This PDF file contains the front matter associated with SPIE Proceedings Volume 9245, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Change detection based on remote sensing imagery is a topic highly on demand with various fields of application. Probably, disaster management is the best known, where it is crucial to get fast and reliable results to enable a suitable supply of the affected region. Another important issue, for example in city or land-use planning, is the regular monitoring of specific regions of interest. For both scenarios, it would be significant to have information about the type or category of the detected changes. Since High-Resolution (HR) Synthetic Aperture Radar (SAR) is in opposite to optical sensors an active technique, it is well-capable for all change detection topics where a regular monitoring is intended. SAR sensors illuminate the investigated scene by their own microwave radiation and most applied microwave wavelengths make SAR nearly independent from atmospheric effects like dust, fog, and clouds. Moreover, the time of day makes no difference using SAR sensors. Acquired in HR SpotLight mode 300 (HS300) by the German satellite TerraSAR-X (TSX), images have a resolution of better than one meter, which allows to separate small objects placed close together. In this paper, a concept of change analysis focusing on small-sized areas is presented. Those change areas can be caused by man-made objects (e.g. vehicles, small construction sites) or natural events like phenologically based changes of the vegetation. Since the presented change analysis concept deals with the analysis of time series imagery, other seasonal also man-made caused changes (e.g. agriculture) can be detected. Furthermore, the concept comprises the categorization of the detected changes, which separates it from many of the existing change detection approaches. It includes five central components given by the change detection itself, the pre-categorization of change pixels, the feature extraction for change blobs, the analysis of their spatial context, and the final decision making forming a categorization statement. In all steps, Object-Based Image Analysis (OBIA) methods are utilized. As test area, the airport of Stuttgart (GER) and its surroundings containing heterogeneous change categories is considered. At current state, one time series consisting of 11 HS300 amplitude images acquired in ascending (ASC) orbit direction is available. For the evaluation of results, several reference data are useable comprising optical satellite, terrestrial information and GIS vector data.

In this paper we propose a GIS-based methodology, using optical and SAR remote sensing data, together with more conventional sources, for the detection of small cattle breeding areas, potentially responsible of hazardous littering. This specific environmental problem is very relevant for the Caserta area, in southern Italy, where many small buffalo breeding farms exist which are not even known to the productive activity register, and are not easily monitored and surveyed. Experiments on a test area, with available specific ground truth, prove that the proposed systems is characterized by very large detection probability and negligible false alarm rate.

The simulation of urban expansion or land cover changes is a major theme in both geographic information science and landscape ecology. Yet till now, almost all of previous studies were based on grid computations at pixel level. With the prevalence of spectral mixture analysis in urban land cover research, the simulation of urban land cover at sub-pixel level is being put into agenda. This study provided a new approach of land cover simulation at sub-pixel level. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover data through supervised classification. Then the two classified land cover data were utilized to extract the transformation rule between 2002 and 2007 using logistic regression. The transformation possibility of each land cover type in a certain pixel was taken as its percent in the same pixel after normalization. And cellular automata (CA) based grid computation was carried out to acquire simulated land cover on 2007. The simulated 2007 sub-pixel land cover was testified with a validated sub-pixel land cover achieved by spectral mixture analysis in our previous studies on the same date. And finally the sub-pixel land cover of 2017 was simulated for urban planning and management. The results showed that our method is useful in land cover simulation at sub-pixel level. Although the simulation accuracy is not quite satisfactory for all the land cover types, it provides an important idea and a good start in the CA-based urban land cover simulation.

Land subsidence can cause severe damage for e.g. infrastructure and buildings and mass movements even can lead to loss of live. Detection and monitoring of these processes by terrestrial measurement techniques remain a challenge due to limitations in spatial coverage and temporal resolution. Since the launch of ERS-1 in 1991 numerous scientific studies demonstrated the capability of differential SAR-Interferometry (DInSAR) for the detection of surface deformation proving the usability of this method. In order to assist the utilization of DInSAR for governmental tasks a national service-concept within the EU-ESA Program “Copernicus” is in the process of preparation. This is done by i) analyzing the user requirements, ii) developing a concept and iii) perform case studies as “proof of concept”. Due to the iterative nature of this procedure governmental users as well as DInSAR experts are involved. This paper introduces the concept, shows the available SAR data archive from ERS-1/2, TerraSAR-X and TanDEM-X as well as the proposed case study. The case study is focusing on the application of advanced DInSAR methods for the detection of subsidence in a region with active gas extraction. The area of interest is located in the state of Lower Saxony in the northwest of Germany. The DInSAR analysis will be based on ERS-1/2 and on TerraSARX/ TanDEM-X SAR data. The usability of the DInSAR products will be discussed with the responsible mining authority (LBEG) in order to adapt the DInSAR products to the user needs and to evaluate the proposed concept.

Western Greece is suffering by landslides. The term landslide includes a wide range of ground movement, such as slides, falls, flows etc. mainly based on gravity with the aid of many conditioning and triggering factors. Landslides provoke enormous changes to the natural and artificial relief. The annual cost of repairing the damage amounts to millions of euros. In this paper a combined use of airphotos time series, high resolution remote sensing data and GIS for the landslide monitoring is presented. Analog and digital air-photos used covered a period of almost 70 years from 1945 until 2012. Classical analog airphotos covered the period from 1945 to 2000, while digital airphotos and satellite images covered the 2008-2012 period. The air photos have been orthorectified using the Leica Photogrammetry Suite. Ground control points and a high accuracy DSM were used for the orthorectification of the air photos. The 2008 digital air photo mosaic from the Greek Cadastral with a spatial resolution of 25 cm and the respective DSM was used as the base map for all the others data sets. The RMS error was less than 0.5 pixel. Changes to the artificial constructions provoked by the landslideswere digitized and then implemented in an ARCGIS database. The results are presented in this paper.

Open quarries are at the same time a necessity but also a source of pollution. Necessity as they supply the necessary fuel for energy production and source of pollution as they affect biodiversity, vegetation cover and threaten water resources. The objective of this work is to indicate a monitoring methodology using Landsat ETM SLC off imagery. On May 31, 2003, the Scan Line Corrector (SLC), which compensates for the forward motion of Landsat 7, failed. Without an operating SLC, the Enhanced Thematic Mapper Plus (ETM+) line of sight now traces a zig-zag pattern along the satellite ground track. As a result, imaged area is duplicated, with width that increases towards the scene edge. An estimated twenty-two percent of any given scene is lost because of the SLC failure. The maximum width of the data gaps along the edge of the image would be equivalent to one full scan line, or approximately 390 to 450 meters. The precise location of the missing scan lines will vary from scene to scene. In this study a gap filling technique for Landsat ETM SLC off imagery is evaluated. Different Landsat 7 ETM+ images SLC off were restored and then compared to historical data and data from other sensors. The restored images have been used in order to monitor the expansion of an open quarry in western Peloponnese and the results are presented.

Remote sensing is a general tool to investigate the different areas of Earth and planets. The development of the implementation capabilities of the optoelectronic devices which are long-term-tested in the laboratory, in the field and are mounted on-board of the remote sensing platforms further improves the capability of instruments to acquire information about the Earth and its resources in different scales. Remote sensing application in the Earth observation begins with the design and the assembling of equipment for carrying out research of the monitored objects remotely and without disturbing their integrity. Ground-truth data in the Earth observation of the environment and in the remote sensing investigations are very important. Remote sensing methods for studying of rocks and minerals are closely related to current programs for the mineral and chemical composition study of the Earth, Mars and Phobos surfaces. The experience and the knowledge from previous experiments in space missions encourage us to continue our efforts to acquire spectral data using different remote sensing systems and to compare the obtained results. The main goal in the geological remote sensing is the determination of the chemical and/or mineral composition and the structure of the rocks. For this purpose the laboratory and the field spectroscopy measurements are performed. These measurements are made to collect, compile and complete guide with spectral characteristics of different rocks for their reliable identification and for the determination of their mineral and chemical composition. The experiments are based on major physical principles such as light scattering, absorption of light, and reflection of light in the electromagnetic spectrum. For the purpose of present paper ex-situ spectroscopy measurements of the granites and their rock-forming minerals from the territory of Bulgaria in visible and near infrared (VNIR) range of the electromagnetic spectrum were performed using following spectrometric systems: SRM, 0.4-0.82 micrometers; SPS-1, 0.55-1.1 micrometers, Thematically Oriented Multi-channel Spectrometer /TOMS/, 0.4-0.9 micrometers, all of them designed and constructed in Remote Sensing Systems /RSS/ Department at SRTI-BAS. The obtained spectral data are compared with similar data from different instruments for Earth observation included in the spectral libraries. They correspond to the shape of the spectral signature in the same spectral range obtained with other spectrometers. Two wavelengths were selected and were applied for the proper comparison between the data obtained by different instruments. The dependence between the reflectance values at the chosen wavelengths and the quantitative content of the rock-forming minerals was established. The achieved results proved that this methodology could be applied for comparing the spectral data from different sources. These promising results encourage us to plan the next campaigns for the field spectroscopy measurements in different regions of Bulgaria.

Earthquake science has entered a new era with the development of space-based technologies to measure surface geophysical parameters and deformation at the boundaries of tectonic plates and large faults. Satellite time-series data, coupled with ground based observations where available, can enable scientists to survey pre-earthquake signals in the areas of strong tectonic activity. Cumulative stress energy in seismic active regions under operating tectonic force manifests various earthquakes’ precursors. Space-time anomalies of Earth’s emitted radiation (thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes, radon in underground water and soil, etc.), and electromagnetic anomalies are considered as pre-seismic signals. Vrancea tectonic active zone in Romania is characterized by a high seismic hazard in European- Mediterranean region, being responsible of intermediate depth and normal earthquakes generation on a confined epicentral area.Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. This paper presents observations made using in-situ data and time series MODIS and NOAA-AVHRR satellite data for derived multi geophysical parameters (land surface temperature -LST, outgoing long-wave radiation- OLR, net surface latent heat flux (LHF) and mean air temperature- AT for some seismic events recorded in Vrancea region in Romania, which is one of the most active intracontinental seismic areas in Europe. Starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (°C) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock.

In this study eight commercial available fusion techniques and more especially the Ehlers, High Pass Filter, HCS (Hyperspherical Color Space), Modified IHS (ModIHS), Pansharp, Pansharp2, PCA, and Wavelet were used for the fusion of a Declassified KH-7 airphoto and a Landsat1 MSS image. Both images were acquired on 1972. The panchromatic data have a spatial resolution of 8m while the multispectral data have a spatial resolution of 80m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q, entropy were examined and the results are presented. As the ultimate purpose of the data fusion is to help in the detection of old landslides, two small areas where historical landslides have been occurred were selected for the evaluation of the fusion algorithms. Both areas are located in western Peloponnese in Achaia Prefecture.

The northern part of Colombia provides an opportunity for lineament collection over all types of rocks and ages, and with contrasting coverage settings, with arid provinces in the northern region, and intermediate and lush southern provinces. Lineaments were interpreted in an area of more than 52000 km2 in derived products from satellite imagery and DEMs. This dataset produced a high-density directional dataset with attributes including rock age, type and province, in addition to interpretation scale and image source. Dense lineament collection applications on fracture prediction, secondary porosity development and tectonic evolution are proposed. Composed lineaments (lineaments of lineaments) are used to compartmentalize Cesar-Rancheria basin, particular fracture intersections are proposed as potential hot spot locations in calcareous rocks and the systematic orientation change of rock age-grouped lineaments is used as additional support for clockwise Perija range rotation.

The "Bi-Directional Scattering Function" BSDF of a diffuser depends on several parameters, such as surface properties, observational conditions and further. This paper describes experimental activities to achieve a better understanding about the interaction between diffuser properties and performance with regards to its scattering behavior. For this purpose a set of 24 diffusers with defined surface properties have been manufactured and systematically been investigated in a dedicated radiometric calibration measurement facility. The experimental data are compared with existing theoretical models.

High resolution topography, by involving Digital Terrain Models (DTMs) and further accurate techniques for a proper displacement identification, is a valuable tool for a good and reliable description of unstable slopes. By comparing multitemporal surveys, the geomorphology of a landslide may be analyzed as well as the changes over time, the volumes transportation and the boundaries evolution. Being aware that a single technique is not sufficient to perform a reliable and accurate survey, this paper discusses the use of multi-platform, multi-source and multi-scale observations (both in terms of spatial scale and time scale) for the study and monitoring of unstable slopes. The final purpose is to highlight and validate a methodology based on multiple sensors and data integration, useful to obtain a comprehensive GIS (Geographic Information System) which can successfully be used to manage natural disasters or to improve the knowledge of a specific phenomenon in order to prevent and mitigate the hydro-geological risk. The novelty of the present research lies in the spatial integration of multiple remote sensing techniques such as: integration of Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) to provide a comprehensive and accurate surface description (DTM) at a fixed epoch (spatial continuity); continuous monitoring by means of spatial integration of Automated Total Station (ATS) and GNSS (Global Navigation Satellite System) to provide accurate surface displacement identification (time continuity). Discussion makes reference to a rockslide located in the northern Apennines of Italy from 2010 to 2013.

This paper addresses the latest advancements concerning both instrumental features and applications to the cultural heritage of a fluorescence LIDAR featuring hyperspectral and time resolution imaging capabilities. In particular, it focuses on the instrument’s technical upgrade in terms of scan speed, enhanced spatial resolution and field of view, which permitted to extend the field of application of the LIDAR technique to wall paintings and to the classification of microbial communities. It also outlines a new concept of fluorescence imaging LIDAR based on the integration of hyperspectral and fluorescence lifetime spectroscopy, which enhances the capabilities of the technique for the characterization of the materials to be investigated in cultural heritage assets. The new prototype is able to acquire full 4D datasets over a remote surface: for each pixel of the image, a 2D datum featuring fluorescence intensity versus wavelength and time is recorded. In this paper we present the results obtained in the lab for the characterization of stone samples and in the field for the investigation of ancient frescoes.

MetaSensing B.V.¹ is a Dutch company producing and operating Synthetic Aperture Radar (SAR) sensors at high resolution and different frequency bands, including P-, L-, C-, X- and Ku. By operating its most recent SAR sensors in the framework of different projects MetaSensing showed how diverse SAR imagery techniques can be applied to different areas of remote sensing, offering an effective tool for monitoring and mapping purposes. The present paper gives an overview about the last achievements of MetaSensing during a recent airborne measurement mission within the AlpTomoExpcampaign, also showing some preliminary results. A fully-polarimetric L-band radar system has been successfully operated in March 2014 within the framework of the future SAOCOM+ mission currently under investigation by the European Space Agency (ESA). At these low frequencies penetration capabilities for several meters is possible on dry snow. More than 40 fully polarimetric images have been focused by MetaSensing of a glacier of the Otzal Alps, in Austria, within the framework of the AlpTomoExp campaign. Thanks to the good interferometric coherence of the acquired images, further tomographic processing has been possible for 3D images generation showing the vertical profile of the monitored scenario.

One of the main difficulties encountered in Differential Interferometry (DInSAR) applications is temporal and spatial decorrelation over time. Single pixels, called Permanent Scatterers (PS), overcome this difficulty since they are coherent over time and over wide look-angle variations. Permanent Scatterers identification using interferographic techniques is unfeasible since they require the use of many acquisitions. Samsonov and Tiampo have presented a technique that selects Permanent Scatterers by analyzing their Polarization Phase Difference (PPD). The PPD approach would work just fine looking for single bounce scatterers because they are invariant to any initial arbitrary rotation between the scatterer and the radar Line of Sight (LOS). We propose to replace the PPD technique with Cameron’s Coherent Target Decomposition (CTD) because it is more accurate in finding the single and double bounce scatterers as it eliminates the initial orientation angle of the scatterer. Additionally, Cameron’s CTD is capable of recognizing more scattering mechanisms which means that more pixels, depending on their amplitude and stability over time, can be classified as Permanent Scatterers. A sample scene of fully polarimetric SAR image depicting the San Francisco bay was employed for experimentation. Our results demonstrate the superiority of the Cameron's CTD approach compared to PPD’s approach for the selection of pixels classified as Permanent Scatterers.

The need of reliable monitoring of old embankment dams is rapidly increasing since a large number of these structures are still equipped with old monitoring devices, usually installed some decades ago, which are generally capable to provide only localized information on specific areas of the embankment. This work discusses the use of Ground-Based Synthetic Aperture Radar (GBSAR) interferometry technique to observe and control the structural behavior of earthfill or rockfill embankments for dam impoundments. This non-invasive technique provides displacements patterns measured with sub-millimeter precision. Monitoring strategies of earthfill dam embankment in Southern Italy are presented.

This paper puts forward a methodology developed at the Institut Cartogràfic i Geològic de Catalunya (ICGC) to quantify upwelling light flux using hyperspectral and photogrammetric airborne data. The work was carried out in the frame of a demonstrative study requested by the municipality of Sant Cugat del Vallès, in the vicinity of Barcelona (Spain), and aimed to envisage a new approach to assess artificial lighting policies and actions as alternative to field campaigns. Hyperspectral and high resolution multispectral/panchromatic data were acquired simultaneously over urban areas. In order to avoid moon light contributions, data were acquired during the first days of new moon phase. Hyperspectral data were radiometrically calibrated. Then, National Center for Environmental Prediction (NCEP) atmospheric profiles were employed to estimate the actual Column Water Vapor (CWV) to be passed to ModTran5.0 for the atmospheric transmissivity τ calculation. At-the-ground radiance was finally integrated using the photopic sensitivity curve to generate a luminance map (cdm-2) of the flown area by mosaicking the different flight tracks. In an attempt to improve the spatial resolution and enhance the dynamic range of the luminance map, a sensor-fusion strategy was finally looked into. DMC Photogrammetric data acquired simultaneously to hyperspectral information were converted into at-the-ground radiance and upscaled to CASI spatial resolution. High-resolution (HR) luminance maps with enhanced dynamic range were finally generated by linearly fitting up-scaled DMC mosaics to the CASI-based luminance information. In the end, a preliminary assessment of the methodology is carried out using non-simultaneous in-situ measurements.

Airborne hyperspectral imaging is widely used for remote sensing of environment. The choice of spectral region usually depends on the availability and cost of the sensor. Visible-to-near infrared (400-1100 nm) spectral range corresponds to spectral sensitivity of relatively cheap Si detectors therefore it is the most commonly used. The implementation of shortwave infrared (1100-3000 nm) requires more expensive solutions, but can provide valuable information about the composition of the substance. Mid wave infrared (3000-8000 nm) is rarely used for civilian applications, but it provides information on the thermal emission of materials. The fusion of different sensors allows spectral analysis of a wider spectral range combining and improving already existing algorithms for the analysis of chemical content and classification. Here we introduce our Airborne Surveillance and Environmental Monitoring System (ARSENAL) that was developed by fusing seven sensors. The first test results from the fusion of three hyperspectral imaging sensors in the visible-to-mid wave infrared (365-5000 nm) are demonstrated. Principal component analysis (PCA) is applied to test correlation between principal components (PCs) and common vegetation indices.

The Tasseled Cap Features, derived by the Tasseled Cap Transformation of the satellite spectral information, provide a way to consistently associate spectral information to biophysical characteristics of land surface features. Since currently there are no Tasseled Cap Coefficients available for RapidEye data, the goal of this study was to obtain Tasseled Cap Coefficients for the RapidEye sensors. As a result the Tasseled Cap Features Brightness, Greenness and Yellowness were derived. Brightness is a weighted sum of all bands and is aligned to the principal direction of soil brightness. Greenness contrasts the visual bands (including the Red Edge band) with the near infrared band, representing the spectral variation of vital vegetation. Yellowness contrast the Blue and Green bands with the Red, Red Edge and, to a lesser extent, NIR bands, and corresponds to the reflectance characteristics of dry, senescent crops. A transferability test of the Tasseled Cap Coefficients showed a successful application of the coefficients to other regions of the world, indicating a wider application potential.

In this research, we present methods for monitoring deforestation and examining implication of the forest policies in forest carbon stocks in the future utilizing ALOS-PALSAR data. Riau Province of central Sumatra is selected for the study as it has received worldwide attention due to high forest–related carbon emissions. An aboveground forest carbon stocks (AFCS) model was calibrated with field measurement data and L-band backscatters from high-resolution slope corrected PALSAR mosaic data of 2009 and 2010. A total of 87 plots of field measured AFCS data ranging 1 - 340 t/ha was used. This AFCS model provides the AFCS map with RMSE of ±45 t/ha. The AFCS modeling results was extrapolated across the province using the mosaic data. The model estimated 315 million tons of AFCS in the province in 2010. A spatial model was used to spatialize three forest policy scenarios. These scenario maps were overlaid with AFCS map for deriving future perspective on AFCS. The future spatial patterns of the AFCS between the policy scenarios are apparent. If the historical trend continues, the forest cover will be consistently disappeared leaving very few small forest patches and releasing 77% of the current AFCS in to the atmosphere by 2030. However, one of the governance scenarios in the province indicates that almost half of the carbon emission can be reduced in the same period.

This work establishes a semi-automatic methodology to define and evaluate the Environmental Protection Areas (EPA), in the Paraíba River Basin, Brazil, taking account the land use and the water quality. The development of this work started from the water capitation point of Guaratingueta city located on the stream that runs through the city. From ASTER GDEM data the drainage network and the basin catchment was automatically extracted. Landsat images for the dates of 1989, 2001 and 2014 were digitally classified and the land uses were mapped, considering the area of permanent protection (APP) for drainage, respecting the limits indicated by Brazilian forest code. Scenes from the RapidEye satellite were used to answer questions of classification, due to good image definition. The study showed that in 1989, the total area classified as APP, 37.59% were anthropized, reaching 37.98% in 2001 and 36.98% in 2014. In a few years it was possible to associate data from water quality, measured directly at the capitation. In 2001 the water quality data showed that the intensive use of fertilizers drained into the Guaratingueta stream by rice paddies was seriously affecting the water supply of the municipality. In 2008 measures for water quality at the capitation point showed that the water resources were still impacted by agricultural activities from the rice fields. So, this work indicates the need for revitalization of the APP inside the EPA Guaratingueta in order to meet the law, protect watersheds and also avoid large investments in water treatment arriving for public consumption.

The Jeseníky Mountains tourism in Czech Republic is unique for its floristic richness, which is caused mainly by the altitude division and polymorphism of the landscape; climate and oil structure are other important factors. This study assesses the impacts of tourism on the land cover in the Jeseniky mountain region by comparing multi-temporal Landsat imagery (1991, 2001 and 2013) to describe the rate and extent of land-cover change throughout the Jeseniky mountain region. This was achieved through spectral classification of different land cover and by assessing the change in forest; settlements; pasture and agriculture in relation to increasing distances (5, 10 and 15 km) from three tourism site. The results indicate that the area was deforested (11.13%) from 1991 to 2001 than experienced forest regrowth (6.71%) from 2001 to 2013. In first decay pasture and agriculture areas was increase and then in next decay it was decrease. The influence of tourism facilities on land cover is also variable. Around each of the tourism site sampled there was a general trend of forest removal decreasing as the distance from each village increased, which indicates tourism does have a negative impact on forests. However, there was an opposite trend from 2001 to 2013 that indicate conservation area. The interplay among global (tourism, climate), regional (national policies, large-river management), and local (construction and agriculture, energy and water sources to support the tourism industry) factors drives a distinctive but complex pattern of land-use and land-cover disturbance.

Recently, frequency and strength of global wildfire are on the increase. The wildfire encourages the climate change through release of GHGs into the atmosphere over one time occur. The effect of wildfire GHGs can be estimate by FRP(fire radiative power), many research using the remote sensing are trying for its efficient produce. A satellite fire product including fire mask and FRP was produced by polar orbit satellite at first, thereafter it was expanded to geostationary satellites for continuous monitoring of wide areas. However, geostationary satellites observing in East Asia no got a standard to produce the fire product yet. This paper described a retrieval of FRP using the COMS(Communication, Ocean and Meteorological satellite) that is a Korean geostationary satellite. The COMS FRP was retrieved MIR(Middle infrared) radiance method which approaches by brightness temperature of single waveband. Our test was presented that large scale wildfires(FRP > 300MW and confidence level > 9) occurred in the each April. The COMS FRP showed MAE = 103.67 MW(16%) with the MODIS. This result represents much as possibility of the FRP in East Asia. This paper is expected to provide to baseline for the FRP in East Asia, and apply to biomass loss and estimate the GHGs. In addition, the COMS FRP will contribute to studies of aerosols, economic losses and ecosystem damages as basic data.

The problem of feature selection is a significant one in classification problems, where the addition of too many features to the classification fails to lead to significant increases in classification accuracy. This problem is especially significant within the context of multitemporal remote sensing classifications, where the costs and efforts associated with the acquisition of additional imagery can be extensive. It would thus be beneficial to identify the most important seasons for acquiring imagery for specific land cover types. This study uses a phenologically-adjusted 21 date RapidEye time-series in order to evaluate two methods of feature selection. The two methods compared in this study are a genetic algorithm (GA) and a semi-exhaustive method (EXH), both of which compare permutations of sequential date and band combinations. These methods are employed using a seven class support vector machine classification on a Normalized Difference Vegetation Index (NDVI)-transformed dataset. Overall accuracy (OAA) is used as the performance metric, and OAA significance is assessed using the McNemar test. The results from the feature selection methods are compared on the basis of phenological seasons selected across all iterations and the ideal number of combinations, based on the ratio of better performing classifications to all other classifications. The results suggest that the GA has a moderate but insignificant correlation when compared with the EXH for identifying ideal phenological seasons (overall Spearman’s ρ= 0.60, p = 0.13), but is comparable when considering the number of seasons and image combinations.

Land use/land cover (LULC) change assessment explores a terrestrial ecosystem in relation to the impact of natural processes and anthropogenic activities towards temporal and spatial change. This study explores spatial and quantitative dynamics of land use change in the semi-arid regions of northwestern Ethiopia using Landsat-5 (1984) and Landsat-8 (2014) which provided recent and historical LULC conditions of the region. Supervised classification algorithm using support vector machines (SVM) was used to map and monitor land use transformations. A post-classification change detection assessment was applied to individual image classification outputs of the best performing SVM model in order to identify respective two-date change trajectories. The change detection analysis with an extended transition matrix showed a net quantity change of 44.0% and total change of 53.7% of the study area, with the latter change is due to swap changes. Post-classification comparisons of the classified imagery identified a major woodland transformation to cropland which is attributed to population size and economic activity. The area of cropland has increased significantly (52.8%) in 2014 contributing to the reduction in native vegetation cover. In the study period, 55.6% of woodland lost signifying a significant change in ecosystems. This significant land use transformation is due to accelerated human impact and subsequent agricultural land expansion. The loss in vegetation cover has exposed the surface and it is common to see a haze of cloud in a most semiarid region of NW Ethiopia.

A number of methods have been developed for the automatic identification and delineation of individual tree crowns from high spatial resolution satellite image to provide support for the management and maintenance of forests both in natural and urban environments. In this paper we present a method that integrates a Marked Point Processes (MPP) model and Template Matching (TM) to extract individual tree crowns in two tropical environments. The MPP is an extension of Markov random fields in which objects are defined by their position within a space of possible positions and their marks (e.g. shape). The MPP has been increasingly used for the recognition of objects but most implementation use an oversimplified model as mark. We argue that the MPP could take better advantage of the geometry of trees by incorporating a three-dimensional model as a mark. Conversely, TM is an approach to pattern recognition that takes the characteristics of the objects into account. Our method uses cross-correlation for determining which objects have been correctly targeted by the MPP. The correlation between the illuminated 3D crown model and the image is an inheritance from TM. The methodology was applied in synthetic images and sub-images of the WorldView satellite in two different contexts in Brazil. The results are validated by counting the correctly identified trees and by comparing their size with our interpreted version. Results are encouraging with 65 to 90% of correctly identified trees. The most difficult cases are mostly related to the existence of clustered tree crowns.

Tree species composition is one of the criteria required for assessing forest reclamation in the province of Alberta in Canada. This information is also very important for forest management and conservation purposes. In this paper the performances of RapidEye data alone and in combination with the Light Detection And Ranging data is assessed for mapping tree species in a boreal forest area in Alberta. Both the random forest and support vector machine classification techniques were evaluated. A significant improvement in the classification outputs was observed when using both data types. Random forest outperformed the support vector machine classifier. Overall, the difference in acquisition time between the RapidEye and Light Detection And Ranging data did not seem to affect significantly the classification results. Using random forest, six input variables were identified as the most important for the classification process including digital elevation model, terrain slope, canopy height, the red-edge normalized difference vegetation index, and the red-edge and near-infrared bands.

Vegetation indices have been commonly used over the past 30 years for studying vegetation characteristics using images collected by remote sensing satellites. One of the most commonly used is the Normalized Difference Vegetation Index (NDVI). The various stages that green vegetation undergoes during a complete growing season can be summarized through time-series analysis of NDVI data. The analysis of such time-series allow for extracting key phenological variables or metrics of a particular season. These characteristics may not necessarily correspond directly to conventional, ground-based phenological events, but do provide indications of ecosystem dynamics. A complete list of the phenological metrics that can be extracted from smoothed, time-series NDVI data is available in the USGS online resources (http://phenology.cr.usgs.gov/methods_deriving.php).This work aims to develop an open source application to automatically extract these phenological metrics from a set of satellite input data. The main advantage of QGIS for this specific application relies on the easiness and quickness in developing new plug-ins, using Python language, based on the experience of the research group in other related works. QGIS has its own application programming interface (API) with functionalities and programs to develop new features. The toolbar developed for this application was implemented using the plug-in NDVIToolbar.py. The user introduces the raster files as input and obtains a plot and a report with the metrics. The report includes the following eight metrics: SOST (Start Of Season – Time) corresponding to the day of the year identified as having a consistent upward trend in the NDVI time series; SOSN (Start Of Season – NDVI) corresponding to the NDVI value associated with SOST; EOST (End of Season – Time) which corresponds to the day of year identified at the end of a consistent downward trend in the NDVI time series; EOSN (End of Season – NDVI) corresponding to the NDVI value associated with EOST; MAXN (Maximum NDVI) which corresponds to the maximum NDVI value; MAXT (Time of Maximum) which is the day associated with MAXN; DUR (Duration) defined as the number of days between SOST and EOST; and AMP (Amplitude) which is the difference between MAXN and SOSN. This application provides all these metrics in a single step. Initially, the data points are interpolated using a moving average graphic with five and three points. The eight metrics previously described are then obtained from the spline using numpy functions. In the present work, the developed toolbar was applied to MODerate resolution Imaging Spectroradiometer (MODIS) data covering a particular region of Portugal, which can be generally applied to other satellite data and study area. The code is open and can be modified according to the user requirements. Other advantage in publishing the plug-ins and the application code is the possibility of other users to improve this application.

The demand of 3D city modeling has been increasing in many applications such as urban planing, computer gaming with realistic city environment, car navigation system with showing 3D city map, virtual city tourism inviting future visitors to a virtual city walkthrough and others. We proposed a simple method for reconstructing a 3D urban landscape from airborne LiDAR point cloud data. The automatic reconstruction method of a 3D urban landscape was implemented by the integration of all connected regions, which were extracted and extruded from the altitude mask images. These mask images were generated from the gray scale LiDAR image by the altitude threshold ranges. In this study we demonstrated successfully in the case of Kanazawa city center scene by applying the proposed method to the airborne LiDAR point cloud data.

Wildfires in forests and forested areas in South Europe, North America, Central Asia and Australia are a diachronic threat with crucial ecological, economic and social impacts. Last decade the frequency, the magnitude and the intensity of fires have increased even more because of the climate change. An efficient response to such disasters requires an effective planning, with an early detection system of the ignition area and an accurate prediction of fire propagation to support the rapid response mechanisms. For this reason, information systems able to predict and visualize the behavior of fires, are valuable tools for fire fighting. Such systems, able also to perform simulations that evaluate the fire development scenarios, based on weather conditions, become valuable Decision Support Tools for fire mitigation planning. A Web-based Information System (WIS) developed in the framework of the FLIRE (Floods and fire risk assessment and management) project, a LIFE+ co-funded by the European Commission research, is presented in this study. The FLIRE WIS use forest fuel maps which have been developed by using generalized fuel maps, satellite data and in-situ observations. Furthermore, it leverages data from meteorological stations and weather forecast from numerical models to feed the fire propagation model with the necessary for the simulations inputs and to visualize the model’s results for user defined time periods and steps. The user has real-time access to FLIRE WIS via any web browser from any platform (PC, Laptop, Tablet, Smartphone).

The proposed work concerns the analysis of Remotely Piloted Aircraft Systems (RPAS), also known as drones, UAV (Unmanned Aerial Vehicle) or UAS (Unmanned Aerial System), on hydrogeological contexts for civil protection purposes, underlying the advantages of using a flexible and relatively low cost system. The capabilities of photogrammetric RPAS multi-sensors platform were examined in term of mapping, creation of orthophotos, 3D models generation, data integration into a 3D GIS (Geographic Information System) and validation through independent techniques such as GNSS (Global Navigation Satellite System). The RPAS used (multirotor OktoXL, of the Mikrokopter) was equipped with a GPS (Global Positioning System) receiver, digital cameras for photos and videos, an inertial navigation system, a radio device for communication and telemetry, etc. This innovative way of viewing and understanding the environment showed huge potentialities for the study of the territory, and due to its characteristics could be well integrated with aircraft surveys. However, such characteristics seem to give priority to local applications for rigorous and accurate analysis, while it remains a means of expeditious investigation for more extended areas. According to civil protection purposes, the experimentation was carried out by simulating operational protocols, for example for inspection, surveillance, monitoring, land mapping, georeferencing methods (with or without Ground Control Points - GCP) based on high resolution topography (2D and 3D information).

The study shows that about 1.4°C rise in mean temperature occurs between the 1900-1910s and 1980-1990s, with an abrupt change around 1990 due to climate shift. We also notice that the rise is 1.6°C in winter, reaching roughly 1.3°C in spring. Nationwide, the strongest warming is found in the northern par of NE China. It is worth noting that from the 1980s to present day the climate remains to be in warming, a phenomenon that has never happened in the last century. 5-model predictions of NE China climate for the future 30-50 years indicate a higher temperature rise in the year 2030 and 2050. The yearly mean would be the 1.94°C rise in 2030, with 2.06, 1.26, 1.79 and 2.66°C increase in spring, summer, autumn and winter, respectively. These results suggest that the highest increase is in winter, in order. The temperature increase is higher in the northern than in the southern part. The increase is expected to be kept in 2050, with annual mean rise of 2.42°C, with the ascent of 2.13, 1.68, 2.56.and 3.21°C, respectively in spring, summer, autumn and winter. The winter rise is the strongest, centered on the northern part of the region. Based on the above findings, the cumulative temperature band of T≥10°C for crop growth would be shifted northward by approximately 5 latitudes. In 2050 the original first band would move to the north of the Daxinganling mountains and the other 4 bands be nearly eliminated. The dominant farming area of rice would be shifted into the Heilongjiang valley, the winter wheat zone be expanded for experiment. For this purpose 6 countermeasures are proposed for the structure of staple grain crops and the necessary adjustment of their regional distribution for the stable and high yields of crops in this region.

A mesoscale observational analysis is performed of an event of rainstorm and intense convective weather as a robust happening during a 100-yr return period on August 10, 2006 in the Song-Nen Plain of NE China by use of minute-level automatic-station data, high-resolution satellite cloud maps, new-generation Doppler soundings and conventional observations. Results show that this event of rainstorm and robust convective weather is bound up with the genesis and development of a squall line, which causes great change in station meteorological elements on its way. At the Tailai station, for instance, precipitation is the strongest, reaching 90.8 mm/h, where temperature drops by 21.5°C, pressure rises by 4.7 hPa and winds peak between 13.3 to 22.6 m/s during the squall passage. The squall line is displayed as an elliptical MCC on satellite cloud maps and in its mature stage as a squall line on radar soundings. This squall shows a comma-form meso -β convective storm before its genesis, followed by propagation towards the southwest part of the storm and showing its bow-like echo zones followed by gradual appearance of multiple super cells, viz., multi-cell storm in the southwestern end of the echoes. These intense convective storm comprise linear strong convective echo bands, ~315 km in total length and 50 km wide, persisting for 7 hrs. When the squall line passes, there are a front low pressure, followed by a thunderstorm high pressure and a wake low differing in intensity.

The increasing developments in Unmanned Aerial Vehicles (UAVs) platforms and associated sensing technologies, offer a broad range of solutions for different applications related to the acquisition of information about objects or phenomenon at the Earth. The huge amount of data, provided by UAVs, represents a new challenge regarding developments of image processing techniques. Object-based image classification (OBIA) is highly suitable for very high resolution imagery, where pixel-based classification is less successful due to the high spatial variability within objects of interest. An OBIA approach using SPRING^® non-commercial software was implemented in this work. The UAV system used was a Swinglet from Sensefly. The ortho-mosaic, with 0.04 m of pixel size, from 20 of January of 2012 of Coimbra (Portugal) region with an apx. 500×400 m area was processed using the original 41 images. Different “similarity” and “area” parameters combination were computed in the segmentation stage (region-based). Firstly, a supervised classification was employed, considering 7 classes based on Corine land cover nomenclature. For several parameter combinations were obtained a Kappa>0.9 and an overall accuracy >90%. However, several objects were not classified. An unsupervised classification was performed and 27 classes were defined. After, a new supervised classification was performed considered 22 of the 27 classes identified, with an overall accuracy of 82.58%, and a Kappa of 0.817. We conclude that the algorithms employed in this work are not the most suitable for this kind of spatial resolution. The use data mining algorithms could improve the results.

Cities are exposed more and more to climate change from greenhouse gas induced radiative forcing, and localized effects from urbanization such as the urban heat island. Urban land covers as the biophysical state of the earth’s surface and immediate subsurface are sources and sinks for most of the material and energy movements and interactions between the geosphere and biosphere. Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. The aim of this paper is to investigate the influences of urban growth on urban thermal environment as well as the relationships of thermal characteristics to other biophysical parameters in Bucharest metropolitan area of Romania based on time series MODIS Terra/Aqua and IKONOS data acquired during 2000-2014 periods. Land Surface Temperature (LST) is a key variable for studying urban land surface processes and surface atmosphere interactions, being a crucial component in the study of the surface energy and water budgets. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LST’s correlations with both impervious and vegetation coverage. City thermal environment risk management strategies for mitigating and adapting to climate change must propose efficient plans to reduce greenhouse gas (GHG) emissions and cool the city through changes in the built environment, land use, and transportation.

The value of remote sensing data to geological exploration has increased as technology has improved. The advent of multispectral and hyperspectral imaging has allowed surface mapping to be performed remotely, thereby enabling vast areas to be mapped in a short time at a fraction of the cost of traditional geologic mapping. Different scanning spectrums enabled researchers to begin cataloguing various reflection and adsorption properties of soils, rock, and vegetation. These spectra could be used to interpret actual surface lithologies from remote sensing images. The study area focused in this work was the Freixeda stretch, district of Mirandela, Portugal. In this work, an ASTER image (March 2011) from the study area was used. ASTER VNIR and SWIR reflectance data have been used to produce colour composite images that seek to maximize the lithological information in the area; ratio images have been used to highlight ferric iron; and relative band depth images of the SWIR bands have been used to predict the occurrence of Alunite/Pyrophyllite, Kaolinite, Illite and Prophylitic group minerals. The VNIR bands were used to define vegetation and also ferric iron (defined by the ratio of band 2/band 1).The vegetation ratio is defined by the ratio of band 3/band 2. The SWIR data consists of 6 bands. Band 4 is located where most cover types have maximum reflectivity. Bands 5-9 cover an area of the SWIR where many-OH bearing minerals and carbonate minerals have absorption features. The presence of Au and Ag mineralization confirm the richness of this area.

Remotely sensed satellite data are critical to understanding the coastal zones’ physical and social systems interaction, complementing ground based methods and providing accurate wide range, objective and comparable, at widely-varying scales, synoptically data. For some environmental agreements remote sensing may provide the only viable means of compliance verification because the phenomena are monitored occurs over large and inaccessible geographic areas. The main aim of this paper was the assessment of coastal zone land cover/use changes based on fusion technique of satellite remote sensing imagery. The evaluation of coastal zone landscapes was based upon different sub-functions which refer to landscape features such as water, soil, land-use, buildings, groundwater, biotope types. A newly proposed sub-pixel mapping algorithm was applied to a set of multispectral and multitemporal satellite data for Danube Delta, Constantza and Black Sea coastal zone areas in Romania. A land cover classification and subsequent environmental quality analysis for change detection was done based on Landsat TM , Landsat ETM, QuickBird satellite images over 1990 to 2013 period of time. Spectral signatures of different terrain features have been used to separate and classify surface units of coastal zone and sub-coastal zone area.The change in the position of the coastline in Constantza area was examined in relation with the urban expansion. A distinction was made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other. We considered the Romanian Black Sea coastal zone dynamics in connection with the spatio-temporal variation of physical and biogeochemical processes and their influences on the environmental state in the near-shore area.

Brazil has 10% of global Mn reserves with its most important mine located in the Amazon region. The Azul deposit is related to sandstones and siltstones of the Águas Claras Formation (Archean), situated in the central portion of the Carajás Strike-Slip System. Vale S.A. mining company operates the Azul mining complex with three simultaneous excavations (mines 1, 2 and 3) conducted on rock materials of low geomechanical qualities. Mining operations are openpit, with 4-8 m-high benches and depth of 80 m. A stack of 19 TerraSAR-X (TSX) images was used for the investigation covering the period of March 20-October 4, 2012. In order to minimize the topography phase error in the interferometric process, a high resolution DEM was generated based on a panchromatic GeoEye-1 stereo pair. Persistent Scatterers Interferometry (PSI) analysis was carried out using the IPTA (Interferometric Point Target Analysis) software and led to the detection of 40,193 point-wise persistent scatterers (PS), with an average density of 5,387 PS/km². It was concluded that most of the mining area can be considered stable during the TSX coverage. High deformation rates related to settlements were mapped over a waste pile, while small deformation rates were detected along the north and south flanks of mine 1and were interpreted as cut slope movements toward the center of the pit. Despite only ground-based radar measurements were available for a short time period during the TSX coverage, and covering a sector of bench walls along the south flank of mine 1, the PSs movement patterns showed concordance with the field measurements. The investigation emphasized the important role that PSI technique can play in planning and risk assessment in this mining area. Monitoring of this type of deformation by PSI can usefully complement other commonly used field geotechnical measurements due to the synoptic SAR coverage over a dense grid, providing ground deformation data independently of field access and with millimeter accuracy.

Satellite remote sensing is a universal tool to investigate the different areas of Earth and environmental sciences. The advancement of the implementation capabilities of the optoelectronic devices which are long-term-tested in the laboratory and the field and are mounted on-board of the remote sensing platforms further improves the capability of instruments to acquire information about the Earth and its resources in global, regional and local scales. With the start of new high-spatial and spectral resolution satellite and aircraft imagery new applications for large-scale mapping and monitoring becomes possible. The integration with Geographic Information Systems (GIS) allows a synergistic processing of the multi-source spatial and spectral data. Here we present the results of a joint project DFNI I01/8 funded by the Bulgarian Science Fund focused on the algorithms of the preprocessing and the processing spectral data by using the methods of the corrections and of the visual and automatic interpretation. The objects of this study are lineaments. The lineaments are basically the line features on the earth's surface which are a sign of the geological structures. The geological lineaments usually appear on the multispectral images like lines or edges or linear shapes which is the result of the color variations of the surface structures. The basic geometry of a line is orientation, length and curve. The detection of the geological lineaments is an important operation in the exploration for mineral deposits, in the investigation of active fault patterns, in the prospecting of water resources, in the protecting people, etc. In this study the integrated approach for the detecting of the lineaments is applied. It combines together the methods of the visual interpretation of various geological and geographical indications in the multispectral satellite images, the application of the spatial analysis in GIS and the automatic processing of the multispectral images by Canny algorithm, Directional Filter and Neural Network. Landsat multispectral images of the Eastern Rhodopes in Bulgaria for carrying out the procedure are used. Canny algorithm for extracting edges represents series of filters (Gaussian, Sobel, etc.) applied to all bands of the image using the free IDL source. Directional Filter is applied to sharpen the image in a specific preferred direction. Another method is the Neural Network algorithm for recognizing lineaments. The lineaments are effectively extracted using different methods of automatic. The results from the above mentioned methods are compared to the results derived from the visual interpretation of satellite images and from the geological map. In conclusion, the rose diagrams of the distribution of the geological lineaments and the maps of their density are completed.

The outputs obtained from satellite image processing generally presents various information based on the interpretation technique, selected objects for object based processing, precision of processing, the number and time of images used for this process. This issue should be managed well during a disaster management process based on satellite images. Very high resolution (VHR) optical satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time. In this paper, we studied tsunami triggered area, which was caused on 11 March 2011 by Tohoku earthquake, using VHR data from GeoEye-1satellite images. A set of pre and post-earthquake images were used to perform visual change analysis through comparison of these data. These images include the data of the same area before the disaster in normal condition and after the disaster which caused changes and also some modification imposed to that area. Upon occurrence of a disaster, the images are used to estimate the extent of the damage. Then based on disaster management criteria and the needs for recovery and reconstruction, the priorities for object based classification indexes are defined. In post-disaster management, they are used for reconstruction and sustainable development activities. Finally a classified characteristic definition has been proposed which can be used as sample indexes prioritization criteria for disaster management based on satellite image processing. This prioritization criteria are based on an object based processing technique and can be further developed for other image processing methods.

In spectral unmixing theory, data reduction techniques play an important role as hyperspectral imagery contains an immense amount of data, posing many challenging problems such as data storage, computational efficiency, and the so called “curse of dimensionality”. Feature extraction and feature selection are the two main approaches for dimensionality reduction. Feature extraction techniques are used for reducing the dimensionality of the hyperspectral data by applying transforms on hyperspectral data. Feature selection techniques retain the physical meaning of the data by selecting a set of bands from the input hyperspectral dataset, which mainly contain the information needed for spectral unmixing. Although feature selection techniques are well-known for their dimensionality reduction potentials they are rarely used in the unmixing process. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information, which is derived by the whole wavelength, in order to define the optimum spectral subspace. These criteria are not associated with any particular application but with the data statistics, such as correlation and entropy values. However, each application is associated with specific land c over materials, whose spectral characteristics present variations in specific wavelengths. In forestry for example, many applications focus on tree leaves, in which specific pigments such as chlorophyll, xanthophyll, etc. determine the wavelengths where tree species, diseases, etc., can be detected. For such applications, when the unmixing process is applied, the tree species, diseases, etc., are considered as the endmembers of interest. This paper focuses on investigating the effects of band selection on the endmember extraction by exploiting the information of the vegetation absorbance spectral zones. More precisely, it is explored whether endmember extraction can be optimized when specific sets of initial bands related to leaf spectral characteristics are selected. Experiments comprise application of well-known signal subspace estimation and endmember extraction methods on a hyperspectral imagery that presents a forest area. Evaluation of the extracted endmembers showed that more forest species can be extracted as endmembers using selected bands.

In the recent years more and more widely accepted by the Space agencies (e.g. NASA, ESA) is the policy toward provision of Earth observation (EO) data and end products concerning air quality especially in large urban areas without cost to researchers and SMEs. Those EO data are complemented by increasing amount of in-situ data also provided at no cost either from national authorities or having crowdsourced origin. This accessibility together with the increased processing capabilities of the free and open source software is a prerequisite for creation of solid framework for air modeling in support of decision making at medium and large scale. Essential part of this framework is web-based GIS mapping tool responsible for dissemination of the output generated. In this research an attempt is made to establish a running framework based solely on openly accessible data on air quality and on set of freely available software tools for processing and modeling taking into account the present status quo in Bulgaria. Among the primary sources of data, especially for bigger urban areas, for different types of gases and dust particles, noted should be the National Institute of Meteorology and Hydrology of Bulgaria (NIMH) and National System for Environmental Monitoring managed by Bulgarian Executive Environmental Agency (ExEA). Both authorities provide data for concentration of several gases just to mention CO, CO₂, NO₂, SO₂, and fine suspended dust (PM10, PM2.5) on monthly (for some data on daily) basis. In the framework proposed these data will complement the data from satellite-based sensors such as OMI instrument aboard EOS-Aura satellite and from TROPOMI instrument payload for future ESA Sentinel-5P mission. Integral part of the framework is the modern map for the land use/land cover which is provided from EEA by initiative GIO Land CORINE. This map is also a product from EO data distributed at European level. First and above all, our effort is focused on provision to the wider public living in urbanized areas with one reliable source of information on the present conditions concerning the air quality. Also this information might be used as indicator for presence of acid rains in agriculture areas close to industrial or electricity plants. Its availability at regular basis makes such information valuable source in case of manmade industrial disasters or incidents such as forest fires. Key issue in developing this framework is to ensure the delivery of reliable data products related to air quality at larger scale that those available at the moment.

The bore-sight calibration procedure and results of a profile laser scanner using a large size exterior calibration field is presented in the paper. The task is a part of Autonomous Mapping Airship (AMA) project which aims to create s surveying system with specific properties suitable for effective surveying of medium-wide areas (units to tens of square kilometers per a day). As is obvious from the project name an airship is used as a carrier. This vehicle has some specific properties. The most important properties are high carrying capacity (15 kg), long flight time (3 hours), high operating safety and special flight characteristics such as stability of flight, in terms of vibrations, and possibility to flight at low speed. The high carrying capacity enables using of high quality sensors like professional infrared (IR) camera FLIR SC645, high-end visible spectrum (VIS) digital camera and optics in the visible spectrum and tactical grade INSGPS sensor iMAR iTracerRT-F200 and profile laser scanner SICK LD-LRS1000. The calibration method is based on direct laboratory measuring of coordinate offset (lever-arm) and in-flight determination of rotation offsets (bore-sights). The bore-sight determination is based on the minimization of squares of individual point distances from measured planar surfaces.

In recent years, there is no doubt that global climate change (CC) has observable development impacts, which seriously threatens the ability of individuals and communities at all levels. During this process, the clear degradation in the situation of ecosystems has produced a global concern of the urgency to mitigate climate threats and related effects. Assessing the impacts and vulnerability of CC requires accurate, up-to-date and improved information. Coupled with the ready availability of historical remote sensing (RS) data, the reduction in data cost and increased resolution from satellite platforms, RS technology appears poised to make a great impact on planning agencies and providing better understanding the dynamics of the climate system, predict and mitigate the expected global changes and the effects on human civilization involved in mapping Land Use Land Cover (LU/LC) at a variety of spatial scales. This research was designed to study the impact of CC in conflict zones and potential flashpoints in Sudan namely Nuba Mountains, where the community in this area living in fragile and unstable conditions, which making them more vulnerable to the risk of violent conflict and CC effects. And to determine the factors that exacerbate vulnerability in the study area as well as to map and assess the LU/LC change during the period 1984 to 2011 covered the years (1999, 2002 and 2009). Multispectral satellite data (i.e. LANDSAT TM and TERRA ASTER) were used. Change detection techniques were applied to analyze the rate of changes, causal factors as well as the drivers of changes. Recent study showed the importance of spatial variables in tackling CC which promoted the use of maps made within a RS. In addition to provide an input for climate models; and thus plan adaptation strategies.

We developed a Raman lidar with ultraspectral resolution for automatic airborne monitoring of pipeline leaks and for oil and gas exploration. Experiments were carried out under the CARS circuit. Minimal concentrations of 200 ppb of heavy hydrocarbon gas have been remotely measured in laboratory tests. Test flights indicate that a sensitivity of 6 ppm for methane and 2 ppm for hydrogen sulfide has been reached for leakage detection. As estimations have shown the reliability of heavy hydrocarbon gas detection by the integration method of seismic prospecting and remote laser sensing in CARS circuit can exceed 80%.