From a9c267f2eaf0a278cb7e4e02565ac6abe9ca1674 Mon Sep 17 00:00:00 2001
From: Robbi Bishop-Taylor <Robbi.BishopTaylor@ga.gov.au>
Date: Mon, 16 Jan 2023 00:37:25 +0000
Subject: [PATCH] Change all time raster thresholds

---
 coastlines/vector.py                          |  24 +-
 data/raw/coastlines_development.geojson       |   9 +-
 .../DEACoastlines_generation_raster.ipynb     | 180 ++++++-------
 .../DEACoastlines_generation_vector.ipynb     | 246 +++++++++++++++---
 4 files changed, 312 insertions(+), 147 deletions(-)

diff --git a/coastlines/vector.py b/coastlines/vector.py
index 786314e..312e0a6 100644
--- a/coastlines/vector.py
+++ b/coastlines/vector.py
@@ -578,14 +578,14 @@ def contours_preprocess(
     thresholded_ds = thresholded_ds.where(temporal_mask)
 
     # Create all time layers by identifying pixels that are land in at
-    # least 15% and 50% of valid observations; the 15% layer is used to
+    # least 20% and 80% of valid observations; the 20% layer is used to
     # identify pixels that contain land for even a small period of time;
     # this is used for producing an all-time buffered "coastal" study area.
-    # The 50% layer is used to more conservatively identify pixels that
-    # are land for a majority of years; this is used for extracting
-    # rivers.
-    all_time_15 = thresholded_ds.mean(dim="year") > 0.15
-    all_time_50 = thresholded_ds.mean(dim="year") > 0.5
+    # The 80% layer is used to more conservatively identify pixels that
+    # are land for most of the time series years; this is used for extracting
+    # rivers as it better accounts for migrating/dynamic river channels.
+    all_time_20 = thresholded_ds.mean(dim="year") > 0.2
+    all_time_80 = thresholded_ds.mean(dim="year") > 0.8
 
     # Identify narrow river and stream features using the `black_tophat`
     # transform. To avoid narrow channels between islands and the
@@ -594,14 +594,14 @@ def contours_preprocess(
     # Use a disk of size 8 to identify any rivers/streams smaller than
     # approximately 240 m (e.g. 8 * 30 m = 240 m).
     island_size = int(5000000 / (30 * 30))  # 5 km^2
-    sieved = xr.apply_ufunc(sieve, all_time_50.astype(np.int16), island_size)
-    rivers = xr.apply_ufunc(black_tophat, (sieved & all_time_50), disk(8)) == 1
+    sieved = xr.apply_ufunc(sieve, all_time_80.astype(np.int16), island_size)
+    rivers = xr.apply_ufunc(black_tophat, (sieved & all_time_80), disk(8)) == 1
 
     # Create a river mask by eroding the all time layer to clip out river
     # mouths, then expanding river features to include stream banks and
     # account for migrating rivers
     river_mouth_mask = xr.apply_ufunc(
-        binary_erosion, all_time_50.where(~rivers, True), disk(12)
+        binary_erosion, all_time_80.where(~rivers, True), disk(12)
     )
     rivers = rivers.where(river_mouth_mask, False)
     river_mask = ~xr.apply_ufunc(binary_dilation, rivers, disk(4))
@@ -624,7 +624,7 @@ def contours_preprocess(
     # "ocean", values of 1 representing "coastal", and values of 2
     # representing non-coastal "inland" pixels.
     coastal_mask = coastal_masking(
-        ds=all_time_15.where(~rivers, True), ocean_da=ocean_da, buffer=buffer_pixels
+        ds=all_time_20.where(~rivers, True), ocean_da=ocean_da, buffer=buffer_pixels
     )
 
     # Add rivers as "inland" pixels in the coastal mask
@@ -684,8 +684,8 @@ def contours_preprocess(
         return (
             masked_ds,
             certainty_masks,
-            all_time_15,
-            all_time_50,
+            all_time_20,
+            all_time_80,
             river_mask,
             ocean_da,
             thresholded_ds,
diff --git a/data/raw/coastlines_development.geojson b/data/raw/coastlines_development.geojson
index 461c745..4077c27 100644
--- a/data/raw/coastlines_development.geojson
+++ b/data/raw/coastlines_development.geojson
@@ -3,8 +3,13 @@
 "name": "coastlines_development",
 "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
 "features": [
-{ "type": "Feature", "properties": { "id": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 132.611126280669453, -11.61081146005043 ], [ 132.610520468361358, -11.687203249183282 ], [ 132.710782405348084, -11.686906622697229 ], [ 132.710479499194008, -11.686016741333162 ], [ 132.710025139962994, -11.610218043672749 ], [ 132.611126280669453, -11.61081146005043 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 6 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 149.695938608467088, -37.492548051212026 ], [ 149.693144400896585, -37.574089636650719 ], [ 149.768588005300415, -37.577632900307499 ], [ 149.818185189676967, -37.562240624449011 ], [ 149.830759123744286, -37.553934118475283 ], [ 149.818464610434035, -37.48445550765247 ], [ 149.737991432403334, -37.484344644804459 ], [ 149.695938608467088, -37.492548051212026 ] ] ] } },
 { "type": "Feature", "properties": { "id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 129.808772974409294, -13.522771535514741 ], [ 129.871062780837775, -13.522362326041147 ], [ 129.873798481795802, -13.6248472833701 ], [ 129.774261054631353, -13.626483410274908 ], [ 129.808772974409294, -13.522771535514741 ] ] ] } },
-{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 122.249386759416581, -17.214313335776648 ], [ 122.186886514452851, -17.185566411563052 ], [ 122.148586701040756, -17.249085110350848 ], [ 122.208772122116898, -17.283046587937186 ], [ 122.249386759416581, -17.214313335776648 ] ] ] } }
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 122.249386759416581, -17.214313335776648 ], [ 122.186886514452851, -17.185566411563052 ], [ 122.148586701040756, -17.249085110350848 ], [ 122.208772122116898, -17.283046587937186 ], [ 122.249386759416581, -17.214313335776648 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 132.611126280669453, -11.61081146005043 ], [ 132.610520468361358, -11.687203249183282 ], [ 132.710782405348084, -11.686906622697229 ], [ 132.710479499194008, -11.686016741333162 ], [ 132.710025139962994, -11.610218043672749 ], [ 132.611126280669453, -11.61081146005043 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 4 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 152.012966156172297, -24.416619325370696 ], [ 151.986652156714598, -24.416246604760904 ], [ 152.002674458606606, -24.449201499566822 ], [ 152.008112685161223, -24.458197381090113 ], [ 152.0276435203142, -24.447923925849551 ], [ 152.012966156172297, -24.416619325370696 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 5 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 141.607135554879449, -12.428496162747486 ], [ 141.610209183207019, -12.39820553756258 ], [ 141.79378862058951, -12.400661665467194 ], [ 141.780655845008113, -12.598712687091243 ], [ 141.632004002256963, -12.594622262913241 ], [ 141.555163294067995, -12.579350769650771 ], [ 141.600988298224365, -12.449233655194789 ], [ 141.607135554879449, -12.428496162747486 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 7 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 147.753134816589125, -37.942890125061354 ], [ 147.821662757255893, -37.943330839584526 ], [ 147.815305935032995, -37.975055338548088 ], [ 147.750620029775661, -37.976982600810167 ], [ 147.753134816589125, -37.942890125061354 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 8 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 145.185450161059464, -38.363443382983867 ], [ 145.186288423330637, -38.382556540880202 ], [ 145.214020933467964, -38.388360683795526 ], [ 145.219329927851931, -38.382775573597662 ], [ 145.216326154713641, -38.375437616515619 ], [ 145.20228526167179, -38.364538829257768 ], [ 145.185450161059464, -38.363443382983867 ] ] ] } }
 ]
 }
diff --git a/notebooks/DEACoastlines_generation_raster.ipynb b/notebooks/DEACoastlines_generation_raster.ipynb
index 0bb076a..ace4f0a 100644
--- a/notebooks/DEACoastlines_generation_raster.ipynb
+++ b/notebooks/DEACoastlines_generation_raster.ipynb
@@ -85,7 +85,7 @@
        "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-828d4d11-801f-11ed-80de-4616e67bc4b6</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-24b8234f-92de-11ed-8bb6-7e1ae5576782</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "\n",
        "        <tr>\n",
@@ -114,7 +114,7 @@
        "    </div>\n",
        "    <div style=\"margin-left: 48px;\">\n",
        "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">fb34dbe6</p>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">b4fe92ad</p>\n",
        "        <table style=\"width: 100%; text-align: left;\">\n",
        "            <tr>\n",
        "                <td style=\"text-align: left;\">\n",
@@ -151,11 +151,11 @@
        "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
        "        <div style=\"margin-left: 48px;\">\n",
        "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-2c4e2bd5-1d2d-45a2-97d5-68c3bbb87446</p>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-0bc0dbc0-2854-4706-8208-cc85376de529</p>\n",
        "            <table style=\"width: 100%; text-align: left;\">\n",
        "                <tr>\n",
        "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:36367\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:41271\n",
        "                    </td>\n",
        "                    <td style=\"text-align: left;\">\n",
        "                        <strong>Workers:</strong> 1\n",
@@ -197,7 +197,7 @@
        "                <table style=\"width: 100%; text-align: left;\">\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:39723\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45749\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Total threads: </strong> 15\n",
@@ -205,7 +205,7 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"/user/robbi.bishoptaylor@ga.gov.au/proxy/39059/status\" target=\"_blank\">/user/robbi.bishoptaylor@ga.gov.au/proxy/39059/status</a>\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/robbi.bishoptaylor@ga.gov.au/proxy/40977/status\" target=\"_blank\">/user/robbi.bishoptaylor@ga.gov.au/proxy/40977/status</a>\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\">\n",
        "                            <strong>Memory: </strong> 117.21 GiB\n",
@@ -213,13 +213,13 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43515\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38899\n",
        "                        </td>\n",
        "                        <td style=\"text-align: left;\"></td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /home/jovyan/Robbi/dea-coastlines/dask-worker-space/worker-1f2h4xr3\n",
+       "                            <strong>Local directory: </strong> /home/jovyan/Robbi/dea-coastlines/dask-worker-space/worker-s_msvmdw\n",
        "                        </td>\n",
        "                    </tr>\n",
        "\n",
@@ -246,7 +246,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:36367' processes=1 threads=15, memory=117.21 GiB>"
+       "<Client: 'tcp://127.0.0.1:41271' processes=1 threads=15, memory=117.21 GiB>"
       ]
      },
      "metadata": {},
@@ -307,7 +307,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "study_area = 3\n",
+    "study_area = 8\n",
     "raster_version = 'development'\n",
     "start_year = 1988\n",
     "end_year = 2021\n",
@@ -343,7 +343,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -383,7 +383,7 @@
     "geopoly = Geometry(gridcell_gdf.iloc[0].geometry, crs=gridcell_gdf.crs)\n",
     "query = {\n",
     "    'geopolygon': geopoly.buffer(0.05),\n",
-    "    'time': (str(start_year - 1), str(end_year + 1)),  # 1999, 2021\n",
+    "    'time': (str(start_year - 1), str(end_year + 1)),\n",
     "    'dask_chunks': {\n",
     "        'time': 1,\n",
     "        'x': 2048,\n",
@@ -761,20 +761,20 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 1110, y: 657, x: 729)\n",
+       "Dimensions:      (time: 1764, y: 461, x: 392)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1987-09-12T00:43:23.359716 ... 2022-12...\n",
-       "  * y            (y) float64 -1.279e+06 -1.279e+06 ... -1.299e+06 -1.299e+06\n",
-       "  * x            (x) float64 2.341e+05 2.341e+05 ... 2.559e+05 2.559e+05\n",
-       "    spatial_ref  int32 32653\n",
+       "  * time         (time) datetime64[ns] 1987-05-27T23:27:59.980430 ... 2022-12...\n",
+       "  * y            (y) float64 -4.242e+06 -4.242e+06 ... -4.256e+06 -4.256e+06\n",
+       "  * x            (x) float64 3.371e+05 3.371e+05 ... 3.488e+05 3.488e+05\n",
+       "    spatial_ref  int32 32655\n",
        "Data variables:\n",
-       "    mndwi        (time, y, x) float32 dask.array&lt;chunksize=(1, 657, 729), meta=np.ndarray&gt;\n",
+       "    mndwi        (time, y, x) float32 dask.array&lt;chunksize=(1, 461, 392), meta=np.ndarray&gt;\n",
        "Attributes:\n",
-       "    crs:           epsg:32653\n",
-       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fc55f648-7dd8-4fa1-be56-19e193d9bb9d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc55f648-7dd8-4fa1-be56-19e193d9bb9d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1110</li><li><span class='xr-has-index'>y</span>: 657</li><li><span class='xr-has-index'>x</span>: 729</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b970f902-9c35-4a85-a1f0-1c2759c4638c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b970f902-9c35-4a85-a1f0-1c2759c4638c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1987-09-12T00:43:23.359716 ... 2...</div><input id='attrs-7662a155-f876-436d-b509-1dc7672a7149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7662a155-f876-436d-b509-1dc7672a7149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34128b00-c132-4d4e-9d43-35bd56ca7a46' class='xr-var-data-in' type='checkbox'><label for='data-34128b00-c132-4d4e-9d43-35bd56ca7a46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1987-09-12T00:43:23.359716000&#x27;, &#x27;1987-09-28T00:43:45.072361000&#x27;,\n",
-       "       &#x27;1987-10-14T00:44:03.606455000&#x27;, ..., &#x27;2022-10-30T01:17:15.544009000&#x27;,\n",
-       "       &#x27;2022-11-15T01:17:10.045673000&#x27;, &#x27;2022-12-17T01:17:04.735970000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.279e+06 ... -1.299e+06</div><input id='attrs-4a52088b-61d1-4c6e-8722-b4f5e61609db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a52088b-61d1-4c6e-8722-b4f5e61609db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e427f7-0dec-46fe-b5eb-d2b8c2829827' class='xr-var-data-in' type='checkbox'><label for='data-27e427f7-0dec-46fe-b5eb-d2b8c2829827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-30.0</dd><dt><span>crs :</span></dt><dd>epsg:32653</dd></dl></div><div class='xr-var-data'><pre>array([-1278930., -1278960., -1278990., ..., -1298550., -1298580., -1298610.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.341e+05 2.341e+05 ... 2.559e+05</div><input id='attrs-21aca219-9f86-4a8e-af1f-a4b1dc7481bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21aca219-9f86-4a8e-af1f-a4b1dc7481bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71704fd5-c2b9-4adb-bfc1-dfe526a3f847' class='xr-var-data-in' type='checkbox'><label for='data-71704fd5-c2b9-4adb-bfc1-dfe526a3f847' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>30.0</dd><dt><span>crs :</span></dt><dd>epsg:32653</dd></dl></div><div class='xr-var-data'><pre>array([234060., 234090., 234120., ..., 255840., 255870., 255900.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32653</div><input id='attrs-5f84910e-346f-4a3d-a8c5-00108f49e6c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f84910e-346f-4a3d-a8c5-00108f49e6c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-847c39a4-07d5-450f-9128-53a102b9399f' class='xr-var-data-in' type='checkbox'><label for='data-847c39a4-07d5-450f-9128-53a102b9399f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 53N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,135],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32653&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32653, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06c035d8-7114-43ef-a0d0-d73da871ea3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06c035d8-7114-43ef-a0d0-d73da871ea3a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mndwi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 657, 729), meta=np.ndarray&gt;</div><input id='attrs-46756f34-bd0e-4383-b956-a3e67238777a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46756f34-bd0e-4383-b956-a3e67238777a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8f3c37e-7a52-4131-bcb6-7496badfa89b' class='xr-var-data-in' type='checkbox'><label for='data-a8f3c37e-7a52-4131-bcb6-7496badfa89b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:32653</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    crs:           epsg:32655\n",
+       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5e95b7e-8572-4ce0-9dae-4da695832021' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5e95b7e-8572-4ce0-9dae-4da695832021' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1764</li><li><span class='xr-has-index'>y</span>: 461</li><li><span class='xr-has-index'>x</span>: 392</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a5933ca2-6356-4068-bc22-683b8b8df21f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5933ca2-6356-4068-bc22-683b8b8df21f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1987-05-27T23:27:59.980430 ... 2...</div><input id='attrs-71e8a14d-00d0-4e5d-958c-a3c84e3da054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71e8a14d-00d0-4e5d-958c-a3c84e3da054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3aec782-6dce-4072-95a3-1d0855cc0013' class='xr-var-data-in' type='checkbox'><label for='data-d3aec782-6dce-4072-95a3-1d0855cc0013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1987-05-27T23:27:59.980430000&#x27;, &#x27;1987-07-30T23:29:25.571665000&#x27;,\n",
+       "       &#x27;1987-08-22T23:35:53.388409000&#x27;, ..., &#x27;2022-12-21T00:10:03.092193000&#x27;,\n",
+       "       &#x27;2022-12-29T00:10:02.868450000&#x27;, &#x27;2022-12-30T00:04:17.864346000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.242e+06 ... -4.256e+06</div><input id='attrs-9a1b54a3-b9a2-4ee7-a0d3-6e8a11b7abb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a1b54a3-b9a2-4ee7-a0d3-6e8a11b7abb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27f4f71a-7bb5-4674-bf78-bbde258f8393' class='xr-var-data-in' type='checkbox'><label for='data-27f4f71a-7bb5-4674-bf78-bbde258f8393' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-30.0</dd><dt><span>crs :</span></dt><dd>epsg:32655</dd></dl></div><div class='xr-var-data'><pre>array([-4242150., -4242180., -4242210., ..., -4255890., -4255920., -4255950.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.371e+05 3.371e+05 ... 3.488e+05</div><input id='attrs-8bbad271-9442-4933-9f7c-fe445fac958b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bbad271-9442-4933-9f7c-fe445fac958b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdfa9502-e1e5-4392-b193-1a7c4a268603' class='xr-var-data-in' type='checkbox'><label for='data-fdfa9502-e1e5-4392-b193-1a7c4a268603' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>30.0</dd><dt><span>crs :</span></dt><dd>epsg:32655</dd></dl></div><div class='xr-var-data'><pre>array([337110., 337140., 337170., ..., 348780., 348810., 348840.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32655</div><input id='attrs-91cd0da3-ca08-4a89-b7f9-dabd79af9f4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91cd0da3-ca08-4a89-b7f9-dabd79af9f4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fef1ccfb-5b14-407a-873a-da1c731580c8' class='xr-var-data-in' type='checkbox'><label for='data-fef1ccfb-5b14-407a-873a-da1c731580c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 55N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,147],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32655&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32655, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcd3fd13-6cf8-49f7-81ed-9e0936957557' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcd3fd13-6cf8-49f7-81ed-9e0936957557' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mndwi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 461, 392), meta=np.ndarray&gt;</div><input id='attrs-4346c28f-017d-4a47-b7be-6d79496a7346' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4346c28f-017d-4a47-b7be-6d79496a7346' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc9598b-b38c-4830-8982-308165051ad1' class='xr-var-data-in' type='checkbox'><label for='data-acc9598b-b38c-4830-8982-308165051ad1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:32655</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table>\n",
@@ -789,19 +789,19 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.98 GiB </td>\n",
-       "                        <td> 1.83 MiB </td>\n",
+       "                        <td> 1.19 GiB </td>\n",
+       "                        <td> 705.91 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1110, 657, 729) </td>\n",
-       "                        <td> (1, 657, 729) </td>\n",
+       "                        <td> (1764, 461, 392) </td>\n",
+       "                        <td> (1, 461, 392) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Count </th>\n",
-       "                        <td> 71042 Tasks </td>\n",
-       "                        <td> 1110 Chunks </td>\n",
+       "                        <td> 115025 Tasks </td>\n",
+       "                        <td> 1764 Chunks </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                    <th> Type </th>\n",
@@ -812,98 +812,98 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"209\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"173\" height=\"163\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"71\" x2=\"80\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"43\" x2=\"80\" y2=\"113\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"71\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"74\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"78\" />\n",
-       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"82\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"85\" />\n",
-       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"89\" />\n",
-       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"93\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"96\" />\n",
-       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"100\" />\n",
-       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"104\" />\n",
-       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"108\" />\n",
-       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"111\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"115\" />\n",
-       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"119\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"122\" />\n",
-       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"126\" />\n",
-       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"130\" />\n",
-       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"134\" />\n",
-       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"137\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"43\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"46\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"50\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"54\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"57\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"61\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"65\" />\n",
+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"69\" />\n",
+       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"72\" />\n",
+       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"76\" />\n",
+       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"80\" />\n",
+       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"83\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"87\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"91\" />\n",
+       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"95\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"98\" />\n",
+       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"102\" />\n",
+       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"106\" />\n",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"109\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"113\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,141.61526232114468 10.0,71.02702702702703\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,113.65079420891769 10.0,43.062558914800036\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"88\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"13\" y1=\"3\" x2=\"92\" y2=\"3\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"96\" y2=\"7\" />\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa771a02-9673-4f34-b413-ac88fefeb105' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa771a02-9673-4f34-b413-ac88fefeb105' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:32653</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7898f849-ee91-4cc5-aed4-aaa40739ac85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7898f849-ee91-4cc5-aed4-aaa40739ac85' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:32655</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:      (time: 1110, y: 657, x: 729)\n",
+       "Dimensions:      (time: 1764, y: 461, x: 392)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1987-09-12T00:43:23.359716 ... 2022-12...\n",
-       "  * y            (y) float64 -1.279e+06 -1.279e+06 ... -1.299e+06 -1.299e+06\n",
-       "  * x            (x) float64 2.341e+05 2.341e+05 ... 2.559e+05 2.559e+05\n",
-       "    spatial_ref  int32 32653\n",
+       "  * time         (time) datetime64[ns] 1987-05-27T23:27:59.980430 ... 2022-12...\n",
+       "  * y            (y) float64 -4.242e+06 -4.242e+06 ... -4.256e+06 -4.256e+06\n",
+       "  * x            (x) float64 3.371e+05 3.371e+05 ... 3.488e+05 3.488e+05\n",
+       "    spatial_ref  int32 32655\n",
        "Data variables:\n",
-       "    mndwi        (time, y, x) float32 dask.array<chunksize=(1, 657, 729), meta=np.ndarray>\n",
+       "    mndwi        (time, y, x) float32 dask.array<chunksize=(1, 461, 392), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    crs:           epsg:32653\n",
+       "    crs:           epsg:32655\n",
        "    grid_mapping:  spatial_ref"
       ]
      },
diff --git a/notebooks/DEACoastlines_generation_vector.ipynb b/notebooks/DEACoastlines_generation_vector.ipynb
index 8148070..4492eeb 100644
--- a/notebooks/DEACoastlines_generation_vector.ipynb
+++ b/notebooks/DEACoastlines_generation_vector.ipynb
@@ -113,11 +113,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
-    "study_area = 2\n",
+    "study_area = 4\n",
     "raster_version = 'development'\n",
     "vector_version = 'development'\n",
     "water_index = \"mndwi\"\n",
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -151,18 +151,18 @@
      "output_type": "stream",
      "text": [
       "<xarray.Dataset>\n",
-      "Dimensions:  (year: 34, y: 729, x: 712)\n",
+      "Dimensions:  (year: 34, y: 525, x: 478)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1988 1989 1990 1991 1992 ... 2017 2018 2019 2020 2021\n",
-      "  * y        (y) float64 -1.895e+06 -1.895e+06 ... -1.917e+06 -1.917e+06\n",
-      "  * x        (x) float64 4.042e+05 4.042e+05 4.042e+05 ... 4.255e+05 4.255e+05\n",
+      "  * y        (y) float64 -2.695e+06 -2.695e+06 ... -2.711e+06 -2.711e+06\n",
+      "  * x        (x) float64 3.922e+05 3.922e+05 3.922e+05 ... 4.065e+05 4.065e+05\n",
       "Data variables:\n",
-      "    mndwi    (year, y, x) float32 0.2759 0.2759 0.2759 ... -0.5615 -0.5664\n",
-      "    count    (year, y, x) int16 7 7 7 7 7 7 7 7 7 ... 15 15 15 15 15 15 15 16 16\n",
-      "    stdev    (year, y, x) float32 0.1667 0.196 0.1673 ... 0.02344 0.02143\n",
+      "    mndwi    (year, y, x) float32 -0.4675 -0.4443 -0.4606 ... 0.819 0.698 0.6623\n",
+      "    count    (year, y, x) int16 10 10 10 10 10 10 10 10 ... 17 17 17 17 17 17 17\n",
+      "    stdev    (year, y, x) float32 0.1232 0.1159 0.1255 ... 0.2513 0.2309 0.2656\n",
       "Attributes:\n",
-      "    transform:      (30.0, 0.0, 404175.0, 0.0, -30.0, -1894725.0)\n",
-      "    crs:            +init=epsg:32651\n",
+      "    transform:      (30.0, 0.0, 392175.0, 0.0, -30.0, -2695125.0)\n",
+      "    crs:            +init=epsg:32656\n",
       "    res:            (30.0, 30.0)\n",
       "    is_tiled:       1\n",
       "    nodatavals:     (nan,)\n",
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -327,13 +327,13 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -350,31 +350,191 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# # DEBUG\n",
-    "# (\n",
-    "#     masked_ds,\n",
-    "#     certainty_masks,\n",
-    "#     all_time_15,\n",
-    "#     all_time_50,\n",
-    "#     river_mask,\n",
-    "#     ocean_da,\n",
-    "#     thresholded_ds,\n",
-    "#     temporal_mask,\n",
-    "#     annual_mask,\n",
-    "#     coastal_mask,\n",
-    "# ) = coastlines.vector.contours_preprocess(\n",
-    "#     yearly_ds,\n",
-    "#     gapfill_ds,\n",
-    "#     water_index,\n",
-    "#     index_threshold,\n",
-    "#     buffer_pixels=33,\n",
-    "#     mask_modifications=modifications_gdf,\n",
-    "#     debug=True,\n",
-    "# )"
+    "# DEBUG\n",
+    "(\n",
+    "    masked_ds,\n",
+    "    certainty_masks,\n",
+    "    all_time_15,\n",
+    "    all_time_50,\n",
+    "    river_mask,\n",
+    "    ocean_da,\n",
+    "    thresholded_ds,\n",
+    "    temporal_mask,\n",
+    "    annual_mask,\n",
+    "    coastal_mask,\n",
+    ") = coastlines.vector.contours_preprocess(\n",
+    "    yearly_ds,\n",
+    "    gapfill_ds,\n",
+    "    water_index,\n",
+    "    index_threshold,\n",
+    "    buffer_pixels=33,\n",
+    "    mask_modifications=modifications_gdf,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f0927b78df0>"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2160x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_time_15.plot(size=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f08b44057f0>"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "river_mask.plot(size=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f08b43faa60>"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_time_15.where(river_mask, 1).plot(size=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f08b43f4070>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ocean_da.plot(size=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f08b4104eb0>"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "annual_mask.isel(year=3).plot(size=20)"
    ]
   },
   {