Spatial statistics for remote sensing

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Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes.

Remote Sensing Basics

In Paper II spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity.

However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used.

In Paper IV human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance.

Paper V showed that generalized linear model GLM is a suitable technique for fire occurrence prediction and for burned area estimation.

Introduction to Geospatial Data Analysis with Python - SciPy 2018 Tutorial - Serge Rey

The ITC has a long history on collecting and analyzing satellite and other remote sensing data, but its involvement into spatial statistics is of a more recent date. Uncertainties in remote sensing images and the large amounts of data in many spectral bands are now considered to be of such an impact that it requires a separate approach from a statistical point of view.

To quote from the justification of the study day, we read: Modern communication means such as remote sensing require an advanced use of collected data. Satellites collect data with different resolution on different spectral bands. Conditional Simulation: An alternative to estimation for achieving mapping objectives.


  • Spatial Statistics for Remote Sensing | A. Stein | Springer.
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JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Buy eBook. Buy Softcover. FAQ Policy. About this book This book is a collection of papers on spatial statistics for remote sensing. Show all.

Spatial statistics for remote sensing — University of Twente Research Information

Table of contents 14 chapters Table of contents 14 chapters Description of the data used in this book Pages Gorte, Ben. Some basic elements of statistics Pages Stein, Alfred. The state vector also includes measures of temperature, pressure, aerosol shape parameters, albedo, and surface wind speed. The algorithm accounts inter alia for the relative geometries of the earth and the sun in relation to the satellite; the solar flux at the top of the atmosphere; Earth's reflectance at the point of measurement; the characteristics of the measuring spectrometer; the state of the atmosphere when the sounding is taken e.

As many of the variables in the state vector are used to quantify the flow of energy in the atmosphere e. Numerical approaches to solving the inverse problem include those based on Twomey-Tikhonov regularisation see Doicu et al, , for a recent review. Statistical approaches include ridge regression, penalised likelihood, and Bayesian posterior analysis e.

Sources of uncertainty

Each of these approaches uses a form of optimal estimation e. A kriging predictor was used to obtain the spatial predictions used in this map.

Banerjee, S. Hierarchical Modeling and Analysis for Spatial Data. Besag, J. Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society, Series B , 36 , Chiles, J. Geostatistics: Modelling Spatial Uncertainty.

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Wiley, New York, NY. Connor, B. Orbiting Carbon Observatory: Inverse method and prospective error analysis.


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Journal of Geophysical Research: Atmospheres , , Cressie, N. Fixed rank kriging for very large spatial data sets. Fixed rank filtering for spatio-temporal data. Journal of Computational and Graphical Statistics , 19 , Applied Stochastic Models in Business and Industry , 29 , Statistics for Spatio-Temporal Data. Wiley, Hoboken, NJ. Crisp, D. Atmospheric Measurement Techniques , 5 ,

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