected seed stands, seed orchards). The previous estimation methods of seed crop were based on visual assessment of cones in the stands for their harvest. Following the rules of FRM transfer is additional difficulty of rational seed management which limits the possibility of the use of planting material in Poland.

 Statements concerning forecast of seed crop and monitoring of seed quality is based on annual reports from the State Forest Service. Forest Research Institute is responsible for preparing and publishing above-mentioned statements. A small extent of its automatization and optimization is a large disadvantage of this procedure. In order to make this process more effective web-based GIS application was designed. Its main performance will give a possibility to upload present-day information on seed efficiency, their spatial pattern and availability. Currently this system is under preparation.

 As a result, the project team will get a possibility to increase participation of seed material collected from selected seed base and to share good practices on this issue in more efficient way. In the future this will make it possible to obtain greater genetic gain of selection strategy.

 Additionally, first results presented in literature showed possible use of unmanned aerial system/vehicle (UAS/V) for supporting of seed crop forecast procedure.
CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)
Authors: Johnson B. A. , Scheyvens H. , Samejima H. , Onoda M.
Published: August 2016

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.
DERIVATION OF FOREST INVENTORY PARAMETERS FOR CARBON ESTIMATION USING TERRESTRIAL LIDAR
Authors: Kalwar Om Prakash Prasad , Hussin Yousif A. , Weir Michael J. C. , Karna Yogendra K.
Published: August 2016

This research was conducted to derive forest sample plot inventory parameters from terrestrial LiDAR (T-LiDAR) for estimating above ground biomass (AGB)/carbon stocks in primary tropical rain forest. Inventory parameters of all sampled trees within circular plots of 500 m2 were collected from field observations while T-LiDAR data were acquired through multiple scanning using Reigl VZ-400 scanner. Pre-processing and registration of multiple scans were done in RSCAN PRO software. Point cloud constructing individual sampled tree was extracted and tree inventory parameters (diameter at breast height-DBH and tree height) were measured manually. AGB/carbon stocks were estimated using Chave et al., (2005) allometric equation. An average 80 % of sampled trees were detected from point cloud of the plots. The average of plots values of R2 and RMSE for manually measured DBHs were 0.95, 2.7 cm respectively. Similarly, the average of plots values of R2 and RMSE for manually measured trees heights were 0.77, 2.96 m respectively. The average value of AGB/carbon stocks estimated from field measurements and T-LiDAR manually derived DBHs and trees heights were 286 Mg ha-1 and 134 Mg ha−1; and 278 M ha-1 and 130 Mg ha−1 respectively. The R2 values for the estimated AGB and AGC were both 0.93 and corresponding RMSE values were 42.4 Mg ha−1 and 19.9 Mg  ha−1 respectively. AGB and AGC were estimated with 14.8 % accuracy.
USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS
Authors: Minařík R. , Langhammer J.
Published: August 2016

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.
WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS
Authors: Monnet J.-M. , Ginzler C. , Clivaz J.-C.
Published: August 2016

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.