Edge enhancement in multispectral satellite images by means of vector operators

Edge enhancement is an element of analysis to derive the spatial structure of satellite images. Two methods to extract edges from multispectral satellite images are presented. A multispectral image is modeled as a vector field with a number of dimensions equal to the number of bands in the image. In this model, a pixel is defined as a vector formed by a number of elements equal to the number of bands. Two vector operators are applied to such vector field. In our first method, we extend the definition of the gradient. In this extension, the vector difference of the window central pixel with neighboring pixels is obtained. A multispectral image is then generated where each pixel represents the maximum change in spectral response in the image in any direction. This image is named a multispectral gradient. The other method, considers the generalization of the Laplacian by means of an η-dimensional Fourier transform. This image is named a multispectral Laplacian. The vector operators perform a simultaneous extraction of edge-content in the spectral bands of a multispectral image. Our methods are parameter-free. Our methods work for a multispectral image of any number of bands. Two examples are discussed that involve multispectral satellite images at two scales. We compare our results with widely used edge enhancement procedures. The evaluation of results shows better performance of proposed methods when compared to widely used edge operators.     

Extracting water-related features using reflectance data and principal component analysis of Landsat images

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.     

Long‐term investigations of the snow cover in a subalpine semi‐forested catchment

To improve spring runoff forecasts from subalpine catchments, detailed spatial simulations of the snow cover in this landscape is obligatory. For more than 30 years, the Swiss Federal Research Institute WSL has been conducting extensive snow cover observations in the subalpine watershed Alptal (central Switzerland). This paper summarizes the conclusions from past snow studies in the Alptal valley and presents an analysis of 14 snow courses located at different exposures and altitudes, partly in open areas and partly in forest. The long‐term performance of a physically based numerical snow–vegetation–atmosphere model (COUP) was tested with these snow‐course measurements. One single parameter set with meteorological input variables corrected to the prevailing local conditions resulted in a convincing snow water equivalent (SWE) simulation at most sites and for various winters with a wide range of snow conditions. The snow interception approach used in this study was able to explain the forest effect on the SWE as observed on paired snow courses. Finally, we demonstrated for a meadow and a forest site that a successful simulation of the snowpack yields appropriate melt rates. Copyright © 2006 John Wiley & Sons, Ltd.     

Derivation of vegetative variables from a landsat tm image for modelling soil erosion

A study was carried out to assess the potential use of satellite thematic mapper (TM) images to produce maps of vegetation-related variables for erosion modelling. In a Mediterranean study area in southern France the (semi-)natural vegetation was described at 33 field plots using four quantitative methods: the Fosberg structural classification system, the cover and management factor of the Universal Soil Loss Equation, the leaf area index and the total percentage cover. After radiometric correction of the image, the spectral TM bands were processed following three different methods. Each method aimed at combining the data of the six spectral TM bands into a single band in such a way that the resulting image displayed optimal information on green vegetation cover. The algorithms used comprise the normalized difference vegetation index, the conventional ‘tasselled cap’ transformation and a locally tuned tasselled cap transformation. Only slight differences were found between the different methods to calculate spectral vegetation indices for this particular case. Furthermore, the correlations between the field variables and image-derived spectral indices are generally small. The largest correlations were found for the normalized vegetation index and the leaf area index (r + 0·71) and for the normalized vegetation index and Fosberg's structural vegetation classes (r + 0·76). However, Fosberg's method results in very general classes, which are of little use for soil erosion models. Furthermore, the spectral indices appeared to be sensitive for the vitality of the vegetation. Consequently, an area covered by a sensed, senescent vegetation will not yield a large value for the spectral index, but its soil is protected against splash erosion. This might lead to a misinterpretation of the soil protective cover when satellite images are used. A final conclusion is that a balance has to be found between the more accurate, but time-consuming field surveys to gather information on erosion-controlling factors and a certain loss of accuracy associated with the use of quick and easy remote sensing methods.     

The influence of land use patterns on water quality at multiple spatial scales in a river system

The influence of land use patterns on water quality in a river system is scale‐dependent. In this study, a four‐order hierarchical arrangement method was used to select water sampling sites and to delineate sub‐basins in the Daliao River Basin, China. The 20 sub‐basins were classified into four spatial scales that represented four different stream orders. Pearson correlation analysis was used to quantify relationships between land use composition and the river's physical‐chemical variables for all samples collected. This analysis showed that the presence of forest cover was associated with higher water quality at the scale of the whole basin. The scale effects of land use patterns on water quality were then examined using stepwise multiple regression analysis that compared different land use types with water quality variables. The results from this analysis showed that urban areas, as opposed to forest areas, became the most significant contributors of water pollutants when scale effects were considered. The influence of urban land cover on water pollution was significantly higher at larger scales. The lack of a significant regression correlation for the forest land use type at smaller scales revealed that forest located upstream of the Daliao River Basin did not provide a buffer for improved water quality. Further analysis showed that this result could be because of disproportionate spatial distributions for forest and urban land use types. The topographic characteristics of sub‐basins, such as average slope (S) and size (A), were determined to be secondary explanatory variables that affected land use impacts on stream water quality. Areas with steep slopes were associated with increased water oxygenation, whereas areas with flatter slopes were associated with higher concentrations of pollutants. These results are significant because they can provide a better understanding of the appropriate spatial scale required for effective river basin management in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

Simulating CO_2 flux of three different ecosystems in ChinaFLUX based on artificial neural networks

The nonlinearity of the relationship between CO2 flux and other micrometeorological variables flux parameters limits the applicability of carbon flux models to accurately estimate the flux dynamics. However, the need for carbon dioxide (CO2) estimations covering larger areas and the limitations of the point eddy covariance technique to address this requirement necessitates the modeling of CO2 flux from other micrometeorological variables. Artificial neural networks (ANN) are used because of their power to fit highly nonlinear relations between input and output variables without explaining the nature of the phenomena. This paper applied a multilayer perception ANN technique with error back propagation algorithm to simulate CO2 flux on three different ecosystems (forest, grassland and cropland) in ChinaFLUX. Energy flux (net radiation, latent heat, sensible heat and soil heat flux) and temperature (air and soil) and soil moisture were used to train the ANN and predict the CO2 flux. Diurnal half-hourly fluxes data of observations from June to August in 2003 were divided into training, validating and testing. Results of the CO2 flux simulation show that the technique can successfully predict the observed values with R2 value between 0.75 and 0.866. It is also found that the soil moisture could not improve the simulative accuracy without water stress. The analysis of the contribution of input variables in ANN shows that the ANN is not a black box model, it can tell us about the controlling parameters of NEE in different ecosystems and micrometeorological environment. The results indicate the ANN is not only a reliable, efficient technique to estimate regional or global CO2 flux from point measurements and understand the spatiotemporal budget of the CO2 fluxes, but also can identify the relations between the CO2 flux and micrometeorological variables.     

A canonical correlation approach to the determination of homogeneous regions for regional flood estimation of ungauged basins

Abstract

A canonical correlation method for determining the homogeneous regions used for estimating flood characteristics of ungauged basins is described. The method emphasizes graphical and quantitative analysis of relationships between the basin and flood variables before the data of the gauged basins are used for estimating the flood variables of the ungauged basin. The method can be used for both homogeneous regions, determined a priori by clustering algorithms in the space of the flood-related canonical variables, as well as for “regions of influence” or “neighbourhoods” centred on the point representing the estimated location of the ungauged basin in that space.     

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R² values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.     

Evaporation from a riparian system in a semi-arid environment

Measurements of micrometeorological variables were made for a complete annual cycle using an automatic weather station and two energy budget–Bowen ratio systems at a field site adjacent to the Santa Cruz River in southern Arizona. These data were used to provide the basis of an estimate of the evaporation from a one-mile long losing reach of a riparian corridor in this semi-arid environment. A remotely sensed map of vegetation cover was used to stratify the corridor into five categories of surface cover. The total evaporation was calculated as the area-weighted average of the measured evaporation for sampled areas of the two most common covers, and appropriate estimates of evaporation for the less common covers. Measurements showed a substantial, seasonally dependent evaporation from the taller, deep-rooted riparian cover in the study reach, while the short, sparse vegetation provided little evaporation. In terms of the volume of water evaporated from the study reach, the evaporation from irrigated agriculture accounts for almost half of the total loss, while the majority of the remaining evaporation is from the taller riparian vegetation covers, with about one-quarter of the total loss estimated as coming from obligatory phreatophytes, primarily cottonwood. © 1998 John Wiley & Sons, Ltd.     

Effect of forest recovery on summer streamflow in small forested watersheds,Northeastern China

The hydrological effect of forest recovery is receiving renewed interest globally because information on forest carbon–water relationship is critically needed to support carbon management through reforestation and sustainable water management. In Northeastern China, summer (June to August) streamflow accounts for about 50% of total annual streamflow and is vital to water supply and management in the region. Understanding how forest recovery may affect streamflow is important to both reforestation campaign and long‐term water sustainability. In this study, we analysed 33 years of summer hydrologic data (1970–2002) from two comparable small‐scale watersheds located in the Xiaoxing'anling, Northeastern China. Time series analysis and two graphic methods (double mass curve and flow duration curve) with statistical testing as well as long‐term data on forest cover changes and climate were used. Our results show that the significant streamflow reduction as a result of reforestation occurred when forest cover reached 70% or 10 years after planting. After forest cover reached 85%, water reduction became stabilized. The accumulative streamflow reduction in 2002 reached 8·61% of the total accumulative streamflow. Among those water reduced, high flows (from 5 to 25 percentiles) were mostly affected, demonstrating that northeastern forests have an important role in reducing high flows. Implications of these results are discussed in the context of climate change, reforestation and water resource management. Copyright © 2011 John Wiley & Sons, Ltd.     

Snow process monitoring in montane forests with time‐lapse photography

A camera network with hourly resolution was used to monitor the complex snow processes in montane forest environments. We developed a semi‐automatic procedure to interpret snow depths from the digital images, which exhibited high consistency with manual measurements and station‐based recordings. To extract snow interception dynamics, six binary classification methods were compared. The MaxEntropy classifier demonstrated better performance than the other methods under conditions of varying illumination and was therefore selected as the method used for quantifying snow in tree canopies. Snow accumulation and ablation on the ground, as well as snow loading and unloading in the forest canopies, were investigated using snow parameters derived from the time‐lapse photography monitoring. The influences of meteorologic conditions, forest cover, and elevation on the snow processes were also considered. Time‐lapse photography proved to be an effective and low‐cost approach for collecting useful information on snow processes and facilitating the set‐up of hydrological models.     

EFFECTS OF FOREST ON THE SNOW PARAMETERS DERIVED FROM MICROWAVE MEASUREMENTS DURING THE BOREAS WINTER FIELD CAMPAIGN

Passive microwave data have been used to infer the areal snow water equivalent (SWE) with some success. However, the accuracy of these retrieved SWE values have not been well determined for heterogeneous vegetated regions. The Boreal Ecosystem–Atmosphere Study (BOREAS) Winter Field Campaign (WFC), which took place in February 1994, provided the opportunity to study in detail the effects of boreal forests on snow parameter retrievals. Preliminary results reconfirmed the relationship between microwave brightness temperature and snow water equivalent. The pronounced effect of forest cover on SWE retrieval was studied. A modified vegetation mixing algorithm is proposed to account for the forest cover. The relationship between the microwave signature and observed snowpack parameters matches results from this model.     

Updating of snow depletion curve with remote sensing data

A method for using remotely sensed snow cover information in updating a hydrological model is developed, based on Bayes' theorem. A snow cover mass balance model structure adapted to such use of satellite data is specified, using a parametric snow depletion curve in each spatial unit to describe the subunit variability in snow storage. The snow depletion curve relates the accumulated melt depth to snow‐covered area, accumulated snowmelt runoff volume, and remaining snow water equivalent. The parametric formulation enables updating of the complete snow depletion curve, including mass balance, by satellite data on snow coverage. Each spatial unit (i.e. grid cell) in the model maintains a specific depletion curve state that is updated independently. The uncertainty associated with the variables involved is formulated in terms of a joint distribution, from which the joint expectancy (mean value) represents the model state. The Bayesian updating modifies the prior (pre‐update) joint distribution into a posterior, and the posterior joint expectancy replaces the prior as the current model state. Three updating experiments are run in a 2400 km² mountainous region in Jotunheimen, central Norway (61°N, 9°E) using two Landsat 7 ETM+ images separately and together. At 1 km grid scale in this alpine terrain, three parameters are needed in the snow depletion curve. Despite the small amount of measured information compared with the dimensionality of the updated parameter vector, updating reduces uncertainty substantially for some state variables and parameters. Parameter adjustments resulting from using each image separately differ, but are positively correlated. For all variables, uncertainty reduction is larger with two images used in conjunction than with any single image. Where the observation is in strong conflict with the prior estimate, increased uncertainty may occur, indicating that prior uncertainty may have been underestimated. Copyright © 2006 John Wiley & Sons, Ltd.     

Total least square method applied to rating curves

The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error‐contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors‐in‐variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study. Copyright © 2013 John Wiley & Sons, Ltd.     

Distribution and seasonal fluctuations in the aquatic biodiversity of the southern Altiplano

We compared the distribution and seasonal fluctuations in the aquatic biota in relation to chemical and physical water variables in the Altiplano watersheds of the Ascotán, Carcote and Huasco salars; Chungará and Cotacotani lakes; Isluga and Lauca Rivers and the Parinacota wetland. We sampled during the austral autumn–winter of 2006 and in the spring–summer of 2006–2007, using three sampling stations for each system. We used canonical correspondence analysis to establish relations between frequency of taxa and environmental variables.We demonstrate that the structure and composition of the aquatic biota in humid areas of the Altiplano is determined by physical and chemical variables of the water. The most relevant one is total nitrogen, which is also the limiting nutrient for phytoplankton production in tropical systems.Benthos and zooplankton showed significant associations with the set of environmental variables (Monte Carlo test, p<0.05); however, the association was not significant for phytoplankton. Lake Chungará showed the greatest variation in composition and abundance of zooplankton between autumn-winter and spring-summer, while in the Huasco salar the physical and chemical characteristics were related to the composition and abundance of the benthonic fauna. Thus, changes in the water volume of these systems would have repercussions in chemical and physical variables, altering the species assemblage and possibly the efficiency and stability of ecosystem functions.     

Evaluation of a water equivalent of snow cover map in a small catchment area using a geostatistical approach

We describe an objective method for evaluating the spatial distribution of water equivalents of the snow cover within a small catchment. Regression analysis is used to quantify the relationship between elevation, presence or absence of forest, and potential direct solar radiation as independent variables and water equivalent as measured at a number of sites. First, this regression relationship is used to interpolate water equivalent data all over the basin area. Then we interpolate the residuals of the regression using a geostatistical approach. Superimposing the results obtained by interpolating the regression relationship and the interpolated residuals eventually yields the water equivalent distribution over the test area. The advantages of the interpolation method used lie in the optimal (effective, unbiased) estimation of the interpolated values as well as in the possibility to quantify the associated estimation variances.     

TENNESSEE VALLEY AUTHORITY DIVISION OF WATER CONTROL PLANNING HYDRAULIC DATA BRANCH

ABSTRACT

Statistical surveys of vegetative cover, soils, and other measures, called hydrologic condition surveys, have been collected by the Tennessee Valley Authority for several years. Attempts to assure that sufficient data were collected for future studies resulted in a total of 53 variables being defined. Because the exact nature of these future studies was unknown it was difficult to reduce the number of variables despite the fact it was known that some of the variables were highly interrelated.

The technique of factor analysis provided a means for determining the number of independent measures available in the survey data. Factor analysis will establish a reduced set of independent, or orthogonal, factors that will represent the original data. The factors can be used as guides to reduce the set of original variables. In the study, these factors were used to indicate the total number of variables that could be justified in a survey, to indicate the number of independent measures available within survey groupings, and to identify variables that were highly interrelated. This information and other criteria were used to reduce the number of variables measured and to establish a new survey that should contain primarily independent measures of hydrologic conditions. Equally important, however, this study provided an opportunity to review systematically and improve a survey procedure that had been developed piecemeal without comprehensive objectives to serve as guidelines.     

Using multicomponent GPR to monitor cracks in a historical building

We tested the usefulness of acquiring multicomponent GPR data to detect cracks in a historical building, and to monitor their dynamics, caused by a slowly and irregularly moving landslide. We used 2 GHz bipolar antennas in a configuration that allows for acquiring the in-line and cross-line electric field components with x- and y-directed antennas. The 2 × 2 data matrix was collected on a floor in the building along transects at four different times over a period of one year. The data were processed with a standard 2D scalar algorithm and with the latest 3D single component vector algorithm that corrects for antenna effects. We have implemented a 3D single component vector migration algorithm in a 2.5D sense to produce 2D slices of a 3D vector migration image by applying the algorithm on line data. This procedure allows for migrating single component line data taking into account all vector effects as well as three-dimensional wave propagation. We show that the 2.5D vector migration images have a much better in-line resolution than the migration images obtained by applying a standard 2D scalar migration algorithm.The GPR profiles agree with the a priori information about the structure of the floor. In particular, we detected two different types of anomalies, only a few of which can be due to utilities and to metallic mesh. Some shallower anomalies agree well with the cracks visible on the tiling, suggesting that some cracks can be directly detected using GPR. Visually there were no changes in the cracks on the floor and no clear changes in the GPR data could be attributed to possible subsurface changes in the cracks. The variations in the GPR images seemed primarily caused by changes in the coupling of the antennas with the investigated structure (floor) depending on the season when the measurements were made. For this reason the monitoring aspect of the survey is not successful.     

Coherence algorithm with a high-resolution time–time transform and feature matrix for seismic data

Traditional coherence algorithms are often based on the assumption that seismic traces are stationary and Gaussian. However, seismic traces are actually non-stationary and non-Gaussian. A constant time window and the canonical correlation analysis in traditional coherence algorithms are not optimal for non-stationary seismic traces and cannot describe the similarity between adjacent seismic traces in detail. To overcome this problem, a new coherence algorithm using the high-resolution time–time transform and the feature matrix is designed. The high-resolution time–time transform used to replace the constant time window can produce a frequency-dependent time local series to analyse non-stationary seismic traces. The feature matrix, constructed by the frequency-dependent time local series and the related local gradients, defines a new correlation metric that enhances more details of the geological discontinuities in seismic images than does the canonical correlation analysis. Additionally, the Riemannian metric is introduced for related calculations because the feature matrices are not defined in a Euclidean space but rather in a manifold space. Application to field data illustrates that the proposed method reveals more details of structural and stratigraphic features.