An accuracy assessment methodology for the remotely sensed discontinuities: a case study in Andesite Quarry area,Turkey

The discontinuities play an important role in both the design and development stages of many geotechnical engineering projects. Therefore, semi‐automatic detection of discontinuities based on remote sensing will save a considerable amount of time and cost. Accuracy of the semi‐automatically detected discontinuities is also as important as the detection process, since the results directly reflect the uncertainty in the geotechnical problem. The current paper presents an accuracy assessment methodology for semi‐automatically detected discontinuities from remotely sensed images. Semi‐automatic lineament analysis is performed by using high‐resolution satellite imagery for identification of rock discontinuities at the Andesite quarry area in Gölba??, Ankara, Turkey. A high‐resolution data IKONOS Precision Plus with 1 m resolution orthorectified image has been used in the present study. The accuracy assessment methodology is developed based on a comparison of manually extracted lineaments using directional filtering and semi‐automatically detected lineaments with LINE module of PCI Geomatica 8.2 and manual intervention. The proposed accuracy assessment methodology considers the accuracy based on location and length of the lineaments. In this respect, it has two dimensions. For the algorithm of the developed methodology, a software called LINECOMP, is coded in Java environment. The LINECOMP is composed of three modules. These are: LINE ADDRESS for identification of pixel addresses of lineaments; which is used for locational accuracy assessment; LINE COMPARE for comparing two lineament maps with respect to lineament pixels, which is used for length‐based accuracy; and LINE CLASSIFICATION for classifying the assessed accuracy with respect to the amount of location and length matching. The performance of the proposed accuracy assessment method is also verified by the field studies carried out in the study area.     

Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures

A new vegetation index, the Normalized Hotspot-signature Vegetation Index (NHVI), is proposed for a better quantitative estimation of leaf area index (LAI) than with the remotely sensed normalized difference vegetation index (NDVI), especially in the boreal forest. To obtain this new index, the Hotspot-Dark-spot index (HDS) (Lacaze et al., 2002) was introduced. HDS is calculated by the difference between the strongest vector (hotspot) and the weakest vector (dark-spot) of bi-directional reflectance, a given tract of vegetation returns in the reflecting solar position, and the geometric structure of the vegetation canopy, which are poorly represented by NDVI alone. The validity of NHVI was statistically tested using two field data sets of multi-angular observations and LAI from the boreal forests of Canada; one set was our own observations, and the other was from the Boreal Ecosystem-Atmosphere Study (BOREAS). The range of linear correspondence of NHVI with LAI is much wider than that of NDVI alone, indicating significant representation of leaf biomass in the canopy geometry captured by HDS. With the technical innovation of multi-angular remote-sensing and kernel-driven models in the future, this index has the potential to provide a more accurate evaluation of regional and global LAIs.     

A novel FPGA-based architecture for the estimation of the virtual dimensionality in remotely sensed hyperspectral images

A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular ways to determine the number of endmembers is by estimating the virtual dimensionality (VD) of the hyperspectral image using the well-known Harsanyi–Farrand–Chang (HFC) method. Due to the complexity and high dimensionality of hyperspectral scenes, this task is computationally expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HFC-VD algorithm. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA’s Airborne Visible Infra-Red Imaging Spectrometer over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the HFC-VD algorithm can significantly outperform an equivalent software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Most important, our implementation exhibits real-time performance with regard to the time that the hyperspectral instrument takes to collect the image data.     

Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines

A data assimilation (DA) methodology that uses two state-of-the-art techniques, relevance vector machines (RVMs) and support vector machines (SVMs), is applied to retrieve surface (0–6 cm) soil moisture content (SMC) and SMC at a depth of 30 cm. RVMs and SVMs are known for their robustness, efficiency and sparseness and provide a statistically sound approach to solve inverse problems and thus to build statistical models. Here, we build a statistical model that produces acceptable estimations of SMC by using inexpensive and readily available data. The study area for this research is the Walnut Creek watershed in Ames, south-central Iowa, USA. The data were obtained from Soil Moisture Experiments 2002 (SMEX02) conducted at Ames, Iowa. The DA methodology combines remotely sensed inputs with field measurements, crop physiological characteristics, soil temperature, soil water-holding capacity and meteorological data to build a two-step model to estimate SMC using both techniques, i.e. RVMs and SVMs. First, the RVM is used to build a model that retrieves surface (0–6 cm) SMC. This information serves as a boundary condition for the second step of this model, which estimates SMC at a depth of 30 cm. An exactly similar routine is followed with an SVM for estimation of surface (0–6 cm) SMC and SMC at a depth of 30 cm. The results from the RVM and SVM models are compared and statistics show that RVMs perform better (root mean square error (RMSE)?= 0.014 m³ m^?3) when compared with SVMs (RMSE?= 0.017 m³ m^?3) with a reduced computational complexity and more suitable real-time implementation. Cross-validation techniques are used to optimize the model. Bootstrapping is used to check over/under-fitting and uncertainty in model estimates. Computations show good agreement with the actual SMC measurements with coefficients of determination (R ²) for RVM equal to 0.92 and for SVM equal to 0.88. Statistics indicate a good model generalization capability with indexes of agreement (IoAs) for RVM equal to 0.97 and for SVM equal to 0.96.     

Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover

Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the effects of water content on the remotely sensed estimates of crop residue cover and to propose a method to mitigate the effects of water content on remotely sensed estimates of crop residue cover. Reflectance spectra of crop residues and soils were measured in the lab over the 400-2400 nm wavelength region. Reflectance of scenes with various residue cover fractions and water contents was simulated using a linear mixture model. Additional spectra of scenes with mixtures of crop residues and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments and different water content conditions. Crop residue cover was linearly related to the cellulose absorption index (CAI), which was defined as the relative intensity of an absorption feature near 2100 nm. Water in the crop residue significantly attenuated CAI and changed the slope of the residue cover vs. CAI relationship. Without an appropriate correction, crop residue covers were underestimated as scene water content increased. Spectral vegetation water indices were poorly related to changes in the water contents of crop residues and soils. A new reflectance ratio water index that used the two bands located on the shoulders of the cellulose absorption feature to estimate scene water conditions was proposed and tested with data from corn, soybean, and wheat fields. The ratio water index was used to describe the changes in the slope of crop residue cover vs. CAI and improve the predictions of crop residue cover. These results indicate that spatial and temporal adjustments in the spectral estimates of crop residue cover are possible. Current mutispectral imaging systems will not provide reliable estimates of crop residue cover when scene water content varies. Hyperspectral data are not required, because the three narrow bands that are used for both CAI and the scene moisture correction could be incorporated in advanced multispectral sensors. Thus, regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using either advanced multispectral or hyperspectral imaging systems.     

An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery

Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification.     

An aftertreatment technique for improving the accuracy of Adomian's decomposition method

Adomian's decomposition method (ADM) is a nonnumerical method which can be adapted for solving nonlinear ordinary differential equations. In this paper, the principle of the decomposition method is described, and its advantages as well as drawbacks are discussed. Then an aftertreatment technique (AT) is proposed, which yields the analytic approximate solution with fast convergence rate and high accuracy through the application of Padé approximation to the series solution derived from ADM. Some concrete examples are also studied to show with numerical results how the AT works efficiently.     

An efficient gradient technique for the solution of optimal control problems

An iterative gradient-like technique — referred to as the double operator gradient method — is described. The method is shown to be generally much more efficient than either the fixed step or steepest descent type gradient methods. In addition, the method is applied to a well known singular optimal control problem which has recently been efficiently solved using a control-averaging technique. The double operator gradient method is shown to give a good suboptimal solution in relatively small computational time, being considerably more efficient than the controlaveraging technique for the extensive range of cases considered.     

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Understanding the spatial scaling behavior of evapotranspiration and its relation to controlling factors on the land surface is necessary to accurately estimate regional water cycling. We propose a method for ascertaining this scaling behavior via a combination of wavelet multi-resolution analysis and information theory metrics. Using a physically-based modeling framework, we are able to compute spatially distributed latent heat fluxes over the tall-grass prairie in North-central Kansas for August 8, 2005. Comparison with three eddy-covariance stations and a large aperture scintillometer demonstrates good agreement, and thus give confidence in the modeled fluxes. Results indicate that the spatial variability in radiometric temperature (a proxy for soil moisture) most closely controls the spatial variability in evapotranspiration. Small scale variability in the water flux can be ascribed to the small scale spatial variance in the fractional vegetation. In addition, correlation analysis indicates general scale invariance and that low spatial resolution data may be adequate for accurately determining water cycling in prairie ecosystems.     

Multisource remotely sensed data,field checks and seismicity for the definition of active tectonics in Ecuadorian Andes

Abstract

The active tectonics of the Ecuadorian Andes were analysed by the correlation of remotely sensed features, and microtectonic and seismological data. Landsat and radar imagery, and airborne stereophotos, were employed to investigate the fault pattern of scarcely accessible areas, with special reference to those lineaments showing young morphology, linear continuity and dragging effect on streams of post-glacial age. Some NNE-SSW long lineaments characterized by right-lateral strike-slip motions, were identified in the eastern Andes as probably very recent faults. Field microtectonic analysis was guided by remotely sensed data and allowed the reconstruction of the active tectonic compressional stress field characterized by a WSW-ENE trending σ. It also confirmed the recent motions along the NNE-SSW set. These data are consistent with published focal mechanism solutions in the same area. The seismicity is explained by a kinematic model of the upper crust, in which west dipping oblique thrusts and strike-slip faults coexist in the eastern Andes. Following the remotely sensed data and the available literature this model is extended southwards to complete the regional geodynamic framework.     

Fault trace parameters as a tool for analysing remotely sensed fault arrays: An example from the eastern Gulf of Corinth,Greece

Data from a remotely sensed fault trace map were sampled using circular scanlines/windows to obtain values of three fault trace parameters: the trace intensity (I), the trace density (ρ) and the mean trace length (μ). The spatial distribution and the interrelation of these trace parameters were then investigated. The constructed contour maps showing the spatial distribution of μ and I are characterized by elongated contours which follow the trend of fault traces individualizing ENE- and WNW-striking fault zones. These zones have lengths ranging between 5 and 20 km and show a good correlation with the current seismicity. Analysis of diagrams showing the variation of I and ρ in respect to μ revealed two major patterns for the arrangements of fault arrays in the area: (a) domains with well-aligned and closely spaced faults are characterized by nearly constant I-values and reduced ρ-values as μ increases; and (b) domains with randomly distributed faults are characterized by increased I-values and nearly constant ρ-values as μ increases. The I, ρ versus μ diagrams for domains where both patterns coexist are also useful for understanding interaction of faults. On such diagrams, the transitional point between the two proposed patterns corresponds to the maximum critical mean fault trace length (μ) value that is required for the onset of the fault linkage process within a fault array.     

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km²) to 6.1% (post-calibration at 3125 km²) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km²) was decreased to 15.4% (post-calibration at 3125 km²). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.     

An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data

An autologistic regression model, which takes into account neighbouring associations, was developed and applied for burned land mapping using Landsat-5 Thematic Mapper data. The integration of the autocovariate component (estimated using a moving window of 3 @ 3 pixels) into the ordinary logistic regression model increased significantly the overall accuracy from 88.18% to 92.44%. In contrast, the accuracy derived with application of post-classification majority filters, which follow the same principles, were not significantly different to that derived with ordinary logistic regression.     

An integrated system optimization and parameter estimation technique for hierarchical control of steady-state systems

This paper presents computer simulation results of an optimal adaptive algorithm (Brdy? and Roberts 1984) and develops a double iterative alternative. Both algorithms are optimal in the sense that Kuhn-Tucker necessary optimality conditions are satisfied. The aim of the latter is to reduce the number of times that information is required from the real system. Simulation results also show that it does not increase the total number of information exchanges during the iteration procedure and may even reduce it.     

An algorithm for synthesis of optimal singular adaptive observers with direct estimation of the initial state vector

An algorithmic synthesis is suggested of a new generation of adaptive observers, including optimal estimators of the initial state vector, identifiers of parameters and optimal singular (full and degenerated) observers of the current state vector, for SISO linear discrete systems. The estimation of the elements of the initial state vector is not typical for the algorithms, inherent to Luenberger's theory of observation with all due consequences. The important fact is emphasized of the dropping off of the procedure for the synthesis of systems with given poles, causing additional difficulties when Luenberger's theory of observation is used. The synthesized algorithm has features such as large-scale granularity and natural parallelism, which also allow its software and hardware interpretation on a transputer basis.     

An adaptive optimization system and point tracking technique for estimation of variable-speed seawater pumped storage

There are few kinds of researches on the capacity optimization of seawater pumped storage with variable-speed units. First, the pumped storage effects are investigated to smoothing the large-scale offshore wind power. From the perspective of energy saving and improving efficiency, a method on maximum efficiency tracking is proposed, based on the synthetic characteristic curve of the pump turbine. Second, under the background of the time-of-use electricity price in the power market, the relevant model is established, taking full account of the characteristics of random seasonal fluctuation of tailwater level. Besides, this model satisfies the constraints of variable-speed units (especially under the pump mode). Finally, the key factors influencing profit growth, such as the fluctuation rate, the unit type, and the optimal capacity, are investigated based on a real case. The results show that the variable-speed unit can give full play to the efficiency advantage when the operating head and power demand of the plant change sharply. Because the output range of the variable-speed unit is extensive and can be continuously adjusted (especially in pump mode), the joint system will actively curtail the offshore wind to seek the maximum profit.

     

An efficient technique for estimating the effect of small errors of control system implementation on the optimal program

The time-optimal problem is considered for a linear system with constant coefficients. For a piecewise constant program, a matrix equality of the form

$P_m^0 (T) = \sum\limits_{i = 0}^{m - 1} {\Delta C_{i,i + 1} (0)} \bullet e^{ - At_i } $

is obtained, where the left-hand side depends on the final point, while each term in the right-hand side depends only on one of the control switchings (t¡ are switching instants). This relation is fulfilled on the trajectories of the original system. The vector deviation from the final point for small errors of switching instants is found from this formula. Furthermore, simple procedures for calculating deviations in the case when the control and coefficient matrices are defined with errors are presented, a new method of solving the time-optimal problem is described, and the Pontryagin’s maximum principle is refined by adding the condition of getting into the final point. A numerical example is considered.

     

Simultaneous retrieval of aerosol and ocean properties by optimal estimation: SeaWiFS case studies for the Santa Barbara Channel

In this work, some sea viewing wide field‐of‐view sensor (SeaWiFS) images over the Santa Barbara Channel (SBC) are analysed. Pixel‐by‐pixel measurements of radiances at eight SeaWiFS channels and analytic Jacobians are simulated using a coupled atmosphere–ocean radiative transfer model. The inverse algorithm is based on optimal estimation with loosely constrained a priori data. The five‐element state vector has two aerosol (optical depth at 865 nm, bimodal fraction of particles) and three marine (chlorophyll concentration, detrital/dissolved‐matter absorption at 443 nm, and backscattering coefficient at 443 nm) parameters. The retrieval is stable and well posed; the results are smoother and show less spread than those derived from the standard SeaDAS v4.8 algorithm. For a 28 February 2003 SeaWiFS image, the average radiance residual is less than 1% for seven SeaWiFS channels, and less than 2% for the 765 nm channel. For a series of SBC SeaWiFS match‐up cases over a 4‐year period, estimated water‐leaving radiances agree well with field measurements.     

Discrete pulse orthogonal functions for the analysis,parameter estimation and optimal control of linear digital systems

Discrete pulse orthogonal functions (DPOFs) are introduced, and their operational matrix is proposed to solve various control problems. The applications of DPOFs in digital control systems are analogous to those of block pulse functions (BPFs) in continuous systems. Applying the DPOFs, the approximate solutions of digital time-invariant systems can be easily obtained by a convenient algorithm. Three examples are presented to demonstrate the applications of DPOFs.