diff --git a/ecoscope/analysis/seasons.py b/ecoscope/analysis/seasons.py
index 2ef062ee..15b8a929 100644
--- a/ecoscope/analysis/seasons.py
+++ b/ecoscope/analysis/seasons.py
@@ -20,23 +20,32 @@ def _min_max_scaler(x):
     return x_std
 
 
-def std_ndvi_vals(aoi=None, start=None, end=None):
-    coll = ee.ImageCollection("MODIS/MCD43A4_006_NDVI").select("NDVI").filterDate(start, end)
-
-    ndvi_vals = (
-        coll.toBands()
-        .reduceRegion("mean", ee.Feature(shapely.geometry.mapping(aoi)).geometry(), bestEffort=True)
-        .values()
-        .getInfo()
+def std_ndvi_vals(aoi=None, img_coll=None, band=None, img_scale=1, start=None, end=None):
+
+    coll = (
+        ee.ImageCollection(img_coll)
+        .select(band)
+        .filterDate(start, end)
+        .map(lambda x: x.multiply(ee.Image(img_scale)).set("system:time_start", x.get("system:time_start")))
     )
-    ndvi_vals_std = _min_max_scaler(ndvi_vals)
-    return pd.DataFrame(
+
+    if aoi:
+        geo = ee.Feature(shapely.geometry.mapping(aoi)).geometry()
+    else:
+        geo = None
+
+    ndvi_vals = coll.toBands().reduceRegion("mean", geo, bestEffort=True).values().getInfo()
+
+    df = pd.DataFrame(
         {
             "img_date": pd.to_datetime(coll.aggregate_array("system:time_start").getInfo(), unit="ms", utc=True),
-            "NDVI": ndvi_vals_std,
+            "NDVI": ndvi_vals,
         }
     ).dropna(axis=0)
 
+    df["NDVI"] = _min_max_scaler(df["NDVI"])
+    return df
+
 
 def val_cuts(vals, num_seasons=2):
     distr = sklearn.mixture.GaussianMixture(n_components=num_seasons, max_iter=500)
diff --git a/ecoscope/version.py b/ecoscope/version.py
index c0f285b0..56dadec6 100644
--- a/ecoscope/version.py
+++ b/ecoscope/version.py
@@ -1 +1 @@
-__version__ = "1.4.4"
+__version__ = "1.4.5"
diff --git a/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb b/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb
index a2820147..5ed4d8d3 100644
--- a/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb	
+++ b/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb	
@@ -29,7 +29,7 @@
    "source": [
     "ECOSCOPE_RAW = \"https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master\"\n",
     "\n",
-    "# !pip install ecoscope"
+    "%pip install ecoscope &> /dev/null"
    ]
   },
   {
@@ -154,8 +154,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract the standardized NDVI ndvi_vals within the AOI\n",
-    "ndvi_vals = seasons.std_ndvi_vals(aoi, start=\"2010-01-01\", end=\"2022-06-18\")\n",
+    "img_coll = \"MODIS/061/MOD13A1\"\n",
+    "band = \"NDVI\"\n",
+    "img_scale = 0.0001\n",
+    "since_filter = \"2010-01-01\"\n",
+    "until_filter = \"2022-06-18\"\n",
+    "\n",
+    "ndvi_vals = seasons.std_ndvi_vals(\n",
+    "    img_coll=img_coll,\n",
+    "    band=band,\n",
+    "    img_scale=img_scale,\n",
+    "    aoi=aoi,\n",
+    "    start=since_filter,\n",
+    "    end=until_filter,\n",
+    ")\n",
+    "\n",
+    "# Calculate the seasonal transition point\n",
+    "cuts = seasons.val_cuts(ndvi_vals, 2)\n",
+    "\n",
+    "# Determine the seasonal time windows\n",
+    "windows = seasons.seasonal_windows(ndvi_vals, cuts, season_labels=[\"dry\", \"wet\"])\n",
     "\n",
     "# Calculate the seasonal transition point\n",
     "cuts = seasons.val_cuts(ndvi_vals, 2)\n",
@@ -247,7 +265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,