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![Appendix
For Further Reading I
M. Moores, A. N. Pettitt & K. Mengersen
Scalable Bayesian inference for the inverse temperature of a hidden
Potts model.
arXiv:1503.08066 [stat.CO], 2015.
M. Moores, C. C. Drovandi, K. Mengersen & C. P. Robert
Pre-processing for approximate Bayesian computation in image
analysis.
Statistics & Computing 25(1): 23–33, 2015.
M. Falk, C. Alston, C. McGrory, S. Clifford, E. Heron, D. Leonte, M.
Moores, C. Walsh, A.N. Pettitt & K. Mengersen
Recent Bayesian approaches for spatial analysis of 2-D images with
application to environmental modelling.
Envir. Ecol. Stat. 22(3): 571–600, 2015.
M. Moores & K. Mengersen
Bayesian approaches to spatial inference: modelling and computational
challenges and solutions.
In Proc. 33rd
Int. Wkshp MaxEnt, AIP Conf. Proc. 1636: 112–117, 2014.](https://image.slidesharecdn.com/mtmwrug20170316-170321144533/75/Importing-satellite-imagery-into-R-from-NASA-and-the-U-S-Geological-Survey-10-2048.jpg)
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Importing satellite imagery into R from NASA and the U.S. Geological Survey
This document discusses the importation of satellite imagery into R from NASA and the U.S. Geological Survey, highlighting case studies on chlorophyll-a measurement, land use classification, and vegetation index from Landsat 8. It provides information on the tools and R packages applicable for remote sensing data analysis and references relevant literature related to Bayesian models and spatial analysis in environmental contexts. The document emphasizes the role of satellite remote sensing in various applications such as environmental monitoring and agricultural development.
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Importing satellite imagery into R from NASA and the U.S. Geological Survey
- 1. Introduction Case Studies Importingsatellite imagery into R from NASA and the U.S. Geological Survey Matt Moores Warwick R Users’ Group March 16, 2017
- 2. Introduction Case Studies Outline 1Introduction 2 Case Studies Chlorophyll-a (MODIS) Land Use Classification Vegetation Index (Landsat 8)
- 3. Introduction Case Studies SatelliteRemote Sensing Many applications: Environmental monitoring Land use (plant biomass) Water quality (chlorophyll a) Total column ozone Agriculture Economic development Much of this data is available in the public domain First Landsat satellite launched in 1972 Landsat 8 launched in February 2013 Landsat 5 decommissioned in June 2013 Land Remote Sensing Policy Act (1992) 15 USC 5641
- 4. Introduction Case Studies MultivariateSpatial Observations Satellite Pixel Width Spectral Bands Landsat 8 30m 9+2 MODIS Aqua/Terra 500m 36 EO-1 Hyperion 30m 220 ESA Sentinel 10m 12
- 5. Introduction Case Studies RPackages raster : read GeoTIFF and other file formats rasterVis : plotting spatial objects (lattice graphics) sp : S4 classes SpatialPoints, etc. rgdal : transform between coordinate systems (R interface to GDAL & PROJ.4) RStoolbox : read meta-data for Landsat MODIS : download and process MODIS images maps : vector shapefiles mapdata : larger and/or higher-resolution shapefiles ggmap : access to Google Maps, OpenStreetMap, etc.
- 6. Introduction Case Studies StatisticalModelling INLA : Gaussian Markov random fields (MRF) http://r-inla.org bayesImageS : pixel classification using discrete MRF (Ising/Potts) https://CRAN.R-project.org/package= bayesImageS PySSM : dynamic state space models https://bitbucket.org/ christophermarkstrickland/pyssm
- 7. Introduction Case Studies MODISOcean Colour Concentration of chlorophyll a (mg/m3): 142 144 146 148 −20−18−16−14−12 (a) longitude latitude 0.00 0.05 0.10 0.15 0.20 0.25
- 8. Introduction Case Studies LandUse Classification National Land Cover Database (NLCD):
- 9. Introduction Case Studies Landsat8 Surface Reflectance Normalised difference vegetation index (NDVI): −3050000 −3045000 −3040000 −3035000 −3030000 490000 495000 500000 505000 510000 515000 −1.0 −0.5 0.0 0.5 1.0
- 10. Appendix For Further ReadingI M. Moores, A. N. Pettitt & K. Mengersen Scalable Bayesian inference for the inverse temperature of a hidden Potts model. arXiv:1503.08066 [stat.CO], 2015. M. Moores, C. C. Drovandi, K. Mengersen & C. P. Robert Pre-processing for approximate Bayesian computation in image analysis. Statistics & Computing 25(1): 23–33, 2015. M. Falk, C. Alston, C. McGrory, S. Clifford, E. Heron, D. Leonte, M. Moores, C. Walsh, A.N. Pettitt & K. Mengersen Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Envir. Ecol. Stat. 22(3): 571–600, 2015. M. Moores & K. Mengersen Bayesian approaches to spatial inference: modelling and computational challenges and solutions. In Proc. 33rd Int. Wkshp MaxEnt, AIP Conf. Proc. 1636: 112–117, 2014.
- 11. Appendix For Further ReadingII H. Rue, S. Martino & N. Chopin Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B 71(2): 319–392, 2009. C. M. Strickland, R. Burdett, K. Mengersen & R. Denham PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models. J. Stat. Soft 57(6), 2014. United States Geological Survey (USGS) Landsat 8 Data Users Handbook. Technical Report LSDS-1574, Version 2.0. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K. Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81(5): 345–354, 2015.