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<h1 class="title toc-ignore">Satellite Remote Sensing</h1>
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<iframe src="09_presentation/09_RemoteSensing.html" width="75%" height="400px">
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<p><a href="09_RemoteSensing.R"><i class="fa fa-file-code-o fa-3x" aria-hidden="true"></i> The R Script associated with this page is available here</a>. Download this file and open it (or copy-paste into a new script) with RStudio so you can follow along.</p>
<div id="libraries" class="section level3">
<h3>Libraries</h3>
<pre class="r"><code>library(raster)
library(rasterVis)
library(rgdal)
library(sp)
library(ggplot2)
library(ggmap)
library(dplyr)
library(reshape2)
library(knitr)
library(tidyr)
# New Packages
library(MODISTools)
library(gdalUtils)
library(rts)</code></pre>
</div>
<div id="specify-directory-to-store-data-absolute-or-relative-to-current-working-directory." class="section level2">
<h2>Specify directory to store data (absolute or relative to current working directory).</h2>
<pre class="r"><code>download.file("http://adamwilson.us/RDataScience/09_data.zip",
destfile=file.path("09_data.zip"))
datadir="09_data"
unzip("09_data.zip",exdir = datadir)</code></pre>
</div>
<div id="working-with-raw-hdf-files" class="section level2">
<h2>Working with <em>raw</em> HDF files</h2>
<p>Will only work if your <code>gdal</code> was compiled with HDF support</p>
<pre class="r"><code>gdalinfo(formats = T) %>% grep(pattern="HDF",value=T)</code></pre>
<pre><code>## [1] " HDF4 -raster- (ros): Hierarchical Data Format Release 4"
## [2] " HDF4Image -raster- (rw+): HDF4 Dataset"
## [3] " HDF5 -raster- (ros): Hierarchical Data Format Release 5"
## [4] " HDF5Image -raster- (ro): HDF5 Dataset"</code></pre>
<pre class="r"><code>hdf=file.path(datadir,"MCD12Q1.A2012001.h12v04.051.2014288200441_subset.hdf")</code></pre>
<p>Use <code>gdalinfo()</code> to print information about the file.</p>
<pre class="r"><code>gdalinfo(hdf)</code></pre>
<div id="subdatasets" class="section level3">
<h3>Subdatasets</h3>
<p>An important component of the metadata of a HDF file is the list of ‘subdatasets’ that are inside the file. HDF files can hold any number of different datasets and you need to use the specific subdataset</p>
<ul>
<li>SUBDATASET_1_NAME=HDF4_EOS:EOS_GRID:"09_data/MCD12Q1.A2012001.h12v04.051.2014288200441_subset.hdf":MOD12Q1:Land_Cover_Type_1</li>
<li>SUBDATASET_1_DESC=[2400x2400] Land_Cover_Type_1 MOD12Q1 (8-bit unsigned integer)<br />
</li>
<li>SUBDATASET_2_NAME=HDF4_EOS:EOS_GRID:"09_data/MCD12Q1.A2012001.h12v04.051.2014288200441_subset.hdf":MOD12Q1:Land_Cover_Type_1_Assessment</li>
<li>SUBDATASET_2_DESC=[2400x2400] Land_Cover_Type_1_Assessment MOD12Q1 (8-bit unsigned integer)</li>
<li>SUBDATASET_3_NAME=HDF4_EOS:EOS_GRID:"09_data/MCD12Q1.A2012001.h12v04.051.2014288200441_subset.hdf":MOD12Q1:Land_Cover_Type_QC.Num_QC_Words_01</li>
<li>SUBDATASET_3_DESC=[2400x2400] Land_Cover_Type_QC.Num_QC_Words_01 MOD12Q1 (8-bit unsigned integer)</li>
</ul>
<div id="translate-to-geotif" class="section level4">
<h4>Translate to GEOtif</h4>
<pre class="r"><code>gdal_translate("HDF4_EOS:EOS_GRID:\"09_data/MCD12Q1.A2012001.h12v04.051.2014288200441_subset.hdf\":MOD12Q1:Land_Cover_Type_1",
"test.tif")
gdalinfo("test.tif",nomd=T)</code></pre>
</div>
<div id="plot-it" class="section level4">
<h4>Plot it</h4>
<pre class="r"><code>d=raster("test.tif")
plot(d)</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-8-1.png" /><!-- --></p>
<p>See also the <code>ModisDownload()</code> function in <code>library(rts)</code>:</p>
<ul>
<li>Downloads series of MODIS images in a specific timeframe for specified tile(s)</li>
<li>MODIS Reproject Tool (MRT) software to mosaic, reproject, reformat</li>
</ul>
</div>
</div>
</div>
<div id="use-modistools-package-to-access-the-modisweb" class="section level1">
<h1>Use MODISTools package to access the MODISweb</h1>
<div id="list-modis-products" class="section level2">
<h2>List MODIS products</h2>
<pre class="r"><code>GetProducts()</code></pre>
<pre><code>## [1] "MCD12Q1" "MCD12Q2" "MCD43A1" "MCD43A2" "MCD43A4"
## [6] "MOD09A1" "MOD11A2" "MOD13Q1" "MOD15A2" "MOD15A2GFS"
## [11] "MOD16A2" "MOD17A2_51" "MOD17A3" "MYD09A1" "MYD11A2"
## [16] "MYD13Q1" "MYD15A2"</code></pre>
<pre class="r"><code>GetBands(Product = "MCD12Q1")</code></pre>
<pre><code>## [1] "LC_Property_1"
## [2] "LC_Property_2"
## [3] "LC_Property_3"
## [4] "Land_Cover_Type_1"
## [5] "Land_Cover_Type_2"
## [6] "Land_Cover_Type_3"
## [7] "Land_Cover_Type_4"
## [8] "Land_Cover_Type_5"
## [9] "Land_Cover_Type_1_Assessment"
## [10] "Land_Cover_Type_2_Assessment"
## [11] "Land_Cover_Type_3_Assessment"
## [12] "Land_Cover_Type_4_Assessment"
## [13] "Land_Cover_Type_5_Assessment"
## [14] "Land_Cover_Type_1_Secondary"
## [15] "Land_Cover_Type_1_Secondary_Percent"</code></pre>
</div>
<div id="selection-locations" class="section level2">
<h2>Selection locations</h2>
<pre class="r"><code>loc=rbind.data.frame(
list("UB Spine",43.000753, -78.788195))
colnames(loc)=c("loc","lat","long")
coordinates(loc)=cbind(loc$long,loc$lat)</code></pre>
</div>
<div id="available-dates" class="section level2">
<h2>Available dates</h2>
<pre class="r"><code>mdates=GetDates(Product = "MOD11A2", Lat = loc$lat[1], Long = loc$long[1])</code></pre>
<div id="modis-date-codes" class="section level3">
<h3>MODIS date codes:</h3>
<p><code>.A2006001</code> - Julian Date of Acquisition (A-YYYYDDD)</p>
<p>Convert to a <em>proper</em> date:</p>
<ul>
<li>Drop the “<code>A</code>”</li>
<li>Specify date format with julian day <code>[1,365]</code></li>
</ul>
<pre class="r"><code>td=mdates[1:5]
td</code></pre>
<pre><code>## [1] "A2000065" "A2000073" "A2000081" "A2000089" "A2000097"</code></pre>
<p><code>sub()</code> to <em>substitute</em> a character in a <code>vector()</code></p>
<pre class="r"><code>sub("A","",td)</code></pre>
<pre><code>## [1] "2000065" "2000073" "2000081" "2000089" "2000097"</code></pre>
<p>Check <code>?strptime</code> for date formats.</p>
<ul>
<li><code>%Y</code> 4-digit year</li>
<li><code>%j</code> 3-digit Julian day</li>
</ul>
<pre class="r"><code>sub("A","",td)%>%
as.Date("%Y%j")</code></pre>
<pre><code>## [1] "2000-03-05" "2000-03-13" "2000-03-21" "2000-03-29" "2000-04-06"</code></pre>
</div>
</div>
<div id="add-start-and-end-dates-to-loc-object" class="section level2">
<h2>Add start and end dates to <code>loc</code> object</h2>
<pre class="r"><code>dates=mdates%>%sub(pattern="A",replacement="")%>%as.Date("%Y%j")
loc$start.date <- min(as.numeric(format(dates,"%Y")))
loc$end.date <- max(as.numeric(format(dates,"%Y")))</code></pre>
</div>
<div id="identify-and-create-download-folders" class="section level2">
<h2>Identify (and create) download folders</h2>
<p>Today we’ll work with:</p>
<ul>
<li>Land Surface Temperature (<code>lst</code>): MOD11A2</li>
<li>Land Cover (<code>lc</code>): MCD12Q1</li>
</ul>
<pre class="r"><code>lstdir=file.path(datadir,"lst")
if(!file.exists(lstdir)) dir.create(lstdir)
lcdir=file.path(datadir,"lc")
if(!file.exists(lcdir)) dir.create(lcdir)</code></pre>
</div>
<div id="download-subset" class="section level2">
<h2>Download subset</h2>
<p><code>Size</code> whole km (integers) for each direction.</p>
<p><code>Size=c(1,1)</code> for 250m resolution data will return a 9x9 pixel tile for each location, centred on the input coordinate.</p>
<p><code>Size=c(0,0)</code> only the central pixel.</p>
<p><strong>Maximum</strong> size tile <code>Size=c(100,100)</code></p>
<p>This can take a few minutes to run, so you can use the file provided in the data folder.</p>
<div id="get-land-surface-temperature-data" class="section level3">
<h3>Get Land Surface Temperature Data</h3>
<pre class="r"><code>MODISSubsets(LoadDat = loc,
Products = c("MOD11A2"),
Bands = c( "LST_Day_1km", "QC_Day"),
Size = c(10,10),
SaveDir=lstdir,
StartDate=T)</code></pre>
</div>
<div id="get-lulc" class="section level3">
<h3>Get LULC</h3>
<pre class="r"><code>MODISSubsets(LoadDat = loc,
Products = c("MCD12Q1"),
Bands = c( "Land_Cover_Type_1"),
Size = c(10,10),
SaveDir=lcdir,
StartDate=T)</code></pre>
<p>List available files:</p>
<pre class="r"><code>lst_files=list.files(lstdir,pattern="Lat.*asc",full=T)
head(lst_files)</code></pre>
<pre><code>## [1] "09_data/lst/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2.asc"</code></pre>
<p>Output:</p>
<ul>
<li>1 file per location in <code>loc</code></li>
<li>Rows: time-steps</li>
<li>Columns: data bands</li>
</ul>
<pre class="r"><code>lst_subset <- read.csv(lst_files[1],header = FALSE, as.is = TRUE)
dim(lst_subset)</code></pre>
<pre><code>## [1] 1180 451</code></pre>
<pre class="r"><code>lst_subset[1:5,1:15]</code></pre>
<pre><code>## V1 V2 V3 V4 V5
## 1 21 21 -6416881 4772121 926.6254
## 2 21 21 -6416881 4772121 926.6254
## 3 21 21 -6416881 4772121 926.6254
## 4 21 21 -6416881 4772121 926.6254
## 5 21 21 -6416881 4772121 926.6254
## V6 V7 V8
## 1 MOD11A2.A2001001.h12v04.005.2006350190920.LST_Day_1km MOD11A2 A2001001
## 2 MOD11A2.A2001009.h12v04.005.2006352085909.LST_Day_1km MOD11A2 A2001009
## 3 MOD11A2.A2001017.h12v04.005.2006354035616.LST_Day_1km MOD11A2 A2001017
## 4 MOD11A2.A2001025.h12v04.005.2006355204922.LST_Day_1km MOD11A2 A2001025
## 5 MOD11A2.A2001033.h12v04.005.2006357090922.LST_Day_1km MOD11A2 A2001033
## V9 V10 V11 V12 V13
## 1 Lat43.000753Lon-78.788195Samp21Line21 2.006350e+12 13321 13306 13296
## 2 Lat43.000753Lon-78.788195Samp21Line21 2.006352e+12 13417 13407 13409
## 3 Lat43.000753Lon-78.788195Samp21Line21 2.006354e+12 13388 13373 13395
## 4 Lat43.000753Lon-78.788195Samp21Line21 2.006355e+12 0 0 0
## 5 Lat43.000753Lon-78.788195Samp21Line21 2.006357e+12 13662 0 0
## V14 V15
## 1 13297 13314
## 2 13400 13396
## 3 13403 13400
## 4 0 0
## 5 0 0</code></pre>
</div>
</div>
<div id="convert-to-ascii-grid-raster-files" class="section level2">
<h2>Convert to ASCII Grid raster files</h2>
<p>Use <code>MODISGrid()</code> to convert to separate <a href="http://resources.esri.com/help/9.3/arcgisdesktop/com/gp_toolref/spatial_analyst_tools/esri_ascii_raster_format.htm">ASCII Grid format</a> files:</p>
<pre><code>NCOLS xxx
NROWS xxx
XLLCENTER xxx | XLLCORNER xxx
YLLCENTER xxx | YLLCORNER xxx
CELLSIZE xxx
NODATA_VALUE xxx
row 1
row 2
...
row n</code></pre>
</div>
<div id="convert-lst-data" class="section level2">
<h2>Convert LST Data</h2>
<pre class="r"><code>MODISGrid(Dir = lstdir,
DirName = "modgrid",
SubDir = TRUE,
NoDataValues=
list("MOD11A2" = c("LST_Day_1km" = 0,
"QC_Day" = -1)))</code></pre>
</div>
<div id="convert-landcover-data" class="section level2">
<h2>Convert LandCover Data</h2>
<pre class="r"><code>MODISGrid(Dir = lcdir,
DirName = "modgrid",
SubDir = TRUE,
NoDataValues=
list("MCD12Q1" = c("Land_Cover_Type_1" = 255)))</code></pre>
</div>
<div id="get-lists-of-.asc-files" class="section level2">
<h2>Get lists of <code>.asc</code> files</h2>
<pre class="r"><code>lst_files=list.files(file.path(lstdir,"modgrid"),recursive=T,
pattern="LST_Day.*asc",full=T)
head(lst_files)</code></pre>
<pre><code>## [1] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001001.asc"
## [2] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001009.asc"
## [3] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001017.asc"
## [4] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001025.asc"
## [5] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001033.asc"
## [6] "09_data/lst/modgrid/Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2/GRID_Lat43.00075Lon-78.78820Start2001-01-01End2013-12-31___MOD11A2_LST_Day_1km_A2001041.asc"</code></pre>
<pre class="r"><code>lstqc_files=list.files(file.path(lstdir,"modgrid"),recursive=T,
pattern="QC_Day.*asc",full=T)</code></pre>
</div>
<div id="create-raster-stacks-of-evi-and-evi-qc-data" class="section level2">
<h2>Create raster stacks of evi and evi qc data</h2>
<pre class="r"><code>lst=stack(lst_files)
plot(lst[[1:2]])</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-25-1.png" /><!-- --></p>
<div id="check-gain-and-offset-in-metadata." class="section level3">
<h3>Check gain and offset in <a href="https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2">metadata</a>.</h3>
<pre class="r"><code>gain(lst)=0.02
offs(lst)=-273.15
plot(lst[[1:2]])</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-26-1.png" /><!-- --></p>
</div>
</div>
</div>
<div id="modland-quality-control" class="section level1">
<h1>MODLAND Quality control</h1>
<p>See a detailed explaination <a href="https://lpdaac.usgs.gov/sites/default/files/public/modis/docs/MODIS_LP_QA_Tutorial-1b.pdf">here</a>. Some code below from <a href="https://stevemosher.wordpress.com/2012/12/05/modis-qc-bits/">Steven Mosher’s blog</a>.</p>
<div id="mod11a2-land-surface-temperature-quality-control" class="section level2">
<h2>MOD11A2 (Land Surface Temperature) Quality Control</h2>
<p><a href="https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2">MOD11A2 QC Layer table</a></p>
<div class="figure">
<img src="09_presentation/09_assets/lst_qc.png" />
</div>
<pre class="r"><code>lstqc=stack(lstqc_files)
plot(lstqc[[1:2]])</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-27-1.png" /><!-- --></p>
<div id="lst-qc-data" class="section level3">
<h3>LST QC data</h3>
<p>QC data are encoded in 8-bit ‘words’ to compress information.</p>
<pre class="r"><code>values(lstqc[[1:2]])%>%table()</code></pre>
<pre><code>## .
## 0 17 65 81
## 625 1 255 1</code></pre>
<div class="figure">
<img src="09_presentation/09_assets/QCdata.png" />
</div>
<pre class="r"><code>intToBits(65)</code></pre>
<pre><code>## [1] 01 00 00 00 00 00 01 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
## [24] 00 00 00 00 00 00 00 00 00</code></pre>
<pre class="r"><code>intToBits(65)[1:8]</code></pre>
<pre><code>## [1] 01 00 00 00 00 00 01 00</code></pre>
<pre class="r"><code>as.integer(intToBits(65)[1:8])</code></pre>
<pre><code>## [1] 1 0 0 0 0 0 1 0</code></pre>
<div id="modis-qc-data-are-big-endian" class="section level4">
<h4>MODIS QC data are <em>Big Endian</em></h4>
<table>
<thead>
<tr class="header">
<th>Format</th>
<th>Digits</th>
<th>value</th>
<th align="left">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Little Endian</td>
<td>1 0 0 0 0 0 1 0</td>
<td>65</td>
<td align="left">2^0 + 2^6</td>
</tr>
<tr class="even">
<td>Big Endian</td>
<td>0 1 0 0 0 0 0 1</td>
<td>65</td>
<td align="left">2^6 + 2^0</td>
</tr>
</tbody>
</table>
<p>Reverse the digits with <code>rev()</code> and compare with QC table above.</p>
<pre class="r"><code>rev(as.integer(intToBits(65)[1:8]))</code></pre>
<pre><code>## [1] 0 1 0 0 0 0 0 1</code></pre>
<p>QC for value <code>65</code>:</p>
<ul>
<li>LST produced, other quality, recommend exampination of more detailed QA</li>
<li>good data quality of L1B in 7 TIR bands</li>
<li>average emissivity error <= 0.01</li>
<li>Average LST error <= 2K</li>
</ul>
<div class="well">
<h2 id="your-turn">Your turn</h2>
<p>What does a QC value of 81 represent?</p>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo1">
Show Solution
</button>
<div id="demo1" class="collapse">
<pre class="r"><code>rev(as.integer(intToBits(81)[1:8]))</code></pre>
<pre><code>## [1] 0 1 0 1 0 0 0 1</code></pre>
<pre class="r"><code># LST produced, other quality, recommend exampination of more detailed QA
# Other quality data
# Average emissivity error <= 0.01
# Average LST error <= 2K</code></pre>
</div>
</div>
</div>
</div>
<div id="filter-the-the-lst-data-using-the-qc-data" class="section level3">
<h3>Filter the the lst data using the QC data</h3>
<pre class="r"><code>## set up data frame to hold all combinations
QC_Data <- data.frame(Integer_Value = 0:255,
Bit7 = NA, Bit6 = NA, Bit5 = NA, Bit4 = NA,
Bit3 = NA, Bit2 = NA, Bit1 = NA, Bit0 = NA,
QA_word1 = NA, QA_word2 = NA, QA_word3 = NA,
QA_word4 = NA)
##
for(i in QC_Data$Integer_Value){
AsInt <- as.integer(intToBits(i)[1:8])
QC_Data[i+1,2:9]<- AsInt[8:1]
}
QC_Data$QA_word1[QC_Data$Bit1 == 0 & QC_Data$Bit0==0] <- "LST GOOD"
QC_Data$QA_word1[QC_Data$Bit1 == 0 & QC_Data$Bit0==1] <- "LST Produced,Other Quality"
QC_Data$QA_word1[QC_Data$Bit1 == 1 & QC_Data$Bit0==0] <- "No Pixel,clouds"
QC_Data$QA_word1[QC_Data$Bit1 == 1 & QC_Data$Bit0==1] <- "No Pixel, Other QA"
QC_Data$QA_word2[QC_Data$Bit3 == 0 & QC_Data$Bit2==0] <- "Good Data"
QC_Data$QA_word2[QC_Data$Bit3 == 0 & QC_Data$Bit2==1] <- "Other Quality"
QC_Data$QA_word2[QC_Data$Bit3 == 1 & QC_Data$Bit2==0] <- "TBD"
QC_Data$QA_word2[QC_Data$Bit3 == 1 & QC_Data$Bit2==1] <- "TBD"
QC_Data$QA_word3[QC_Data$Bit5 == 0 & QC_Data$Bit4==0] <- "Emiss Error <= .01"
QC_Data$QA_word3[QC_Data$Bit5 == 0 & QC_Data$Bit4==1] <- "Emiss Err >.01 <=.02"
QC_Data$QA_word3[QC_Data$Bit5 == 1 & QC_Data$Bit4==0] <- "Emiss Err >.02 <=.04"
QC_Data$QA_word3[QC_Data$Bit5 == 1 & QC_Data$Bit4==1] <- "Emiss Err > .04"
QC_Data$QA_word4[QC_Data$Bit7 == 0 & QC_Data$Bit6==0] <- "LST Err <= 1"
QC_Data$QA_word4[QC_Data$Bit7 == 0 & QC_Data$Bit6==1] <- "LST Err > 2 LST Err <= 3"
QC_Data$QA_word4[QC_Data$Bit7 == 1 & QC_Data$Bit6==0] <- "LST Err > 1 LST Err <= 2"
QC_Data$QA_word4[QC_Data$Bit7 == 1 & QC_Data$Bit6==1] <- "LST Err > 4"
kable(head(QC_Data))</code></pre>
<table>
<thead>
<tr class="header">
<th align="right">Integer_Value</th>
<th align="right">Bit7</th>
<th align="right">Bit6</th>
<th align="right">Bit5</th>
<th align="right">Bit4</th>
<th align="right">Bit3</th>
<th align="right">Bit2</th>
<th align="right">Bit1</th>
<th align="right">Bit0</th>
<th align="left">QA_word1</th>
<th align="left">QA_word2</th>
<th align="left">QA_word3</th>
<th align="left">QA_word4</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="left">LST GOOD</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
<tr class="even">
<td align="right">1</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
<tr class="odd">
<td align="right">2</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">0</td>
<td align="left">No Pixel,clouds</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
<tr class="even">
<td align="right">3</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">1</td>
<td align="left">No Pixel, Other QA</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
<tr class="odd">
<td align="right">4</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="left">LST GOOD</td>
<td align="left">Other Quality</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
<tr class="even">
<td align="right">5</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Other Quality</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
</tr>
</tbody>
</table>
</div>
<div id="select-which-qc-levels-to-keep" class="section level3">
<h3>Select which QC Levels to keep</h3>
<pre class="r"><code>keep=QC_Data[QC_Data$Bit1 == 0,]
keepvals=unique(keep$Integer_Value)
keepvals</code></pre>
<pre><code>## [1] 0 1 4 5 8 9 12 13 16 17 20 21 24 25 28 29 32
## [18] 33 36 37 40 41 44 45 48 49 52 53 56 57 60 61 64 65
## [35] 68 69 72 73 76 77 80 81 84 85 88 89 92 93 96 97 100
## [52] 101 104 105 108 109 112 113 116 117 120 121 124 125 128 129 132 133
## [69] 136 137 140 141 144 145 148 149 152 153 156 157 160 161 164 165 168
## [86] 169 172 173 176 177 180 181 184 185 188 189 192 193 196 197 200 201
## [103] 204 205 208 209 212 213 216 217 220 221 224 225 228 229 232 233 236
## [120] 237 240 241 244 245 248 249 252 253</code></pre>
</div>
<div id="how-many-observations-will-be-dropped" class="section level3">
<h3>How many observations will be dropped?</h3>
<pre class="r"><code>qcvals=table(values(lstqc)) # this takes a minute or two
QC_Data%>%
dplyr::select(everything(),-contains("Bit"))%>%
mutate(Var1=as.character(Integer_Value),
keep=Integer_Value%in%keepvals)%>%
inner_join(data.frame(qcvals))%>%
kable()</code></pre>
<pre><code>## Joining, by = "Var1"</code></pre>
<table>
<thead>
<tr class="header">
<th align="right">Integer_Value</th>
<th align="left">QA_word1</th>
<th align="left">QA_word2</th>
<th align="left">QA_word3</th>
<th align="left">QA_word4</th>
<th align="left">Var1</th>
<th align="left">keep</th>
<th align="right">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">0</td>
<td align="left">LST GOOD</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
<td align="left">0</td>
<td align="left">TRUE</td>
<td align="right">62819</td>
</tr>
<tr class="even">
<td align="right">2</td>
<td align="left">No Pixel,clouds</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err <= 1</td>
<td align="left">2</td>
<td align="left">FALSE</td>
<td align="right">30750</td>
</tr>
<tr class="odd">
<td align="right">17</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Err >.01 <=.02</td>
<td align="left">LST Err <= 1</td>
<td align="left">17</td>
<td align="left">TRUE</td>
<td align="right">11244</td>
</tr>
<tr class="even">
<td align="right">65</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err > 2 LST Err <= 3</td>
<td align="left">65</td>
<td align="left">TRUE</td>
<td align="right">47211</td>
</tr>
<tr class="odd">
<td align="right">81</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Err >.01 <=.02</td>
<td align="left">LST Err > 2 LST Err <= 3</td>
<td align="left">81</td>
<td align="left">TRUE</td>
<td align="right">99852</td>
</tr>
<tr class="even">
<td align="right">129</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Error <= .01</td>
<td align="left">LST Err > 1 LST Err <= 2</td>
<td align="left">129</td>
<td align="left">TRUE</td>
<td align="right">8</td>
</tr>
<tr class="odd">
<td align="right">145</td>
<td align="left">LST Produced,Other Quality</td>
<td align="left">Good Data</td>
<td align="left">Emiss Err >.01 <=.02</td>
<td align="left">LST Err > 1 LST Err <= 2</td>
<td align="left">145</td>
<td align="left">TRUE</td>
<td align="right">8306</td>
</tr>
</tbody>
</table>
<p>Do you want to update the values you are keeping?</p>
</div>
<div id="filter-the-lst-data-keeping-only-keepvals" class="section level3">
<h3>Filter the LST Data keeping only <code>keepvals</code></h3>
<p>These steps take a couple minutes.</p>
<p>Make logical flag to use for mask</p>
<pre class="r"><code>lstkeep=calc(lstqc,function(x) x%in%keepvals)</code></pre>
<p>Plot the mask</p>
<pre class="r"><code>gplot(lstkeep[[4:8]])+
geom_raster(aes(fill=as.factor(value)))+
facet_grid(variable~.)+
scale_fill_manual(values=c("blue","red"),name="Keep")+
coord_equal()+
theme(legend.position = "bottom")</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-36-1.png" /><!-- --></p>
<p>Mask the lst data using the QC data</p>
<pre class="r"><code>lst2=mask(lst,mask=lstkeep,maskval=0)</code></pre>
</div>
</div>
<div id="add-dates-to-z-dimension" class="section level2">
<h2>Add Dates to Z dimension</h2>
<pre class="r"><code>tdates=names(lst)%>%
sub(pattern=".*_A",replacement="")%>%
as.Date("%Y%j")
names(lst2)=1:nlayers(lst2)
lst2=setZ(lst2,tdates)</code></pre>
</div>
<div id="summarize-to-seasonal-climatologies" class="section level2">
<h2>Summarize to Seasonal climatologies</h2>
<p>Use <code>stackApply()</code> with a seasonal index.</p>
<pre class="r"><code>tseas=as.numeric(sub("Q","",quarters(getZ(lst2))))
tseas[1:20]</code></pre>
<pre><code>## [1] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2</code></pre>
<pre class="r"><code>lst_seas=stackApply(lst2,
indices = tseas,
mean,na.rm=T)
names(lst_seas)=c("Q1_Winter",
"Q2_Spring",
"Q3_Summer",
"Q4_Fall")</code></pre>
<pre class="r"><code>gplot(lst_seas)+geom_raster(aes(fill=value))+
facet_wrap(~variable)+
scale_fill_gradientn(colours=c("blue",mid="grey","red"))+
coord_equal()+
theme(axis.text.x=element_text(angle=60, hjust=1))</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-40-1.png" /><!-- --></p>
<div class="well">
<h2 id="your-turn-1">Your turn</h2>
<p>Use <code>stackApply()</code> to generate and plot monthly median lst values.</p>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo2">
Show Solution
</button>
<div id="demo2" class="collapse">
<pre class="r"><code>tmonth=as.numeric(format(getZ(lst2),"%m"))
lst_month=stackApply(lst2,indices = tmonth,mean,na.rm=T)
names(lst_month)=sprintf("%02d",1:12)
gplot(lst_month)+geom_raster(aes(fill=value))+
facet_wrap(~variable)+
scale_fill_gradientn(colours=c("blue",mid="grey","red"))+
coord_equal()</code></pre>
<p><img src="09_RemoteSensing_files/figure-html/unnamed-chunk-41-1.png" /><!-- --></p>
</div>
</div>
</div>
<div id="extract-timeseries-for-a-point" class="section level2">
<h2>Extract timeseries for a point</h2>
<pre class="r"><code>lw=SpatialPoints(
data.frame(
x= -78.791547,
y=43.007211))
projection(lw)="+proj=longlat"
lw=spTransform(lw,projection(lst2))
lwt=data.frame(date=getZ(lst2),
lst=t(raster::extract(
lst2,lw,
buffer=1000,
fun=mean,na.rm=T)))