Tropospheric delay is a major error source that affects the performance of the Global navigation Satellite systems (gnss) real Time kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by gnss carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new gps/bds rtk algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from nwp is assessed through comparisons with the results of gamit (gnss at mit). The positioning performance with standard gps rtk, standard gps/bds rtk, the developed nwp-constrained gps rtk and nwp-constrained gps/bds rtk over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the nwp and gamit zenith tropospheric delay (ZTD) are between.50 mm and.60 mm, and the rms values of the nwp ztd residuals are from.02 mm.62. A reduction in the initialization time of over 41 and 58 for medium- and long-range baselines can be achieved with the nwp-constrained rtk (both gps alone and gps/bds rtk solutions) compared to the standard rtk solution, respectively.
PhD, thesis, remote, sensing, laboratory (rslab)
Past studies showed that such models are prone to overfitting, especially when there are discrepancies between the lsts that are not related to the viewing geometry (e.g., emissivity, atmospheric correction). To reduce such effects, pixels with similar characteristics are first grouped by means of a cluster analysis. The models calibration is then performed on each one of the selected clusters. The derived coefficients reflect the expected impact of vegetation and topography on the anisotropy kind of lst. Furthermore, when tested with independent data, the calibrated models are shown to maintain the capability of representing the angular dependency of the differences between lst derived from polar-orbiter (modis) and geostationary (Meteosat, goes and Himawari) satellites. The methodology presented here is currently being used to estimate the deviation of lst products with respect to what would be obtained for a reference view angle (e.g., nadir therefore contributing to the harmonization of lst products. Full article figures Open AccessArticle gps/bds medium/Long-Range rtk constrained with Tropospheric Delay parameters from nwp model by ying xu, chen wu, lei li, lizi yan, min liu and Shengli wang Remote sens. 2018, 10 (7 1113; https doi. Org/10.3390/rs bstract Tropospheric delay sales is a major error source that affects the performance of the Global navigation Satellite systems (gnss) real Time kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by gnss carrier phase measurement, together with.
Full article figures Open AccessArticle a methodology to simulate lst directional Effects Based on Parametric Models and Landscape Properties by sofia. Dacamara and Ana. 2018, 10 (7 1114; https doi. Org/10.3390/rs bstract The correction of directional effects on satellite-retrieved land surface temperature (LST) is of high relevance for a proper interpretation of spatial and temporal features contained in lst fields. This study presents a methodology to correct such directional effects in an operational setting. The correction of directional effects on satellite-retrieved land surface temperature (LST) is of high relevance for a proper interpretation of spatial and temporal features contained in lst fields. This methodology relies on parametric models, which are computationally efficient and require few input information, making them particularly appropriate for operational use. The models are calibrated with lst data collocated in time and space from modis (Aqua and Terra) and seviri (Meteosat for an area covering the writings entire seviri disk and encompassing the full year of 2011.
The eruption started with powerful explosive activity, lava fountains, and a substantial ash column accompanying the opening of an eruptive fissure. Fogo volcano erupted in producing an extensive lava flow field in the summit caldera that destroyed two villages, portela and Bangaeira. Lava flows spreading from the base of the eruptive fissure produced three arterial lava flows. By a week after the start of the eruption, a master lava tube had already developed within the eruptive fissure and along the arterial flow. In this paper, we analyze the emplacement processes based on observations carried out directly on the lava flow field, remote sensing measurements carried out with a thermal camera, so2 fluxes, and satellite images, to unravel the key reviews factors leading to the development of lava tubes. These were responsible for the rapid expansion of lava for the.9 km length of the flow field, as well as the destruction of the portela and Bangaeira villages. The key factors leading to the development of tubes were the low topography and the steady magma supply rate along the arterial lava flow. Comparing time-averaged discharge rates (tadr) obtained from satellite and Supply rate (SR) derived from SO2 flux data, we estimate the amount and timing of the lava flow field endogenous growth, with the aim of developing a tool that could be used for hazard assessment and.
In the north Atlantic Subtropical Gyre (nast) province, ppaph was 355 higher than pppsm, whereas ppvgpm was 215 higher. A sensitivity analysis indicated that chlorophyll- a (Chl a or the absorption of phytoplankton, at 443 nm ( aph (443) caused the largest error in the estimation of pp, followed by the photosynthetic rate terms and then the irradiance functions used for each model. Full article figures Open AccessArticle satellite and Ground Remote sensing Techniques to Trace the hidden Growth of a lava flow field: The Effusive eruption at Fogo volcano (Cape verde) by sonia calvari, gaetana ganci, sónia silva victória, pedro. Perez, josé barrancos, vera Alfama, samara dionis, jeremias Cabral, nadir Cardoso, paulo fernandes, gladys Melian, josé. Pereira, hélio semedo, germán Padilla and Fátima rodriguez remote sens. 2018, 10 (7 1115; https doi. Org/10.3390/rs bstract Fogo volcano erupted in producing an extensive lava flow field in the summit caldera that destroyed two villages, portela and Bangaeira.
Gis msc thesis /
Org/10.3390/rs bstract The accuracy of three satellite models of primary morrison production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the north East Atlantic Ocean (NEA). The models were run using the european Space Agency (esa ocean Colour Climate. The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the north East Atlantic Ocean (NEA). The models were run using the european Space Agency (esa ocean Colour Climate Change Initiative (oc-cci) version.0 data. The objectives of the study were to determine which is the most accurate pp model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (weekly) temporal resolution oc-cci data, and whether the performance.
The Platt-Sathyendranath primary production model (pppsm) was the most accurate over all nea provinces and, specifically, in the Atlantic Arctic province (arct) and North Atlantic Drift (nadr) provinces. The implementation of a photoinhibition function in the pppsm reduced its accuracy, especially at lower range. The vertical Generalized Production Model-vgpm (ppvgpm) tended to over-estimate pp, especially in summer and in the nadr. The accuracy of ppvgpm improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PPAph with and without photoinhibition, was the least accurate model for the nea. Mapped images of each model showed that the ppvgpm was 150 higher in the nadr compared to pppsm.
Pullanagari, gabor Kereszturi and Ian Yule remote sens. 2018, 10 (7 1117; (registering DOI) - bstract Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF).
The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for cp was R2CV (cross-validated coefficient of determination). Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using rf combined with recursive feature elimination (RFE) (CP: R2cv.80, rmsecv.68, rpdcv.23; ME: R2cv.78, rmsecv.61 MJ/kg dm, rpdcv.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits. Full article figures Open AccessArticle Accuracy Assessment of Primary Production Models with and without Photoinhibition Using Ocean-Colour Climate Change Initiative data in the north East Atlantic Ocean by polina lobanova, gavin. Tilstone, igor Bashmachnikov and Vanda Brotas Remote sens. 2018, 10 (7 1116; https doi.
When does human personhood begin?
(2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the slstr instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German Firebird tet-1 small satellite and with the nightfire product based on viirs on-board the suomi-npp satellite show general agreement for an individual flaring site in Siberia and for several. Small systematic differences write to viirs nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified daddy and flared gas volumes and pollutant emissions can be calculated with previously published methods. Full article figures Open AccessArticle Integrating Airborne hyperspectral, topographic, and soil Data for Estimating Pasture quality Using Recursive feature Elimination with Random Forest Regression by rajasheker.
Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the visible Infrared Imaging Radiometer suite (viirs) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the sea and Land Surface temperature radiometer (slstr) observations on-board the sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (swir and mir) channels presentation of slstr. The swir band centered.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available swir, mir and thermal infra-red channels of slstr. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original viirs nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity.
that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, resNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies.17,.81, and.57, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are.89 and.08, respectively, considerably inferior relative to cnns. Importantly, inceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands. Full article figures Open AccessArticle persistent Hot Spot Detection and Characterisation Using slstr by Alexandre caseiro, gernot Rücker, joachim tiemann, david leimbach, eckehard Lorenz, olaf Frauenberger and Johannes. 2018, 10 (7 1118; (registering DOI) - bstract Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible. Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space.
Open AccessFeature paperArticle, very deep Convolutional neural Networks for Complex Land cover Mapping Using Multispectral Remote sensing Imagery by, masoud Mahdianpari, bahram Salehi, mohammad rezaee, fariba mohammadimanesh and, yun Zhang, remote sens. 2018, 10 (7 1119; (registering DOI) - abstract, despite recent advances of resume deep Convolutional neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep cnns using optical remote sensing data have focused. Despite recent advances of deep Convolutional neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep cnns using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral Rapideye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely denseNet121, InceptionV3, vgg16, vgg19, Xception, resNet50, and InceptionResNetV2, for wetland mapping in Canada.
Task 2 » ielts, writing
Journal Menu, remote sensing (issn ) is a peer-reviewed open access journal about the science and application of remote sensing technology, and is published monthly online by mdpi. Open Access free for readers, with article processing charges (APC) paid by authors or their institutions. High visibility: indexed by the, science citation Index Expanded (Web of Science scopus, ei compendex, and other databases. Rapid publication: shredder manuscripts are peer-reviewed and a first decision provided to authors approximately 19 days after submission; acceptance to publication is undertaken.68 days (median values for papers published in the first six months of 2018).). Recognition of reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the apc of their next publication in any mdpi journal, in appreciation of the work done. Impact Factor:.406 (2017) ; 5-year Impact Factor:.952 (2017). Full Imprint Information, download journal Flyer, latest Articles.