Simulating and mapping spatial complexity using multi-scale techniques

Cola, Lee De

doi:10.1080/02693799408902011

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…The monotonic decrease in image variance with increasing pixel size exhibited by the rescaled series conforms to a well-established theory for spatial aggregation of geographic data (De Cola, 1994).…”

Section: Discussionsupporting

confidence: 67%

“…However, before we can use rescaled data to infer fields of biogeophysical values, a pressing question must be addressed: ''How good is the correspondence between the actual, observed data and the simulated, rescaled data?'' Studying how the spatial resolution affects observed spatial structure can provide insights into both the process of rescaling and spatial pattern dynamics, which is crucial to understand cross-scale phenomena (De Cola, 1994).…”

Section: Introductionmentioning

confidence: 99%

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Change of spatial information under rescaling: A case study using multi-resolution image series

Chen

Henebry

2009

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Change of spatial information under rescaling: A case study using multi-resolution image series

Chen

Henebry

2009

ISPRS Journal of Photogrammetry and Remote Sensing

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“…To capture spatial heterogeneity effectively using remotely sensed data, its resolution must be high enough to characterize the scale of the typical length of interest [9] In this work, relatively high resolution data, Landsat 5 (i.e., TM imagery with 30 m spatial resolution) was used, as it is fine enough to describe the length scale of agricultural landscapes [10]. The image dataset was acquired at the best time in the growing season for crops (i.e., 31/8/2006).…”

Section: Study Area and Data Descriptionmentioning

confidence: 99%

“…For instance, by integrating the spatial variability and the autocorrelation extent, D c represents the mean extent of the image spatial structures at the landscape scale. Defining an appropriate pixel size at which the spatial structure and the spatial variability could be captured, is therefore possible, based on D c , if the sampling space (i.e., spatial resolution in this study) is less than D c /2, according to the Nyquist-Shannon sampling theorem [10]. In this study, we have the smallest D c , i.e., 164 m, occurring in Site 1, thus the largest pixel size of monitoring remotely-sensed data should be less than 82 m [9](i.e., half of the smallest D c ) at the data acquisition time (i.e., 31/8/2006).…”

Section: B Omnidirection Heterogeneity Analysismentioning

confidence: 99%

Variogram Analysis for Assessing Landscape Spatial Heterogeneity in NDVI: an Example Applied to Agriculture in the Jiansanjiang Reclamation area, Northeast China

Wen

Feng

et al. 2013

Proceedings of the 2013 International Conference on Remote Sensing,Environment and Transportation Engineering

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It is necessary to characterize and monitor the spatial heterogeneity of agricultural landscapes, to assist the spatial understanding of some related agricultural processes and to predict food production patterns. In the study, variogram models fitted to empirical variograms are proposed to retrieve spatial heterogeneity characteristics, and they are applied to NDVI data generated from Landsat TM imagery at a 30 m pixel scale. Four representative study sites with distinct landscape patterns, located in the Honghe farm of Jiansanjiang Reclamation Area, Northeast China, were selected for spatial heterogeneity testing and analyzing. The results provided quantitative agricultural landscape knowledge: (1) the agricultural landscape heterogeneity at different directions within the study area could be effectively quantified with the variogram model and then interpreted; (2) the spatial heterogeneity of the dry land matrix was normally larger than that of the paddy field matrix; and (3) the agricultural spatial heterogeneity was affected by two factors: the land use type and the distribution pattern of land use types. As remote sensing techniques can now provide various types of surface monitoring data, we argue that quantitative variogram analysis of the data for spatial heterogeneity can help identify explain related ecological phenomena. It could also be used to improve quantitative agricultural remote sensing monitoring in a spatial heterogeneity area as well.

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“…Let there also be a set of points Ao = {(i, j): i = 1, ... ,N;j = 1, ... , T} at N locations and Ttime instants, and let Zo(i, j) be an Nx T data matrix of values recorded atAo C A (Knorr-Held and Besag 1998;De Cola 1994). In the case of a disease, for example, a fine-scale representation of the data might be an animated multifractal "dust" of infection events (x, t) in space-time (Mandelbrot 1983) or a detailed geographic timeline tracing the continuous location of each infected organism in space (Haggett 2000).…”

Section: Spatial and Temporal Forecastingmentioning

confidence: 99%

Spatial Forecasting of Disease Risk and Uncertainty

Cola¹

2002

Cartography and Geographic Information Science

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Because maps typically represent the value of a single variable over 2-dimensional space, cartographers must simplify the display of multiscale complexity, temporal dynamics, and underlying uncertainty. A choropleth disease risk map based on data for polygonal regions might depict incidence (cases per 100,000 people) within each polygon for a year but ignore the uncertainty that results from finer-scale variation, generalization, misreporting, small numbers, and future unknowns. In response to such limitations, this paper reports on the bivariate mapping of data "quantity" and "quality" of Lyme disease forecasts for states of the United States. Historical state data for 1990-2000 are used in an autoregressive model to forecast 2001-2010 disease incidence and a probability index of confidence, each of which is then kriged to provide two spatial grids representing continuous values over the nation. A single bivariate map is produced from the combination of the incidence grid (using a blue-to-red hue spectrum), and a probabilistic confidence grid (used to control the saturation of the hue at each grid cell). The resultant maps are easily interpretable, and the approach may be applied to such problems as detecting unusual disease occurrences, visualizing past and future incidence, and assembling a consistent regional disease atlas showing patterns of forecasted risks in light of probabilistic confidence.

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Simulating and mapping spatial complexity using multi-scale techniques

Cited by 24 publications

References 16 publications

Change of spatial information under rescaling: A case study using multi-resolution image series

Change of spatial information under rescaling: A case study using multi-resolution image series

Variogram Analysis for Assessing Landscape Spatial Heterogeneity in NDVI: an Example Applied to Agriculture in the Jiansanjiang Reclamation area, Northeast China

Spatial Forecasting of Disease Risk and Uncertainty

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