Validation of Remote Sensing Data and Products

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Calibration, validation and the NERC Airborne Remote Sensing Facility

Calibration, validation and the NERC Airborne Remote Sensing Facility

‘NCAVEO’ is a recently established network of Earth Observation experts and data users committed to exchanging knowledge and understanding in the area of remote sensing data calibration and validation (cal/val). The aim is to provide a co-ordinated resource for users from industry and academia and also to facilitate access to benchmark methods and algorithms as well as identifying areas where additional research and improved methods are required. The emphasis of the network will be on the validation and traceability of EO products. The network will promote the paradigm of remote sensing as a quantitative physical science, based upon traceable physical measurements and repeatable methodologies. The aim of the network will be to support this view, to facilitate its extension to application areas currently dominated by qualitative approaches, and to educate and inform end-users of remote sensing products about the importance of cal/val and its relevance to their scientific applications.
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Empirical validation and proof of added value of MUSICA’s tropospheric D remote sensing products

Empirical validation and proof of added value of MUSICA’s tropospheric D remote sensing products

In this context the objective of our paper is to empirically document the quality and prove the scientific value of tropo- spheric water vapour isotopologue remote sensing products for investigating moisture transport pathways. To do so we use tropospheric δD reference data obtained by continuously calibrated in situ instruments. The study is made for the Izaña observatory and the surroundings of the island of Tenerife, where tropospheric water isotopologues have been measured in coincidence from different platforms and by different tech- niques: (1) since 1999 by ground-based FTIR remote sensing within NDACC, (2) since 2007 by space-based remote sens- ing using IASI aboard METOP, (3) since 2012/2013 by com- mercial Picarro in situ instruments from ground at two differ- ent altitudes, and (4) in July 2013 during six aircraft profile measurements using the dedicated ISOWAT in situ instru- ment. All these data have been generated within the project MUSICA (www.imk-asf.kit.edu/english/musica). The Izaña observatory and the surroundings of Tenerife comprise the principal water vapour isotopologue reference area of MU- SICA and allow a first empirical validation with dedicated
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Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

The methodological approach considered in this work allowed us to examine the feasibility to use Saildrone data for evaluating the validation quality of satellite-derived SST and SSS data in coastal regions with high oceanographic complexity such as frontal activity. We compared values from three satellite-based SSS datasets derived from NASA’s Soil Moisture Active Passive Mission (SMAP) and six SST datasets from the Group for High Resolution Sea Surface Temperature (GHRSST), against SSS and SST measurements from the remotely-controlled unmanned surface vehicle Saildrone. The primary goal here was not to validate each of these satellite-derived SST and SSS products individually, but to compare directly with Saildrone, thus validating the satellite- derived products in a coastal upwelling region. The process would highlight the use of Saildrone for coastal validation. Several types of calculations were derived in comparing the datasets. The three SMAP-derived SSS datasets used were the Jet Propulsion Laboratory 60 km product (JPLSMAP), the Remote Sensing System 40 km product (RSS40), and the Remote Sensing Systems 70 km product (RSS70). All these products are available through the Physical Oceanography Distributed Active Center (PO.DAAC) (http://podaac.jpl.nasa.gov) as described in the previous section.
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Urban sprawl: The GIS and remote sensing data assessments

Urban sprawl: The GIS and remote sensing data assessments

This paper brings out the extent of sprawl using Geographic Information System (GIS) and Remote Sensing imagery. These techniques based study is carried out to comprehend the process of sprawl. In one hand, geographic positioning, topology and surface measurements are basic GIS properties which enable highly precise locational referencing of spatial phenomena. The growing uses of Remote Sensed imagery are contributing to unprecedented surveillance of the environment and to monitor and measure urban sprawl in other hand. For this purpose, this paper tries to give some possible reflections that help us to develop the analytical tool that may help us to improving the way towards the amplification of the analysis paradigm.
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Daily streamflow estimation using remote sensing data

Daily streamflow estimation using remote sensing data

The history of RS is intricately linked to World Wars I and II. The rapid development of RS technology took place during World War I. After this war, the technology and corresponding experts were adapted and used for civilian applications. (Lillesand and Kiefer, 1999; Joseph, 2005). The launch of Earth Resources Technology Satellite-1 (ERTS 1) by NASA in 1972 which was the parts of work started in the 1960s, was the landmark in the history of RS, and is arguably the beginning of modern RS (Estes, 2005; Irons, 2011) with respect to the civilian applications. This series was later named ‘Landsat’, and was the first of several earth-orbiting satellites designed specifically for land observation for civil applications. The Landsat program provides systematic and repetitive observation of the oceans, atmosphere and land areas (Taylor, 2014). In the recent past, various stakeholders such as the Indian Space Research Organization (ISRO), European Space Agency (ESA), Centre national d'études spatiales – the French space agency (CNES) and Japan Aerospace Exploration Agency (JAXA) have developed numerous sensors with various capabilities and have launched satellites to provide better data for the user community.
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Big Data Analytical Architecture for Remote Sensing Application

Big Data Analytical Architecture for Remote Sensing Application

In this paper design proposed architecture for the remote sensing application. The three main units comprises the advanced framework the three units are First, Remote sensing data accession unit (RSDU) takes the information from the satellite and transfers to the earth Station, wherever processing starts in this unit. Second, Data processing unit (DPU) is the main role in the architecture, the real time data will process efficiently by filtering, load balancing and parallel processing and Third, Data Analyzing and Decision unit (DADU) this unit is responsible for the storing the output and generates the opinion based on the results of the data processing unit.
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Remote Sensing in Agriculture

Remote Sensing in Agriculture

II. DEFINITION OF REMOTE SENSING The term remote sensing was coined by Fischer in 1960 A.D. Remote sensing is defined as the art and science of gathering information about objects or areas from a distance without having physical contact with objects/areas being investigated. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Remote sensing is a tool to monitor the earth’s resources using space technology in addition to ground observations. It can be used in soil mapping, land use pattern, forest mapping, geological and hydrological purpose, drought & flood monitoring in addition to crop coverage crop output estimates. Remote sensing techniques are used in agriculture & allied fields. This involves collection of basic data, monitoring of crop growth, soil moisture condition irrigation drainage & outbreak of pest & disease infestation. Spectral reflectance:
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Unmanned Aerial Vehicle Remote Sensing Monitoring And Image Acquisition System

Unmanned Aerial Vehicle Remote Sensing Monitoring And Image Acquisition System

one is the detachable array CCD digital back installed in large and medium format camera to replace the rear film digital imaging; the other is the CCD imaging module directly integrated with the body, it is our common 135 small format digital camera. The former usually has high resolution for professional photography, while the latter emphasizes convenience for home and general photography. Unmanned aerial vehicle remote sensing platform requires high resolution and small size of camera, so it adopts the scheme of large area CCD digital back with 120 medium format camera. We require higher image quality, so we must reduce the impact of the image on the image, requiring less than 0.5 pixels. If we choose the large surface array CCD back the pixel size of 9 m * 9 m, the UAV speed 33m/s, height 500m, the camera focal length is about 50mm, can calculate the camera exposure time is 1/733s, so we use the highest shutter camera should be more than 1/1000s. The main parameters of the camera lens are the focal length f, the focal length f and the FOV angle theta, and the imaging surface width L are closely related. The relation between the focal length f and the field of view theta and the size of the imaging surface L is as follows: TG (theta /2) = (L/2) /f, the correlation calculation of the focal length can be carried out according to this relation. The master computer completes the control of the camera, the transmission and storage of the image. PC/104+ embedded computer better meets this requirement, and has the advantages of short development cycle. PC/104+ embedded computer can complete the functions of exposure control, image acquisition, transmission, storage, GPS decoding calculation, remote instruction execution and status reporting in two instruments.
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Cropping System Analysis of Kurukshetra District using Remote Sensing and GIS

Cropping System Analysis of Kurukshetra District using Remote Sensing and GIS

crop area and, predict yield at small scales (Kanemasu, 1974). The structure of most crops is identical causing spectral mixes within crops and other types of vegetation. These high spectral overlaps make attempts to understand the relationships between crops and the ecosystems within which they occur in order to classify remote sensing imagery difficult. The spectral signature for vegetation is highly variable in nature since it changes completely during the seasonal cycle of many plants (Schowengerdt, 1997). Therefore, a number of contexts like spatial, spectral and temporal have been used over the years in order to carry out crop identification in remotely sensed data (Byeungwoo and Landgrebe, 1992; Wharton, 1982).
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Vol 3, No 7 (2015)

Vol 3, No 7 (2015)

and get ready for work. As she strolls to her auto, she peruses the morning's news on her advanced cell that was prefetched for her and disseminated to her telephone by her cloud administrations. In the event that she has not completed her perusing when she gets in her auto, the cloud can perform a content to- discourse change of her news articles and read them to her as she drives. Endless supply of the news, she can stream her music storehouse to her auto. Once at work, Alice can flawlessly move from her office to meeting rooms for the duration of the day while maintaing access to every last bit of her data, applications, and sessions because of her cloud administrations being open from any machine. Indeed, even subsequent to returning home, she can unwind with her most loved PC amusement through her desktop PC. On the off chance that facilitated by her cloud supplier, she won't lose advance in her diversion regardless of the fact that the force fizzles; rather, she can keep playing through her advanced cell. To empower such a situation, we must address new difficulties in securing the data put away on the cloud and the entrance to it. The appropriation of distributed computing as a piece of pervasive frameworks will influence security in pervasive frameworks. By utilizing the cloud as a preparing and stockpiling powerhouse for pervasive frameworks, the center of security in these frameworks will move to guaranteeing that the information and handling controlled by an outsider is secure, and the transmission of information between the cloud and the pervasive framework is secured. Further, following pervasive frameworks regularly empower clients to sign on from any number of gadgets, as exhibited in the sample above, verification components will likewise be of high significance
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Remote sensing of coral reefs for monitoring and management: a review

Remote sensing of coral reefs for monitoring and management: a review

When considering the efficacy of habitats as surrogates of other variables, there are two critical questions: (1) does the variable of interest vary systematically and consistently among habitats? and (2) how much variation is there within each habitat? The use of habitats as surrogates of biodiversity has a long history in terrestrial ecosystems [265], and is increasingly being used for corals reefs. This is exemplified by recommendations to include every habitat type within networks of marine reserves in order to maximise the chances of including every species [266]. Benthic and fish communities clearly vary among both geomorphological zones and habitat types on reefs (e.g., [267]), but there are surprisingly few studies explicitly addressing the use of maps from remotely sensed imagery as surrogates of biodiversity [268]. In Panama, Andréfouët and Guzman [269] mapped geomorphological zones with Landsat and demonstrated a weak positive correlation between benthic diversity and the number of geomorphological zones in an area, and suggested habitats would be a better surrogate. The study only investigated the number of each species in each zone, rather than analysing multivariate community characteristics, but the results are similar to those of Lindsay et al. [270] who demonstrated that habitats were more effective surrogates for fishes when they were defined at higher resolutions. Dalleau et al. [271] extend this finding to corals and algae, but not commercial invertebrates. Furthermore, data from The Bahamas highlights which combinations of habitats routinely discernible with IKONOS are required to represent 95% or 100% of benthic or fish species in a seascape [272]. Detailed habitat maps using CASI have also been used to generate two-dimensional maps of beta diversity (species turnover among habitats) in order to study the environmental factors controlling this important facet of biodiversity [42]. Knudby et al. [273] used a statistical relationship between geomorphic zonation and benthic composition derived from satellite imagery, and field data to determine fish species richness and biomass, demonstrating how satellite imagery can form important input in biodiversity studies (Figure 8).
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Remote sensing big data computing: challenges and opportunities

Remote sensing big data computing: challenges and opportunities

In spite of the tremendous computation capacity and outstand- ing scalability, the cluster systems or petascale supercomputers are not good at loading, transferring and processing extremely large volume of data. The main reason account for that is the data lo- cality optimization of the data storage architecture is not under consideration in supercomputers where computation is the main concern. Actually, we are currently witnessing a transition to sys- tems that are arranged in multiple hierarchical levels. These sys- tems are trend to have a higher dimensional connection topology, and multilevel storage architecture as well. For example, the Blue- Gene/Q system is composed of several islands where non-blocking communication is only available within an island. The Gordon sys- tem [52] is designed for the data-centric applications. It has a five-level memory hierarchy (excluding caches) with a node-local shared memory, a virtual shared memory within a super node, a distributed memory, the flash memory (SSDs) and disk arrays. Wherein, with multi-level structured storage, the requested data would be much more possibly reside in local memory, local flash or even local disks. However, for performance efficiency it is crit- ically important to take data locality into account [53]. Program- ming for these multiple levels of locality and routes for a certain dimensionality is anything but easy.
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Satellite communication and remote sensing intervention by rural development

Satellite communication and remote sensing intervention by rural development

Among the many societal development related initiatives, I would like to emphasize on a unique programme of ISRO called ‘Village Resource Centres (VRC)’. It is being implemented in association with the stakeholders at local levels, to reach the benefits of space and other IT enabled services directly to the common man. The VRCs are a step to bridge the societal divides, and are proving to be vital in improving the quality of life in villages - by way of providing locale-specific advisories for farm sector development, livestock management, local governance, skill development for livelihood support, awareness creation, market information, building disaster resilience, etc. All these services are reaching the doorsteps of common man, in local language. So far over 470 VRCs are set up in 22 States and Union Territories, and many more are in the offing. The uniqueness of VRCs is the knowledge connectivity between the experts at agricultural universities, research institutes and doctors at hospitals, and the village community. We are also making the natural resources data available at VRCs, which could be used to work out development plans at local levels.
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Modeling soil processes: Review, key challenges, and new perspectives

Modeling soil processes: Review, key challenges, and new perspectives

Sequential data assimilation has been the method of choice for model–data fusion in land surface modeling for more than a decade (e.g., Reichle et al., 2002) and more recently also for groundwater modeling (Chen and Zhang, 2006). In land surface modeling, this involves updating of soil moisture contents with remote sensing information, (e.g., Dunne et al., 2007), or in situ measurements (e.g., De Lannoy et al., 2007), and updating of soil carbon pools in biogeochemistry models, (e.g., Zhou et al., 2013). Soil parameters are in general not updated in those applications. In the following, we focus on parameter estimation with SDA for soil hydrologi- cal models, which is a less studied subject. Early applications of SDA in soil hydrology are the one-dimensional synthetic experi- ments with the assimilation of soil moisture data by Montzka et al. (2011) with the particle filter and Wu and Margulis (2011) with EnKF. They updated both states and soil hydraulic parameters of the van Genuchten model. Montzka et al. (2013) estimated also time-dependent variables of a radiative transfer model with the particle filter and applied the filter on a site in Colorado, USA. Wu and Margulis (2013) extended their framework for the assimila- tion of electrical conductivity data and applied the filter to data at site in California, USA. Although these works showed promising results, other one-dimensional studies pointed to the limitations of EnKF. Erdal et al. (2014) pointed out that a wrong concep- tual model of the vertical distribution of soil horizons affects soil hydraulic parameter estimation, and they suggested the inclusion of an additional bias term to improve the filter performance. Erdal et al. (2015) stressed that especially under dry conditions the pdf of pressure is highly skewed and EnKF unstable. They showed that a normal score transformation (Zhou et al., 2011) strongly improved filter performance. Song et al. (2014) estimated two-dimensional spatially distributed saturated hydraulic conductivities of the unsaturated zone with an iterative variant of EnKF. However, their work made various simplifications, like perfect knowledge of the other soil hydraulic parameters and a constant rainfall rate. Integrated hydrological models also model flow in the unsaturated
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Impact of tsunami on mangrove forest cover along the cuddalore coast using remote sensing data

Impact of tsunami on mangrove forest cover along the cuddalore coast using remote sensing data

Mangrove forests form one of the primary coastal ecosystems in the tropical and subtropical region of the world. They are biologically diverse and have therefore traditionally been utilized for food resources, firewood, charcoal, timber and other minor products. However, mangrove ecosystems are very sensitive and fragile. In recent years, the pressures of increasing population, and the resulting expansion of agricultural land and industrial and urban development, have caused a significant proportion of the world’s mangrove resource to be destroyed. In addition, significant areas of mangrove swamps in Indonesia and other regions of Southeast Asia have been developed to create ponds for the commercial production of fish and shrimps. The mangrove ecosystem forms as a natural barriers and protects the coast from natural disasters like, seasonal cyclones and tsunamis. Hence the present study aims to map the mangrove forest using optical remote sensing data during the pre and post Asian tsunami periods; and also to assess the impact of mangrove forest cover and how this has been a protective cover to the coast in Cuddalore coast zone. For this purpose Indian remote sensing digital data have been used to study the pre and post tsunami conditions and the mangrove ecosystem.
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Adoption of M- Wallet: A way Ahead

Adoption of M- Wallet: A way Ahead

It was barely a year ago that Uttarakhand, another Himalayan state, was devasted by floods. In Kashmir, the rainfall was unexpected and extremely high, and authorities had ignored warnings. A report prepared by Department of Environment, Ecology and Remote Sensing (DEERS) in collaboration with Hyderabad-based National Remote Sensing Centre, ISRO reveals that the floods in Jammu and Kashmir are a result of High rainfall in the catchments over short period of time, which were not less than cloud bursts and is a combine effect of the extreme event due to climate change and less capacity of our drainage system that failed to hold the quantum of water and it overflowed, which ultimately lead to floods The human tragedy and property loss in both Uttarakhand and Kashmir was enormous. The fact behind the both Himalayan states is that there is evidence proving a change in global weather patterns and its natural variability, i.e., climate change, brought about by man-made carbon emissions is heating up the atmosphere faster than normal.
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Spatio-temporal Features of Urban Heat Island and its Relationship with Land Use/Cover in Mountainous City: A Case Study in Chongqing

Spatio-temporal Features of Urban Heat Island and its Relationship with Land Use/Cover in Mountainous City: A Case Study in Chongqing

on remote sensing data often used empirical parameters to estimate the land surface temperature 49.. (LST).[r]

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Map Archive Mining: Visual-analytical Approaches to Explore Large Historical Map Collections

Map Archive Mining: Visual-analytical Approaches to Explore Large Historical Map Collections

techniques such as image information mining [7] used for the exploration of large remote sensing 105.. data archives.[r]

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Identification of Hydrothermal Alteration Minerals

Identification of Hydrothermal Alteration Minerals

remote sensing image classification technology [ 48 – 52 ], was trained using alteration sample data by. 153[r]

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EXTRACTION OF HYDRO-MORPHOLOGICAL FEATURES FROM GEO-SPATIAL DATASETS A Study for Ghataprabha River Basin, Karnataka

EXTRACTION OF HYDRO-MORPHOLOGICAL FEATURES FROM GEO-SPATIAL DATASETS A Study for Ghataprabha River Basin, Karnataka

Remote Sensing (RS), Geographical Information System (GIS) and Global Positioning System (GPS) have proved to be an efficient tool to extract information from the spatial datasets like Topographical maps, Satellite imageries about drainage pattern, surface hydrology and hydro-morphology of an area. In the present study, an attempt has been made to extract the hydro-morphological features of Ghataprabha River Left Bank Canal (GLBC) Command Area (CA) in Karnataka, India from the spatial datasets using SIT. The study region lies between 16° 12’ 16’’ N to 16° 30’ 1’’ N latitude and 74° 45’ 18’’ E to 75° 44’ 58’’ E longitude of northern Karnataka. The spatial datasets namely, CARTOSAT-1 DEM data, RESOURCESAT -2 LISS III along with Survey of India (SOI) Toposheets at 1:50,000 scale have been used to create various thematic maps of surface morphology with the help of Erdas Imagine and ArcGIS software’s for the study area. The resulted drainage pattern, landuse/ landcover, slope, contour and DEM maps of this analysis would be useful in determining the effect of hydro-morphological characteristics such as contour, slope of the area and distribution of stream network within the command area which intern useful for planning command area management.
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