Remote Sensing

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Remote sensing for precision agriculture

Remote sensing for precision agriculture

This study presents a method for data fusion based on evidential reasoning in the agricultural con- text. With the Transferable Belief Model, satellite data and GIS data can be fused independently of their unit and spatial resolution to model yield zones. These yield zones can then be used as ma- nagement zones in precision farming applications, because they represent vitality differences in the field, which can be addressed by precision farming measures. The TBM calculates with quantified beliefs, not probabilities, because probabilities are very difficult to determine in an agricultural con- text. The beliefs allow the expert knowledge and experience of the user - e.g. a farmer or a consul- tant - to be integrated into the model. The calculation of the quantified beliefs is easy to understand and transparent. A wheat field in north-eastern Germany was used to show how the method works and what values the parameters influencing the TBM could have. The method leaves the farmer a lot of freedom in decision making and does not risk patronizing him with an intransparent, finis- hed solution. In practice, however, the determination of this large number of parameters can be an obstacle to the successful implementation of the method. A further development of the method could therefore be to automatically develop a standard ruleset on the basis of past yield maps and the data used as sources of evidence. The farmer could then still adapt this standard rule set indivi- dually but would not have to work without reference. An analysis of a large amount of yield data in similar habitats and the existing GIS data as well as the large archive of remote sensing data could be a reliable data basis for such a ruleset. Especially if the farmer does not have his own yield data. Data mining algorithms would be very effective for the analysis.
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Inversion technique for optical remote sensing in shallow water

Inversion technique for optical remote sensing in shallow water

This work deals with remote sensing of optically complex waters: shallow water in coastal re- gions and in lakes, where the bottom is visible. Water masses in coastal areas and in lakes are often designated as case-2 waters. The nomenclature arises from a commonly used classification scheme for optically deep water, according to which oceanic waters are partitioned into case-1 and case-2 waters. This was introduced by Morel and Prieur (1977) and refined by Prieur and Sathyendranath (1981) and Gordon and Morel (1983). By definition, case-1 waters are those in which phytoplankton (index P) and its accompanying substances are the principal components for variations in optical properties of the water. Case-2 waters are influenced not only by phy- toplankton, but also by other substances, notably suspended material (index X) and gelbstoff (index Y). Other names for gelbstoff are yellow substance, coloured dissolved organic matter (CDOM), and gilvin. A diagrammatic representation of the two cases is adapted from the third report of the International Ocean Colour Coordinating Group (IOCCG, 2000) and is shown in figure 1.2. The classification scheme is based on relative contributions of the three substances and does not depend on the magnitude of each substance. For example, case-1 waters can range from phytoplankton-poor (oligotrophic) to phytoplankton-rich (eutrophic).
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Remote-Sensing of Planetary Surface Using Infrared Spectroscopy.

Remote-Sensing of Planetary Surface Using Infrared Spectroscopy.

The nature of planetary surfaces provides key information about the geologic, physical, and chemical structure as well as the evolution of a planetary body. Key goals of comparative planetology are to unveil common origin processes and divergent evolutionary paths. For most bodies in the solar systems the remote-sensing view onto the surface is still the main if not the only available data source for comparative planetology analyses. Spacecraft studies have made it possible to make meaningful observations of a large number of different planetary objects including small bodies like asteroids and comets, the terrestrial and outer planets and their moons.. Over the last decades, these spacecraft studies have strongly changed our view on the origin, the current similarities, differences, and the evolutionary paths of the single bodies. Missions have succeeded in recording different evolutionary stages of our solar system studying the whole spectrum of planetary objects. These objects ranging from poorly differentiated bodies like asteroid 2867 Šteins [1] (ESA Rosetta mission) over protoplanet type bodies like asteroid (4) Vesta [2] (NASA DAWN mission) to differentiated objects like planets. This enables reconstructing the planetary system’s formation starting from early processes up to the current stage of the highly differentiated objects. Apart from this time line, new geoscientific results like the geologic activity of the icy outer moons driven by tidal forces [3] have led to a fundamental review of habitability in our solar system.
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DEMMIN - Calibration and Validation Site for Remote Sensing

DEMMIN - Calibration and Validation Site for Remote Sensing

(2014): Automated Derivation of Value Added Information Products on Basis of In-Situ-Data for Validation of Remote Sensing Data.- 12th International Conference on Computational Science[r]

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DEMMIN - Test Site for Remote Sensing in Agricultural Application

DEMMIN - Test Site for Remote Sensing in Agricultural Application

• The DLR wants to develop the test site DEMMIN into a technical and technological blueprint for the calibration and validation of remote sensing data, missions, applicatio[r]

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DEMMIN - Test Site for Remote Sensing in Agricultural Application

DEMMIN - Test Site for Remote Sensing in Agricultural Application

(2014): Automated Derivation of Value Added Information Products on Basis of In-Situ-Data for Validation of Remote Sensing Data.- 12th International Conference on Computational Science[r]

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Remote Sensing for the Analysis of Global Urbanization

Remote Sensing for the Analysis of Global Urbanization

definitive. They are like a journey that never ends” ( UN-Habitat, 2008 ). The current transformation of urban landscapes at various scales on our planet is challenging for the global society. This habilitation elaborated applied research directions based on remote sensing and other geodata sources in urban geography. It shows that remote sensing data are crucial to document the physical developments and outcomes of this transformation in ever-increasing speed. It also reveals that this spatial approach towards urban form allows uncovering underlying demographic, social, economic, political or environmental patterns. This makes clear that remote sensing must play a crucial role for a more comprehensive documentation and understanding of these (physical) processes and related urban forms across the globe. These uncovered capabilities of remote sensing data, however, contain a commitment for this discipline and its communities that on-going transitions on our planet need to be documented, observed and analyzed in a more systematic way to really gain societal impact.
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Aerosol distribution above Munich using remote sensing techniques

Aerosol distribution above Munich using remote sensing techniques

At the moment, the best source for height resolved aerosol information around the globe is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathnder Satellite Observations (CALIPSO). It was launched as part of the A-Train of NASA in April 2006 and oers a unique possibility to get vertically resolved optical properties of aerosols and clouds [Winker et al., 2009]. With CALIPSO, a multitude of dierent aerosol studies is possible, for example to examine the air quality above polluted areas like Bangkok [Bridhikitti, 2013] or the transportation of the Eyjafjallajökull ash plume [Winker et al., 2012], or to determine the distribution of aerosols in the arctic [Di Pierro et al., 2013]. Huang et al. [2013] derived the seasonal and diurnal variations of aerosol extinction proles and analyzed the aerosol types from CALIPSO 5- year observations. With CALIPSO it is possible to study the aerosol distribution above any region, like the variation of the aerosol optical depth (AOD) along the ight path. Additionally, CALIPSO can also be used to describe the variation of the PBL [McGrath- Spangler and Denning, 2013]. Also the aerosol interaction with clouds is an important topic, for which CALIPSO data  mostly in combination with passive remote sensing instruments can be a great advantage [e.g. Costantino and Bréon, 2013, Várnai and Marshak, 2011].
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Understanding Forest Health with Remote Sensing - Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

Understanding Forest Health with Remote Sensing - Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.
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Monitoring agricultural plantations with remote sensing imagery

Monitoring agricultural plantations with remote sensing imagery

Suggested Citation: Slave, Camelia; Rotman, Anca (2013) : Monitoring agricultural plantations with remote sensing imagery, In: Agrarian Economy and Rural Development - Realities and Perspectives for Romania. 4th Edition of the International Symposium, November 2013, Bucharest, The Research Institute for Agricultural Economy and Rural Development (ICEADR), Bucharest, pp. 112-117

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Providing data quality information for remote sensing applications

Providing data quality information for remote sensing applications

The last few years have seen a growth in availability and accessibility of remote sensing (RS) data. Several of the Sentinels, the Earth Observation (EO) satellite fleet of Copernicus, have become operational with more to come (European Commission, 2016). The development of Data and Information Access Services (DIAS) is on the way that will provide cloud-processing platforms as part of the Copernicus downstream services, and a large industry provides and develops new information products and services (ibid.). This leads to a larger and more diverse user community for RS data: new ideas for applications are implemented as shown by success stories from different initiatives, e.g. the Copernicus Masters competition (https://www.copernicus-masters.com/) and the users’ testimonials on operational satellite applications gathered by Eurisy (https://www.eurisy.org/good-practices.php). The raised awareness of the benefits of RS data may help to address new research questions in a diverse set of scientific disciplines. Many of them are organized in the Group on Earth Observations (GEO) community activities and, with a global focus, target the UN 2030 Agenda for Sustainable Development, the Paris Agreement on Climate Change and the Sendai Framework for Disaster Risk Reduction (Group on Earth Observations, 2017). Researchers and developers accordingly have to come up with quality criteria to assess the suitability of their information
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Interoperability of geo-information in remote sensing-based biodiversity monitoring

Interoperability of geo-information in remote sensing-based biodiversity monitoring

transferability and comparability since there is no shared conceptualisation in the developed applications ( Nieland et al. , 2015b ). At present, there have been no standardised classication methodologies to get harmonised output products, and no benchmarking system even exists for the great variance of remote sensing-based analysis techniques. In practice, remote sensing always depends on data availability in combination with the right classication procedure. Forcing data producers to use standardised approaches would in many cases lead to less customisation and less quality of remote sensing output products ( Vanden Borre et al. , 2011 ). Hence, the remote sensing community is increasingly aware of the discrepancy between tailored regional products and comprehensive applicability. However, international policies (such as those mentioned in section 1.2.1) strongly rely on the comparability of spatial data collected on a regional level. Therefore, traditional remote sensing classication approaches do not always reach an operational user-basis especially when used to deliver very detailed and often locally-adapted environmental information. In fact, they are often driven by application examples and intended to measure and classify specics that are dened in existing directives, guidelines, and the subsequent nomenclatures. A possible solution to overcome this heterogeneity problem is to build up well-formalised shared vocabularies and link them to the tailored remote-sensing products to be able to infer comparability automatically and thereby preserve higher-level interoperability.
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Remote Sensing in Mapping Mangrove Ecosystems: An Object-Based Approach

Remote Sensing in Mapping Mangrove Ecosystems: An Object-Based Approach

for a particular focus. For a detailed overview of the numerous techniques and approaches applicable to the mapping of mangrove ecosystems, readers can refer to their paper. Pixel-based classification approaches are most frequently applied for mapping mangrove forests [28–34]. Tong et al. [31], for example, applied Maximum Likelihood classification to map the mangrove distribution in Ca Mau province based on SPOT 4 images. Pixel-based approaches are the subject of a study by Béland et al. [35], who investigated land cover changes related to aquaculture in the Red River Delta (Vietnam). The authors used multi-temporal Landsat data (1986, 1992 and 2001) to detect changes from mangrove forest to aquaculture using Tasseled Cap-derived information. In addition to these pixel-based approaches, several applications use spatial neighborhood information in object-based classification. Recently, object-based approaches have been applied successfully in many ecology-related remote sensing studies, such as landslide inventories [36], mapping burned areas using different sensors [37,38], monitoring land conversion [39], or assessing forest structural complexity [40]. In mangrove studies, for example, Conchedda et al. [41] used an object-based approach to map mangrove cover change in Casamance (Senegal) based on SPOT XS data. The authors performed a change-detection analysis based on object-based mapping results. For their mapping, they applied a multi-resolution segmentation and class-specific rules incorporating spectral properties and spectral/spatial relationships between image objects. Also, Wang et al. [42] demonstrated in their study on mangrove mapping for the coast of Panama that an improvement of classification accuracy resulted from object-based classification in comparison to pixel-based classification. Heumann [43] applied object-based image analysis and support vector machines for differentiating fringe-mangrove and true mangrove species. The result showed an overall accuracy greater than 94% (kappa = 0.863). Myint et al. [44] used spatial data as an input into the image object segmentation process and reported an accuracy greater than 90%. The superiority of object- based approaches over traditional pixel-based classification exercises for high-resolution satellite data has been demonstrated in numerous studies [26,33,40,42,43,45].
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Remote Sensing Image Classification: No Features, No Clustering

Remote Sensing Image Classification: No Features, No Clustering

Since 1981, he has been a Professor in electronics and telecommunications with UPB. Since 1993, he has been a Scientist with the German Aerospace Center (DLR), Munich, Germany. Currently, he is a Senior Scientist and Image Analysis Research Group Leader with the Remote Sensing Technology Insti- tute (IMF), DLR, Coordinator of the CNES-DLR-ENST Competence Centre on Information Extraction and Image Understanding for Earth Observation, and a Professor with Paris Institute of Technology/GET Telecom Paris. From 1991 to 1992, he was a Visiting Professor with the Department of Mathematics, University of Oviedo, Oviedo, Spain, and from 2000 to 2002 with the Universitouis Pasteur, and the International Space University, both in Strasbourg, France. From 1992 to 2002, he was a Longer Invited Professor with the Swiss Federal Institute of Technology ETH Zrich, Zrich, Switzerland. In 1994, he was a Guest Scientist with the Swiss Center for Scientific Computing (CSCS), Manno, Switzerland, and in 2003, he was a Visiting Professor with the University of Siegen, Siegen, Germany. His research interests include Bayesian inference, information and complexity theory, stochastic processes, model-based scene understanding, image information mining, for applications in information retrieval and understanding of high- resolution SAR and optical observations.
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Detection of traffic congestion in optical remote sensing imagery

Detection of traffic congestion in optical remote sensing imagery

During the past years, increasing traffic appears to be one of the major problems in urban and sub-urban areas [1]. Traffic congestion and jams are one of the main reasons for immensely increasing transportation costs due to the wasted time and extra fuel. A new type of information is needed for a more efficient use of road networks. Remote sensing sensors installed on aircrafts or satellites enable data collection on a large scale and thus seem to be very suitable for various traffic monitoring applications. Several airborne optical remote sensing systems are already in experimental use at German Aerospace Center DLR, e.g. airborne 3K camera system [2], consisting of three digital cameras capable of acquiring three images per second, and LUMOS [3]. Automatic detection of vehicles and estimation of their velocities in sequences of optical images is still a challenge and at present still results in a too low completeness (less than 70%) thus being not yet suitable e.g. for the estimating of the traffic density.
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[Assessment of urban location factors from remote sensing]

[Assessment of urban location factors from remote sensing]

Remote sensing is not able to deliver absolutely accurate census data. In fact, the question is, if the assessment of the example population density matches the dimension of the real world. An accuracy assessment is difficult because of a lack of reliable data. A census in 2000 in the whole quarter Üsküdar in Istanbul resulted in a population density of 12,837 inhabitants per km². For this analysis, only the central old part of Üsküdar was part of the sampling of the field work because the IKONOS imagery from 2003 displayed only this area. In this central high dense areas of old Üsküdar the result from the remote sensing analysis are 17,000 inhabitants per km². This seems to be a close approximation to the real values due to an expected higher population density in this central part with higher building density and lower portions of open spaces then in the peripheral areas of the quarter.
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Digital Beamforming Techniques for Spaceborne Radar Remote Sensing

Digital Beamforming Techniques for Spaceborne Radar Remote Sensing

The combination of spatiotemporal radar waveform en- coding and digital beamforming on receive is an innova- tive concept which enables new and very powerful SAR imaging modes for a wide range of remote sensing appli- cations. Examples are improved performance and ambi- guity suppression, moving object indication, as well as redundancy reduction in large receiver arrays. The oppor- tunity for beamforming on transmit enables furthermore a flexible distribution of the RF signal energy on the ground, which allows for the combination of different im- aging modes like a simultaneous high resolution spot- light like mapping of areas of high interest in combination with a simultaneous wide swath mapping. Such a hybrid mode is well suited to satisfy otherwise contradicting user requirements like e.g. the conflict between a continuous interferometric background mission and a high resolution data acquisition request within a wide incident angle range. The data acquisition could even be made adaptive where more system resources are devoted to areas of high interest and/or low SNR, thereby maximizing the infor- mation content for a given RF power budget. This can be regarded as a first step towards a cognitive radar which directs its resources to areas of high interest in analogy to the selective attention mechanisms of the human visual system with its saccadic eye movements. The rising inter- est in such systems is also well documented in a recent issue of the IEEE signal processing magazine (01/2006). 5 References
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Remote Sensing in Environmental Justice Research - A Review

Remote Sensing in Environmental Justice Research - A Review

Building upon the theoretical approach by Bolte et al. [ 10 ], environmental justice is defined as the interplay between individual and social influences as well as environmental exposures. Remote sensing technologies and modern ‘big earth data’ are able to provide highly detailed data for the spatial neighborhood of the urban living environment and which can greatly support advances in environmental justice research. Especially since remote sensing images cover large areas on nationwide or even global scales, data models of environmental justice are no longer limited by geographic extent. Multi-level analyses further incorporate highly detailed socioeconomic surveys which provide in-depth descriptions of the population’s SES and vulnerability. In particular, when large-scale comparative studies are performed on national or even international level, remote sensing imagery is a valuable data source without restrictions concerning the quantity and quality of in situ measurements or national bias. Overall, modern big earth geo data provided by satellite imagery has great potential of providing many health relevant features on international level. It should hence be considered for environmental justice research to close the gap of such data as highlighted by the WHO [ 36 ].
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Remote sensing and GIS in hydrogeology

Remote sensing and GIS in hydrogeology

Satellites and airplanes are fascinating. The technology is still young enough for many to remember when it was not around. Airborne and spaceborne imagery have also received their share of attention as they have become better and increasingly detailed, making progress highly visible. The recent development of viewers like Google Earth has yet again amazed and inspired many people. However, as “any sufficiently advanced technology is indistinguishable from magic ” (Arthur Clarke, Profiles of The Future, 1961), it is dif ficult for people unfamiliar with remote-sensing technology to judge what is and is not possible today. Fascinating satellite imagery frequently leads to over-expectation as to what satellite observations are feasible. However, if satellites can do so much, why should mapping groundwater flow from satellites present a problem?
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Cloud geometry for passive remote sensing

Cloud geometry for passive remote sensing

An important cause for disagreements between current climate models is lack of under- standing of cloud processes. In order to test and improve the assumptions of such models, detailed and large scale observations of clouds are necessary. Passive remote sensing meth- ods are well-established to obtain cloud properties over a large observation area in a short period of time. In case of the visible to near infrared part of the electromagnetic spectrum, a quick measurement process is achieved by using the sun as high-intensity light source to illuminate a cloud scene and by taking simultaneous measurements on all pixels of an imaging sensor. As the sun as light source can not be controlled, it is not possible to measure the time light travels from source to cloud to sensor, which is how active remote sensing determines distance information. But active light sources do not provide enough radiant energy to illuminate a large scene, which would be required to observe it in an instance. Thus passive imaging remains an important remote sensing method. Distance information and accordingly cloud surface location information is nonetheless crucial infor- mation: cloud fraction and cloud optical thickness largely determines the cloud radiative effect and cloud height primarily influences a cloud’s influence on the Earth’s thermal ra- diation budget. In combination with ever increasing spatial resolution of passive remote sensing methods, accurate cloud surface location information becomes more important, as the largest source of retrieval uncertainties at this spatial scale, influences of 3D radiative transfer effects, can be reduced using this information. This work shows how the miss- ing location information is derived from passive remote sensing. Using all sensors of the improved hyperspectral and polarization resolving imaging system specMACS, a unified dataset, including classical hyperspectral measurements as well as cloud surface location information and derived properties, is created.
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