Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Using frameworks to develop a distributed conferencing system: an experience report

Application frameworks are a powerful means to reduce software development costs while improving quality. However, at the same time they are difficult to select and understand, as well as hard to learn, use, and debug effectively and efficiently. In this paper we report the story of eConference, a distributed conferencing system that was developed as part of a broader research effort. Here we discuss the lessons learned from the evolution of our conferencing tool over four generations, which have been necessary to find good frameworks and build a flexible distributed tool. Copyright © 2009 John Wiley & Sons, Ltd.     

Space and time localization for the estimation of distributed parameters in a finite element model

In this paper we study the problem of estimating the possibly non-homogeneous material coefficients inside a physical system, from transient excitations and measurements made in a few points on the boundary. We assume there is available an adequate Finite Element (FEM) model of the system, whose distributed physical parameters must be estimated from the experimental data.We propose a space–time localization approach that gives a better conditioned estimation problem, without the need of an expensive regularization. Some experimental results obtained on an elastic system with random coefficients are given.     

The subsequence composition of a string

Words that appear as constrained subsequences in a text-string are considered as possible indicators of the host string structure, hence also as a possible means of sequence comparison and classification. The constraint consists of imposing a bound on the number ω$\omega$ of positions in the text that may intervene between any two consecutive characters of a subsequence. A subset of such ω$\omega$-sequences is then characterized that consists, in intuitive terms, of sequences that could not be enriched with more characters without losing some occurrence in the text. A compact spatial representation is then proposed for these representative sequences, within which a number of parameters can be defined and measured. In the final part of the paper, such parameters are empirically analyzed on a small collection of text-strings endowed with various degrees of structure.     

A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data

The selection of the optimal band combination for the estimation of specific crop variables is a key aspect in order to obtain reliable estimation of in-field variability from multi- and hyperspectral remote-sensing data. The selection of the bands is strongly influenced by the phenological stage of the crop at the acquisition time. In this work, the influence of the growing stage on the combination of spectral bands related to grain nitrogen (N) uptake in wheat was evaluated using multispectral (Satellite Pour l’Observation de la Terre – SPOT) and hyperspectral (Compact High Resolution Imaging Spectrometer – CHRIS-PROBA) satellite images at different growth stages over two wheat growth seasons in central Italy. In order to identify the more appropriate covariates (spectral bands) for each phenological stage, stepwise regression with backward selection was combined with stepwise variance inflation factors (VIFs) analysis and linear mixed effect model (LMEM). The results obtained in this study suggest that the spectral region most related to N uptake varies over the growing season of the wheat crop. For SPOT data, near-infrared (NIR) region was selected at all the phenological stages in both growing seasons, except for the latest stage, with low chlorophyll content due to the onset of senescence, in which the red band was selected. At stem elongation, the shortwave infrared (SWIR) band of SPOT data was also selected. At this stage, the best N estimation accuracy was obtained using an LMEM (root mean square error, RMSE = 0.012 t ha^?1). The inclusion of a spatial component in the estimation model by means of LMEMs provided a more accurate estimation than ordinary least square (OLS) models at all growth stages. The test carried out with CHRIS-PROBA data at the fourth stage confirmed the importance of NIR and in particular of the red-edge region for N uptake prediction. A novel methodology is proposed, which involves two crucial aspects in the context of the use of remote-sensing data in precision agriculture: i) the standardization of the spatial resolution for in-field and satellite data by a geostatistical data technique (data fusion); and ii) the selection of the most appropriate spectral bands for each phenological stage, taking into account both correlation with the target variable and collinearity.     

Many-Objective Virtual Machine Placement

The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.     

The “Always Best Packet Switching” architecture for SIP-based mobile multimedia services

This paper presents a distributed architecture for the provision of seamless and responsive mobile multimedia services. This architecture allows its user applications to use concurrently all the wireless network interface cards (NICs) a mobile terminal is equipped with. In particular, as mobile multimedia services are usually implemented using the UDP protocol, our architecture enables the transmission of each UDP datagram through the “most suitable” (e.g. most responsive, least loaded) NIC among those available at the time a datagram is transmitted. We term this operating mode of our architecture Always Best Packet Switching (ABPS). ABPS enables the use of policies for load balancing and recovery purposes. In essence, the architecture we propose consists of the following two principal components: (i) a fixed proxy server, which acts as a relay for the mobile node and enables communications from/to this node regardless of possible firewalls and NAT systems, and (ii) a proxy client running in the mobile node responsible for maintaining a multi-path tunnel, constructed out of all the node's NICs, with the above mentioned fixed proxy server. We show how the architecture supports multimedia applications based on the SIP and RTP/RTCP protocols, and avoids the typical delays introduced by the two way message/response handshake of the SIP signaling protocol. Experimental results originated from the implementation of a VoIP application on top of the architecture we propose show the effectiveness of our approach.     

Robust stabilization of second-order image-based affine systems

In this paper, the problem of robust relative positioning between a 6-DOF robot camera and an object of interest is considered. Assuming weak perspective camera model and local linear approximation of visible object's surface, an image-based state space representation of robot camera–object interaction model is derived, based on the matrix of 2-D affine transformations. Dynamic extension of the visual model permits to estimate 3-D parameters directly as functions of state variables. The proposed nonlinear robust control law ensures asymptotic stability but at image singularities, assuming exact model and state measurements. In the presence of bounded uncertainties, under appropriate choice of control gains, ultimate boundedness of the state error is also formally proved. Simulation results validate the theoretical framework both in terms of system convergence and control robustness.     

Managing Workflow Authorization Constraints through Active Database Technology

The execution of workflow processes requires authorizations for enforcing the assignment of tasks to agents, either human or automated, according to the security policy of the organization. This paper presents a workflow authorization framework based on roles and organizational levels, and on authorization constraints. To facilitate the assignment of tasks to agents, roles and organizational levels are organized into hierarchies. Authorization constraints are introduced to specify instance-dependent, time-dependent, and history-dependent authorizations. Authorization constraints are specified in terms of active rules, used also for authorization management. The Workflow Management System determines authorized agents on the basis of the contents of an authorization base maintained through the active rules defined in the system.     

Outdoor Visual Position Estimation for Planetary Rovers

This paper describes (1) a novel, effective algorithm for outdoor visual position estimation; (2) the implementation of this algorithm in the Viper system; and (3) the extensive tests that have demonstrated the superior accuracy and speed of the algorithm. The Viper system (Visual Position Estimator for Rovers) is geared towards robotic space missions, and the central purpose of the system is to increase the situational awareness of a rover operator by presenting accurate position estimates. The system has been extensively tested with terrestrial and lunar imagery, in terrains ranging from moderate—the rounded hills of Pittsburgh and the high deserts of Chile—to rugged—the dramatic relief of the Apollo 17 landing site—to extreme—the jagged peaks of the Rockies. Results have consistently demonstrated that the visual estimation algorithm estimates position with an accuracy and reliability that greatly surpass previous work.