bluuebunny/crossref_metadata_2025 huggingface dataset:
Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity
Authors: Bach H. , Klug P. , Ruf T. , Migdall S. , Schlenz F. , Hank T. , Mauser W.
Published: April 2015

Abstract. To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2’s continuous and manifold data stream.
APPLICATION OF A NEW POLARIMETRIC FILTER TO RADARSAT-2 DATA OF DECEPTION ISLAND (ANTARCTIC PENINSULA REGION) FOR SURFACE COVER CHARACTERIZATION
Authors: Guillaso S. , Schmid T. , López-Martínez J. , D'Hondt O.
Published: April 2015

Abstract. In this paper, we describe a new approach to analyse and quantify land surface covers on Deception Island, a volcanic island located in the Northern Antarctic Peninsula region by means of fully polarimetric RADARSAT-2 (C-Band) SAR image. Data have been filtered by a new polarimetric speckle filter (PolSAR-BLF) that is based on the bilateral filter. This filter is locally adapted to the spatial structure of the image by relying on pixel similarities in both the spatial and the radiometric domains. Thereafter different polarimetric features have been extracted and selected before being geocoded. These polarimetric parameters serve as a basis for a supervised classification using the Support Vector Machine (SVM) classifier. Finally, a map of landform is generated based on the result of the SVM results.
NAVIGATION ASSISTANCE FOR ICE-INFESTED WATERS THROUGH AUTOMATIC ICEBERG DETECTION AND ICE CLASSIFICATION BASED ON TERRASAR-X IMAGERY
Authors: Ressel R. , Frost A. , Lehner S.
Published: April 2015

Abstract. Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.
ALIGNMENT OF HYPERSPECTRAL IMAGERY AND FULL-WAVEFORM LIDAR DATA FOR VISUALISATION AND CLASSIFICATION PURPOSES
Authors: Miltiadou M. , Warren M. A. , Grant M. , Brown M.
Published: April 2015

Abstract. The overarching aim of this paper is to enhance the visualisations and classifications of airborne remote sensing data for remote forest surveys. A new open source tool is presented for aligning hyperspectral and full-waveform LiDAR data. The tool produces coloured polygon representations of the scanned areas and aligned metrics from both datasets. Using data provided by NERC ARSF, tree coverage maps are generated and projected into the polygons. The 3D polygon meshes show well-separated structures and are suitable for direct rendering with commodity 3D-accelerated hardware allowing smooth visualisation. The intensity profile of each wave sample is accumulated into a 3D discrete density volume building a 3D representation of the scanned area. The 3D volume is then polygonised using the Marching Cubes algorithm. Further, three user-defined bands from the hyperspectral images are projected into the polygon mesh as RGB colours. Regarding the classifications of full-waveform LiDAR data, previous work used extraction of point clouds while this paper introduces a new approach of deriving information from the 3D volume representation and the hyperspectral data. We generate aligned metrics of multiple resolutions, including the standard deviation of the hyperspectral bands and width of the reflected waveform derived from the volume. Tree coverage maps are then generated using a Bayesian probabilistic model and due to the combination of the data, higher accuracy classification results are expected.
Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data
Authors: Charou E. , Gyftakis S. , Bratsolis E. , Tsenoglou T. , D. Papadopoulou Th. , Vassilas N.
Published: April 2015

Abstract. Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.
Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology
Authors: Themistocleous K. , Agapiou A. , Cuca B , Hadjimitsis D. G.
Published: April 2015

Abstract. Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.
Determining and Monitoring the Water Quality of Kizilirmak River of Turkey: First Results
Authors: Gürsoy Ö. , Birdal A. C. , Özyonar F. , Kasaka E.
Published: April 2015

Abstract. Water resources are getting more and more important with each passing day in case of survival of humanity. For this reason, assessing water resources’ quality and also monitoring them have attracted lots of attention in the recent years. Remote sensing has been growing widely in the last decade and its resources are very usable when it comes to water resources management. In this study, by using remote sensing technology, satellite images that have 350 to 1050 nanometres wavelength band sensors (e.g. CHRIS Proba) are used to determine the quality of the Kizilirmak River’s water. Kizilirmak River is born and also pours out to sea in country limits of Turkey. It is the longest river of the country by the length of 1355 kilometres. Through the river’s resources, ground based spectral measurements are made to identify the quality differences of the water at the test spots that have been determined before. In this context at Imranli, where the river contacts civilization for the first time, which is located in Sivas city of Turkey, samples are gathered in order to do ground based spectroradiometer measurements. These samples are gathered simultaneously with the image acquiring time of CHRIS Proba satellite. Spectral signatures that are obtained from ground measurements are used as reference data in order to classify CHRIS Proba satellite’s hyperspectral images over the study area. Satellite images are classified based on Chemical Oxygen Demand (COD), Turbidity and Electrical Conductivity (EC) attributes. As a result, interpretations obtained from classified CHRIS Proba satellite hyperspectral images of the study area are presented.
Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery
Authors: Zillmann E. , Schönert M , Lilienthal H. , Siegmann B. , Jarmer T , Rosso P. , Weichelt T.
Published: April 2015

Abstract. Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.
NDVI dynamics of the taiga zone in connection with modern climate changes
Authors: Bobkov A. , Panidi E. , Torlopova N. , Tsepelev V.
Published: April 2015

Abstract. This research is dedicated to the investigation of the relations between the XXI century climate changes and Normalized Difference Vegetation Index (NDVI) variability of the taiga zone. For this purposes was used the observations of vegetation variability on the test area located nearby Syktyvkar city (Komi Republic, Russia), 16-day averages of NDVI data derived from TERRA/MODIS space imagery (spatial resolution is about 250 meters), and the air temperature and precipitation observations from Syktyvkar meteorological station. The research results confirmed the statistically significant positive correlation between NDVI and air temperature for all vegetation types of the test area, for both spring and autumn seasons. The weakest correlation was found for coniferous forest, namely, pine forest on poor soils, and the strongest correlation was found for meadows and bogs. Additionally the map of NDVI trends of the test area shows that the sectors of greatest positive trend located on the territories with non-forest cover, and as a result, the positive trend of air temperature is indicated most brightly on vegetation of non-forest lands. Thereby these lands can serve as climate changes indicator in the investigated region. The study was partially supported by Russian Foundation for Basic Research (RFBR), research project No. 14-05-00858 a.
Satellite radiothermovision of atmospheric mesoscale processes: case study of tropical cyclones
Authors: Ermakov D. M. , Sharkov E. A. , Chernushich A. P.
Published: April 2015

Abstract. Satellite radiothermovision is a set of processing techniques applicable for multisource data of radiothermal monitoring of oceanatmosphere system, which allows creating dynamic description of mesoscale and synoptic atmospheric processes and estimating physically meaningful integral characteristics of the observed processes (like avdective flow of the latent heat through a given border). The approach is based on spatiotemporal interpolation of the satellite measurements which allows reconstructing the radiothermal fields (as well as the fields of geophysical parameters) of the ocean-atmosphere system at global scale with spatial resolution of about 0.125° and temporal resolution of 1.5 hour. The accuracy of spatiotemporal interpolation was estimated by direct comparison of interpolated data with the data of independent asynchronous measurements and was shown to correspond to the best achievable as reported in literature (for total precipitable water fields the accuracy is about 0.8 mm). The advantages of the implemented interpolation scheme are: closure under input radiothermal data, homogeneity in time scale (all data are interpolated through the same time intervals), automatic estimation of both the intermediate states of scalar field of the studied geophysical parameter and of vector field of effective velocity of advection (horizontal movements). Using this pair of fields one can calculate the flow of a given geophysical quantity though any given border. For example, in case of total precipitable water field, this flow (under proper calibration) has the meaning of latent heat advective flux. This opportunity was used to evaluate the latent heat flux though a set of circular contours, enclosing a tropical cyclone and drifting with it during its evolution. A remarkable interrelation was observed between the calculated magnitude and sign of advective latent flux and the intensity of a tropical cyclone. This interrelation is demonstrated in several examples of hurricanes and tropical cyclones of August, 2000, and typhoons of November, 2013, including super typhoon Haiyan.
Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area
Authors: Ali A. , Darvishzadeh R. , Skidmore A.-K. , Duren I.-V. , Heiden U. , Heurich M.
Published: April 2015

Abstract. Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf samples were measured during a field campaign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf samples were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R2 and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.
ASTER and Worldview-2 satellite data comparison for identification of groundwater salinization effects on the Classe pine forest vegetation (Ravenna, Italy)
Authors: Barbarella M. , De Giglio M. , Greggio N. , Panciroli L.
Published: April 2015

Abstract. The availability of a large number of data acquired by satellite sensors with different spatial and spectral resolutions has always required an evaluation of their synergistic use. The integration of dataset of images coming from different sources can be an optimal solution for the study of various environmental problems which need a continuous monitoring (coastal development, forest evolution, land use changes etc.). The Classe pinewood, an important safeguarded biodiversity hot spot near Ravenna city (Italy), is historically affected by the groundwater salinization. Since changes in the water concentration are able to induce variations of the leaf properties and vegetation cover, recognizable by surveys carried out with different spectral bands, the comparison between ASTER and Worldview-2 data was performed using the (Normalized Difference Vegetation Index) NDVI. For each satellite data, the same Areas of Interest (AOIs) were selected within the most widespread cover, Thermophilic Deciduous Forest (TDF). The NDVI was calculated, statistically evaluated and the AOI rankings were built. In order to evaluate the difference between the results provided by the two images, statistical tests were applied on the average NDVI values. Finally the calculated NDVI were compared with groundwater salinity data collected during a contemporary field monitoring campaign. Based on groundwater salinity the same AOIs ranking was reached for both satellite sensors. This study suggests the opportunity to employ the medium resolution Aster images in continuity with high resolution WarldView-2 dataset.
Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative
Authors: Bontemps S. , Boettcher M. , Brockmann C. , Kirches G. , Lamarche C. , Radoux J. , Santoro M. , Vanbogaert E. , Wegmüller U. , Herold M. , Achard F. , Ramoino F. , Arino O. , Defourny P.
Published: April 2015

Abstract. Essential Climate Variables were listed by the Global Climate Observing System as critical information to further understand the climate system and support climate modelling. The European Space Agency launched its Climate Change Initiative in order to provide an adequate response to the set of requirements for long-term satellite-based products for climate. Within this program, the CCI Land Cover project aims at revisiting all algorithms required for the generation of global land cover products that are stable and consistent over time, while also reflecting the land surface seasonality. To this end, the land cover concept is revisited to deliver a set of three consistent global land cover products corresponding to the 1998-2002, 2003-2007 and 2008-2012 periods, along with climatological 7-day time series representing the average seasonal dynamics of the land surface over the 1998-2012 period. The full Envisat MERIS archive (2003-2012) is used as main Earth Observation dataset to derive the 300-m global land cover maps, complemented with SPOT-Vegetation time series between 1998 and 2012. Finally, a 300-m global map of open permanent water bodies is derived from the 2005-2010 archive of the Envisat Advanced SAR imagery mainly acquired in the 150m Wide Swath Mode.
Forest Cover Change and Soil Erosion in Toledo's Rio Grande Watershed
Authors: Chicas S. , Omine K.
Published: April 2015

Abstract. Toledo, the southernmost district, is the hub of Belize’s Mayan population, descendants of the ancient Mayan civilization. The Toledo District is primarily inhibited by Kekchi and Mopan Mayans whose subsistence needs are met by the Milpa slash-and-burn agricultural system and the extraction of forest resources. The poverty assessment in the country indicates that Toledo is the district with the highest percentage of household an individual indigence of 37.5 % and 49.7 % respectively. Forest cover change in the area can be attributed to rapid population growth among the Maya, together with increase in immigration from neighboring countries, logging, oil exploration and improvement and construction of roads. The forest cover change analysis show that from 2001 to 2011 there was a decrease of Lowland broad-leaved wet forest of 7.53 km sq, Shrubland of 4.66 km sq, and Wetland of 0.08 km sq. Forest cover change has resulted in soil erosion which is causing the deterioration of soils. The land cover types that are contributing the most to total erosion in the Rio Grande watershed are no-forest, lowland broad-leaved wet forest and submontane broad-leaved wet forest. In this study the Revised Universal Soil Loss Equation (RUSLE) was employed in a GIS platform to quantify and assess forest cover change and soil erosion. Soil erosion vulnerability maps in Toledo’s Rio Grande watershed were also created. This study provides scientifically sound information in order to understand and respond effectively to the impacts of soil erosion in the study site.
Forest and Forest Change Mapping with C- and L-band SAR in Liwale, Tanzania
Authors: Haarpaintner J. , Davids C. , Hindberg H. , Zahabu E. , Malimbwi R. E.
Published: April 2015

Abstract. As part of a Tanzanian-Norwegian cooperation project on Monitoring Reporting and Verification (MRV) for REDD+, 2007-2011 Cand L-band synthetic aperture radar (SAR) backscatter data from Envisat ASAR and ALOS Palsar, respectively, have been processed, analysed and used for forest and forest change mapping over a study side in Liwale District in Lindi Region, Tanzania. Land cover observations from forest inventory plots of the National Forestry Resources Monitoring and Assessment (NAFORMA) project have been used for training Gaussian Mixture Models and k-means classifier that have been combined in order to map the study region into forest, woodland and non-forest areas. Maximum forest and woodland extension masks have been extracted by classifying maximum backscatter mosaics in HH and HV polarizations from the 2007-2011 ALOS Palsar coverage and could be used to map efficiently inter-annual forest change by filtering out changes in non-forest areas. Envisat ASAR APS (alternate polarization mode) have also been analysed with the aim to improve the forest/woodland/non-forest classification based on ALOS Palsar. Clearly, the combination of C-band SAR and L-band SAR provides useful information in order to smooth the classification and especially increase the woodland class, but an overall improvement for the wall-to-wall land type classification has yet to be confirmed. The quality assessment and validation of the results is done with very high resolution optical data from WorldView, Ikonos and RapidEye, and NAFORMA field observations.
Comparison of field and airborne laser scanning based crown cover estimates across land cover types in Kenya
Authors: Heiskanen J. , Korhonen L. , Hietanen J. , Heikinheimo V. , Schäfer E. , Pellikka P. K. E.
Published: April 2015

Abstract. Tree crown cover (CC) provides means for the continuous land cover characterization of complex tropical landscapes with multiple land uses and variable degrees of degradation. It is also a key parameter in the international forest definitions that are basis for monitoring global forest cover changes. Recently, airborne laser scanning (ALS) has emerged as a practical method for accurate CC mapping, but ALS derived CC estimates have rarely been assessed with field data in the tropics. Here, our objective was to compare the various field and ALS based CC estimates across multiple land cover types in the Taita Hills, Kenya. The field data was measured from a total of 178 sample plots (0.1 ha) in 2013 and 2014. The most accurate field measurement method, line intersect sampling using Cajanus tube, was used in 37 plots. Other methods included CC estimate based on the tree inventory data (144 plots), crown relascope (43 plots) and hemispherical photography (30 plots). Three ALS data sets, including two scanners and flying heights, were acquired concurrently with the field data collection. According to the results, the first echo cover index (FCI) from ALS data had good agreement with the most accurate field based CC estimates (RMSD 7.1% and 2.7% depending on the area and scan). The agreement with other field based methods was considerably worse. Furthermore, we observed that ALS cover indices were robust between the different scans in the overlapping area. In conclusion, our results suggest that ALS provides a reliable method for continuous CC mapping across tropical land cover types although dense shrub layer and tree-like herbaceous plants can cause overestimation of CC.
Regional scale crop mapping using multi-temporal satellite imagery
Authors: Kussul N. , Skakun S. , Shelestov A. , Lavreniuk M. , Yailymov B. , Kussul O.
Published: April 2015

Abstract. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.
Applications of Photogrammetry for Analysis of Forest Plantations. Preliminary study: Analysis of individual trees
Authors: Mora R. , Barahona A. , Aguilar H.
Published: April 2015

Abstract. This paper presents a method for using high detail volumetric information, captured with a land based photogrammetric survey, to obtain information from individual trees. Applying LIDAR analysis techniques it is possible to measure diameter at breast height, height at first branch (commercial height), basal area and volume of an individual tree. Given this information it is possible to calculate how much of that tree can be exploited as wood. The main objective is to develop a methodology for successfully surveying one individual tree, capturing every side of the stem a using high resolution digital camera and reference marks with GPS coordinates. The process is executed for several individuals of two species present in the metropolitan area in San José, Costa Rica, Delonix regia (Bojer) Raf. and Tabebuia rosea (Bertol.) DC., each one with different height, stem shape and crown area. Using a photogrammetry suite all the pictures are aligned, geo-referenced and a dense point cloud is generated with enough detail to perform the required measurements, as well as a solid tridimensional model for volume measurement. This research will open the way to develop a capture methodology with an airborne camera using close range UAVs. An airborne platform will make possible to capture every individual in a forest plantation, furthermore if the analysis techniques applied in this research are automated it will be possible to calculate with high precision the exploit potential of a forest plantation and improve its management.
Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia
Authors: Sakti A. D. , Tsuyuki S.
Published: April 2015

Abstract. Indonesian peatland, one of the world’s largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation (peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.
Using the Landsat data archive to assess long-term regional forest dynamics assessment in Eastern Europe, 1985-2012
Authors: Turubanova S. , Potapov P. , Krylov A. , Tyukavina A. , McCarty J. L. , Radeloff V. C. , Hansen M. C.
Published: April 2015

Abstract. Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers.
Bio-optical data integration based on a 4 D database system approach
Authors: Imai N. N. , Shimabukuro M. H. , Carmo A. F. C. , Alcântara E. H. , Rodrigues T. W. P. , Watanabe F. S. Y.
Published: April 2015

Abstract. Bio-optical characterization of water bodies requires spatio-temporal data about Inherent Optical Properties and Apparent Optical Properties which allow the comprehension of underwater light field aiming at the development of models for monitoring water quality. Measurements are taken to represent optical properties along a column of water, and then the spectral data must be related to depth. However, the spatial positions of measurement may differ since collecting instruments vary. In addition, the records should not refer to the same wavelengths. Additional difficulty is that distinct instruments store data in different formats. A data integration approach is needed to make these large and multi source data sets suitable for analysis. Thus, it becomes possible, even automatically, semi-empirical models evaluation, preceded by preliminary tasks of quality control. In this work it is presented a solution, in the stated scenario, based on spatial – geographic – database approach with the adoption of an object relational Database Management System – DBMS – due to the possibilities to represent all data collected in the field, in conjunction with data obtained by laboratory analysis and Remote Sensing images that have been taken at the time of field data collection. This data integration approach leads to a 4D representation since that its coordinate system includes 3D spatial coordinates – planimetric and depth – and the time when each data was taken. It was adopted PostgreSQL DBMS extended by PostGIS module to provide abilities to manage spatial/geospatial data. It was developed a prototype which has the mainly tools an analyst needs to prepare the data sets for analysis.
3D-information fusion from very high resolution satellite sensors
Authors: Krauss T. , d'Angelo P. , Kuschk G. , Tian J. , Partovi T.
Published: April 2015

Abstract. In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl´eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.
Topographic Correction Module at Storm (TC@Storm)
Authors: Zakšek K. , Čotar K. , Veljanovski T. , Pehani P. , Oštir K.
Published: April 2015

Abstract. Different solar position in combination with terrain slope and aspect result in different illumination of inclined surfaces. Therefore, the retrieved satellite data cannot be accurately transformed to the spectral reflectance, which depends only on the land cover. The topographic correction should remove this effect and enable further automatic processing of higher level products. The topographic correction TC@STORM was developed as a module within the SPACE-SI automatic near-real-time image processing chain STORM. It combines physical approach with the standard Minnaert method. The total irradiance is modelled as a three-component irradiance: direct (dependent on incidence angle, sun zenith angle and slope), diffuse from the sky (dependent mainly on sky-view factor), and diffuse reflected from the terrain (dependent on sky-view factor and albedo). For computation of diffuse irradiation from the sky we assume an anisotropic brightness of the sky. We iteratively estimate a linear combination from 10 different models, to provide the best results. Dependent on the data resolution, we mask shades based on radiometric (image) or geometric properties. The method was tested on RapidEye, Landsat 8, and PROBA-V data. Final results of the correction were evaluated and statistically validated based on various topography settings and land cover classes. Images show great improvements in shaded areas.
Automatic Processing of Chinese GF-1 Wide Field of View Images
Authors: Zhang Y. , Wan Y. , Wang B. , Kang Y. , Xiong J.
Published: April 2015

Abstract. The wide field of view (WFV) imaging instrument carried on the Chinese GF-1 satellite includes four cameras. Each camera has 200km swath-width that can acquire earth image at the same time and the observation can be repeated within only 4 days. This enables the applications of remote sensing imagery to advance from non-scheduled land-observation to periodically land-monitoring in the areas that use the images in such resolutions. This paper introduces an automatic data analysing and processing technique for the wide-swath images acquired by GF-1 satellite. Firstly, the images are validated by a self-adaptive Gaussian mixture model based cloud detection method to confirm whether they are qualified and suitable to be involved into the automatic processing workflow. Then the ground control points (GCPs) are quickly and automatically matched from the public geo-information products such as the rectified panchromatic images of Landsat-8. Before the geometric correction, the cloud detection results are also used to eliminate the invalid GCPs distributed in the cloud covered areas, which obviously reduces the ratio of blunders of GCPs. The geometric correction module not only rectifies the rational function models (RFMs), but also provides the self-calibration model and parameters for the non-linear distortion, and it is iteratively processed to detect blunders. The maximum geometric distortion in WFV image decreases from about 10-15 pixels to 1-2 pixels when compensated by self-calibration model. The processing experiments involve hundreds of WFV images of GF-1 satellite acquired from June to September 2013, which covers the whole mainland of China. All the processing work can be finished by one operator within 2 days on a desktop computer made up by a second-generation Intel Core-i7 CPU and a 4-solid-State-Disk array. The digital ortho maps (DOM) are automatically generated with 3 arc second Shuttle Radar Topography Mission (SRTM). The geometric accuracies of the generated DOM are 20m for camera-2 and 3, and 30m accuracy for camera-1 and 4. These products are now widely used in the fields of land and resource investigation, environment protection, and agricultural research.
The Potential Of Geomatics In The Realization Of A Map Of Desertification Sensitivity Southern Massif Belezma - Batna - (Algeria)
Authors: Benmessaoud H. , Chergui F. , Sahnouni R. , Chafai C.
Published: April 2015

Abstract. Desertification is the gradual and sustained reduction in the quantity and quality of the biological productivity of arid and semi-arid land. The study area is located in the North Eastern part of Algeria, it has a rich heritage in its biodiversity, however weather conditions and adverse human reality, induce a degradation of the physical environment in the form of a regression of vegetation cover. To assess desertification in our study area map of desertification sensitivity is a tool for decision support. For the realization of this Map we used the ArcGis software applied a methodology which is inspired by the concept MEDALUS (Mediterranean Desertification and Land Use, 1999) by crossing four thematic layers that may have an impact on the process of desertification. The results of Cartography and statistical analysis permit the classification of our region in terms of sensitivity to desertification in four very important classes. (Not affected, Insensitive, Sensitive and highly sensitive). More than 69.92% of the surface area were classified sensitive to very sensitive, For against 30.07% is classified in unallocated insensitive. Planning restoration work and the fight against desertification are expected to limit the risk of desertification in the study area perspectives.
Real-time stream processing for active fire monitoring on Landsat 8 direct reception data
Authors: Böhme C. , Bouwer P. , Prinsloo M. J.
Published: April 2015

Abstract. Some remote sensing applications are relatively time insensitive, for others, near-real-time processing (results 30-180 minutes after data reception) offer a viable solution. There are, however, a few applications, such as active wildfire monitoring or ship and airplane detection, where real-time processing and image interpretation offers a distinct advantage. The objective of real-time processing is to provide notifications before the complete satellite pass has been received. This paper presents an automated system for real-time, stream–based processing of data acquired from direct broadcast push-broom sensors for applications that require a high degree of timeliness. Based on this system, a processing chain for active fire monitoring using Landsat 8 live data streams was implemented and evaluated. The real-time processing system, called the FarEarth Observer, is connected to a ground station’s demodulator and uses its live data stream as input. Processing is done on variable size image segments assembled from detector lines of the push broom sensor as they are streamed from the satellite, enabling detection of active fires and sending of notifications within seconds of the satellite passing over the affected area, long before the actual acquisition completes. This approach requires performance optimized techniques for radiometric and geometric correction of the sensor data. Throughput of the processing system is kept well above the 400Mbit/s downlink speed of Landsat 8. A latency of below 10 seconds from sensor line acquisition to anomaly detection and notification is achieved. Analyses of geometric and radiometric accuracy and comparisons in latency to traditional near-real-time systems are also presented.