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 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.

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.

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

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*

Discriminant Analysis with Spatial Weights for Urban Land Cover Classification

Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape resulting in mixed pixels and classes with highly variable spectral ranges. Approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas. Appropriate ancillary data, however, may not always be available. The goal of this study is to compare the results of the discriminant analysis statistical technique with discriminant analysis with spatial weights to classify urban land cover. Discriminant analysis is a statistical technique used to predict group membership for a target based on the linear combination of independent variables. Strict per pixel statistical analysis however does not consider the spatial dependencies among neighbouring pixels. Our study shows that approaches using ancillary data continue to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results show that when the discriminant analysis technique works well then the spatially weighted approach performs better. However, when the discriminant analysis performs poorly, those poor results are magnified in the spatially weighted approach in the same study area. The study shows that for dominant classes, adding spatial weights improves the classification accuracy.

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

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

Microhabitats and canopy cover moderate high summer temperatures in a fragmented Mediterranean landscape

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Extreme heat events will become more frequent under anthropogenic climate change, especially in Mediterranean ecosystems. Microhabitats can considerably moderate (buffer) the effects of extreme weather events and hence facilitate the persistence of some components of the biodiversity. We investigate the microclimatic moderation provided by two important microhabitats (cavities formed by the leaves of the grass-tree Xanthorrhoea semiplana F.Muell., Xanthorrhoeaceae; and inside the leaf-litter) during the summer of 2015/16 on the Fleurieu Peninsula of South Australia. We placed microsensors inside and outside these microhabitats, as well as above the ground below the forest canopy. Grass-tree and leaf-litter microhabitats significantly buffered against high temperatures and low relative humidity, compared to ground-below-canopy sensors. There was no significant difference between grass-tree and leaf-litter temperatures: in both microhabitats, daily temperature variation was reduced, day temperatures were 1–5°C cooler, night temperatures were 0.5–3°C warmer, and maximum temperatures were up to 14.4°C lower, compared to ground-below-canopy sensors. Grass-tree and leaf-litter microhabitats moderated heat increase at an average rate of 0.24°C temperature per 1°C increase of ambient temperature in the ground-below-canopy microhabitat. The average daily variation in temperature was determined by the type (grass-tree and leaf-litter versus ground-below-canopy) of microhabitat (explaining 67%), the amount of canopy cover and the area of the vegetation fragment (together explaining almost 10% of the variation). Greater canopy cover increased the amount of microclimatic moderation provided, especially in the leaf-litter. Our study highlights the importance of microhabitats in moderating macroclimatic conditions. However, this moderating effect is currently not considered in species distribution modelling under anthropogenic climate change nor in the management of vegetation. This shortcoming will have to be addressed to obtain realistic forecasts of future species distributions and to achieve effective management of biodiversity