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Modern Applied Statistics With S

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Remote Sensing And Image Interpretation

We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

https://pure.tue.nl/ws/files/3887204/760250.pdf

A review of recent research on green road freight transportation

Environmental hazards: Assessing risk and reducing disaster

Modelling of light pollution in suburban areas using remotely sensed imagery and GIS

In this paper, bivariate statistical analysis modeling was applied and validated to derive a landslide susceptibility map of Peloponnese (Greece) at a regional scale. For this purpose, landslide-conditioning factors such as elevation, slope, aspect, lithology, land cover, mean annual precipitation (MAP) and peak ground acceleration (PGA), and a landslide inventory were analyzed within a GIS environment. A landslide dataset was realized using two main landslide inventories. The landslide statistical index method (LSI) produced a susceptibility map of the study area and the probability level of landslide occurrence was classified in five categories according to the best classification method from three different methods tested. Model performance was checked by an independent validation set of landslide events. The accuracy of the final result was evaluated by receiver operating characteristics (ROC) analysis. The prediction ability was found to be 75.2% indicating an acceptable susceptibility map obtained from the GIS-based bivariate statistical model.

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

The main purpose of this study is to compare the performance of two statistical analysis models like weight of evidence and logistic regression (LR) with a soft computing model like artificial neural networks for landslide susceptibility assessment. These models were applied for the Selinous River drainage basin (northern Peloponnese, Greece) in order to map landslide susceptibility and rate the importance of landslide causal factors. A landslide inventory was prepared using satellite imagery interpretation and field surveys. Eight causal factors including altitude, slope angle, slope aspect, distance to road network, distance to drainage network, distance to tectonic elements, land cover, and lithology were considered. Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models show promising results since they give good accuracy values. However, the LR model proved to be relatively superior in estimating landslide susceptibility throughout the study area.

Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models

Waste collection and transpor t (WC&T) constitutes a large fraction of the total municipal solid waste (MSW) management costs worldwide. In Greece currently this may account for 70-100% of the total MSW costs, most of it being spent on salaries and fuel. It is therefore crucial to improve the WC&T system through routing optimisation. Geographic Information System (GIS) technology provides an advanced modelling framework for decision makers in order to analyse and simulate various spatial waste management problems, including waste collection. In this study a methodology for the optimisation of the waste collection and transport system, based on GIS, was developed. A model in ArcGIS Network Analyst was developed in order to improve the efficiency of WC&T in the Municipality of Nikea (MoN), Athens, Greece via the reallocation of waste collection bins and the optimisation of vehicle routing in terms of distance and time travelled. Two scenarios were compared with the current empirical collection scheme: S1-collection vehicle routing optimisation, and S2-reallocation of bins and routing optimisation. Results demonstrate that both scenarios provided savings compared to the current situation in terms of collection time (3.0% and 17.0% for S1 and S2 respectively) and travel distance (5.5% and 12.5% for S1 and S2 respectively). Time and distance reduction relate to similar CO2 emissions and fuel consumption savings. These figures indicate that GIS based models can offer significant improvements to the WC&T system and, consequently, to its financial and environmental costs.

/pdf/a-gis-based-model-for-the-optimisation-of-municipal-solid-2zyq2iyigq.pdf

Christos Chalkias

Papers

Modelling of light pollution in suburban areas using remotely sensed imagery and GIS

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models

A GIS based model for the optimisation of municipal solid waste collection: the case study of Nikea, Athens, Greece

Geographical heterogeneity of the relationship between childhood obesity and socio-environmental status: Empirical evidence from Athens, Greece