An Ensemble Approach to Space-Time Interpolation

There has been much excitement and activity in recent years related to the relatively sudden availability of earth-related data and the computational capabilities to visualize and analyze these data. Despite the increased ability to collect and store large volumes of data, few individual data sets exist that provide both the requisite spatial and temporal observational frequency for many urban and/or regional-scale applications. The motivating view of this paper, however, is that the relative temporal richness of one data set can be leveraged with the relative spatial richness of another to fill in the gaps. We also note that any single interpolation technique has advantages and disadvantages. Particularly when focusing on the spatial or on the temporal dimension, this means that different techniques are more appropriate than others for specific types of data. We therefore propose a space- time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem in order to maximize the quality of the result. We call our ensemble approach the Space-Time Interpolation Environment (STIE). The primary steps within this environment include a spatial interpolator, a time-step processor, and a calibration step that enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In the current paper, we describe STIE conceptually including the structure of the data inputs and output, details of the primary steps (the STIE processors), and the mechanism for coordinating the data and the 1 processors. We then describe a case study focusing on urban land cover in Phoenix Arizona. Our empirical results show that STIE was effective as a space-time interpolator for urban land cover with an accuracy of 85.2% and furthermore that it was more effective than a single technique.

Scenario planning including ecosystem services for a coastal region in South Australia

Coastal regions provide vital ecosystem services for the human well-being. Rapid economic growth and increasing population in coastal regions is exerting more pressure on coastal environments. Here we develop four plausible scenarios to the year 2050 that address above issues in the northern Adelaide coastline, South Australia. Four scenarios were named after their characteristics, Lacuna, Gold Coast SA, Down to Earth, and Green & Gold. Lacuna and Gold Coast SA. Economy declined significantly in Lacuna, whereas, there is highest annual GDP growth (3.5%) in Gold Coast SA, which was closely followed by Green & Gold scenario (3%), GDP under Down to Earth grows at moderate 1.5%. There is highest population growth in Gold Coast SA followed by Green & Gold, Down to Earth and Lacuna. Gold Coast SA scenario led to high inequality as estimated by the Gini co-efficient of 0.45 compared to the current value of 0.33. Ecosystem services declined rapidly under Green & Gold and Lacuna as compared to the other two scenarios. The combination of scenario planning and ecosystem services valuation provides the capacity to guide coastal planning by illustrating enhanced social, environmental and economic benefits. © 2018 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Harpinder Sandhu” is provided in this record*

Satellite Image Restoration using the VMCA Model

One of the most common patterns of the geographic landscape is the fractal or nearfractal form. Unfortunately, most traditional methods of spatial interpolation assume some type of continuous and regionalizeable variation of the underlying geographic form, an assumption at odds with the observed fractal properties of many landscapes. An extremely simple iterative algorithm, the voter model cellular automata (CA), produces discontinuous fractal patterns useful for interpolation while at the same preserving a realistic amount of spatial autocorrelation, extracted from neighboring existing data, also found in these landscapes. This adaptive algorithm is based on the principle of iteratively interpolating a missing data point using the value of a randomly selected neighbor cell. The model can also be extended to interpolate field-like variables by adding random deviations from the randomly chosen neighbor cell value. In this paper we explore the effect of satellite image restoration using a simple VMCA over obscured by clouds areas. This model is computationally advantageous, given its localty and restricted underlying computational model. Thus, an adequate computer implementation may perform significantly faster than other restoration methods, with roughly similar overall results. Also the local/scalable/parallelizable nature of CAs allows hardware FPGA implementation that might be embedded within the imager devices in satellites and remote sensors. On the other end, a GPU implementation might take advantage of highly specialized parallel processors capablde of restoring huge images in real time.Eje: Computación gráfica, visualización e imágenesRed de Universidades con Carreras en Informática (RedUNCI

Interpolación de imágenes de sensado remoto utilizando VMCA

El voter model cellular automata (VMCA) es un modelo sencillo y efectivo que puede ser utilizado para la interpolación de información perdida en imágenes (nubes en imágenes satelitales, ruido en imágenes fotográficas, etc.). En este trabajo se presentan los resultados de la interpolación resultante para varios tipos de imágenes de sensado remoto, y se demuestra que la utilidad del método es significativa para la mayor parte de los usos probables en diversos contextos.IV Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

The global value of coastal wetlands for storm protection

Coastal wetlands provide a range of valuable ecosystem services, including protecting coastal communities from storms. We estimated for the first time the global value of these storm protection services for all coastal wetlands for both damages avoided and lives saved. We used the historical tracks of 1,014 tropical cyclones since 1902 that recorded property damage and/or human casualties in 71 countries/regions. We used Bayesian and OLS statistical techniques to relate storm damages and lives lost to: wind speed, storm forward speed, the year of the storm, the volume of ocean water proximal to landfall, and GDP, population, and coastal wetlands in the swath of the storm. Based on current storm probabilities, we estimate the median annual global value of coastal wetlands for storm protection at $447 billion/yr (2015$US) ($213 -$837 billion/yr, 90% CI) and 4,620 lives saved per year (3,320 – 6,550, 90% CI). The 40 million hectares of coastal wetlands in storm prone areas provided an average of $11,000/ha/yr in avoided storm damages. The frequency and intensity of tropical cyclones has been increasing in recent decades and is projected to further increase with climate change. Consequently, the already significant benefits from protecting and restoring coastal wetlands will become increasingly important and valuable in the future. These results justify much larger investments in conservation and restoration of coastal wetlands