diff --git a/README.md b/README.md
index 2f65537b..b598680b 100644
--- a/README.md
+++ b/README.md
@@ -14,11 +14,10 @@
 
 ### Lesson Maintainers:
 
-- [Jemma Stachelek][stachelek_jemma]
 - [Ivo Arrey][arreyves]
 - [Jon Jablonski][jonjab]
-- Drake Asberry
+- [Braden Owsley][owsleybc]
 
-[stachelek_jemma]: https://carpentries.org/instructors/#jsta
 [arreyves]: https://carpentries.org/instructors/#arreyves
 [jonjab]: https://carpentries.org/instructors/#jonjab
+[owsleybc]: https://carpentries.org/instructors/#owsleybc
diff --git a/episodes/04-raster-calculations-in-r.Rmd b/episodes/04-raster-calculations-in-r.Rmd
index 1c0b850e..75c9ce7f 100644
--- a/episodes/04-raster-calculations-in-r.Rmd
+++ b/episodes/04-raster-calculations-in-r.Rmd
@@ -43,13 +43,13 @@ DTM_HARV <-
 
 DTM_HARV_df <- as.data.frame(DTM_HARV, xy = TRUE)
 
-# DSM data for SJER
+# DSM (treetop) data for SJER
 DSM_SJER <- 
   rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop.tif")
 
 DSM_SJER_df <- as.data.frame(DSM_SJER, xy = TRUE)
 
-# DTM data for SJER
+# DTM (bare-earth) data for SJER
 DTM_SJER <- 
   rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmCrop.tif")
 
@@ -64,6 +64,35 @@ See the [lesson homepage](.) for detailed information about the software,
 data, and other prerequisites you will need to work through the examples in 
 this episode.
 
+### Load the Data
+
+For this episode, we will use the DTM and DSM from the NEON Harvard Forest 
+Field site and San Joaquin Experimental Range. If you don't still have
+them loaded, do so now and turn them into dataframes:
+
+# DSM (tree top) data for Harvard Forest
+DSM_HARV <- 
+  rast("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
+
+DSM_HARV_df <- as.data.frame(DSM_HARV, xy = TRUE)
+
+# DTM (bare earth) data for Harvard Forest
+DTM_HARV <- 
+  rast("data/NEON-DS-Airborne-Remote-Sensing/HARV/DTM/HARV_dtmCrop.tif")
+
+DTM_HARV_df <- as.data.frame(DTM_HARV, xy = TRUE)
+
+# DSM data for SJER
+DSM_SJER <- 
+  rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop.tif")
+
+DSM_SJER_df <- as.data.frame(DSM_SJER, xy = TRUE)
+
+# DTM data for SJER
+DTM_SJER <- 
+  rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmCrop.tif")
+
+DTM_SJER_df <- as.data.frame(DTM_SJER, xy = TRUE)
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -95,11 +124,7 @@ with the influence of ground elevation removed.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-### Load the Data
 
-For this episode, we will use the DTM and DSM from the NEON Harvard Forest 
-Field site and San Joaquin Experimental Range, which we already have loaded 
-from previous episodes.
 
 :::::::::::::::::::::::::::::::::::::::  challenge
 
diff --git a/episodes/05-raster-multi-band-in-r.Rmd b/episodes/05-raster-multi-band-in-r.Rmd
index 126e5571..e817c8b3 100644
--- a/episodes/05-raster-multi-band-in-r.Rmd
+++ b/episodes/05-raster-multi-band-in-r.Rmd
@@ -1,7 +1,7 @@
 ---
 title: Work with Multi-Band Rasters
-teaching: 40
-exercises: 20
+teaching: 30
+exercises: 15
 source: Rmd
 ---
 
@@ -77,6 +77,8 @@ ggplot() +
   coord_quickmap()
 ```
 
+To import the green band, we would use `lyrs = 2`.
+
 :::::::::::::::::::::::::::::::::::::::  challenge
 
 ## Challenge
@@ -124,51 +126,8 @@ represent pixels with less red in them (less red was reflected). To plot an RGB
 image, we mix red + green + blue values into one single color to create a full 
 color image - similar to the color image a digital camera creates.
 
-### Import A Specific Band
-
-We can use the `rast()` function to import specific bands in our raster object
-by specifying which band we want with `lyrs = N` (N represents the band number we
-want to work with). To import the green band, we would use `lyrs = 2`.
-
-```{r read-specific-band}
-RGB_band2_HARV <-  
-  rast("data/NEON-DS-Airborne-Remote-Sensing/HARV/RGB_Imagery/HARV_RGB_Ortho.tif", 
-       lyrs = 2)
-```
-
-We can convert this data to a data frame and plot the same way we plotted the red band:
-
-```{r}
-RGB_band2_HARV_df <- as.data.frame(RGB_band2_HARV, xy = TRUE)
-```
+ 
 
-```{r rgb-harv-band2}
-ggplot() +
-  geom_raster(data = RGB_band2_HARV_df,
-              aes(x = x, y = y, alpha = HARV_RGB_Ortho_2)) + 
-  coord_equal()
-```
-
-:::::::::::::::::::::::::::::::::::::::  challenge
-
-## Challenge: Making Sense of Single Band Images
-
-Compare the plots of band 1 (red) and band 2 (green). Is the forested area 
-darker or lighter in band 2 (the green band) compared to band 1 (the red band)?
-
-:::::::::::::::  solution
-
-## Solution
-
-We'd expect a *brighter* value for the forest in band 2 (green) than in band 1 
-(red) because the leaves on trees of most often appear "green" - healthy leaves 
-reflect MORE green light than red light.
-
-
-
-:::::::::::::::::::::::::
-
-::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Raster Stacks in R