Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Remote Sens., Volume 5, Issue 11 (November 2013), Pages 5424-6158

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-35
Export citation of selected articles as:
Open AccessArticle Quantitative Analysis of the Waterline Method for Topographical Mapping of Tidal Flats: A Case Study in the Dongsha Sandbank, China
Remote Sens. 2013, 5(11), 6138-6158; https://doi.org/10.3390/rs5116138
Received: 7 October 2013 / Revised: 6 November 2013 / Accepted: 13 November 2013 / Published: 21 November 2013
Cited by 11 | PDF Full-text (2744 KB) | HTML Full-text | XML Full-text
Abstract
Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This
[...] Read more.
Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This study performs a quantitative analysis of the relationship between the accuracy of tidal flat digital elevation models (DEMs) that are based on the waterline method and the factors that influence the DEMs. The three major conclusions of the study are as follows: (1) the coverage rate of the waterline points and the number of satellite images used to create the DEM are highly linearly correlated with the error of the resultant DEMs, and the former is more significant in indicating the accuracy of the resultant DEMs than the latter; (2) both the area and the slope of the tidal flats are linearly correlated with the error of the resultant DEMs; and (3) the availability analysis of the archived satellite images indicates that the waterline method can retrieve tidal flat terrains from the past forty years. The upper limit of the temporal resolution of the tidal flat DEM can be refined to within one year since 1993, to half a year since 2004 and to three months since 2009. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Open AccessArticle Live Coral Cover Index Testing and Application with Hyperspectral Airborne Image Data
Remote Sens. 2013, 5(11), 6116-6137; https://doi.org/10.3390/rs5116116
Received: 8 October 2013 / Revised: 13 November 2013 / Accepted: 18 November 2013 / Published: 20 November 2013
Cited by 11 | PDF Full-text (9338 KB) | HTML Full-text | XML Full-text
Abstract
Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image
[...] Read more.
Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image pixels may represent mixed proportions of coverage. In order to address this, we describe the development, application, and testing of a spectral index for mapping live coral cover using CASI-2 airborne hyperspectral high spatial resolution imagery of Heron Reef, Australia. Field surveys were conducted in areas of varying depth to quantify live coral cover. Image statistics were extracted from co-registered imagery in the form of reflectance, derivatives, and band ratios. Each of the spectral transforms was assessed for their correlation with live coral cover, determining that the second derivative around 564 nm was the most sensitive to live coral cover variations(r2 = 0.63). Extensive field survey was used to transform relative to absolute coral cover, which was then applied to produce a live coral cover map of Heron Reef. We present the live coral cover index as a simple and viable means to estimate the amount of live coral over potentially thousands of km2 and in clear-water reefs. Full article
Figures

Graphical abstract

Open AccessArticle Spectral Properties of ENVISAT ASAR and QuikSCAT Surface Winds in the North Sea
Remote Sens. 2013, 5(11), 6096-6115; https://doi.org/10.3390/rs5116096
Received: 30 September 2013 / Revised: 25 October 2013 / Accepted: 8 November 2013 / Published: 18 November 2013
Cited by 3 | PDF Full-text (289 KB) | HTML Full-text | XML Full-text
Abstract
Spectra derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) and QuikSCAT near-surface ocean winds are investigated over the North Sea. The two sensors offer a wide range of spatial resolutions, from 600 m to 25 km, with different spatial coverage over the area
[...] Read more.
Spectra derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) and QuikSCAT near-surface ocean winds are investigated over the North Sea. The two sensors offer a wide range of spatial resolutions, from 600 m to 25 km, with different spatial coverage over the area of interest. This provides a unique opportunity to study the impact of the spatial resolution on the spectral properties of the wind over a wide range of length scales. Initially, a sub-domain in the North Sea is chosen, due to the overlap of 87 wind scenes from both sensors. The impact of the spatial resolution is manifested as an increase in spectral density over similar wavenumber ranges as the spatial resolution increases. The 600-m SAR wind product reveals a range of wavenumbers in which the exchange processes between micro- and meso-scales occur; this range is not captured by the wind products with a resolution of 1.5 km or lower. The lower power levels of coarser resolution wind products, particularly when comparing QuikSCAT to ENVISAT ASAR, strongly suggest that the effective resolution of the wind products should be high enough to resolve the spectral properties. Spectra computed from 87 wind maps are consistent with those obtained from several thousands of samples. Long-term spectra from QuikSCAT show that during the winter, slightly higher energy content is identified compared to the other seasons. Full article
Figures

Graphical abstract

Open AccessArticle Fraunhofer Lidar Prototype in the Green Spectral Region for Atmospheric Boundary Layer Observations
Remote Sens. 2013, 5(11), 6079-6095; https://doi.org/10.3390/rs5116079
Received: 8 October 2013 / Revised: 27 October 2013 / Accepted: 13 November 2013 / Published: 18 November 2013
Cited by 1 | PDF Full-text (961 KB) | HTML Full-text | XML Full-text
Abstract
A lidar detects atmospheric parameters by transmitting laser pulse to the atmosphere and receiving the backscattering signals from molecules and aerosol particles. Because of the small backscattering cross section, a lidar usually uses the high sensitive photomultiplier and avalanche photodiode as detector and
[...] Read more.
A lidar detects atmospheric parameters by transmitting laser pulse to the atmosphere and receiving the backscattering signals from molecules and aerosol particles. Because of the small backscattering cross section, a lidar usually uses the high sensitive photomultiplier and avalanche photodiode as detector and uses photon counting technology for collection of weak backscatter signals. Photon Counting enables the capturing of extremely weak lidar return from long distance, throughout dark background, by a long time accumulation. Because of the strong solar background, the signal-to-noise ratio of lidar during daytime could be greatly restricted, especially for the lidar operating at visible wavelengths where solar background is prominent. Narrow band-pass filters must therefore be installed in order to isolate solar background noise at wavelengths close to that of the lidar receiving channel, whereas the background light in superposition with signal spectrum, limits an effective margin for signal-to-noise ratio (SNR) improvement. This work describes a lidar prototype operating at the Fraunhofer lines, the invisible band of solar spectrum, to achieve photon counting under intense solar background. The photon counting lidar prototype in Fraunhofer lines devised was used to observe the atmospheric boundary layer. The SNR was improved 2-3 times by operating the lidar at the wavelength in solar dark lines. The aerosol extinctions illustrate the vertical structures of aerosol in the atmospheric boundary over Qingdao suburban during summer 2011. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
Open AccessArticle Autonomous Navigation Airborne Forward-Looking SAR High Precision Imaging with Combination of Pseudo-Polar Formatting and Overlapped Sub-Aperture Algorithm
Remote Sens. 2013, 5(11), 6063-6078; https://doi.org/10.3390/rs5116063
Received: 17 September 2013 / Revised: 25 October 2013 / Accepted: 4 November 2013 / Published: 15 November 2013
Cited by 3 | PDF Full-text (1708 KB) | HTML Full-text | XML Full-text
Abstract
Autonomous navigation airborne forward-looking synthetic aperture radar (SAR) observes the anterior inferior wide area with a short cross-track dimensional linear array as azimuth aperture. This is an application scenario that is drastically different from that of side-looking space-borne or air-borne SAR systems, which
[...] Read more.
Autonomous navigation airborne forward-looking synthetic aperture radar (SAR) observes the anterior inferior wide area with a short cross-track dimensional linear array as azimuth aperture. This is an application scenario that is drastically different from that of side-looking space-borne or air-borne SAR systems, which acquires azimuth synthetic aperture with along-track dimension platform movement. High precision imaging with a combination of pseudo-polar formatting and overlapped sub-aperture algorithm for autonomous navigation airborne forward-looking SAR imaging is presented. With the suggested imaging method, range dimensional imaging is operated with wide band signal compression. Then, 2D pseudo-polar formatting is operated. In the following, azimuth synthetic aperture is divided into several overlapped sub-apertures. Intra sub-aperture IFFT (Inverse Fast Fourier Transform), wave front curvature phase error compensation, and inter sub-aperture IFFT are operated sequentially to finish azimuth high precision imaging. The main advantage of the proposed algorithm is its extremely high precision and low memory cost. The effectiveness and performance of the proposed algorithm are demonstrated with outdoor GBSAR (Ground Based Synthetic Aperture Radar) experiments, which possesses the same imaging geometry as the airborne forward-looking SAR (short azimuth aperture, wide azimuth swath). The profile response of the trihedral angle reflectors, placed in the imaging scene, reconstructed with the proposed imaging algorithm and back projection algorithm are compared and analyzed. Full article
Open AccessArticle Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011
Remote Sens. 2013, 5(11), 6043-6062; https://doi.org/10.3390/rs5116043
Received: 30 September 2013 / Revised: 25 October 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 16 | PDF Full-text (1064 KB) | HTML Full-text | XML Full-text
Abstract
Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere
[...] Read more.
Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere models to identify the trends and driving mechanisms related to GPP and NDVI in Asia. A satellite-based time-series data analysis showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI are dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation and CO2 fertilization are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Figures

Graphical abstract

Open AccessArticle Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping
Remote Sens. 2013, 5(11), 6026-6042; https://doi.org/10.3390/rs5116026
Received: 9 September 2013 / Revised: 10 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 46 | PDF Full-text (4632 KB) | HTML Full-text | XML Full-text
Abstract
Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study
[...] Read more.
Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. Full article
Open AccessArticle Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations
Remote Sens. 2013, 5(11), 5999-6025; https://doi.org/10.3390/rs5115999
Received: 4 September 2013 / Revised: 8 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 5 | PDF Full-text (3259 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case
[...] Read more.
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet. Full article
Figures

Graphical abstract

Open AccessArticle Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh
Remote Sens. 2013, 5(11), 5969-5998; https://doi.org/10.3390/rs5115969
Received: 7 September 2013 / Revised: 31 October 2013 / Accepted: 31 October 2013 / Published: 15 November 2013
Cited by 39 | PDF Full-text (5765 KB) | HTML Full-text | XML Full-text
Abstract
Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP)
[...] Read more.
Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures. Full article
Figures

Graphical abstract

Open AccessArticle Knowledge-Based Modeling of Buildings in Dense Urban Areas by Combining Airborne LiDAR Data and Aerial Images
Remote Sens. 2013, 5(11), 5944-5968; https://doi.org/10.3390/rs5115944
Received: 11 July 2013 / Revised: 28 October 2013 / Accepted: 5 November 2013 / Published: 14 November 2013
Cited by 9 | PDF Full-text (2863 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas,
[...] Read more.
In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas, in which houses are typically located close to each other and their heights are similar. This makes it difficult to separate point clouds into individual buildings. A combination of airborne LiDAR and aerial images can be an effective approach to resolve this issue. Information about individual building boundaries, derived by segmentation of images, can be utilized for modeling. However, shadows cast by adjacent buildings cause segmentation errors. The algorithm proposed in this paper uses an improved segmentation algorithm (Susaki, J. 2012.) that functions even for shadowed buildings. In addition, the proposed algorithm uses assumptions about the geometry of building arrangement to calculate normal vectors to candidate roof segments. By considering the segmented regions and the normals, models of four common roof types—gable-roof, hip-roof, flat-roof, and slant-roof buildings—are generated. The proposed algorithm was applied to two areas of Higashiyama ward, Kyoto, Japan, and the modeling was successful even in dense urban areas. Full article
Figures

Graphical abstract

Open AccessArticle A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US
Remote Sens. 2013, 5(11), 5926-5943; https://doi.org/10.3390/rs5115926
Received: 26 September 2013 / Revised: 6 November 2013 / Accepted: 7 November 2013 / Published: 14 November 2013
Cited by 22 | PDF Full-text (2337 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to
[...] Read more.
Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas. Full article
Open AccessArticle A Water Index for SPOT5 HRG Satellite Imagery, New South Wales, Australia, Determined by Linear Discriminant Analysis
Remote Sens. 2013, 5(11), 5907-5925; https://doi.org/10.3390/rs5115907
Received: 22 September 2013 / Revised: 7 November 2013 / Accepted: 7 November 2013 / Published: 13 November 2013
Cited by 16 | PDF Full-text (6151 KB) | HTML Full-text | XML Full-text
Abstract
A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis
[...] Read more.
A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis classification. The index uses all four image bands, and is better at separating water and non-water pixels than the two commonly used variations of the normalized difference water index (NDWI), which each only use two image bands. Compared across 2,400 validation pixels, from six images spanning four years, the LDAWI attained an overall accuracy of 98%, a producer’s accuracy for water of 100%, and a user’s accuracy for water of 97%. These accuracy measures increase to 99%, 100% and 98% if cloud shadow and topographic shadow masks are applied to the imagery. The NDWI achieved consistently lower accuracies, with the NDWI calculated from the green and shortwave infrared (IR) bands performing slightly better (91% overall accuracy) than the NDWI calculated from the green and near IR bands (89% overall accuracy). The LDAWI is now being routinely used on an archive of over 2,000 images from across NSW, as part of an operational environmental monitoring program. Full article
Open AccessArticle Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms
Remote Sens. 2013, 5(11), 5871-5906; https://doi.org/10.3390/rs5115871
Received: 15 August 2013 / Revised: 18 September 2013 / Accepted: 23 October 2013 / Published: 12 November 2013
Cited by 20 | PDF Full-text (13289 KB) | HTML Full-text | XML Full-text
Abstract
Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a
[...] Read more.
Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets. Full article
Figures

Graphical abstract

Open AccessArticle GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru)
Remote Sens. 2013, 5(11), 5851-5870; https://doi.org/10.3390/rs5115851
Received: 19 August 2013 / Revised: 31 October 2013 / Accepted: 1 November 2013 / Published: 11 November 2013
Cited by 19 | PDF Full-text (7239 KB) | HTML Full-text | XML Full-text
Abstract
Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions,
[...] Read more.
Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions, such as water storage capacity. Thus, erosion monitoring is highly advisable. Erosion quantification and monitoring can be supported by high-resolution terrestrial Light Detection and Ranging (LiDAR). In this study, a novel Geographic Information System (GIS)-based method for the automatic delineation and geomorphometric description of gullies in cushion peatlands is presented. The approach is a multi-step workflow based on a gully edge extraction and a sink filling algorithm applied to a conditioned digital terrain model. Our method enables the creation of GIS-ready polygons of the gullies and the derivation of geomorphometric parameters along the entire channel course. Automatically derived boundaries and gully area values correspond to a high degree (93%) with manually digitized reference polygons. The set of methods developed in this study offers a suitable tool for the monitoring and scientific analysis of fluvial morphology in cushion peatlands. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Figures

Graphical abstract

Open AccessArticle Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter
Remote Sens. 2013, 5(11), 5825-5850; https://doi.org/10.3390/rs5115825
Received: 29 August 2013 / Revised: 23 October 2013 / Accepted: 24 October 2013 / Published: 8 November 2013
Cited by 28 | PDF Full-text (1425 KB) | HTML Full-text | XML Full-text
Abstract
Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is
[...] Read more.
Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Back to Top