hakimabdi

hakimabdi

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# title         : Perform a Mann-Kendall trend test of satellite image time series
# purpose       : Mann-Kendall test of monotonic trend and associated statistical significance
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Raster stack/brick comprising a time series of observations
# output        : Raster object of monotoic trend (Kendall's tau), significance or both (i.e. brick) 
# data          : Test data: https://1drv.ms/u/s!AsHsKb_qtbkwg4ti8pJCxvUer9rMqg?e=BGcH1K
# notes         : Depending on the time series, it is might be useful prewhiten the data to remove serial correlation 
#               : prior to extracting the trend. See blog post on www.hakimabdi.com for more information. 
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hakimabdi

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# title         : Machine learning exercise for Sentinel-2 data
# purpose       : Implementing a machine learning workflow in R 
# author        : Abdulhakim M. Abdi (Twitter: @HakimAbdi / www.hakimabdi.com)
# input         : A multi-temporal raster stack of Sentinel-2 data comprising scenes from four dates 
# output        : One classified land cover map from each of three machine learning algorithms  
# Note 1        : This brief tutorial assumes that you are already well-grounded in R concepts and are 
#               : familiar with image classification procedure and terminology
# Reference		  : Please cite Abdi (2020): "Land cover and land use classification performance of machine learning 
#				        : algorithms in a boreal landscape using Sentinel-2 data" in GIScience & Remote Sensing if you find this 

hakimabdi

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# title         : mosaicGTiffs.R
# purpose       : Takes in projected GeoTIFF outputs from projHDF2GTiff or  
#               : other GeoTIFFS and mosaics them into on larger file
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Individual GeoTIFF files
# output        : A mosaicked GeoTIFF file with a larger extent
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mosaicGTiffs = function(proj.loc, gtiffs, mosaicName, overwrite){ 

hakimabdi

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# title         : projHDF2GTiff.R
# purpose       : Converts desired HDF layer to a GeoTIFF and projects it 
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : HDF-EOS files
# output        : Projected GeoTIFF files 
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projHDF2GTiff = function(loc, hdfs, gtiffs, lyr, fromSRS, toSRS){ 
  if("gdalUtils" %in% rownames(installed.packages()) == FALSE){

hakimabdi

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# title         : Gridded Correlation of Time Series Raster Data (Gridcorts)
# purpose       : Pixelwise time series correlation and significance based on Pearson's r, 
#               : Spearman's rho or Kendall's tau
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Raster brick comprising two time series of equal number of layers
# output        : Raster object of pixelwise correlation, significance or both (i.e. brick) 
#               : based on the chosen method
# update        : Minor based suggestion from Tao Xiong of Northeast Normal University in China.
# data          : Test data: https://1drv.ms/u/s!AsHsKb_qtbkwgvoQuHnPaFazPr_XnA?e=fJ9fue

hakimabdi

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# title         : pload.R
# purpose       : Install and load uninstalled packages, or load installed ones.
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Package name in quotes
# output        : Installation and loading of uninstalled packages, or loading of installed ones.
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pload <- function(x){
  if(x %in% rownames(installed.packages()))

hakimabdi

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# title         : ManhattanLST.R
# purpose       : Converts Landsat ETM+ thermal imagery to degrees Celsius
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Landsat TM / ETM+ band 6
# output        : Land surface temperature map in degrees Celsius
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# Install and load the required packages
install.packages(c("rgdal" "sp", "raster"), repos='http://cran.r-project.org')

hakimabdi

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# This code ingests binary GIMMS FAPAR and LAI data and converts them to GeoTIFFs. 
# The data was produced by the Climate and Vegetation Research Group of Boston University. 
# The GIMMS data are available from: http://cliveg.bu.edu/modismisr/lai3g-fpar3g.html

# title         : GIMMS3g_LAI_FPAR_Binary2Geotiff.r
# purpose       : Converts binary GIMMS FAPAR/LAI to GeoTIFFs
# author        : Abdulhakim Abdi (@HakimAbdi)
# input         : Binary 8-bit unsigned integer with ieee-be byte order
# output        : Georeferenced TIFF, ArcGIS-ready files