README.md

In RichardLemoine/LSTtools: LSTtools: An R package to process thermal data derived from Landsat and MODIS images

LSTtools: An R package to process thermal data derived from Landsat and MODIS images ================

Authors:

* Richard Lemoine-Rodriguez, Institute of Geography, Ruhr-Universität Bochum

* Jean François Mas, Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México

Description

The LSTtools package provides 21 tools for the analysis of thermal data derived from Landsat and MODIS satellites. It includes a number of pre-processing, processing and post-processing functions to conduct Urban Heat Island (UHI) assessments. Quality filtering, data uncompression, spectral indices focused on green and grey urban infrastructure, hot-cold spot analysis and spatial statistics are included.

This vignette describes the main use of the LSTtools package, including: (1) the steps to compute Land Surface Temperature (LST) based on Landsat 8 atmospherically corrected bands and (2) filter the quality of LST and NDVI MODIS pixels.

Install and load the packages that will be employed

#install.packages("devtools")
#install.packages("raster")
#install.packages("RcolorBrewer")
library(devtools)
install_github("RichardLemoine/LSTtools")
library(LSTtools)
library(raster)
library(RColorBrewer)

Load and define Landsat 8 bands

# Load the data
data(land8)
# Define the Landsat 8 bands that will be employed
red <- land8[[3]]
nir <- land8[[4]]
tir <- land8[[7]]

Compute NDVI based on the NIR and Red bands (Rouse et al., 1974)

veg <- ndvi(nir, red)
plot(veg, col=rev(colorRampPalette(c("green4", "yellow", "firebrick"))(255)), main = "NDVI")

Convert the DN contained in the Landsat 8 TIR band (10) to TOA brightness temperature employing the radiance multiplicative and additive band rescaling factors and K1 and K2 constants, according to USGS (2019)

br <- br_temp(tir, conv = TRUE, mult = 0.0003342, add = 0.1, k1 = 774.89, k2 = 1321.08)
plot(br, col=brewer.pal(9, 'YlOrRd'), main = "Brightness temperature (°C)")

Compute the per-pixel land surface emissivity employing the NDVI threshold method (Sobrino et al., 2008)

emis <- emissivity(veg, enonveg = 0.95, eveg = 0.99, pveg = FALSE)
plot(emis, main = "Emissivity", col=colorRampPalette(c("white", "black"))(255))

Compute LST based on the inversion of the Planck function employing the brightness temperature and emissivity bands, according to USGS (2019)

lst <- landsat_lst(br, emis, sensor = "L8", conv = FALSE)
plot(lst, col=brewer.pal(9, 'YlOrRd'), main = "LST (°C)")

Compute hot and cold spots analysis based on the Getis-Ord Gi* statistic (Getis and Ord, 1996) and the False Discovery Rate (FDR) correction (Benjamini and Hochberg, 1995)

# Subset extent to avoid time-consuming test
e <- as(extent(716747.19, 719585.16, 2160408.248, 2163349.293), 'SpatialPolygons') 
crs(e) <- crs(lst)
lst_e <- crop(lst, e)

# Compute Getis-Ord Gi* statistic
spots <- getis(lst_e, dist = 65, p = 0.05)

# Aggregate by cluster type and plot with proper colors
r <- aggregate(spots, by = "cluster")
r$color <- ifelse(r$cluster == "Hot spot", "red", 
                  ifelse(r$cluster == "Cold spot", "blue", "grey"))
{plot(r, col=r$color, border=NA, axes=F, main = "Hot-cold spots")  
legend("bottom", ncol = 3, fill=r$color, legend = c("Cold", "Hot", "No sig."))}

Filter MODIS LST from a .hdf file according to Wan (2013) and Gawuc and Struzewska (2016)

# Load the MOD11A1 LST and Emissivity MODIS product
data(system.file("data/MOD11A1.hdf", package="LSTtools"))

# For LST day MOD11A1 product, filtering pixels with LST error <= 1 and view zenith angle <= 35
r <- lst_filter(MOD11A1, time = "day", flag  = 1, angle = 35, conv = TRUE)
plot(r[[1]], col=brewer.pal(9, 'YlOrRd'), main = "Filtered LST (°C)")

plot(r[[2]], main = "LST error (°C)")

plot(r[[3]], main = "Viewing zenith angle (°)")

Filter MODIS vegetation index from a .hdf file according to Didan et al. (2005)

# Load the MOD13A1 vegetation index MODIS product
data(system.file("data/MOD13A1.hdf", package="LSTtools"))

# For NDVI MOD13Q1 product, filtering pixels with usefulness <= 2 and view zenith angle <= 35
v <- veg_filter(MOD13A1, vi = "NDVI", rel = FALSE, usef = 2, angle = 35)
plot(v[[1]], col=rev(colorRampPalette(c("green4", "yellow", "firebrick"))(255)), main = "Filtered NDVI")

plot(v[[2]], main = "Reliability")

plot(v[[3]], main = "Usefulness")

plot(v[[4]], main = "Viewing zenith angle (°)")

Acknowledgements

Richard Lemoine-Rodriguez acknowledges the PhD scholarship and financial support provided by the Consejo Nacional de Ciencia y Tecnología (CONACyT-México 308198/471027) and the Deutscher Akademischer Austauschdienst (DAAD-Deutschland 91680266), as well as the funding from the Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT-Chile), number 11160643, of the Agencia Nacional de Investigación y Desarrollo. Both authors acknowledge the funding and support of the project “Herramientas para la Enseñanza de la Geomática con programas de Código Abierto” of the Programa de Apoyo a Proyectos para la Innovación y Mejoramiento de la Enseñanza (PAPIME), number PE117519, from the National Autonomous University of Mexico (UNAM).

References

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289–300.

Didan, K. Barreto-Munoz, A., Solano, R. & Huete, A. (2015). MODIS Vegetation Index User’s Guide (MOD13 Series) Collection-6. Arizona, USA, 32.

Gawuc, L., & Struzewska, J. (2016). Impact of MODIS quality control on temporally aggregated urban surface temperature and long-term surface urban heat island intensity. Remote Sensing, 8(5).

Getis, A. and Ord, J. K. (1996). Local spatial statistics: an overview. In P. Longley and M. Batty (eds) Spatial analysis: modelling in a GIS environment (Cambridge: Geoinformation International), 261–277.

Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS, In: S.C. Freden, E.P. Mercanti, and M. Becker (eds) Third Earth Resources Technology Satellite–1 Syposium. Volume I: Technical Presentations, NASA SP-351, NASA, Washington, D.C., pp. 309-317.

Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J. & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316–327.

USGS. (2019). Landsat 8 data users handbook version 4. USGS Earth Resources Observation and Science (EROS). Sioux Falls, South Dakota. USA. 106.

Wan, Z. (2013). Collection-6 MODIS Land Surface Temperature Products User’s guide. Santa Barbara, USA, 33.

RichardLemoine/LSTtools documentation built on Oct. 19, 2020, 12:50 a.m.