The Experts below are selected from a list of 93387 Experts worldwide ranked by ideXlab platform

Feng Ling - One of the best experts on this subject based on the ideXlab platform.

  • sfsdaf an enhanced fsdaf that incorporates sub pixel class fraction change information for spatio temporal image fusion
    Remote Sensing of Environment, 2020
    Co-Authors: Giles M Foody, Yihang Zhang, Doreen S Boyd, Feng Ling
    Abstract:

    Abstract Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both Fine spatial and temporal Resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives Fine spatial Resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a Fine Resolution image at the time of acquisition of coarse Resolution images using only one prior coarse and Fine Resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of Fine Resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.

  • improvement of the example regression based super Resolution land cover mapping algorithm
    IEEE Geoscience and Remote Sensing Letters, 2015
    Co-Authors: Yihang Zhang, Feng Ling
    Abstract:

    Super-Resolution mapping (SRM) is a method for generating a Fine-Resolution land cover map from coarse-Resolution fraction images. Example-regression-based SRM algorithms can estimate a Fine-Resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated Fine-Resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the Fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.

  • a spatial temporal hopfield neural network approach for super Resolution land cover mapping with multi temporal different Resolution remotely sensed images
    Isprs Journal of Photogrammetry and Remote Sensing, 2014
    Co-Authors: Feng Ling, Qi Feng, Yihang Zhang
    Abstract:

    Abstract The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-Resolution mapping (SRM) can produce land cover maps with a Finer spatial Resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-Resolution image as input. Uncertainty always exists in resultant Fine-Resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of Fine spatial Resolution remotely sensed images. These data can provide Fine-Resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-Resolution image and a previous Fine-Resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-Resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.

  • super Resolution land cover mapping with spatial temporal dependence by integrating a former Fine Resolution map
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Feng Ling, Fei Xiao
    Abstract:

    Super-Resolution mapping (SRM) is a technique to predict spatial locations of land cover classes at the subpixel scale within coarse Resolution remotely sensed image pixels. Due to the lack of information about the spatial pattern of land covers, uncertainty always exists in resultant Fine-Resolution land cover maps. In the present work, by integrating a former Fine-Resolution land cover map, the spatial dependence used in existing SRM algorithms is extended into a novel spatial–temporal dependence used in the SRM algorithm (SRM_STD). The spatial–temporal dependence consists of the spatial dependences of former Fine-Resolution land cover map, the spatial dependences of latter coarse Resolution fraction images, and the corresponding dependence between former and latter land cover maps. By considering the spatial–temporal dependences of subpixels, SRM_STD can inherit valuable land cover information from the former Fine-Resolution land cover map, and reduce the uncertainty of SRM to a large extent. The performance of the proposed SRM_STD algorithm is assessed using a subset of the National Land Cover Database datasets and land cover maps produced by Landsat imagery in an area of rapid urban expansion. The results of two experiments show that the former dependence has little influence on the result, whereas the corresponding dependence plays a crucial role on the result. With a large weight of corresponding dependence, the proposed SRM_STD algorithm can produce Fine-Resolution land cover maps with higher accuracy than those of hard classification and the pixel swapping algorithm.

Yihang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • sfsdaf an enhanced fsdaf that incorporates sub pixel class fraction change information for spatio temporal image fusion
    Remote Sensing of Environment, 2020
    Co-Authors: Giles M Foody, Yihang Zhang, Doreen S Boyd, Feng Ling
    Abstract:

    Abstract Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both Fine spatial and temporal Resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives Fine spatial Resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a Fine Resolution image at the time of acquisition of coarse Resolution images using only one prior coarse and Fine Resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of Fine Resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.

  • improvement of the example regression based super Resolution land cover mapping algorithm
    IEEE Geoscience and Remote Sensing Letters, 2015
    Co-Authors: Yihang Zhang, Feng Ling
    Abstract:

    Super-Resolution mapping (SRM) is a method for generating a Fine-Resolution land cover map from coarse-Resolution fraction images. Example-regression-based SRM algorithms can estimate a Fine-Resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated Fine-Resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the Fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.

  • a spatial temporal hopfield neural network approach for super Resolution land cover mapping with multi temporal different Resolution remotely sensed images
    Isprs Journal of Photogrammetry and Remote Sensing, 2014
    Co-Authors: Feng Ling, Qi Feng, Yihang Zhang
    Abstract:

    Abstract The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-Resolution mapping (SRM) can produce land cover maps with a Finer spatial Resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-Resolution image as input. Uncertainty always exists in resultant Fine-Resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of Fine spatial Resolution remotely sensed images. These data can provide Fine-Resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-Resolution image and a previous Fine-Resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-Resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.

Wataru Ohfuchi - One of the best experts on this subject based on the ideXlab platform.

  • mesoscale spectrum of atmospheric motions investigated in a very Fine Resolution global general circulation model
    Journal of Geophysical Research, 2008
    Co-Authors: Kevin Hamilton, Yoshiyuki O Takahashi, Wataru Ohfuchi
    Abstract:

    [1] The horizontal spectrum of wind variance, conventionally referred to as the kinetic energy spectrum, is examined in experiments conducted with the Atmospheric GCM for the Earth Simulator (AFES) global spectral general circulation model. We find that the control version of AFES run at T639 horizontal spectral Resolution simulates a kinetic energy spectrum that compares well at large scales with global observational reanalyses and, at smaller scales, with available aircraft observations at near-tropopause levels. Specifically there is a roughly −3 power-law dependence on horizontal wave number for wavelengths between about 5000 and 500 km, transitioning to a shallower mesoscale regime at smaller wavelengths. This is seen for both one-dimensional spectra and for the two-dimensional total wave number spectrum based on a spherical harmonic analysis. The simulated spectrum at midtropospheric levels is similar in that there is a transition to a shallower mesoscale regime, but the spectrum in the mesoscale is clearly steeper at midtroposphere than near the tropopause. There seem to be no extensive observations of horizontal spectra available in the midtroposphere, so it is not known whether the contrast seen in the model between upper and mid tropospheric levels is realistic. The dependence of the model simulated variability on the subgrid-scale moist convection parameterization is examined. The space-time variability of rainfall is shown to depend strongly on the convection scheme employed. The tropospheric kinetic energy spectrum in the mesoscale seems to be correlated with the precipitation behavior, so that in a version with a more variable precipitation field the kinetic energy in the mesoscale is enhanced. This suggests that the mesoscale motions in the model may be directly forced to a significant extent by the variability in the latent heating field. Experiments were also performed with a dry dynamical core version of the model run at both T639 and T1279 Resolutions. This version also simulated a shallow mesoscale range, supporting the view that the mesoscale regime in the atmosphere is energized, at least in part, by a predominantly forward (i.e., downscale) nonlinear spectral cascade. Experiments with various formulations of the hyperdiffusion horizontal mixing parameterization show that the kinetic energy spectrum over about the last half of the resolved wave number range is under strong control by the parameterized mixing. However, the T1279 model simulates almost a decade of the shallow mesoscale regime (i.e., for horizontal wavelengths from about 80 to 500 km) that appears to be fairly independent of the diffusion employed. Finally, experiments are conducted in the dry version to see the effects on the kinetic energy spectrum of changing the thermal Rossby number for the simulations.

  • topographic effects on the solar semidiurnal surface tide simulated in a very Fine Resolution general circulation model
    Journal of Geophysical Research, 2008
    Co-Authors: Kevin Hamilton, Steven Ryan, Wataru Ohfuchi
    Abstract:

    [1] We present an examination of the solar tidal variation of surface pressure in integrations conducted with the Atmospheric GCM for the Earth Simulator (AFES) global general circulation model run at very Fine Resolution (roughly 10 km horizontal grid spacing and 96 numerical levels from the ground up to 0.1 hPa pressure). The basic features of the observed diurnal and semidiurnal surface pressure oscillations are reasonably well simulated by the model, although simulated amplitudes of the semidiurnal oscillation have an overall enhancement of about 25% over those observed, a deficiency which is reasonably attributed to the effects of the upper boundary condition in the model. The focus of our analysis is the local-/regional-scale modulation of the semidiurnal tidal oscillation in the tropics and subtropics associated with high and steep topography. The results show that the first-order effect of high topography is a reduction in the semidiurnal pressure amplitude with surface elevation, a feature that is consistent with our understanding of the semidiurnal tide as a vertically propagating inertia-gravity wave primarily excited in the ozone layer. We also find evidence in the model for systematically weak semidiurnal pressure amplitudes to the west of very high and steep topography, which is reasonably attributed to a shadowing effect of topography on the global-scale westward propagating tide. Support for these effects is presented in our analysis of previously published station observations of the semidiurnal pressure oscillation. In addition, we present new determinations of the semidiurnal pressure oscillation based on barometric data from a special array of nine sensors established on the island of Hawaii, representing an unprecedented sampling of surface tides over a large range of elevations in a relatively small geographical region. The results of the AFES simulations agree quite well with these new detailed observations in Hawaii.

Helin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • vallai_crop a validation dataset for coarse Resolution satellite lai products over chinese cropland
    Scientific Data, 2021
    Co-Authors: Bowen Song, Liangyun Liu, Xiao Zhang, Xidong Chen, Helin Zhang
    Abstract:

    Numerous validation efforts have been conducted over the last decade to assess the accuracy of global leaf area index (LAI) products. However, such efforts continue to face obstacles due to the lack of sufficient high-quality field measurements. In this study, a Fine-Resolution LAI dataset consisting of 80 reference maps was generated during 2003–2017. The direct destructive method was used to measure the field LAI, and Fine-Resolution LAI images were derived from Landsat images using semiempirical inversion models. Eighty reference LAI maps, each with an area of 3 km × 3 km and a percentage of cropland larger than 75%, were selected as the Fine-Resolution validation dataset. The uncertainty associated with the spatial scale effect was also provided. Ultimately, the Fine-Resolution reference LAI dataset was used to validate the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The results indicate that the Fine-Resolution reference LAI dataset builds a bridge to link small sampling plots and coarse-Resolution pixels, which is extremely important in validating coarse-Resolution LAI products. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15124524

Holt J. - One of the best experts on this subject based on the ideXlab platform.

  • Kilometric scale modeling of the North West European shelf seas: exploring the spatial and temporal variability of internal tides
    'Wiley', 2018
    Co-Authors: Guihou K., Polton J., Harle J., Wakelin S., O'dea E., Holt J.
    Abstract:

    The North West European shelf-break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the Fine-scale processes over the whole region. In this context, under the FASTNEt programme, a new NEMO configuration of the North West European Shelf and Atlantic margin at 1/60° (∼1.8km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and Fine-Resolution temperature profiles is assessed against a basin-scale (1/12°, ∼9km) configuration and a standard regional configuration (7 km Resolution). The seasonal cycle is well reproduced in all configurations though the Fine-Resolution allows the simulation of smaller scale processes. Time-series of temperature at various locations on the shelf show the presence of internal waves with a strong spatio-temporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semi-diurnal, inertial and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2m for 30% of the stratified domain. With sufficiently Fine-Resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern

  • Kilometric Scale Modeling of the North West European Shelf Seas: Exploring the Spatial and Temporal Variability of Internal Tides
    'Wiley', 2018
    Co-Authors: Guihou Karen, Polton J., Harle J., Wakelin S., O'dea E., Holt J.
    Abstract:

    The North West European Shelf break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the Fine-scale processes over the whole region. In this context, under the FASTNEt program, a new NEMO configuration of the North West European Shelf and Atlantic Margin at 1/60° (∼1.8 km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and Fine-Resolution temperature profiles is assessed against a basin-scale (1/12°, ∼9 km) configuration and a standard regional configuration (7 km Resolution). The seasonal cycle is well reproduced in all configurations though the Fine-Resolution allows the simulation of smaller scale processes. Time series of temperature at various locations on the shelf show the presence of internal waves with a strong spatiotemporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semidiurnal, inertial, and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2 m for 30% of the stratified domain. With sufficiently Fine Resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern.Fil: Guihou, Karen. National Oceanography Centre; Reino Unido. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografía Naval; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Polton, J.. National Oceanography Centre; Reino UnidoFil: Harle, J.. National Oceanography Centre; Reino UnidoFil: Wakelin, S.. National Oceanography Centre; Reino UnidoFil: O'Dea, E.. Met Office; Reino UnidoFil: Holt, J.. National Oceanography Centre; Reino Unid