This program generates atmospherically corrected images of NDVI and MSAVI2. The images of multiple dates can be processed in a single run. The images for the bands 4 and 3 corresponding to each date (previously subsetted to the region of analysis) must be in the working directory. A table with information for each image as described in the parameter tab (see also the example) is needed for processing the information.
eco.NDVI(tab, correct = c("COST", "DOS"), method = c("NDVI", "MSAVI2"), landsat = c("LT5", "LT7.L", "LT7.H"), datatype = c("FLT4S", "FLT8S", "INT4U", "INT4S", "INT2U", "INT2S", "INT1U", "INT1S", "LOG1S"))
| tab | data.frame with 7 columns: The date of the images (format: YYYY/MM/DD), the sun elevation (both values could be extracted from Landsat headers), the name of the band 4, the name of the band 3, the starting haze value of the band 4, the starting haze value of the band 3, and the name of the output file. Each row corresponds to an image of different date. |
|---|---|
| correct | Correction method ("COST", "DOS"). |
| method | Vegetation index ("NDVI", "MSAVI2"). |
| landsat | Satellite data source ("LT5" for Landsat 5, "LT7.L" for Landsat 7 low gain and "LT7.H" for Landsat 7 high gain). |
| datatype | type of data, see |
Chander G., B. Markham, and D. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113: 893-903.
Chavez P. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing, 55: 1285-1294.
Chavez P. 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62: 1025-1035.
Goslee S. 2011. Analyzing remote sensing data in R: the landsat package. Journal of Statistical Software, 43: 1-25.
Song C., C. Woodcock, K. Seto, M. Lenney and S. Macomber. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?. Remote sensing of Environment, 75: 230-244.
Tucker C. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8: 127-150.
# NOT RUN { require(raster) data(tab) temp <-list() # we create 4 simulated rasters for the data included in the object tab: for(i in 1:4) { temp[[i]] <- runif(19800, 0, 254) temp[[i]] <- matrix(temp[[i]], 180, 110) temp[[i]] <- raster(temp[[i]], crs="+proj=utm") extent(temp[[i]])<-c(3770000, 3950000, 6810000, 6920000) } writeRaster(temp[[1]], "20040719b4.tif", overwrite=T) writeRaster(temp[[2]], "20040719b3.tif", overwrite=T) writeRaster(temp[[3]], "20091106b4.tif", overwrite=T) writeRaster(temp[[4]], "20091106b3.tif", overwrite=T) # Computing NDVI images: eco.NDVI(tab, "COST", "NDVI", "LT5") example <- raster("NDVICOST20040719.tif") image(example) # }