eBook - PDF

Remote Sensing Data Analysis Using R

Name: Remote Sensing Data Analysis Using R
Author: Nirmal Kumar

Nirmal Kumar,

English
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eBook - PDF

Remote Sensing Data Analysis Using R

Nirmal Kumar,

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Citations

About This Book

This book provides a comprehensive guided tour to the users for performing remote sensing and GIS operations in free and open source software i.e. R. This book is suitable for the users who have basic knowledge of remote sensing and GIS, but no or little knowledge about R software. It introduces the R software to users along with the procedures for its downloading and installation. It provides R-codes for loading and plotting of both raster and vector data; pre-processing, filtering, enhancement and transformations of raster data; processing of vector data; unsupervised and supervised classification of raster data; and thematic mapping of both raster and vector data. In addition to it, this book provides R-codes for performing advanced machine learning algorithms like random forest, support vector machine, etc. for supervised classification of raster data.This book is apt for the users who don't have access to the sophisticated paid software of GIS and digital image processing. Sample data for practice is provided in an additional DVD so that users can get hands on training of the R-codes given in this book. This book can serve as a training manual for performing digital image analysis and GIS operations in R software.

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Information

Publisher

NEW INDIA PUBLISHING AGENCY (NIPA)

Year

2020

ISBN

9789390175772

Topic

Technology & Engineering

Subtopic

Civil Engineering

Index

Technology & Engineering

Bibliography

353

https://datacarpentry.org/r-raster-vector-geospatial/06-vector-open-shapefile-in-r/

https://datascienceplus.com/random-forests-in-r/

https://docs.qgis.org/2.8/en/docs/user_manual/processing_algs/gdalogr/gdal_analysis/

triterrainruggednessindex.html

https://earthexplorer.usgs.gov/

https://ecosystems.psu.edu/research/labs/walter-lab/manual/chapter-6-three-dimensional-

analysis/6.2-three-dimensional-exploration-of-digital-elevation-models-dems

https://eden.dei.uc.pt/~ruipedro/publications/Tutorials/slidesML.pdf

https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/CLARA

https://geoawesomeness.com/list-of-top-10-sources-of-free-remote-sensing-data/

https://geocompr.robinlovelace.net/adv-map.html

https://gisgeography.com/flow-direction/

https://homepages.inf.ed.ac.uk/rbf/HIPR2/filtops.htm

https://johnwilson.usc.edu/wp-content/uploads/2016/09/geoinfoscience_n209.pdf

https://mgimond.github.io/Spatial/coordinate-systems-in-r.html

https://nature.berkeley.edu/~penggong/textbook/chapter6/html/home65.htm

https://nptel.ac.in/courses/105108077/module5/lecture21.pdf

https://paginas.fe.up.pt/~ec/files_1112/week_06_Clustering_part_II.pdf

https://pro.arcgis.com/en/pro-app/help/data/imagery/curvature-function.htm

https://proj.org/usage/projections.html

https://rdrr.io/cran/RStoolbox/man/spectralIndices.html

https://rpubs.com/huanfaChen/ggplotShapefile

https://rstudio-pubs-static.s3.amazonaws.com/

72662_b874adb74477422ca4464e5e542a41fc.html

https://segoptim.bitbucket.io/docs/index.html

https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781783982103/

5/ch05lvl1sec53/the-clara-algorithm

https://topepo.github.io/caret/train-models-by-tag.html#Support_Vector_Machines

https://uc-r.github.io/random_forests

https://webapps.itc.utwente.nl/librarywww/papers_2003/misca/hengl_digital.pdf

https://www.datanovia.com/en/lessons/clara-in-r-clustering-large-applications/

https://www.diva-gis.org/gdata, 

http://download.geofabrik.de/asia.html

https://www.eanswers.net/wiki/en/Positive_and_negative_predictive_values/

https://www.eanswers.net/wiki/en/Topographic_wetness_index/?ext=t&cid=5036

https://www.esri.com/about/newsroom/arcuser/create-amazing-hillshade-effects-quickly-and-

easily-in-arcgis-pro/

https://www.hydrol-earth-syst-sci.net/10/101/2006/hess-10-101-2006.pdf

https://www.latlong.net/category/states-102-14.html

https://www.leg.state.mn.us/docs/2016/mandated/160722.pdf

https://www.listendata.com/2014/11/random-forest-with-r.html

https://www.machinelearningplus.com/machine-learning/evaluation-metrics-classification-

models-r/

https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/

OverviewCoordinateReferenceSystems.pdf

https://www.neonscience.org/dc-open-shapefiles-r

https://www.neonscience.org/image-raster-data-r

https://www.neonscience.org/raster-data-r

https://www.neonscience.org/vector-data-series#scrollNav-6

https://www.neonscience.org/vector-data-series#scrollNav-7

https://www.r-bloggers.com/how-to-implement-random-forests-in-r/

354

Remote 

Sensing 

Data 

Analysis 

in

R

https://www.rdocumentation.org/packages/caret/versions/6.0-84/topics/negPredValue

https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/partialPlot

https://www.rdocumentation.org/packages/raster/versions/2.9-23/topics/terrain

https://www.rdocumentation.org/packages/raster/versions/3.0-2/topics/terrain

https://www.rdocumentation.org/packages/RStoolbox/versions/0.2.4/topics/panSharpen

https://www.rdocumentation.org/packages/spatialEco/versions/1.2-0/topics/curvature

https://www.r-exercises.com/2018/02/28/advanced-techniques-with-raster-data-part-1-

unsupervised-classification/

https://www.r-project.org/

https://www.rstudio.com/products/rstudio/download/

https://www.slideshare.net/BinteAdaam/resampling-gis

https://www.srbc.net/pennsylvania-lidar-working-group/docs/twi-srbc.pdf

https://www.tutorialspoint.com/dip

https://www.usna.edu/Users/oceano/pguth/md_help/html/geomorph_curvature.htm

https://www.usna.edu/Users/oceano/pguth/md_help/html/topo_rugged_index.htm

https://www.wrc.umn.edu/sites/wrc.umn.edu/files/terrain_analysis_slides.pdf

https://zia207.github.io/geospatial-data-science.github.io/support-vector-machine.html

Books 

consulted

Kaufman, 

Leonard, 

and

Peter 

Rousseeuw. 

1990. 

Finding 

Groups 

in

Data: 

An

Introduction 

to

Cluster 

Analysis

.

Lillesand, 

T.

M.,

Kiefer, 

R.

W.,

&

Chipman, 

J.

W.

(2008). 

Remote 

sensing 

and

image

interpretation

.

Hoboken, 

NJ:

John 

Wiley 

&

Sons.

Panda, 

B.C. 

(2005). 

Remote 

Sensing: 

Principles 

and

Applications

.

Viva 

Books 

Private 

Limited.

remote_sensing_data_analysis_in_r_uncurved - Copy.pdf
Remote Sensing Data Analysis With R.pdf
remote_sensing_data_analysis_in_r_uncurved.pdf