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%.

Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

A lightweight balloon-carried cloud charge sensor

Despite the importance of microphysical cloud processes on the climate system, some topics are under-explored. For example, few measurements of droplet charges in nonthunderstorm clouds exist. Balloon carried charge sensors can be used to provide new measurements. A charge sensor is described for use with meteorological balloons, which has been tested over a range of atmospheric temperatures from -60 to 20 degrees C, in cloudy and clear air. The rapid time response of the sensor (to >10 V s(-1)) permits charge densities from 100 fC m(-3) to 1 nC m(-3) to be determined, which is sufficient for it to act as a cloud edge charge detector at weakly charged horizontal cloud boundaries.

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

A review and classification of the existing models of cyanobacteria

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

Assessing the sources and magnitude of diurnal nitrate variability in the San Joaquin River (California) with an in-situ optical nitrate sensor and dual nitrate isotopes

We investigated diurnal nitrate (NO3-) concentration variability in the San Joaquin River using an in situ optical NO3- sensor and discrete sampling during a 5-day summer period characterized by high algal productivity. Dual NO3- isotopes (delta N-15(NO3) and delta O-18(NO3)) and dissolved oxygen isotopes (delta O-18(DO)) were measured over 2 days to assess NO3- sources and biogeochemical controls over diurnal time-scales. Concerted temporal patterns of dissolved oxygen (DO) concentrations and delta O-18(DO) were consistent with photosynthesis, respiration and atmospheric O-2 exchange, providing evidence of diurnal biological processes independent of river discharge. Surface water NO3- concentrations varied by up to 22% over a single diurnal cycle and up to 31% over the 5-day study, but did not reveal concerted diurnal patterns at a frequency comparable to DO concentrations. The decoupling of delta N-15(NO3) and delta O-18(NO3) isotopes suggests that algal assimilation and denitrification are not major processes controlling diurnal NO3- variability in the San Joaquin River during the study. The lack of a clear explanation for NO3- variability likely reflects a combination of riverine biological processes and time-varying physical transport of NO3- from upstream agricultural drains to the mainstem San Joaquin River. The application of an in situ optical NO3- sensor along with discrete samples provides a view into the fine temporal structure of hydrochemical data and may allow for greater accuracy in pollution assessment.

Soft topographic map for clustering and classification of bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification or erroneous annotations in the database.

Ag dopedWO(3)-based powder sensor for the detection of NO gas in air

WO3-based materials as sensors for the monitor of environmental gases such as NO2 (NO + NO2) have been rapidly developed for various potential applications (stationary and mobile uses). It has been reported that these materials are highly sensitive to NOx with the sensitivity further enhanced by adding precious group metals (PGM such as Pt, Pd, Au, etc.). However, there has been limited work in revealing the sensing mechanism for these gases over the WO3-based sensors. In particular, the role of promoter is not yet clear though speculations on their catalytic, electronic and structural effects have been made in the past. In parallel to these PGM promoters here we report,for the first time, that Ag promotion can also enhance WO3 sensitivity significantly. In addition, this promotion decreases the optimum sensor temperature of 300 degreesC for Most WO3-based sensors, to below 200 degreesC. Characterizations (XRD, TEM, and impedance measurement) reveal that there is no significant bulk structure change nor particle size alteration in the WO3 phases during the NO exposure. However, it is found that the Ag doping creates a high concentration of oxygen vacancies in form of coordinated crystallographic shear (CS) planes onto the underneath WO3. It is thus proposed that the Ag particle facilitates the oxidative conversion of NO to NO2 followed by a subsequent NO2 adsorption on the defective WO, sites created at the Ag-WO3 interface; hence, accounting for the high molecular sensitivity. (C) 2002 Elsevier Science B.V. All rights reserved.

Bis[1,1 '-N,N '-(2-picolyl)aminomethyl]ferrocene as a redox sensor for transition metal ions

The compound bis[1,1'-N,N'-(2-picolyl) aminomethyl] ferrocene, L-1, was synthesized. The protonation constants of this ligand and the stability constants of its complexes with Ni2+, Cu2+, Zn2+, Cd2+ and Pb2+ were determined in aqueous solution by potentiometric methods at 25degreesC and at ionic strength 0.10 mol dm(-3) in KNO3. The compound L-1 forms only 1:1 (M:L) complexes with Pb2+ and Cd2+ while with Ni2+ and Cu2+ species of 2:1 ratio were also found. The complexing behaviour of L-1 is regulated by the constraint imposed by the ferrocene in its backbone, leading to lower values of stability constants for complexes of the divalent first row transition metals when compared with related ligands. However, the differences in stability are smaller for the larger metal ions. The structure of the copper complex with L-1 was determined by single-crystal X-ray diffraction and shows that a species of 2:2 ratio is formed. The two copper centres display distorted octahedral geometries and are linked through the two L1 bridges at a long distance of 8.781(10) Angstrom. The electrochemical behaviour of L-1 was studied in the presence of Ni2+, Cu2+, Zn2+, Cd2+ and Pb2+, showing that upon complexation the ferrocene-ferrocenium half-wave potential shifts anodically in relation to that of the free ligand. The maximum electrochemical shift (DeltaE(1/2)) of 268 mV was found in the presence of Pb2+, followed by Cu2+ (218 mV), Ni2+ (152 mV), Zn2+ (111 mV) and Cd2+ (110 mV). Moreover, L-1 is able to electrochemically and selectively sense Cu2+ in the presence of a large excess of the other transition metal cations studied.

A sensor based intelligent building assessment model

While building provides shelter for human being, the previous models for assessing the intelligence of a building seldom consider the responses of occupants. In addition, the assessment is usually conducted by an authority organization on a yearly basis, thus can seldom provide timely assistance for facility manager to improve his daily facility maintenance performance. By the extending the law of entropy into the area of intelligent building, this paper demonstrate that both energy consumption and the response of occupants are important when partially assessing the intelligence of a building. This study then develops a sensor based real time building intelligence (BI) assessment model. An experimental case study demonstrates how the model can be implemented. The developed model can address the two demerits of the previous BI assessment model.