bluuebunny/crossref_metadata_2025 huggingface dataset:
INVERSION OF THREE LAYERS MULTI-SCALE SPM MODEL BASED ON NEURAL NETWORK TECHNIQUE FOR THE RETRIEVAL OF SOIL MULTI-SCALE ROUGHNESS AND MOISTURE PARAMETERS
Authors: Hosni I. , JaafriGhamki M. , Bennaceur Farah L. , Naceur M. S.
Published: April 2015

Abstract. In this paper, a multi-layered multi-scale backscattering model for a lossy medium and a neural network inversion procedure has been presented. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. An adapted three layers 2D MLS small perturbations (SPM) model has been used to describe radar backscattering response of semiarid sub-surfaces. The total reflection coefficients of the natural soil are computed using the multilayer model, and volumetric scattering is approximated by the internal reflections between layers. The original multi-scale SPM model includes only the surface scattering of the natural bare soil, while the multilayer soil modified 2D MLS SPM model includes both the surface scattering and the volumetric scattering within the soil. This multi-layered model has been used to calculate the total surface reflection coefficients of a natural soil surface for both horizontal and vertical co-polarizations. A parametric analysis presents the dependence of the backscattering coefficient on multi scale roughness and soil. The overall objective of this work is to retrieve soil surfaces parameters namely roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. To perform the inversion of the modified three layers 2D MLS SPM model, we used a multilayer neural network (NN) architecture trained by a back-propagation learning rule.
INITIAL CHECKOUT RESULTS OF THE COMPACT INFRARED CAMERA (CIRC) FOR EARTH OBSERVATION
Authors: Kato E. , Katayama H. , Sakai M. , Nakajima Y. , Kimura T. , Nakau K. , Tonooka H.
Published: April 2015

Abstract. Compact Infrared Camera (CIRC) is a technology-demonstration instrument equipped with an uncooled infrared array detector (microbolometer) for space application. CIRC is the first microbolometer sensor without a calibration function in orbit, like a shutter system or an onboard blackbody. The main objective of the CIRC is to detect wildfires, which are major and chronic disasters affecting various countries of Southeast Asia, particularly considering the effects of global warming and climate change. The CIRC achieves a small size (approximately 200 mm), light mass (approximately 3 kg), and low electrical power consumption (<20 W) by employing athermal optics and a shutterless system. The CIRC can be consequently mounted on multiple satellites to enable highfrequency observation. Installation of CIRCs on the ALOS-2 and on the JEM/CALET is expected to increase observation frequency. We present the initial check-out results of the CIRC onboard ALOS-2. Since the initial check-out phase (July 4–14, 2014), the CIRC has acquired the images of Earth. CIRC was demonstrated to function according to its intended design. After the early calibration validation phase, which confirmed the temperature accuracy of observed data, CIRC data has been available to the public January 2015 onward. We also introduce a few observational results about wildfire, volcanoes, and heat-island.
SIGNIFICANT WAVE HEIGHT DETERMINED FROM SEQUENCE OF X-BAND RADAR IMAGES USING TEAGER-HUANG TRANSFORM
Authors: Mortazavi M. R. , Huang C. J. , Wu L. C.
Published: April 2015

Abstract. This work introduces a nonlinear and data-dependant method for extracting the significant wave height from a sequence of X-band radar images, which is based on the Teager-Huang Transform (THH). The THH comprises two parts, which are empirical mode decomposition (EMD) and application of the Teager-Kaiser energy operator (TKEO). EMD is applied to decompose the images into various decompositions, which are narrow-banded and have mono-components; TKEO separates the aforementioned narrow-banded components into their amplitude and frequency. The standard deviation of the separated amplitude is related to Hs , and, the relation is obtained by calibrating radar data with in situ data (buoy). The separated frequencies reveal the orientation and intensity of data, which are directly related to the direction of the waves. For validation, the method was applied to sequences of radar images that were obtained from the west coast of Taiwan. The results obtained using the method indicate that THH can be used specifically to estimate Hs with a root mean square error (RMSE) of 0.34 m. Furthermore, the developed method can efficiently measure the direction of waves at each specific point in an image.
VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS
Authors: Salami E. , Soler J. A. , Cuadrado R. , Barrado C. , Pastor E.
Published: April 2015

Abstract. Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.
Research on bathymetry estimation by Worldview-2 based with the semi-analytical model
Authors: Sheng L. , Bai J. , Zhou G.-W. , Zhao Y. , Li Y.-C.
Published: April 2015

Abstract. South Sea Islands of China are far away from the mainland, the reefs takes more than 95% of south sea, and most reefs scatter over interested dispute sensitive area. Thus, the methods of obtaining the reefs bathymetry accurately are urgent to be developed. Common used method, including sonar, airborne laser and remote sensing estimation, are limited by the long distance, large area and sensitive location. Remote sensing data provides an effective way for bathymetry estimation without touching over large area, by the relationship between spectrum information and bathymetry. Aimed at the water quality of the south sea of China, our paper develops a bathymetry estimation method without measured water depth. Firstly the semi-analytical optimization model of the theoretical interpretation models has been studied based on the genetic algorithm to optimize the model. Meanwhile, OpenMP parallel computing algorithm has been introduced to greatly increase the speed of the semi-analytical optimization model. One island of south sea in China is selected as our study area, the measured water depth are used to evaluate the accuracy of bathymetry estimation from Worldview-2 multispectral images. The results show that: the semi-analytical optimization model based on genetic algorithm has good results in our study area;the accuracy of estimated bathymetry in the 0-20 meters shallow water area is accepted.Semi-analytical optimization model based on genetic algorithm solves the problem of the bathymetry estimation without water depth measurement. Generally, our paper provides a new bathymetry estimation method for the sensitive reefs far away from mainland.
POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS
Authors: Tran G. , Nguyen D. , Milenkovic M. , Pfeifer N.
Published: April 2015

Abstract. Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.
Research on rice acreage estimation in fragmented area based on decomposition of mixed pixels
Authors: Zhang H. , Li Q. Z. , Lei F. , Du X. , Wei J. D.
Published: April 2015

Abstract. Rice acreage estimation is a key aspect to guarantee food security and also important to support government agricultural subsidy system. In this paper, we explored a sophisticated method to improve rice estimation accuracy at county scale and we developed our approach with China Environment Satellite HJ-1A/B data in Hunan Province, a fragmented area with complex rice cropping patterns. Our approach improved the estimation accuracy by combing supervised and unsupervised classification upon decomposition of mixed pixels model, and the rice estimation results, validated by ground survey data, showed a close relationship (RMSE≈3.40) with survey figures, the estimated accuracy (EA) reached 83.74% at county level according to the sub-pixel method, and the accuracy can be increased about 12% compared to the pure-pixel method. The results suggest that decomposition of mixed pixels method has great significance to the improvement of rice acreage estimation accuracy, and can be used in mountainous and broken planting area.
USE OF LANDSAT-SERIES DATA IN NATIONAL GEOGRAPHIC CONDITION MONITORING IN CHINA
Authors: Bai J. , Zhao Y. , Sheng L. , Li Y. , Lv G.
Published: April 2015

Abstract. To fully grasp the nature and human geography situation information, solve the problem of ecological environment, economic and social development of the country, monitoring the state of geographic condition by uniform index system has great significance. By collecting the existing standard documents, our paper established a suit of index system considering the characteristics of long time series remote sensing data. The index system includes basic, subject, composite statistical indexes, and statistical indexes based on basic geographic element. The spatial and temporal distribution of geographic condition with Landsat TM image in Haidian district of Beijing from 1983 to 2013 are studies. Results show that farmland decreases by 28.60%, build-up land increases by 38.95% in this period. The amount of land resources in different elevation/slope shows that, with the increase of elevation/slope, farmland and build-up land is gradually reduced, while grassland area is gradually increasing. In plains areas of elevation less than 50m and within the scope of the 0 to 3° slope, farmland and build-up land are the main land cover types, and both show the characteristic of tradeoffs. Urban area extended to the west and the north, meanwhile mass center of Haidian also moves to the northwest. The urban compactness decreases and the fractal index increased gradually, reflecting the city saturation degree become reduced, the city boundary becomes complicated gradually. The comprehensive land cover dynamic degree after the first decrease and then increases. Finally, based on the above statistic results, the spatial distribution of land cover in 2015 is predicted.
Innovation Technologies and Applications for Coastal Archaeological sites
Authors: Di Iorio A. , Biliouris D. , Guzinski R. , Hansen L. B. , Bagni M.
Published: April 2015

Abstract. Innovation Technologies and Applications for Coastal Archaeological sites project (ITACA) aims to develop and test a management system for underwater archaeological sites in coastal regions. The discovering and monitoring service will use innovative satellite remote sensing techniques combined with image processing algorithms. The project will develop a set of applications integrated in a system pursuing the following objectives: – Search and location of ancient ship wrecks; – Monitoring of ship wrecks, ruins and historical artefacts that are now submerged; – Integration of resulting search and monitoring data with on-site data into a management tool for underwater sites; – Demonstration of the system’s suitability for a service. High resolution synthetic aperture radar (TerraSAR-X, Cosmo-SkyMed) and multispectral satellite data (WorldView) will be combined to derive the relative bathymetry of the bottom of the sea up to the depth of 50 meters. The resulting data fusion will be processed using shape detection algorithms specific for archaeological items. The new algorithms, the physical modelling and the computational capabilities will be integrated into the Web-GIS, together with data recorded from surface (2D and 3D modelling) and from underwater surveys. Additional specific archaeological layers will be included into the WebGIS to facilitate the object identification through shape detection techniques and mapping. The system will be verified and validated through an extensive onground (sea) campaign carried out with both cutting edge technologies (side-scan sonar, multi beam echo sounder) and traditional means (professional scuba divers) in two test sites in Italy and Greece. The project is leaded by Planetek Hellas E.P.E. and include ALMA Sistemi sas for the “shape detection” and dissemination tasks, DHI-GRAS and Kell Srl for multispectral and SAR bathymetry. The complete consortium is composed by eleven partners and the project Kick-Off has been held in January 2014. The present contribution aims to present the project research achievements and finding at the mid-term review.
Using Light-at-Night (LAN) Satellite Data for Identifying Clusters of Economic Activities in Europe
Authors: Rybnikova N. A. , Portnov B. A.
Published: April 2015

Abstract. Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying clusters of economic activities (EAs) thus becomes an important step in devising regional development policies, aimed at facilitating regional economic development. However, a major problem with cluster identification stems from limited reporting of specific EAs by individual countries and administrative entities. Even Eurostat, which maintains most advances regional databases, provides data for less than 50% of all regional subdivisions of the 3rd tier of the Nomenclature of Territorial Units for Statistics (NUTS3). Such poor reporting impedes identification of EA clusters and economic forces behind them. In this study, we test a possibility that missing data on geographic concentrations of EAs can be reconstructed using Light-at-Night (LAN) satellite measurements, and that such reconstructed data can then be used for the identification of EA clusters. As we hypothesize, LAN, captured by satellite sensors, is characterized by different intensity, depending on its source – production facilities, services, etc., – and this information can be used for EA identification. The study was carried out in three stages. First, using nighttime satellite images, we determined what types of EAs can be identified, with a sufficient degree of accuracy, by LAN they emit. Second, we calculated multivariate statistical models, linking EAs concentrations with LAN intensities and several locational and development attributes of NUTS3 regions in Europe. Next, using the obtained statistical models, we restored missing data on EAs across NUTS3 regions in Europe and identified clusters of EAs, using spatial analysis tools.
Analysis of urban development by means of multi-temporal fragmentation metrics from LULC data
Authors: Sapena M. , Ruiz L. A.
Published: April 2015

Abstract. The monitoring and modelling of the evolution of urban areas is increasingly attracting the attention of land managers and administration. New data, tools and methods are being developed and made available for a better understanding of these dynamic areas. We study and analyse the concept of landscape fragmentation by means of GIS and remote sensing techniques, particularly focused on urban areas. Using LULC data obtained from the European Urban Atlas dataset developed by the local component of Copernicus Land Monitoring Services (scale 1:10,000), the urban fragmentation of the province of Rome is studied at 2006 and 2012. A selection of indices that are able to measure the land cover fragmentation level in the landscape are obtained employing a tool called IndiFrag, using as input data LULC data in vector format. In order to monitor the urban morphological changes and growth patterns, a new module with additional multi-temporal metrics has been developed for this purpose. These urban fragmentation and multi-temporal indices have been applied to the municipalities and districts of Rome, analysed and interpreted to characterise quantity, spatial distribution and structure of the urban change. This methodology is applicable to different regions, affording a dynamic quantification of urban spatial patterns and urban sprawl. The results show that urban form monitoring with multi-temporal data using these techniques highlights urbanization trends, having a great potential to quantify and model geographic development of metropolitan areas and to analyse its relationship with socioeconomic factors through the time.
Adapting and improving resilience to climate change in communities (moravian community as a pilot), by creating new capabilities based on the implementation of a new water culture; protection and management of natural resources
Authors: Campos Gallo A.
Published: April 2015

Abstract. Water, in all its dimensions and scope, concerns humans as civilization, individuals and communities immersed in an environment that faces serious environmental threats and changes. The efficient way to deal with this crisis is education of present and future generations, breaking paradigms, creating awareness and new development models, seeking community groups and forces to empower their water resource and care, manage and renew it in an efficient and sustainable manner. The multiple uses of water in personal uses, irrigation, agro-industry and clean energy production, transforms this resource in a strategic element to any nation. With support from the Centro Nacional de Alta Tecnología (CeNAT), it was possible to formulate the “Agenda Ambiental de Moravia”, agreeing to be the “Consejo Técnico de Fuerzas Vivas” (CTFV) from Moravia - articulated network of stakeholders – the one that coordinate all actions refered to water Resources, pollution and cleaner technologies and protected Areas. CeNAT and CTFV have developed distinguished efforts to improve the Moravians quality of life, and this has led the initiative of constitution of a whole education and training project in rescuing the Upper Basin of the Río Tárcoles, through the implementation of an ecological – recreative garden ("Parque Comunitario Pulmón Verde de Moravia"), fostersing good use of natural resources, and also works as a platform for training and awareness program in Sustainable Development, based on “Hacia una Nueva Cultura del Agua” (powered through the United Nations by Dr. Pedro Arrojo Agudo and his " Feria de Aguas, Ríos y Pueblos”, presented in many countries). This initiative is projected to the national and international communities, through the “Water International Conference”, which propel initiatives, laws and decisions which enable the development of Costa Rica and other countries under a sustainable model, focused on this essential component for life on the planet.
Integrationof Remote Sensing and Geographic information system in Ground Water Quality Assessment and Management
Authors: Shakak N.
Published: April 2015

Abstract. Spatial variations in ground water quality in the Khartoum state, Sudan, have been studied using geographic information system (GIS) and remote sensing technique. Gegraphical informtion system a tool which is used for storing, analyzing and displaying spatial data is also used for investigating ground water quality information. Khartoum landsat mosac image aquired in 2013was used, Arc/Gis software applied to extract the boundary of the study area, the image was classified to create land use/land cover map. The land use map,geological and soil map are used for correlation between land use , geological formations, and soil types to understand the source of natural pollution that can lower the ground water quality. For this study, the global positioning system (GPS), used in the field to identify the borehole location in a three dimentional coordinate (Latitude, longitude, and altitude), water samples were collected from 156 borehole wells, and analyzed for physico-chemical parameters like electrical conductivity, Total dissolved solid,Chloride, Nitrate, Sodium, Magnisium, Calcium,and Flouride, using standard techniques in the laboratory and compared with the standards.The ground water quality maps of the entire study area have been prepared using spatial interpolation technique for all the above parameters.then the created maps used to visualize, analyze, and understand the relationship among the measured points. Mapping was coded for potable zones, non-potable zones in the study area, in terms of water quality sutability for drinking water and sutability for irrigation. In general satellite remote sensing in conjunction with geographical information system (GIS) offers great potential for water resource development and management.
Changes in the land cover and land use of the Itacaiunas River watershed, arc of deforestation, Carajas, southeastern Amazon
Authors: Souza-Filho P. W. M. , Nascimento Jr. W. R. , Versiani de Mendonça B. R. , Silva Jr. R. O. , Guimarães J. T. F. , Dall'Agnol R. , Siqueira J. O.
Published: April 2015

Abstract. Human actions are changing the Amazon’s landscape by clearing tropical forest and replacing it mainly by pasturelands. The focus of this work is to assess the changes in the Itacaiúnas River watershed; an area located in the southeastern Amazon region, near Carajás, one of the largest mining provinces of the World. We used a Landsat imagery dataset to map and detect land covers (forest and montane savanna) and land use (pasturelands, mining and urban) changes from 1984 to 2013. We employed standard image processing techniques in conjunction with visual interpretation and geographic object-based classification. Land covers and land use (LCLU) “from-to” change detection approach was carried out to recognize the trajectories of LCLU classes based on object change detection analysis. We observed that ~47% (~1.9 million ha) of forest kept unchanged; almost 41% (~1.7 million ha) of changes was associated to conversion from forest to pasture, while 8% (~333,000 ha) remained unchanged pasture. The conversion of forest and montane savannah to mining area represents only 0.24% (~9,000 ha). We can conclude that synergy of visual interpretation to discriminate fine level objects with low contrast associated to urban, mining and savanna classes; and automatic classification of coarse level objects related to forest and pastureland classes is most successfully than use these methods individually. In essence, this approach combines the advantages of the human quality interpretation and quantitative computing capacity.
Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?
Authors: Theologou I. , Patelaki M. , Karantzalos K.
Published: April 2015

Abstract. Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn’t establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.
The Influence of the Time Equation on Remote Sensing Data Interpretation
Authors: Fichtelmann B. , Borg E. , Schwarz E.
Published: April 2015

Abstract. The interpretation of optical Earth observation data (remote sensing data from satellites) requires knowledge of the exact geographic position of each pixel as well as the exact local acquisition time. But these parameters are not available in each case. If a satellite has a sun-synchronous orbit, equator crossing time (ECT) can be used to determine the local crossing time (LCT) and its corresponding solar zenith distance. Relation between local equator crossing time (LECT) and LCT is given by orbit geometry. The calculation is based on ECT of satellite. The method of actual ECT determination for different satellites on basis of the two-line-elements (TLE), available for their full lifetime period and with help of orbit prediction package is well known. For land surface temperature (LST) studies mean solar conditions are commonly used in the relation between ECT given in Coordinated Universal Time (UTC) and LECT given in hours, thus neglecting the difference between mean and real Sun time (MST, RST). Its difference is described by the equation of time (ET). Of particular importance is the variation of LECT during the year within about ±15 minutes. This is in each case the variation of LECT of a satellite, including satellites with stable orbit as LANDSAT (L8 around 10:05 a.m.) or ENVISAT (around 10:00 a.m.). In case of NOAA satellites the variation of LECT is overlaid by a long-term orbital drift. Ignatov et al. (2004) developed a method to describe the drift-based variation of LECT that can be viewed as a formal mathematical approximation of a periodic function with one or two Fourier terms. But, nevertheless, ET is not included in actual studies of LST. Our paper aims to demonstrate the possible influence of equation of time on simple examples of data interpretation, e.g. NDVI.
Climate Absolute Radiance and Refractivity Observatory (CLARREO)
Authors: Leckey J.
Published: April 2015

Abstract. The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a mission, led and developed by NASA, that will measure a variety of climate variables with an unprecedented accuracy to quantify and attribute climate change. CLARREO consists of three separate instruments: an infrared (IR) spectrometer, a reflected solar (RS) spectrometer, and a radio occultation (RO) instrument. The mission will contain orbiting radiometers with sufficient accuracy, including on orbit verification, to calibrate other space-based instrumentation, increasing their respective accuracy by as much as an order of magnitude. The IR spectrometer is a Fourier Transform spectrometer (FTS) working in the 5 to 50 μm wavelength region with a goal of 0.1 K (k = 3) accuracy. The FTS will achieve this accuracy using phase change cells to verify thermistor accuracy and heated halos to verify blackbody emissivity, both on orbit. The RS spectrometer will measure the reflectance of the atmosphere in the 0.32 to 2.3 μm wavelength region with an accuracy of 0.3% (k = 2). The status of the instrumentation packages and potential mission options will be presented.
Environmental assessment of Mangrove communities in Tarut Bay, Eastern Arabian Peninsula, based on multidisciplinary approach
Authors: Al-Ali A. M. , Del Campo A. G. , Rocha C.
Published: April 2015

Abstract. Tarut Bay contains some of the most important mangrove forests in the Arabian Gulf countries, which are facing significant anthropogenic pressures. A multidisciplinary approach based on remote sensing, fieldwork, and chemical analysis was adopted in order to identify the pressures facing the mangrove communities in Tarut Bay and subsequently evaluate its current environmental condition. The analysis focused on the 2011 to 2014 timeframe in particular. The results indicate that the mangrove communities in Tarut Bay cover currently around 11.32 square km. Two major mangrove cover changes were identified in the area during the studied period, with a net loss of around 0.675 square km happening in the interim. The analysis adopted the Normalized Difference Vegetation Index (NDVI) to evaluate the temporal change in vegetation concentration. Moreover, the study evaluated the overall pollution condition of 19 mangrove communities in Tarut Bay to be medium based on the Pollution Load Index (PLI). Nevertheless, some of the mangrove communities in Tarut Bay are very healthy while others are very polluted and require immediate action to improve their pollution status. The findings of this work led to recommending mangrove improvement measures to address further deterioration and disappearance of mangrove communities, including the re-opening of natural inlets and avoiding residential developments on the mangrove coast.
Estimation of grassland use intensities based on high spatial resolution LAI time series
Authors: Asam S. , Klein D. , Dech S.
Published: April 2015

Abstract. The identification and surveillance of agricultural management and the measurement of biophysical canopy parameters in grasslands is relevant for environmental protection as well as for political and economic reasons, as proper grassland management is partly subsidized. An ideal monitoring tool is remote sensing due to its area wide continuous observations. However, due to small-scaled land use patterns in many parts of central Europe, a high spatial resolution is needed. In this study, the feasibility of RapidEye data to derive leaf area index (LAI) time series and to relate them to grassland management practices is assessed. The study area is the catchment of river Ammer in southern Bavaria, where agricultural areas are mainly grasslands. While extensively managed grasslands are maintained with one to two harvests per year and no or little fertilization, intensive cultivation practices compass three to five harvests per year and turnover pasturing. Based on a RapidEye time series from 2011 with spatial resolution of 6.5 meters, LAI is derived using the inverted radiation transfer model PROSAIL. The LAI in this area ranges from 1.5 to 7.5 over the vegetation period and is estimated with an RMSE between 0.7 and 1.1. The derived LAI maps cover 85 % of the study area’s grasslands at least seven times. Using statistical metrics of the LAI time series, different grassland management types can be identified: very intensively managed meadows, intensively managed meadows, intensively managed pastures, and extensively managed meadows and moor. However, a precise identification of the mowing dates highly depends on the coincidence with satellite data acquisitions. Further analysis should focus therefor on the selection of the temporal resolution of the time series as well as on the performance of further vegetation parameters and indices compared to LAI.
Spectral Discrimination and Reflectance Properties of Various Vine Varieties from Satellite, UAV and Proximate Sensors
Authors: Karakizi C. , Oikonomou M. , Karantzalos K.
Published: April 2015

Abstract. An assessment of the spectral discrimination between different vine varieties was undertaken using non-destructive remote sensing observations at the véraison period. During concurrent satellite, aerial and field campaigns, in-situ reflectance data were collected from a spectroradiometer, hyperspectral data were acquired from a UAV and multispectral data from a high-resolution satellite imaging sensor. Data were collected during a three years period (i.e, 2012, 2013 and 2014) over five wine-growing regions, covering more than 1000ha, in Greece. Data for more than twenty different vine varieties were processed and analysed. In particular, reflectance hyperspectral data from a spectroradiometer (GER 1500, Spectra Vista Corporation, 350-1050nm, 512 spectral bands) were calculated from the raw radiance values and then were correlated with the corresponding reflectance observations from the UAV and satellite data. Reflectance satellite data (WorldView-2, 400nm-1040nm, 8 spectral bands, DigitalGlobe), after the radiometric and atmospheric correction of the raw datasets, were classified towards the detection and the discrimination of the different vine varieties. The concurrent observations from in-situ hyperspectral, aerial hyperspectral and satellite multispectral data over the same vines were highly correlated. High correlations were, also, established for the same vine varieties (e.g., Syrah, Sauvignon Blanc) cultivated in different regions. The analysis of in-situ reflectance indicated that certain vine varieties, like Merlot, Sauvignon Blanc, Ksinomavro and Agiorgitiko possess specific spectral properties and detectable behaviour. These observations were, in most cases, in accordance with the classification results from the high resolution satellite data. In particular, Merlot and also Sauvignon Blanc were detected and discriminated with high accuracy rates. Surprisingly different clones from the same variety could be separated (e.g., clones of Syrah), while they were confused with other varieties (e.g., with Riesling).
National Scale Monitoring Reporting and Verification of Deforestation and Forest Degradation in Guyana
Authors: Bholanath P. , Cort K.
Published: April 2015

Abstract. Monitoring deforestation and forest degradation at national scale has been identified as a national priority under Guyana‟s REDD+ Programme. Based on Guyana‟s MRV (Monitoring Reporting and Verification) System Roadmap developed in 2009, Guyana sought to establish a comprehensive, national system to monitor, report and verify forest carbon emissions resulting from deforestation and forest degradation in Guyana. To date, four national annual assessments have been conducted: 2010, 2011, 2012 and 2013. Monitoring of forest change in 2010 was completed with medium resolution imagery, mainly Landsat 5. In 2011, assessment was conducted using a combination of Landsat (5 and 7) and for the first time, 5m high resolution imagery, with RapidEye coverage for approximately half of Guyana where majority of land use changes were taking place. Forest change in 2013 was determined using high resolution imagery for the whole of Guyana. The current method is an automated-assisted process of careful systematic manual interpretation of satellite imagery to identify deforestation based on different drivers of change. The minimum mapping unit (MMU) for deforestation is 1 ha (Guyana‟s forest definition) and a country-specific definition of 0.25 ha for degradation. The total forested area of Guyana is estimated as 18.39 million hectares (ha). In 2012 as planned, Guyana‟s forest area was reevaluated using RapidEye 5 m imagery. Deforestation in 2013 is estimated at 12 733 ha which equates to a total deforestation rate of 0.068%. Significant progress was made in 2012 and 2013, in mapping forest degradation. The area of forest degradation as measured by interpretation of 5 m RapidEye satellite imagery in 2013 was 4 352 ha. All results are subject to accuracy assessment and independent third party verification.
Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data
Authors: Colditz R. R. , Llamas R. M. , Ressl R. A.
Published: April 2015

Abstract. Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.
Tropical Forest Remote Sensing Services for the Democratic Republic of Congo inside the EU FP7 ReCover Project (Final Results 2000-2012)
Authors: Haarpaintner J. , de la Fuente Blanco D. , Enßle F. , Datta P. , Mazinga A. , Singa C. , Mane L.
Published: April 2015

Abstract. ‘ReCover’ was a 3-year EU-FP7 project (Nov. 2010 – Dec. 2013), aiming to develop and improve science based remote sensing services to support tropical forest management and activities to reduce emission from deforestation and forest degradation (REDD) in the tropical region (Häme et al., 2012). This is an overview of the final ReCover service delivery of 2000-2012 single-year optical (Landsat, ALOS AVNIR-2, RapidEye) and C-and L-band SAR (Envisat ASAR and ALOS Palsar, respectively) image mosaics, their derived forest/non-forest maps, a multi-sensor forest change map (2000-2010) and a biomass map (based on 2003-2009 ICESat GLAS) o he user of he De ocr ic Repub ic of Congo DRC), he Observatoir Satellitale des Forê s d’Afrique Cen r e OSFAC). The results are an improvement from a first iteration service delivery in 2012 after a critical review and validation process by both, the user and service providers, further method development and research, like a prior statistical data analysis considering temporal/seasonal variability, improved data pre-processing, and through the use of ground reference data collected in March 2013 for classification training. Validation with Kompsat-2 VHR data for the 2010 forest/non-forest maps revealed accuracies of 87% and 88% for optical and radar sensors, respectively.
Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program
Authors: Hojas-Gascon L. , Belward A. , Eva H. , Ceccherini G. , Hagolle O. , Garcia J. , Cerutti P.
Published: April 2015

Abstract. The forthcoming European Space Agency’s Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, ‘SPOT4 take 5’, to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 program.
Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation
Authors: Miller M. E. , Billmire M. , Elliot W. J. , Endsley K. A. , Robichaud P. R.
Published: April 2015

Abstract. Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site.
Vegetation Height Estimation Near Power transmission poles Via satellite Stereo Images using 3D Depth Estimation Algorithms
Authors: Qayyum A. , Malik A. S. , Saad M. N. M. , Iqbal M. , Abdullah F. , Rahseed W. , Abdullah T. A. R. B. T. , Ramli A. Q.
Published: April 2015

Abstract. Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites’ imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.
Long-term Soil Moisture Time Series Analyses based on Active Microwave Backscatter Measurements
Authors: Wagner W. , Reimer C. , Bauer-Marschallinger B. , Enenkel M. , Hahn S. , Melzer T. , Naeimi V. , Paulik C. , Dorigo W.
Published: April 2015

Abstract. Active microwave sensors operating at lower microwave frequencies in the range from 1 to 10 GHz provide backscatter measurements that are sensitive to the moisture content of the soil. Thanks to a series of European C-band (5.3 GHz) scatterometers, which were first flown on board of the European Remote Sensing satellites ERS-1 and ERS-2, and later on board of MetOp-A and MetOp -B, we are now in the possession of a long-term soil moisture time series starting in 1991. The creation of globally consistent long-term soil moisture time series is a challenging task. The TU-Wien soil moisture algorithm is adopted to tackle these challenges. In this paper we present two methodologies that were developed to ensure radiometric stability of the European C-band scatterometers. The objective of sensor intra-calibration is to monitor and correct for radiometric instabilities within one scatterometer mission, while sensor inter-calibration aims to remove radiometric differences across several missions. In addition, a novel vegetation modelling approach is presented that enables the estimation of vegetation parameters for each day across several years to account for yearly to longer-term changes in vegetation phenology and land cover.