diff --git a/src/data/csv_to_geojson.py b/src/data/csv_to_geojson.py
index d7481b3..41b5c5a 100644
--- a/src/data/csv_to_geojson.py
+++ b/src/data/csv_to_geojson.py
@@ -37,10 +37,11 @@ def lat_lon_from_profile_x_coord(center_lat_lon, orientation, center_profile_x,
 
 @click.command()
 @click.option("--sites-csv", required=True, help=".csv file to convert")
-@click.option("--profile-csv", required=True, help=".csv file to convert")
+@click.option("--profiles-csv", required=True, help=".csv file to convert")
+@click.option("--crest-toes-csv", required=True, help=".csv file to convert")
 @click.option("--impacts-csv", required=True, help=".csv file to convert")
 @click.option("--output-geojson", required=True, help="where to store .geojson file")
-def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
+def R_high_to_geojson(sites_csv, profiles_csv, crest_toes_csv, impacts_csv, output_geojson):
     """
     Converts impact R_high into a lat/lon geojson that we can plot in QGIS
     :param sites_csv:
@@ -49,13 +50,9 @@ def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
     :param output_geojson:
     :return:
     """
-    sites_csv = "./data/interim/sites.csv"
-    profiles_csv = "./data/interim/profiles.csv"
-    impacts_csv = "./data/interim/impacts_forecasted_mean_slope_sto06.csv"
-    output_geojson = "./data/interim/R_high_forecasted_mean_slope_sto06.geojson"
-
     df_sites = pd.read_csv(sites_csv, index_col=[0])
     df_profiles = pd.read_csv(profiles_csv, index_col=[0, 1, 2])
+    df_crest_toes = pd.read_csv(crest_toes_csv, index_col=[0, 1])
     df_impacts = pd.read_csv(impacts_csv, index_col=[0])
 
     # Create geojson file
@@ -77,9 +74,10 @@ def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
             df_profile = df_profiles.loc[(site_id, "prestorm")]
             int_x = crossings(df_profile.index.get_level_values("x").tolist(), df_profile.z.tolist(), R_high_z)
 
-            # Take most landward interesection. Continue to next site if there is no intersection
+            # Take the intersection closest to the dune face.
             try:
-                int_x = max(int_x)
+                dune_face_x = np.mean(df_crest_toes.loc[(site_id, 'prestorm'), :].x)
+                int_x = min(int_x, key=lambda x:abs(x-dune_face_x))
             except:
                 continue
 
@@ -100,7 +98,7 @@ def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
 @click.option("--sites-csv", required=True, help=".csv file to convert")
 @click.option("--profile-features-csv", required=True, help=".csv file to convert")
 @click.option("--output-geojson", required=True, help="where to store .geojson file")
-def profile_features_to_geojson(sites_csv, profile_features_csv, output_geojson):
+def profile_features_crest_toes_to_geojson(sites_csv, profile_features_csv, output_geojson):
     """
     Converts profile_features containing dune toes and crest locations to a geojson we can load into QGIS
     :param sites_csv: