bluuebunny/crossref_metadata_2025 huggingface dataset:
SPACEBORNE QUANTITATIVE ASSESSMENT OF PRIMARY PRODUCTION VARIATIONS IN THE ARCTIC OCEAN OVER THE PREVIOUS DECADE
Authors: Pozdnyakov D. , Petrenko D.
Published: April 2015

Abstract. Spaceborne one month averaged data, predominantly from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and partly from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used to investigate changes in primary production (PP) by phytoplankton in the Arctic Ocean from 1998 till 2010. Several PP retrieval algorithms were tested against the collected in situ data, and it was shown that the algorithm by Behrenfeld and Falkowski gave the best results (with the coefficient of correlation, r equal to 0.8 and 0.75, respectively, for the pelagic and shelf zones. Based on the performed test, the Behrenfeld and Falkowski algorithm was further applied for determining both the annual PP in the Arctic and the PP trend over the aforementioned time period. The results of our analysis indicate that PP in the Arctic has increased by ~ 15.9% over 13 years. This finding, as well as the absolute annual values of PP remotely quantified in the present study, is at odds with analogous numerical assessments by other workers. These disagreements are thought to be due to differences in the applied methodologies of satellite data processing, such as cloud masking and determination of phytoplankton concentration within (i) overcast areas, and (ii) areas of massive growth of coccolithophore algae, as well as (iii) in the shelf zone prone to a significant influence of land and river runoff. Hindcast (a decadal long) and forecast projections of PP variations are performed.
THE MATHEMATICAL MODEL OF OPTICAL REMOTE SENSING SYSTEM SIGNAL CONSIDERING BROKEN CLOUDINESS EFFECTS
Authors: Budak V. P. , Shagalov O. V.
Published: April 2015

Abstract. With increasing of the accuracy of measuring equipment for the optical remote sensing in recent years the requirements for speed and accuracy of the algorithms for satellite data processing has greatly increased. It became necessary accurately to account all of the known factors, which affect the signal significantly. At each time, more than half of the planet is covered with clouds, so it is almost always necessary to take measurements into breaks in clouds. Cloudiness is among those factors which affect significantly the signal and its neglect in extreme cases can lead to an error of 140%. Here we propose a new solution of the radiative transfer equation (RTE) for a slab of a turbid medium with consideration of broken clouds. We use the classical approach to solving RTE: complete solution is represented as the sum of the anisotropic and regular parts. We express anisotropic part using small-angle modification of the spherical harmonics method. For the regular part we propose to use quasi two-stream approximation. This method is a special case of the synthetic iterations method. The method is based on splitting the ordinary iteration into two stages. At the first step one of approximate methods is used, and on the second step one ordinary iteration is used. We use two-stream approximation as an approximate method. In this paper we proposed a solution for the simplest case of broken clouds - cylindrical hole in the slab. Comparison of the algorithm was performed with established program MDOM, and showed good agreement.
CITIZEN SCIENCE FOR EARTH OBSERVATION: APPLICATIONS IN ENVIRONMENTAL MONITORING AND DISASTER RESPONSE
Authors: Kotovirta V. , Toivanen T. , Tergujeff R. , Häme T. , Molinier M.
Published: April 2015

Abstract. Citizen science is a promising way to increase temporal and spatial coverages of in-situ data, and to aid in data processing and analysis. In this paper, we present how citizen science can be used together with Earth observation, and demonstrate its value through three pilot projects focusing on forest biomass analysis, data management in emergencies and water quality monitoring. We also provide recommendations and ideas for follow-up activities. In the forest biomass analysis pilot, in the state of Durango (Mexico), local volunteers make in-situ forest inventory measurements with mobile devices. The collected data is combined with Landsat-8 imagery to derive forest biomass map of the area. The study area includes over 390 permanent sampling plots that will provide reference data for concept validation and verification. The emergency data management pilot focuses in the Philippines, in the areas affected by the typhoons Haiyan in November 2013 and Hagupit in December 2014. Data collected by emergency workers and citizens are combined with satellite data (Landsat-8, VHR if available) to intensify the disaster recovery activities and the coordination efforts. Simple processes for citizens, nongovernmental organisations and volunteers are developed to find and utilize up to date and freely available satellite imagery for coordination purposes and for building new not-for-profit services in disaster situations. In the water quality monitoring pilot, citizens around the Baltic Sea area contribute to the algae situation awareness by collecting algae observations using a mobile application. In-situ observations are compared with surface algal bloom products based on the satellite imagery, e.g. Aqua MODIS images with 500 meter resolution. As an outcome, the usability of the citizen observations together with satellite data in the algae monitoring will be evaluated.
COMPARISON OF UNSUPERVISED VEGETATION CLASSIFICATION METHODS FROM VHR IMAGES AFTER SHADOWS REMOVAL BY INNOVATIVE ALGORITHMS
Authors: Movia A. , Beinat A. , Crosilla F.
Published: April 2015

Abstract. The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the classification of the image components and for the extraction of the needed information. The aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental biochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been experimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned by the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of them are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based on RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based on Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called “not-centered oblique Procrustes” and “anisotropic Procrustes” methods to efficiently restore brightness with respect to a linear correlation correction based on the Cholesky decomposition. Some experimental results obtained by different classification methods after shadows removal carried out with the innovative algorithms are presented and discussed.
MAPPING LAND COVER IN THE TAITA HILLS, SE KENYA, USING AIRBORNE LASER SCANNING AND IMAGING SPECTROSCOPY DATA FUSION
Authors: Piiroinen R. , Heiskanen J. , Maeda E. , Hurskainen P. , Hietanen J. , Pellikka P.
Published: April 2015

Abstract. The Taita Hills, located in south-eastern Kenya, is one of the world’s biodiversity hotspots. Despite the recognized ecological importance of this region, the landscape has been heavily fragmented due to hundreds of years of human activity. Most of the natural vegetation has been converted for agroforestry, croplands and exotic forest plantations, resulting in a very heterogeneous landscape. Given this complex agro-ecological context, characterizing land cover using traditional remote sensing methods is extremely challenging. The objective of this study was to map land cover in a selected area of the Taita Hills using data fusion of airborne laser scanning (ALS) and imaging spectroscopy (IS) data. Land Cover Classification System (LCCS) was used to derive land cover nomenclature, while the height and percentage cover classifiers were used to create objective definitions for the classes. Simultaneous ALS and IS data were acquired over a 10 km × 10 km area in February 2013 of which 1 km × 8 km test site was selected. The ALS data had mean pulse density of 9.6 pulses/m2, while the IS data had spatial resolution of 1 m and spectral resolution of 4.5–5 nm in the 400–1000 nm spectral range. Both IS and ALS data were geometrically co-registered and IS data processed to at-surface reflectance. While IS data is suitable for determining land cover types based on their spectral properties, the advantage of ALS data is the derivation of vegetation structural parameters, such as tree height and crown cover, which are crucial in the LCCS nomenclature. Geographic object-based image analysis (GEOBIA) was used for segmentation and classification at two scales. The benefits of GEOBIA and ALS/IS data fusion for characterizing heterogeneous landscape were assessed, and ALS and IS data were considered complementary. GEOBIA was found useful in implementing the LCCS based classification, which would be difficult to map using pixel-based methods.
Preliminary Study on the Radar Vegetation Index (RVI) Application to Actual Paddy Fields by ALOS/PALSAR Full-polarimetry SAR Data
Authors: Yamada Y.
Published: April 2015

Abstract. Kim and van Zyl (2001) proposed a kind of radar vegetation index (RVI). RVI = 4*min(λ1, λ2, λ3) / (λ1 + λ2 + λ3) They modified the equation as follows. (2009) RVI = 8 * σ0hv / (σ0hh + σ0vv +σ0hv ) by L-band full-polarimetric SAR data. They applied it into rice crop and soybean. (Y.Kim, T.Jackson et al., 2012) They compared RVI for L-, C- and X-bands to crop growth data, LAI and NDVI. They found L-band RVI was well correlated with Vegetation Water Content, LAI and NDVI. But the field data were collected by the multifrequency polarimetric scatterometer. The platform height was 4.16 meters from the ground. The author tried to apply the method to actual paddy fields near Tsukuba science city in Japan using ALOS/PALSAR, full-polarimetry L-band SAR data. The staple crop in Eastern Asia is rice and paddy fields are dominant land use. A rice-planting machine comes into wide use in this areas. The young rice plants were bedded regularly ridged line in the paddy fields by the machine. The space between two ridges of rice plants is about 30 cm and the wave length of PALSAR sensor is about 23 cm. Hence the Bragg scattering will appear depending upon the direction of the ridges of paddy fields. Once the Bragg scattering occurs, the backscattering values from the pixels should be very high comparing the surrounding region. Therefore the radar vegetation index (RVI) would be saturated. The RVI did not follow the increasing of vegetation anymore. Japan has launched ALOS-2 satellite and it has PALSAR-2, L-band SAR. Therefore RVI application product by PALSAR-2 will be watched with deep interest.
Application of Lidar Data and 3D-City Models in Visual Impact Simulations of Tall Buildings
Authors: Czyńska K.
Published: April 2015

Abstract. The paper examines possibilities and limitations of application of Lidar data and digital 3D-city models to provide specialist urban analyses of tall buildings. The location and height of tall buildings is a subject of discussions, conflicts and controversies in many cities. The most important aspect is the visual influence of tall buildings to the city landscape, significant panoramas and other strategic city views. It is an actual issue in contemporary town planning worldwide. Over 50% of high-rise buildings on Earth were built in last 15 years. Tall buildings may be a threat especially for historically developed cities – typical for Europe. Contemporary Earth observation, more and more available Lidar scanning and 3D city models are a new tool for more accurate urban analysis of the tall buildings impact. The article presents appropriate simulation techniques, general assumption of geometric and computational algorithms – available methodologies and individual methods develop by author. The goal is to develop the geometric computation methods for GIS representation of the visual impact of a selected tall building to the structure of large city. In reference to this, the article introduce a Visual Impact Size method (VIS). Presented analyses were developed by application of airborne Lidar / DSM model and more processed models (like CityGML), containing the geometry and it’s semantics. Included simulations were carried out on an example of the agglomeration of Berlin.
The Application of Modified Normalized Difference Water Index (MNDWI) by Leaf Area Index in the Retrieval of Regional Drought Monitoring
Authors: Zhang H.-w. , Chen H.-l.
Published: April 2015

Abstract. The vegetation coverage is one of the important factors that restrict the accuracy of remote sensing retrieval of soil moisture. In order to effectively improve the accuracy of the remote sensing retrieval of soil moisture and to reduce the impact of vegetation coverage variation on the retrieval accuracy, the Leaf Area Index (LAI) is introduced to the Normalized Difference Water Index (NDWI) to greatly improve the accuracy of the soil moisture retrieval. In its application on the regional drought monitoring, the paper uses the relative LAI from two places which locate in the north and south of Henan Province respectively (Xin Xiang and Zhu Ma Dian) as indicators. It uses the days after turned-green stage to conduct difference value correction on the Relative Leaf Area Index (RLAL) of the entire province, so as to acquire the distribution of RLAI of the province’s wheat producing area. After this, the local remote sensing NDWI will be Modified （MNDWI = NDWI ×RLAI ） to acquire the soil moisture distribution status of the entire province’s wheat producing area. The result shows that, the Modified Normalized Difference Water Index of LAI which based on the days after turned-green stage can improve the real time retrieval accuracy of soil moisture under different vegetation coverage.
Rainfall in an experimental watershed: a comparison between observed and TRMM 3B42V7 dataset
Authors: Barbosa L. R. , Freitas E. S. , Almeide C. N. , Melo D. C. D.
Published: April 2015

Abstract. In 2003, a network for hydrology of the semiarid region (REHISA in Portuguese) was created in Brazil. Since then, experimental watersheds in this region has been providing hydrometeorological data collected in automatic stations. However, the spatial distribution of these gauges might be insufficient to thoroughly understand the hydrological processes occurring in the area. Remotely sensed hydrological variables presents a possible way to overcome such limitations as long as these estimates prove to have enough accuracy. This paper compares the monitored yearly and monthly rainfall in the Guaraíra experimental watershed with data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version-7 product. The study area has a drainage area of 5.84 km² and is located in the coastal region of Paraíba State, where the mean annual rainfall is 1,700 mm. Two automatic stations provided rainfall data from 2004 to 2011 to assess the satellite estimates in annual and monthly basis. TRMM 3B42V7 performance was evaluated based on graphical analysis. In the annual analysis, relative error ranged from 3 to -51%, however due to the monthly variation, such errors seemed to be insufficient to draw any conclusion, regarding the monthly results. For instance, when the relative error was 3% (difference of 48.3 mm for year 2004), the monthly analysis showed that this was due to a compensation occurred during the year, this is, a month for which rainfall was significantly underestimated by TRMM was compensated by another one when the satellite rainfall was overestimated. On the opposite, in 2007 (relative error 51%, 855 mm of difference), the monthly data analysis showed that just 4 months presented observed data overestimation, but it was enough to result in such annual overestimation. The monthly analysis showed that 29% of the months presented difference between observed rainfall and TRMM data greater than 70 mm and less than 386 mm, which can be considered a relevant error. 72.4% of these cases (monthly analyses) occurred in years in which the annual rainfall were within the ordinary mean (from 1,205 to 1,760 mm/year). Another important result is the underestimation cases were concentrated on the second part of the rainy period. Thus, conclusions points out that TRMM estimates can provide useful information on annual basis, but users should be aware concerning the underestimation, specially on monthly basis for the studied region.
Technology targeting for sustainable intensification of crop production in the Delta region of Bangladesh
Authors: Schulthess U. , Krupnik T.  J. , Ahmed Z. U. , McDonald A. J.
Published: April 2015

Abstract. Remote sensing data are nowadays being acquired within short intervals and made available at a low cost or for free. This opens up opportunities for new remote sensing applications, such as the characterization of entire regions to identify most suitable areas for technology targeting. Increasing population growth and changing dietary habits in South Asia call for higher cereal production to ensure future food security. In the Delta area of Bangladesh, surface water is considered to be available in quantities large enough to support intensification by adding an irrigated dry season crop. Fuel-efficient, low lift axial flow pumps have shown to be suitable to carry water to fields that are within a buffer of four hundred meters of the rivers. However, information on how and where to target surface water irrigation efforts is currently lacking. We describe the opportunities and constraints encountered in developing a procedure to identify cropland for which axial flow pumps could be successfully deployed upon in a 43’000 km2 area. First, we isolated cropland and waterways using Landsat 5 and 7 scenes using image segmentation followed by classification with the random forest algorithm. Based on Landsat 7 and 8 scenes, we extracted maximum dry season enhanced vegetation index (EVI) values, which we classified into fallow, low-, and high-intensity cropland for the last three years. Last, we investigated the potential for surface water irrigation on fallow and low-intensity land by applying a cropping risk matrix to address the twin threats of soil and water salinity. Our analysis indicates that there are at least 20,000 ha of fallow land under the low-risk category, while more than 100,000 ha of low-intensity cropland can be brought into intensified production. This information will aid in technology targeting for the efficient deployment of surface water irrigation as a tool for intensification.
Hydrological characterization of the Usumacinta River Basin towards the preservation of environmental services
Authors: Tapia-Silva F.-O. , Contreras-Silva A.-I. , Rosales-Arriaga E.-R.
Published: April 2015

Abstract. The Usumacinta basin is characterized by aboundance of natural and scenic resources. It also houses a vast biodiversity (wich also means invaluable genetic resources). Its river is the longest in Mexico (main channel length of 927 km). Therefore it is one of the most productive regions of the country in terms of ecosystem services (such as habitat for biodiversity, maintenance of wetlands, water flows generation, carbon sequestration, soil retention, etc.) that are directly related to the hydrological functioning. During the last centuries, human activities on the Usumacinta basin have drastically changed its geomorphology and vegetation cover. As a result the hydrological cycle has been greatly modified. Therefore, it is necessary to conduct studies in order to support planning activities and implementation of public policies, as well as, to generate a permanent scientific ecosystem monitoring system. This paper presents the results of a study focused on estimating the water balance of this frontier basin. Satellite and field data is used. Variables as vegetation cover (generated by classification of Landsat and MODIS), digital terrain model (SRTM), surface temperature (MODIS), potential evaporation, precipitation and runoff measurements were processed. Various techniques of Remote Sensing, geospatial models (as SSEB and the model for definition of surface hydrological connectivity) and spatial analysis (geostatistics and map algebra) were implemented. The results were integrated into the environment of a Geographic Information System. These are estimates of actual evapotranspiration, soil moisture and runoff, among other biophysical parameters. For the lower part of the basin a balance was performed to estimate inputs of water from runoff and precipitation to the large amount of wetlands in the area. Additionally, areas generating runoff and areas in which most of the precipitaion infiltrates were also mapped. The geo-information obtained is requiered for purposes of planning and land management.
Evaluation method of water quality for river based on multi-spectral remote sensing data
Authors: Xiao X. , Jian X. , Xiongfei W. , Chengfang H. , Xuejun C. , Zhaohui W. , Dengzhong Z.
Published: April 2015

Abstract. With the rapid development of the regional economy, water pollution has gradually become an environmental problem that cannot be ignored. As an important water source in central China, the Han River should strengthen water quality monitoring and management in order to ensure the sustainable development of watershed and related areas. Taking typical sections of middle and lower reaches of the Han River as the study area, this paper focuses on rapid river water quality assessment using multispectral remote sensing images. Based on measured water quality data and synchronous spatial high and medium-resolution remote sensing data (multi-spectral data of ZY3 and HJ1A) in 2013, neural network algorithm is used to establish water quality index retrieval model for the study area, and then water quality status is assessed accordingly. The results show that BP neural network retrieval model of water quality index that is established based on multispectral data of ZY3 satellite has higher accuracy and that its assessment results are of high credibility and strong applicability, which can really reflect changes in water quality and better achieve water quality assessment for the study area. In addition, water quality assessment results show that major excessive factors in the study area are total nitrogen and total phosphorus; the polluting type is organic pollution; water quality varies greatly with seasons.
Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling
Authors: Fernandes A. , Riffler M. , Ferreira J. , Wunderle S. , Borrego C. , Tchepel O.
Published: April 2015

Abstract. Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx - Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.
Preliminary Research on Radiance Fog Detection based on time series MTSAT data
Authors: Wen X. , Li Z. , Zhang S. , Shen S. , Hu D. , Xiao X.
Published: April 2015

Abstract. Fog is a kind of disastrous weather phenomenon. In this paper, the geostationary satellite MTSAT imagery is selected as the main data source to radiance fog detection. According to the unique feature of radiance fog from its generation to dissipation, especially considering the difference between clouds and fog during their lifecycle, the characteristics in frequency domain was constructed to discriminate fog from clouds, The time series MTSAT images were register with a modified Gauss Newton optimization method firstly, then, the Savitzky-Golay smoothing filter was applied to the time series remote sensing imageries to process the noises in the original signal, after that the non-orthogonal Haar wavelets was applied to convert the signal from time domain into frequency domain. The coefficient of high frequency component, including the properties: “max”, “min”, “the location of the min”, “the interval length between the max and min”, “the coefficient of linear fit for the high frequency”, these properties are selected as the characteristic parameters to distinguish fog from clouds. The experiment shows that using the algorithm proposed in this paper, the radiance fog could be monitored effectively, and it is found that although it is difficult to calculate the thickness of the fog directly, while the duration of fog could be obtained by using the frequency feature.
Land-use and land-cover dynamics monitored by NDVI multitemporal analysis in a selected southern Amazonian area (Brazil) for the last three decades
Authors: Borini Alves D. , Pérez-Cabello F. , Rodrigues Mimbrero M.
Published: April 2015

Abstract. This study aims to analyse the dynamics of land-use and land-cover (LULC) in a selected southern Amazonian area (Brazil), monitoring and distinguishing trajectories in NDVI (Normalized Difference Vegetation Index) variations for the last three decades. The area, with a total of 17336 km², has been subject to significant LULC changes associated with deforestation progress and use of fire. Considering available Landsat time series, it was selected an image per year from 1984 to 2013 (path/row -231/66), at a particular period of year, atmospherically corrected using LEDAPS tools. NDVIs values were generated for each selected image. Furthermore, the images of 1984 and 2010 still underwent a classification of LULC differentiate five categories: water, forest, secondary/degraded forest, savannah/pasture and crop/bare soil. The trajectories in NDVI variation values were analysed by R software, considering intersections of classified categories. The pixels identified as forests on the images of 1984 and 2010 displayed stable trajectories of NDVI values, with average value 0.824 and coefficient of variation 3.9%. While the pixels of savannah/pasture, which was periodically affected by fire, had an average NDVI value 0.585 and coefficient of variation 15,1%. The main regressive trajectory was the transition “forest to crop/bare soil", identifying 1999 as the starting point in the drop in NDVI values, associated with an increase of the deforested areas. Therefore, the results show distinct trajectories associated with NDVIs and LULC changes that assist in better understanding the dynamics of ecological processes and the human impacts operating in the area.
Performance of the Enhanced Vegetation Index to Detect Inner-annual Dry Season and Drought Impacts on Amazon Forest Canopies
Authors: Brede B. , Verbesselt J. , Dutrieux L. , Herold M.
Published: April 2015

Abstract. The Amazon rainforests represent the largest connected forested area in the tropics and play an integral role in the global carbon cycle. In the last years the discussion about their phenology and response to drought has intensified. A recent study argued that seasonality in greenness expressed as Enhanced Vegetation Index (EVI) is an artifact of variations in sun-sensor geometry throughout the year. We aimed to reproduce these results with the Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD43 product suite, which allows modeling the Bidirectional Reflectance Distribution Function (BRDF) and keeping sun-sensor geometry constant. The derived BRDF-adjusted EVI was spatially aggregated over large areas of central Amazon forests. The resulting time series of EVI spanning the 2000-2013 period contained distinct seasonal patterns with peak values at the onset of the dry season, but also followed the same pattern of sun geometry expressed as Solar Zenith Angle (SZA). Additionally, we assessed EVI’s sensitivity to precipitation anomalies. For that we compared BRDF-adjusted EVI dry season anomalies to two drought indices (Maximum Cumulative Water Deficit, Standardized Precipitation Index). This analysis covered the whole of Amazonia and data from the years 2000 to 2013. The results showed no meaningful connection between EVI anomalies and drought. This is in contrast to other studies that investigate the drought impact on EVI and forest photosynthetic capacity. The results from both sub-analyses question the predictive power of EVI for large scale assessments of forest ecosystem functioning in Amazonia. Based on the presented results, we recommend a careful evaluation of the EVI for applications in tropical forests, including rigorous validation supported by ground plots.
Comparison of interferometric and stereo-radargrammetric 3D metrics in mapping of forest resources
Authors: Karila K. , Karjalainen M. , Yu X. , Vastaranta M. , Holopainen M. , Hyyppä J.
Published: April 2015

Abstract. Accurate forest resources maps are needed in diverse applications ranging from the local forest management to the global climate change research. In particular, it is important to have tools to map changes in forest resources, which helps us to understand the significance of the forest biomass changes in the global carbon cycle. In the task of mapping changes in forest resources for wide areas, Earth Observing satellites could play the key role. In 2013, an EU/FP7-Space funded project “Advanced_SAR” was started with the main objective to develop novel forest resources mapping methods based on the fusion of satellite based 3D measurements and in-situ field measurements of forests. During the summer 2014, an extensive field surveying campaign was carried out in the Evo test site, Southern Finland. Forest inventory attributes of mean tree height, basal area, mean stem diameter, stem volume, and biomass, were determined for 91 test plots having the size of 32 by 32 meters (1024 m2). Simultaneously, a comprehensive set of satellite and airborne data was collected. Satellite data also included a set of TanDEM-X (TDX) and TerraSAR-X (TSX) X-band synthetic aperture radar (SAR) images, suitable for interferometric and stereo-radargrammetric processing to extract 3D elevation data representing the forest canopy. In the present study, we compared the accuracy of TDX InSAR and TSX stereo-radargrammetric derived 3D metrics in forest inventory attribute prediction. First, 3D data were extracted from TDX and TSX images. Then, 3D data were processed as elevations above the ground surface (forest canopy height values) using an accurate Digital Terrain Model (DTM) based on airborne laser scanning survey. Finally, 3D metrics were calculated from the canopy height values for each test plot and the 3D metrics were compared with the field reference data. The Random Forest method was used in the forest inventory attributes prediction. Based on the results InSAR showed slightly better performance in forest attribute (i.e. mean tree height, basal area, mean stem diameter, stem volume, and biomass) prediction than stereo-radargrammetry. The results were 20.1% and 28.6% in relative root mean square error (RMSE) for biomass prediction, for TDX and TSX respectively.
Feature extraction from geoeye-1 stereo pairs data for forested area
Authors: Stournara P. , Georgiadis C. , Kaimaris D. , Tsakiri-Strati M. , Tsioukas V.
Published: April 2015

Abstract. Remote sensing facilitates the extraction of information for earth’s surface through its capability of acquiring images covering large areas and the availability of commercial software for their processing. The aim of this study is the feature extraction from three Geoeye-1 stereo pairs for forested area. The study area is located in central mountainous forested peninsula of Chalkidiki, in northern Greece. Dominant forest tree species of the site are oak (Quercus conferta), beech (Fagus moesiaca), black pine (Pinus nigra) and calabrian pine (Pinus brutia). Very High Resolution (VHR) Geoeye-1 stereo pair satellite images were utilized in panchromatic and multispectral mode. The panchromatic mode was employed for Digital Surface Model (DSM) generation and its evaluation. In this study the High Pass Filter (HPF) data fusion technique was applied between panchromatic and multispectral mode for acquiring a new image with the benefits of both contracting images. Because of the fact that the feature extraction was attempted in a forested region, NDVI index and Tasseled Cap transformation were applied in the fused images’ evaluation procedure. Optical assessment was also applied. The accuracy of the generated DSM and the evaluation results of the fused images were remarkable.
A laboratory procedure for measuring and georeferencing soil colour
Authors: Marqués-Mateu Á. , Balaguer-Puig M. , Moreno-Ramón H. , Ibáñez-Asensio S.
Published: April 2015

Abstract. Remote sensing and geospatial applications very often require ground truth data to assess outcomes from spatial analyses or environmental models. Those data sets, however, may be difficult to collect in proper format or may even be unavailable. In the particular case of soil colour the collection of reliable ground data can be cumbersome due to measuring methods, colour communication issues, and other practical factors which lead to a lack of standard procedure for soil colour measurement and georeferencing. In this paper we present a laboratory procedure that provides colour coordinates of georeferenced soil samples which become useful in later processing stages of soil mapping and classification from digital images. The procedure requires a laboratory setup consisting of a light booth and a trichromatic colorimeter, together with a computer program that performs colour measurement, storage, and colour space transformation tasks. Measurement tasks are automated by means of specific data logging routines which allow storing recorded colour data in a spatial format. A key feature of the system is the ability of transforming between physically-based colour spaces and the Munsell system which is still the standard in soil science. The working scheme pursues the automation of routine tasks whenever possible and the avoidance of input mistakes by means of a convenient layout of the user interface. The program can readily manage colour and coordinate data sets which eventually allow creating spatial data sets. All the tasks regarding data joining between colorimeter measurements and samples locations are executed by the software in the background, allowing users to concentrate on samples processing. As a result, we obtained a robust and fully functional computer-based procedure which has proven a very useful tool for sample classification or cataloging purposes as well as for integrating soil colour data with other remote sensed and spatial data sets.
A modified approach for change detection using change vector analysis in posterior probability space
Authors: Azzouzi S. A. , Vidal A. , Bentounes H. A.
Published: April 2015

Abstract. The multispectral and multitemporal data coming from satellites allow us to extract valuable spatiotemporal change. Consequently, Earth surface change detection analysis has been used in the past to monitor land cover changes caused by different reasons. Several techniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automatic spatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysis in Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposed technique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at the same region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected method for supervised classification since it provides good properties in the posterior probability map. An improved method for threshold determination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequate threshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the region of Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pair of Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed and unchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area of study that was selected in the literature.
Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data
Authors: Espinoza Molina D. , Datcu M
Published: April 2015

Abstract. The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform’s goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.
The Swarm Archiving Payload Data Facility, an Instance Configuration of the ESA Multi-Mission Facility
Authors: Pruin B. , Martini A. , Shanmugam P. , Lopes C.
Published: April 2015

Abstract. The Swarm mission consists of 3 satellites, each carrying an identical set of instruments. The scientific algorithms for processing are organized in 11 separate processing steps including automated product quality control. In total, the mission data consists of data products of several hundred distinct types from raw to level 2 product types and auxiliary data. The systematic production for Swarm within the ESA Archiving and Payload Data Facility (APDF) is performed up to level 2. The production up to L2 (CAT2-mature algorithm) is performed completely within the APDF. A separate systematic production chain from L1B to L2 (CAT1-evolving algorithm) is performed by an external facility (L2PS) with output files archived within the APDF as well. The APDF also performs re-processing exercises. Re-processing may start directly from the acquired data or from any other intermediate level resulting in the need for a refined product version and baseline management. Storage, dissemination and circulation functionality is configurable in the ESA generic multi-mission elements and does not require any software coding. The control of the production is more involved. While the interface towards the algorithmic entities is standardized due to the introduction of a generic IPF interface by ESA, the orchestration of the individual IPFs into the overall workflows is distinctly mission-specific and not as amenable to standardization. The ESA MMFI production management system provides extension points to integrate additional logical elements for the build-up of complex orchestrated workflows. These extension points have been used to inject the Swarm-specific production logic into the system. A noteworthy fact about the APDF is that the dissemination elements are hosted in a high bandwidth infrastructure procured as a managed service, thus affording users a considerable access bandwidth. This paper gives an overview of the Swarm APDF data flows. It describes the elements of the solution with particular focus on how the available generic multi-mission functionality of the ESA MMFI was utilized and where there was a need to implement missionspecific extensions and plug-ins. The paper concludes with some statistics on the system output during commissioning and early operational phases as well as some general considerations on the utilization of a framework like the ESA MMFI, discussing benefits and pitfalls of the approach.
A Robust False Matching Points Detection Method for Remote Sensing Image Registration
Authors: Shan X. J. , Tang P.
Published: April 2015

Abstract. Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.
Comparison among operators for detecting and/or extracting or roads using the matlab software and the cartomorph software
Authors: Silva E. A. , Chaves C. D. , Santos A. F.
Published: April 2015

Abstract. FCT / UNESP has been developing CARTOMORPH to be a public domain software which can be operated by users needing to extract and / or detect features from digital images. In this work, two methods were applied for the extraction and / or detection of the contours of the feature of interest (the highway) using a digital image containing part of a highway. One method was applied through the use of operators contained in the Mathematical Morphology toolbox, which is a private domain, SDC Information Systems, and the other was applied using the routine contained in the CARTOMORPH software. In the toolbox, the operators used were mmreadgray, responsible for opening the original image, mmhisteq, which softens the image, mmneg which can reverse the grayscale of pixels, and mmareaclose, which aims to remove image segmentation. This set of operators has resulted in a routine capable of extracting and / or detecting the contour of a road. The Blur and Minimum (Gblur) operator was used in the process using the CARTOMORPH software. This can detect the edges of objects present in the original blurred image, and transform them into an ideal edge ramp. Results from both methods were compared and the conclusion is that the blur and minimum (GBlur) operator gave a better performance. This finding indicates that the set of operators implemented in CARTOMORPH will be able to be operated by users in the field of cartography and related fields, thus enabling the use of a public domain software with efficient results.
Enhancing DInSAR capabilities for landslide monitoring by applying GIS-based multicriteria filtering analysis
Authors: Beyene F. , Knospe S. , Busch W.
Published: April 2015

Abstract. Landslide detection and monitoring remain difficult with conventional differential radar interferometry (DInSAR) because most pixels of radar interferograms around landslides are affected by different error sources. These are mainly related to the nature of high radar viewing angles and related spatial distortions (such as overlays and shadows), temporal decorrelations owing to vegetation cover, and speed and direction of target sliding masses. On the other hand, GIS can be used to integrate spatial datasets obtained from many sources (including radar and non-radar sources). In this paper, a GRID data model is proposed to integrate deformation data derived from DInSAR processing with other radar origin data (coherence, layover and shadow, slope and aspect, local incidence angle) and external datasets collected from field study of landslide sites and other sources (geology, geomorphology, hydrology). After coordinate transformation and merging of data, candidate landslide representing pixels of high quality radar signals were filtered out by applying a GIS based multicriteria filtering analysis (GIS-MCFA), which excludes grid points in areas of shadow and overlay, low coherence, non-detectable and non-landslide deformations, and other possible sources of errors from the DInSAR data processing. At the end, the results obtained from GIS-MCFA have been verified by using the external datasets (existing landslide sites collected from fieldworks, geological and geomorphologic maps, rainfall data etc.).