Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms

We provide a unified framework for a range of linear transforms that can be used for the analysis of terahertz spectroscopic data, with particular emphasis on their application to the measurement of leaf water content. The use of linear transforms for filtering, regression, and classification is discussed. For illustration, a classification problem involving leaves at three stages of drought and a prediction problem involving simulated spectra are presented. Issues resulting from scaling the data set are discussed. Using Lagrange multipliers, we arrive at the transform that yields the maximum separation between the spectra and show that this optimal transform is equivalent to computing the Euclidean distance between the samples. The optimal linear transform is compared with the average for all the spectra as well as with the Karhunen–Loève transform to discriminate a wet leaf from a dry leaf. We show that taking several principal components into account is equivalent to defining new axes in which data are to be analyzed. The procedure shows that the coefficients of the Karhunen–Loève transform are well suited to the process of classification of spectra. This is in line with expectations, as these coefficients are built from the statistical properties of the data set analyzed.

Optical sensors for monitoring water uptake in plants

The monitoring of water uptake in plants is becoming increasingly important. Optical sensors offer considerable advantages over conventional methods and several sensors have been developed including an optical potometer that monitors water uptake from individual roots, the detection of xylem cavitation using audio acoustic emissions with an interferometric force feedback microphone, and an optical fiber displacement transducer that detects changes in leaf thickness in relation to leaf-water potential.

Changes in fat, fat-free mass and body water in human normal pregnancy

Measurements of body weight, total body water and total body potassium (40K) were made serially on three occasions during pregnancy and once post partum in 27 normal pregnant women. Skinfold thickness and fat cell diameter were also measured. A model of body composition was formulated to permit the estimation of changes in fat, lean tissue and water content of the maternal body. Total maternal body fat increased during pregnancy, reaching a peak towards the end of the second trimester before diminishing. Serial measurements of fat cell diameter showed poor correlation, whilst total body fat calculated from skinfold thickness correlated well with our estimated values for total body fat in pregnancy.

Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼20%. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.8.083690]

Stalagmite water content as a proxy for drip water supply in tropical and subtropical areas

In this pilot study water was extracted from samples of two Holocene stalagmites from Socotra Island, Yemen, and one Eemian stalagmite from southern continental Yemen. The amount of water extracted per unit mass of stalagmite rock, termed "water yield" hereafter, serves as a measure of its total water content. Based on direct correlation plots of water yields and δ18Ocalcite and on regime shift analyses, we demonstrate that for the studied stalagmites the water yield records vary systematically with the corresponding oxygen isotopic compositions of the calcite (δ18Ocalcite). Within each stalagmite lower δ18Ocalcite values are accompanied by lower water yields and vice versa. The δ18Ocalcite records of the studied stalagmites have previously been interpreted to predominantly reflect the amount of rainfall in the area; thus, water yields can be linked to drip water supply. Higher, and therefore more continuous drip water supply caused by higher rainfall rates, supports homogeneous deposition of calcite with low porosity and therefore a small fraction of water-filled inclusions, resulting in low water yields of the respective samples. A reduction of drip water supply fosters irregular growth of calcite with higher porosity, leading to an increase of the fraction of water-filled inclusions and thus higher water yields. The results are consistent with the literature on stalagmite growth and supported by optical inspection of thin sections of our samples. We propose that for a stalagmite from a dry tropical or subtropical area, its water yield record represents a novel paleo-climate proxy recording changes in drip water supply, which can in turn be interpreted in terms of associated rainfall rates.

Using laboratory and field measurements to constrain a single habit shortwave optical parameterization for cirrus

A single habit parameterization for the shortwave optical properties of cirrus is presented. The parameterization utilizes a hollow particle geometry, with stepped internal cavities as identified in laboratory and field studies. This particular habit was chosen as both experimental and theoretical results show that the particle exhibits lower asymmetry parameters when compared to solid crystals of the same aspect ratio. The aspect ratio of the particle was varied as a function of maximum dimension, D, in order to adhere to the same physical relationships assumed in the microphysical scheme in a configuration of the Met Office atmosphere-only global model, concerning particle mass, size and effective density. Single scattering properties were then computed using T-Matrix, Ray Tracing with Diffraction on Facets (RTDF) and Ray Tracing (RT) for small, medium, and large size parameters respectively. The scattering properties were integrated over 28 particle size distributions as used in the microphysical scheme. The fits were then parameterized as simple functions of Ice Water Content (IWC) for 6 shortwave bands. The parameterization was implemented into the GA6 configuration of the Met Office Unified Model along with the current operational long-wave parameterization. The GA6 configuration is used to simulate the annual twenty-year short-wave (SW) fluxes at top-of-atmosphere (TOA) and also the temperature and humidity structure of the atmosphere. The parameterization presented here is compared against the current operational model and a more recent habit mixture model.

Downwelling solar irradiance in the biomass burning region of the southern Amazon: Dependence on aerosol intensive optical properties and role of water vapor

The sensitivity of solar irradiance at the surface to the variability of aerosol intensive optical properties is investigated for a site (Alta Floresta) in the southern portion of the Amazon basin using detailed comparisons between measured and modeled irradiances. Apart from aerosol intensive optical properties, specifically single scattering albedo (omega(o lambda)) and asymmetry parameter (g(lambda)), which were assumed constant, all other relevant input to the model were prescribed based on observation. For clean conditions, the differences between observed and modeled irradiances were consistent with instrumental uncertainty. For polluted conditions, the agreement was significantly worse, with a root mean square difference three times larger (23.5 Wm(-2)). Analysis revealed a noteworthy correlation between the irradiance differences (observed minus modeled) and the column water vapor (CWV) for polluted conditions. Positive differences occurred mostly in wet conditions, while the differences became more negative as the atmosphere dried. To explore the hypothesis that the irradiance differences might be linked to the modulation of omega(o lambda) and g(lambda) by humidity, AERONET retrievals of aerosol properties and CWV over the same site were analyzed. The results highlight the potential role of humidity in modifying omega(o lambda) and g(lambda) and suggest that to explain the relationship seen between irradiances differences via aerosols properties the focus has to be on humidity-dependent processes that affect particles chemical composition. Undoubtedly, there is a need to better understand the role of humidity in modifying the properties of smoke aerosols in the southern portion of the Amazon basin.

Effect of Different Solvent Ratios (Water/Ethylene Glycol) on the Growth Process of CaMoO(4) Crystals and Their Optical Properties

In this paper, calcium molybdate (CaMoO(4)) crystals (meso- and nanoscale) were synthesized by the coprecipitation method using different solvent volume ratios (water/ethylene glycol). Subsequently, the obtained suspensions were processed in microwave-assisted hydrothermal/solvothermal systems at 140 degrees C for 1 h. These meso- and nanocrystals processed were characterized by X-ray diffraction (X R I)), Fourier transform Raman (FT-Raman), Fourier transform infrared (FT-IR). ultraviolet visible (UV-vis) absorption spectroscopies, held-emission gun scanning electron microscopy (FEG-SEM). transmission electron microscopy (TEM). and photoluminescence (PL) measurements. X RI) patterns and FT-Raman spectra showed that these meso- and nanocrystals have a scheelite-type tetragonal structure without the presence of deleterious phases. FT-IR spectra exhibited a large absorption band situated at around 827 cm(-1), which is associated with the Mo-O anti-symmetric stretching vibrations into the [MoO(4)] clusters. FEG-SEM micrographs indicated that the ethylene glycol concentration in the aqueous solution plays an important role in the morphological evolution of CaMoO(4) crystals. High-resolution TEM micrographs demonstrated that the mesocrystals consist of several aggregated nanoparticles with electron diffraction patterns of monocrystal. In addition, the differences observed in the selected area electron diffraction patterns of CaMoO(4) crystals proved the coexistence of both nano- and mesostructures, First-principles quantum mechanical calculations based on the density functional theory at the B3LYP level were employed in order to understand the band structure find density of states For the CaMoO(4). UV-vis absorption measurements evidenced a variation in optical band gap values (from 3.42 to 3.72 cV) for the distinct morphologies. The blue and green PI. emissions observed in these crystals were ascribed to the intermediary energy levels arising from the distortions on the [MoO(4)] clusters clue to intrinsic defects in the lattice of anisotropic/isotropic crystals.

Very low optical absorptions and analyte concentrations in water measured by Optimized Thermal Lens Spectrometry

Thermal Lens Spectrometry has traditionally been carried out in the single-beam and the mode-mismatched dual-beam configurations. Recently, a much more sensitive dual-beam TL setup was developed, where the probe beam is expanded and collimated. This feature optimizes Thermal Lens (TL) signal and allows the use of thicker samples, further improving the sensitivity. In this paper, we have made comparisons between the conventional and optimized TL configurations, and presented applications such as measurements of very low absorptions and concentrations in water and Cr(III) aqueous solution in the UV-vis range. For pure water we found linear absorption coefficients as low as the Raman scattering one due to the stretching vibrational modes of OH group. The detection limit was estimated 1 x 10(-6) cm(-1) with a 180-mW excitation power using a 100-mm cell length. This sensitivity is very high, considering that water has a photothermal enhancement factor similar to 33 times smaller than CCl(4), for example. For Cr(III) species in aqueous solution, the limit of detection (LOD) was estimated in similar to 40 ng mL(-1) at 514 nm, or similar to 10ng mL(-1) at 405 nm, which is similar to 30 times smaller than the LOD achieved with conventional transmission techniques. The more recent TL configuration is very attractive to obtain absorption spectra, since the result does not depend critically on the beam parameters, unlike the other configurations. The main drawbacks of this optimized TL configuration are the longer acquisition time and the need for larger samples. (C) 2011 Published by Elsevier B.V.

Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters

Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO (4) (3-) , and SO (4) (2-) concentrations were as follows: Cu (3.1-120 A mu g L(-1)), Fe (60-330 A mu g L(-1)), Mg (48-123 mg L(-1)), Mn (0.4-4.0 mg L(-1)), PO (4) (3-) (55-212 mg L(-1)), and SO (4) (2-) (19-136 mg L(-1)). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey-Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.

Evaluation of the QuEChERS Method and Gas Chromatography-Mass Spectrometry for the Analysis Pesticide Residues in Water and Sediment

A method for the determination of pesticide residues in water and sediment was developed using the QuEChERS method followed by gas chromatography - mass spectrometry. The method was validated in terms of accuracy, specificity, linearity, detection and quantification limits. The recovery percentages obtained for the pesticides in water at different concentrations ranged from 63 to 116%, with relative standard deviations below 12%. The corresponding results from the sediment ranged from 48 to 115% with relative standard deviations below 16%. The limits of detection for the pesticides in water and sediment were below 0.003 mg L(-1) and 0.02 mg kg(-1), respectively.

Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network has become exceedingly serious. As urban water supply network is hidden underground asset, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network, decision tree algorithm (C4.5) has been carried out to classify the specific situation of water supply pipeline. Part of the historical data was used to establish a decision tree classification model, and the remaining historical data was used to validate this established model. Adopting statistical methods were used to access the decision tree model including basic statistical method, Receiver Operating Characteristic (ROC) and Recall-Precision Curves (RPC). These methods has been successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model was to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable (AU), near perfect condition (NPC) and serious deterioration (SD). Finally, this research focused on pipe classification which plays a significant role in maintaining water supply networks in the future.