diff --git a/NEWS.md b/NEWS.md
new file mode 100644
index 0000000..9da1467
--- /dev/null
+++ b/NEWS.md
@@ -0,0 +1,3 @@
+# MDCSpatialData (development version)
+
+* Fixed NLCD-correcting the mask 
diff --git a/R/data.R b/R/data.R
index 8107d15..f0b5da3 100644
--- a/R/data.R
+++ b/R/data.R
@@ -65,7 +65,7 @@
 #'
 #' See vignettes for examples on how to batch process
 #'
-#'  Cropping performed with terra::crop
+#'  Cropping performed with terra::crop and the terra:mask
 #'
 #' Area converted to square km based on a 30x30m cell size and summing all
 #' all pixels with in a given class. No other processing occurred.
@@ -73,24 +73,61 @@
 #'
 #' @examples
 #' \dontrun{
-#' #pixel classes were condensed as follows:
+#' #data processed as follows:
+#'
+#'
+#'
+#' # list projected NLCD files in a folder
+#'list.nlcd<-list.files(path='./prj_NLCD/',
+#'                      pattern='.tif$',
+#'                      full.names=TRUE)
+#'
+#'
+#'
+#' MDCSpatialData::counties.mo->co
+#'
+#'
+#'
+#'co%>%split(.$NAME)%>%
+#'  map(.,~terra::vect(.x))->tmp
 #'
+#'nlcd.fxn<-function(nlcd,
+#'                   county,year){
+#'  terra::rast(nlcd)->Z
 #'
-#' terra::rast(nlcd)->Z
 #'  mo.co.list[[county]]->Q
-#'  terra::crop(Z,Q)%>%
-#'  terra::freq()%>%
-#'  filter(str_detect(value,
-#'  'Water|Developed|Forest|Shrub|Grass|Hay|Crops|Wetlands'))%>%
-#'  mutate(LC=case_when(str_detect(value,'Developed')~"develop",
-#'                    str_detect(value,'Forest')~"forest",
-#'                    str_detect(value,'Shrub|Grassland|Hay')~'grass',
-#'                    str_detect(value,'Crops')~'crop',
-#'                    str_detect(value,'Wetlands')~'wetland',
-#'                    str_detect(value,'Water')~'water',
-#'                    TRUE~as.character("other")
-#'                    ))
-#'                    }
+#'
+#'  terra::crop(Z,Q)->P
+#'  terra::mask(P,Q)%>%
+#'    terra::freq()%>%
+#'    rename(LC=value)%>%
+#'    select(LC,count)%>%
+#'    group_by(LC)%>%
+#'    summarize(n.cells=sum(count))%>%
+#'    ungroup()%>%
+#'    mutate(pct.area=n.cells/sum(n.cells),
+#'           area.sqkm=(n.cells*(30*30)/1E6),
+#'           cty=county,
+#'           yr=year)
+#'}
+#'
+#'
+#'
+#'
+#'crossing(list.nlcd,names(mo.co.list))%>%
+#'  rename(path=1,
+#'         county=2)%>%
+#'  mutate(year=str_extract(path,pattern='20\\d{2}'))%>%
+#'  pmap(~nlcd.fxn(..1,..2,..3))->nlcd.tab
+#'
+#'
+#' bind_rows(nlcd.tab)%>%
+#'   select(-n.cells,-pct.area)%>%
+#'   mutate(LC=paste0(LC,'.sqkm'))%>%
+#'   pivot_wider(names_from=LC,
+#'               values_from=area.sqkm)->nlcd.area.tab
+#'}
+#'
 #'
 #' @format
 #' \describe{
@@ -124,23 +161,59 @@
 #'
 #' @examples
 #' \dontrun{
-#' #pixel classes were condensed as follows:
+#' #' #data processed as follows:
+#'
+#'
+#'
+#' # list projected NLCD files in a folder
+#'list.nlcd<-list.files(path='./prj_NLCD/',
+#'                      pattern='.tif$',
+#'                      full.names=TRUE)
+#'
+#'
+#'
+#' MDCSpatialData::counties.mo->co
+#'
+#'
 #'
+#'co%>%split(.$NAME)%>%
+#'  map(.,~terra::vect(.x))->tmp
+#'
+#'nlcd.fxn<-function(nlcd,
+#'                   county,year){
+#'  terra::rast(nlcd)->Z
 #'
-#' terra::rast(nlcd)->Z
 #'  mo.co.list[[county]]->Q
-#'  terra::crop(Z,Q)%>%
-#'  terra::freq()%>%
-#'  filter(str_detect(value,
-#'  'Water|Developed|Forest|Shrub|Grass|Hay|Crops|Wetlands'))%>%
-#'  mutate(LC=case_when(str_detect(value,'Developed')~"develop",
-#'                    str_detect(value,'Forest')~"forest",
-#'                    str_detect(value,'Shrub|Grassland|Hay')~'grass',
-#'                    str_detect(value,'Crops')~'crop',
-#'                    str_detect(value,'Wetlands')~'wetland',
-#'                    str_detect(value,'Water')~'water',
-#'                    TRUE~as.character("other")
-#'                    ))
+#'
+#'  terra::crop(Z,Q)->P
+#'  terra::mask(P,Q)%>%
+#'    terra::freq()%>%
+#'    rename(LC=value)%>%
+#'    select(LC,count)%>%
+#'    group_by(LC)%>%
+#'    summarize(n.cells=sum(count))%>%
+#'    ungroup()%>%
+#'    mutate(pct.area=n.cells/sum(n.cells),
+#'           area.sqkm=(n.cells*(30*30)/1E6),
+#'           cty=county,
+#'           yr=year)
+#'}
+#'
+#'
+#'
+#'
+#'crossing(list.nlcd,names(mo.co.list))%>%
+#'  rename(path=1,
+#'         county=2)%>%
+#'  mutate(year=str_extract(path,pattern='20\\d{2}'))%>%
+#'  pmap(~nlcd.fxn(..1,..2,..3))->nlcd.tab
+#'
+#'  bind_rows(nlcd.tab)%>%
+#'  select(-n.cells,-area.sqkm)%>%
+#'    mutate(LC=paste0(LC,'.pct'))%>%
+#'    pivot_wider(names_from=LC,
+#'                values_from=pct.area)->nlcd.pct.tab
+#'
 #'                    }
 #'
 #' @format
diff --git a/data/nlcd.area.tab.rda b/data/nlcd.area.tab.rda
index 43cfc8d..6202775 100644
Binary files a/data/nlcd.area.tab.rda and b/data/nlcd.area.tab.rda differ
diff --git a/data/nlcd.pct.tab.rda b/data/nlcd.pct.tab.rda
index aa64ce2..8d2c7ce 100644
Binary files a/data/nlcd.pct.tab.rda and b/data/nlcd.pct.tab.rda differ
diff --git a/man/nlcd.area.tab.Rd b/man/nlcd.area.tab.Rd
index 5143fa0..35e7908 100644
--- a/man/nlcd.area.tab.Rd
+++ b/man/nlcd.area.tab.Rd
@@ -28,31 +28,68 @@ to NAD83 UTM 15 N before areal calculations.
 \details{
 See vignettes for examples on how to batch process
 
-Cropping performed with terra::crop
+Cropping performed with terra::crop and the terra:mask
 
 Area converted to square km based on a 30x30m cell size and summing all
 all pixels with in a given class. No other processing occurred.
 }
 \examples{
 \dontrun{
-#pixel classes were condensed as follows:
+#data processed as follows:
 
 
-terra::rast(nlcd)->Z
+
+# list projected NLCD files in a folder
+list.nlcd<-list.files(path='./prj_NLCD/',
+                     pattern='.tif$',
+                     full.names=TRUE)
+
+
+
+MDCSpatialData::counties.mo->co
+
+
+
+co\%>\%split(.$NAME)\%>\%
+ map(.,~terra::vect(.x))->tmp
+
+nlcd.fxn<-function(nlcd,
+                  county,year){
+ terra::rast(nlcd)->Z
+
  mo.co.list[[county]]->Q
- terra::crop(Z,Q)\%>\%
- terra::freq()\%>\%
- filter(str_detect(value,
- 'Water|Developed|Forest|Shrub|Grass|Hay|Crops|Wetlands'))\%>\%
- mutate(LC=case_when(str_detect(value,'Developed')~"develop",
-                   str_detect(value,'Forest')~"forest",
-                   str_detect(value,'Shrub|Grassland|Hay')~'grass',
-                   str_detect(value,'Crops')~'crop',
-                   str_detect(value,'Wetlands')~'wetland',
-                   str_detect(value,'Water')~'water',
-                   TRUE~as.character("other")
-                   ))
-                   }
+
+ terra::crop(Z,Q)->P
+ terra::mask(P,Q)\%>\%
+   terra::freq()\%>\%
+   rename(LC=value)\%>\%
+   select(LC,count)\%>\%
+   group_by(LC)\%>\%
+   summarize(n.cells=sum(count))\%>\%
+   ungroup()\%>\%
+   mutate(pct.area=n.cells/sum(n.cells),
+          area.sqkm=(n.cells*(30*30)/1E6),
+          cty=county,
+          yr=year)
+}
+
+
+
+
+crossing(list.nlcd,names(mo.co.list))\%>\%
+ rename(path=1,
+        county=2)\%>\%
+ mutate(year=str_extract(path,pattern='20\\\\d{2}'))\%>\%
+ pmap(~nlcd.fxn(..1,..2,..3))->nlcd.tab
+
+
+bind_rows(nlcd.tab)\%>\%
+  select(-n.cells,-pct.area)\%>\%
+  mutate(LC=paste0(LC,'.sqkm'))\%>\%
+  pivot_wider(names_from=LC,
+              values_from=area.sqkm)->nlcd.area.tab
+}
+
 
 }
 \keyword{datasets}
diff --git a/man/nlcd.pct.tab.Rd b/man/nlcd.pct.tab.Rd
index ac7a8cd..38abeb8 100644
--- a/man/nlcd.pct.tab.Rd
+++ b/man/nlcd.pct.tab.Rd
@@ -34,23 +34,59 @@ all pixels with in a given class. No other processing occurred.
 }
 \examples{
 \dontrun{
-#pixel classes were condensed as follows:
+#' #data processed as follows:
 
 
-terra::rast(nlcd)->Z
+
+# list projected NLCD files in a folder
+list.nlcd<-list.files(path='./prj_NLCD/',
+                     pattern='.tif$',
+                     full.names=TRUE)
+
+
+
+MDCSpatialData::counties.mo->co
+
+
+
+co\%>\%split(.$NAME)\%>\%
+ map(.,~terra::vect(.x))->tmp
+
+nlcd.fxn<-function(nlcd,
+                  county,year){
+ terra::rast(nlcd)->Z
+
  mo.co.list[[county]]->Q
- terra::crop(Z,Q)\%>\%
- terra::freq()\%>\%
- filter(str_detect(value,
- 'Water|Developed|Forest|Shrub|Grass|Hay|Crops|Wetlands'))\%>\%
- mutate(LC=case_when(str_detect(value,'Developed')~"develop",
-                   str_detect(value,'Forest')~"forest",
-                   str_detect(value,'Shrub|Grassland|Hay')~'grass',
-                   str_detect(value,'Crops')~'crop',
-                   str_detect(value,'Wetlands')~'wetland',
-                   str_detect(value,'Water')~'water',
-                   TRUE~as.character("other")
-                   ))
+
+ terra::crop(Z,Q)->P
+ terra::mask(P,Q)\%>\%
+   terra::freq()\%>\%
+   rename(LC=value)\%>\%
+   select(LC,count)\%>\%
+   group_by(LC)\%>\%
+   summarize(n.cells=sum(count))\%>\%
+   ungroup()\%>\%
+   mutate(pct.area=n.cells/sum(n.cells),
+          area.sqkm=(n.cells*(30*30)/1E6),
+          cty=county,
+          yr=year)
+}
+
+
+
+
+crossing(list.nlcd,names(mo.co.list))\%>\%
+ rename(path=1,
+        county=2)\%>\%
+ mutate(year=str_extract(path,pattern='20\\\\d{2}'))\%>\%
+ pmap(~nlcd.fxn(..1,..2,..3))->nlcd.tab
+
+ bind_rows(nlcd.tab)\%>\%
+ select(-n.cells,-area.sqkm)\%>\%
+   mutate(LC=paste0(LC,'.pct'))\%>\%
+   pivot_wider(names_from=LC,
+               values_from=pct.area)->nlcd.pct.tab
+
                    }
 
 }