diff --git a/.github/PULL_REQUEST_TEMPLATE/content.md b/.github/PULL_REQUEST_TEMPLATE/content.md
index 439ab2763..490b6cb98 100644
--- a/.github/PULL_REQUEST_TEMPLATE/content.md
+++ b/.github/PULL_REQUEST_TEMPLATE/content.md
@@ -6,13 +6,13 @@ _Replace with brief description/list of the content being added_
## Content checklist
-Check the [Content Documentation](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/CONTENT.md) for more details.
+Check the [Content Documentation](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/content/CONTENT.md) for more details.
### Any content
- [ ] Cover image with proper attribution (when relevant) is present.
- [ ] Cover images do not contain any text (they're used as a background and text may not be readable).
- [ ] All images are not too big/heavy. As a general rule of thumb, they should not weigh more than 500KB, or be larger than 2000px. See the [media guide](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/frontmatter/media.md#media) for more info.
-- [ ] Content was added using the available [MDX Blocks](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/MDX_BLOCKS.md).
+- [ ] Content was added using the available [MDX Blocks](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/content/MDX_BLOCKS.md).
### Datasets
- [ ] [Layer description](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/frontmatter/layer.md#properties) is defined.
@@ -20,4 +20,4 @@ Check the [Content Documentation](https://github.com/NASA-IMPACT/veda-ui/blob/ma
- [ ] The Dataset is showing up on the map correctly
### Stories
-- [ ] [Publication date](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/CONTENT.md#stories) value is set
+- [ ] [Publication date](https://github.com/NASA-IMPACT/veda-ui/blob/main/docs/content/CONTENT.md#stories) value is set
diff --git a/datasets/camp-fire-albedo-wsa-diff.data.mdx b/datasets/camp-fire-albedo-wsa-diff.data.mdx
new file mode 100644
index 000000000..121ef80c4
--- /dev/null
+++ b/datasets/camp-fire-albedo-wsa-diff.data.mdx
@@ -0,0 +1,57 @@
+---
+id: campfire_albedo_wsa_difference_2015_2022
+name: "Camp Fire burn scar Albedo WSA difference"
+description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived Albedo WSA over the 2018 Camp Fire burn scar domain."
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+
+
+layers:
+ - id: modis-albedo-wsa-diff-2015-2022
+ stacCol: campfire-albedo-wsa-diff
+ name: MODIS Albedo WSA Difference
+ type: raster
+ description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived Albedo WSA over the 2018 Camp Fire burn scar domain."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap_name: bwr
+ rescale:
+ - -0.1
+ - 0.1
+ nodata: -9999
+ legend:
+ type: gradient
+ min: "-0.1"
+ max: "0.1"
+ stops:
+ - "#4575b4"
+ - "#91bfdb"
+ - "#e0f3f8"
+ - "#ffffff"
+ - "#fee090"
+ - "#fc8d59"
+ - "#d73027"
+
+
+
+
+---
+
+
+
+ In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis.
+
+
+
+
diff --git a/datasets/camp-fire-background.jpg b/datasets/camp-fire-background.jpg
new file mode 100644
index 000000000..a7adb01c7
Binary files /dev/null and b/datasets/camp-fire-background.jpg differ
diff --git a/datasets/camp-fire-lst-day-diff.data.mdx b/datasets/camp-fire-lst-day-diff.data.mdx
new file mode 100644
index 000000000..bae42dc19
--- /dev/null
+++ b/datasets/camp-fire-lst-day-diff.data.mdx
@@ -0,0 +1,59 @@
+---
+id: campfire_lst_day_difference_2015_2022
+name: "Camp Fire burn scar LST Day difference"
+description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived LST Day over the 2018 Camp Fire burn scar domain."
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+
+
+layers:
+ - id: modis-lst-day-diff-2015-2022
+ stacCol: campfire-lst-day-diff
+ name: MODIS LST Day Difference
+ type: raster
+ description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived LST Day over the 2018 Camp Fire burn scar domain."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap_name: bwr
+ rescale:
+ - -7.5
+ - 7.5
+ nodata: -9999
+ legend:
+ unit:
+ label: °C
+ type: gradient
+ min: "-7.5"
+ max: "7.5"
+ stops:
+ - "#4575b4"
+ - "#91bfdb"
+ - "#e0f3f8"
+ - "#ffffff"
+ - "#fee090"
+ - "#fc8d59"
+ - "#d73027"
+
+
+
+
+---
+
+
+
+ In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Day difference portion of that analysis.
+
+
+
+
diff --git a/datasets/camp-fire-lst-night-diff.data.mdx b/datasets/camp-fire-lst-night-diff.data.mdx
new file mode 100644
index 000000000..4ead02a1b
--- /dev/null
+++ b/datasets/camp-fire-lst-night-diff.data.mdx
@@ -0,0 +1,59 @@
+---
+id: campfire_lst_night_difference_2015_2022
+name: "Camp Fire burn scar LST Night difference"
+description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived LST Night over the 2018 Camp Fire burn scar domain."
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+
+
+layers:
+ - id: modis-lst-night-diff-2015-2022
+ stacCol: campfire-lst-night-diff
+ name: MODIS LST Night Difference
+ type: raster
+ description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived LST Night over the 2018 Camp Fire burn scar domain."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap_name: bwr
+ rescale:
+ - -1.75
+ - 1.75
+ nodata: -9999
+ legend:
+ unit:
+ label: °C
+ type: gradient
+ min: "-1.75"
+ max: "1.75"
+ stops:
+ - "#4575b4"
+ - "#91bfdb"
+ - "#e0f3f8"
+ - "#ffffff"
+ - "#fee090"
+ - "#fc8d59"
+ - "#d73027"
+
+
+
+
+---
+
+
+
+ In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Night difference portion of that analysis.
+
+
+
+
diff --git a/datasets/camp-fire-ndvi-diff.data.mdx b/datasets/camp-fire-ndvi-diff.data.mdx
new file mode 100644
index 000000000..97a6c69b5
--- /dev/null
+++ b/datasets/camp-fire-ndvi-diff.data.mdx
@@ -0,0 +1,57 @@
+---
+id: campfire_ndvi_difference_2015_2022
+name: "Camp Fire burn scar NDVI difference"
+description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived NDVI over the 2018 Camp Fire burn scar domain."
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+
+
+layers:
+ - id: modis-ndvi-diff-2015-2022
+ stacCol: campfire-ndvi-diff
+ name: MODIS NDVI Difference
+ type: raster
+ description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived NDVI over the 2018 Camp Fire burn scar domain."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap_name: bwr
+ rescale:
+ - -0.5
+ - 0.5
+ nodata: -9999
+ legend:
+ type: gradient
+ min: "-0.5"
+ max: "0.5"
+ stops:
+ - "#4575b4"
+ - "#91bfdb"
+ - "#e0f3f8"
+ - "#ffffff"
+ - "#fee090"
+ - "#fc8d59"
+ - "#d73027"
+
+
+
+
+---
+
+
+
+ In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the NDVI difference portion of that analysis.
+
+
+
+
diff --git a/datasets/camp-fire-nlcd.data.mdx b/datasets/camp-fire-nlcd.data.mdx
new file mode 100644
index 000000000..76923c23f
--- /dev/null
+++ b/datasets/camp-fire-nlcd.data.mdx
@@ -0,0 +1,97 @@
+---
+id: campfire-nlcd
+name: "NLCD Land Cover identification 2016 vs 2019"
+description: "Land Use-Land Cover (LULC) is a product derived from Landsat NLCD (national Land Cover Database) data, who’s most recent iteration is from 2019 satellite imagery."
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+
+
+layers:
+ - id: campfire-nlcd
+ stacCol: campfire-nlcd
+ name: NLCD Land Use - Land Cover Classification
+ type: raster
+ description: "30 meter LULC classification provided by the NLCD."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap: '{"11":"#486DA2", "12":"#E7EFFC", "21": "#E1CDCE", "22": "#DC9881", "23": "#F10100", "24": "#AB0101",
+ "31":"#B3AFA4", "41":"#6BA966", "42": "#1D6533", "43": "#BDCC93", "51": "#B29C46", "52": "#D1BB82",
+ "71":"#EDECCD", "72":"#D0D181", "73": "#A4CC51", "74": "#82BA9D", "81": "#DDD83E", "82": "#AE7229",
+ "90":"#BBD7ED", "95":"#71A4C1","0":"#00BFFF"}'
+ nodata: 0
+ rescale:
+ - 0
+ - 255
+ legend:
+ type: categorical
+ min: "0"
+ max: "255"
+ stops:
+ # - color: "#486DA2"
+ # label: "Open Water"
+ # - color: "#E7EFFC"
+ # label: "Perennial Ice/Snow"
+ - color: "#E1CDCE"
+ label: "Urban"
+ - color: "#DC9881"
+ # label: "Developed, Low Intensity"
+ - color: "#F10100"
+ # label: "Developed, Medium Intensity"
+ - color: "#AB0101"
+ # label: "Developed High Intensity"
+ # - color: "#B3AFA4"
+ # label: "Barren Land (Rock/Sand/Clay)"
+ - color: "#6BA966"
+ label: "Vegetation"
+ # label: "Deciduous Forest"
+ - color: "#1D6533"
+ # label: "Evergreen Forest"
+ - color: "#BDCC93"
+ # label: "Mixed Forest"
+ # - color: "#B29C46"
+ # label: "Dwarf Scrub"
+ - color: "#D1BB82"
+ # label: "Shrub/Scrub"
+ - color: "#EDECCD"
+ # label : "Grassland/Herbaceous"
+ - color: "#D0D181"
+ # label: "Sedge/Herbaceous"
+ - color: "#A4CC51"
+ # label: "Lichens"
+ # - color: "#82BA9D"
+ # label: "Moss"
+ # - color: "#DDD83E"
+ # label: "Pasture/Hay"
+ - color: "#AE7229"
+ # label: "Cultivated Crops"
+ label: "Agriculture"
+ # - color: "#BBD7ED"
+ # label: "Woody Wetlands"
+ # - color: "#71A4C1"
+ # label: "Emergent Herbaceous Wetlands"
+ compare:
+ datasetId: campfire-nlcd
+ layerId: campfire-nlcd
+
+
+
+---
+
+
+
+ We utilized the National Land Cover Database (NLCD), which provides a classification of land cover categories at 30m spatial resolution over geographical locations within the Continental United States (CONUS). The NLCD is derived from Landsat satellite sensors data and is available at approximately three-year time intervals. We used the NLCD maps for the years 2016 and 2019 to examine changes in land cover type resulting from the Camp Fire event, to examine LULC before and after the Camp Fire. This analysis shows that the dominant vegetation cover type that was present within the region per-wildfire are evergreen forest and shrub/scrub cover, while post-wildfire are grasslands and herbaceous vegetation.
+
+
+
+
diff --git a/datasets/lahaina-fire-background.jpg b/datasets/lahaina-fire-background.jpg
new file mode 100644
index 000000000..b91c94e44
Binary files /dev/null and b/datasets/lahaina-fire-background.jpg differ
diff --git a/datasets/lahaina-fire.data.mdx b/datasets/lahaina-fire.data.mdx
new file mode 100644
index 000000000..3bcf2f45d
--- /dev/null
+++ b/datasets/lahaina-fire.data.mdx
@@ -0,0 +1,207 @@
+---
+id: lahaina-fire
+name: 'Lahaina Fire'
+description: "HLS (BAIS2 and SWIR FalseColor composites) and Landsat-8 thermal imagery supporting the Lahaina, HI Wildfire Story"
+media:
+ src: ::file ./lahaina-fire-background.jpg
+ alt: Wildfire erupting over Lahaina, HI, August 8, 2023
+ author:
+ name: Matthew Thayer/AP
+ url: https://www.sfchronicle.com/travel/article/hawaii-fire-maui-lahaina-18289213.php
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+ - name: Source
+ values:
+ - UAH
+layers:
+ - id: hls-bais2-v2
+ stacCol: hls-bais2-v2
+ name: BAIS-2 Burned Area
+ type: raster
+ description: 'Experimental burned-area calculation from the HLS scene taken on August 13,2023 over Lahaina, HI'
+ zoomExtent:
+ - 4
+ - 20
+ sourceParams:
+ colormap_name: rdylbu_r
+ rescale: 0.5,1
+ resampling: bilinear
+ bidx: 1
+ nodata: -9999
+ compare:
+ datasetId: lahaina-fire
+ layerId: landsat-nighttime-thermal
+ mapLabel: |
+ ::js ({ dateFns, datetime, compareDatetime }) => {
+ return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
+ }
+ legend:
+ type: gradient
+ min: "Low Burn Confidence"
+ max: "High Burn Confidence"
+ stops:
+ - "#313695"
+ - "#74add1"
+ - "#e0f3f8"
+ - "#fee090"
+ - "#f46d43"
+ - "#a50026"
+
+ - id: hls-swir-falsecolor-composite
+ stacCol: hls-swir-falsecolor-composite
+ name: HLS SWIR FalseColor Composite
+ type: raster
+ description: 'HLS falsecolor composite imagery using S30 Bands 12, 8A, and 4, over Lahaina, HI.'
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ rescale:
+ - 0,5000
+ resampling: bilinear
+ bidx: [1,2,3]
+ compare:
+ datasetId: lahaina-fire
+ layerId: hls-swir-falsecolor-composite
+ mapLabel: |
+ ::js ({ dateFns, datetime, compareDatetime }) => {
+ return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
+ }
+
+ - id: landsat-nighttime-thermal
+ stacCol: landsat-nighttime-thermal
+ name: Landsat-8 Thermal Band
+ type: raster
+ description: 'Nighttime Thermal band from Landsat-8 on August 8, 2023 shows the extent of the ongoing Lahaina Fire.'
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+
+ sourceParams:
+ colormap_name: inferno
+ nodata: 0
+ resampling: bilinear
+ bidx: 1
+ rescale:
+ - 180
+ - 255
+ compare:
+ datasetId: lahaina-fire
+ layerId: hls-bais2-v2
+ mapLabel: |
+ ::js ({ dateFns, datetime, compareDatetime }) => {
+ return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
+ }
+
+ legend:
+ type: gradient
+ min: "No Active Fire"
+ max: "Active Fire"
+ stops:
+ - '#08041d'
+ - '#1f0a46'
+ - '#52076c'
+ - '#f57c16'
+ - '#f7cf39'
+
+---
+
+
+
+## Overview
+
+On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th.
+
+
+
+
+
+
+ SWIR False color HLS imagery showing the burned area along Front Street in Lahaina, HI after the wildfire (2023 August 13).
+
+
+
+
+
+
+
+## Scientific Research
+
+Three datasets were utilized in the analysis of the Lahaina Fire. The first dataset is an experimental index was calculated using HLS imagery captured after the fire on August 13th, 2023. This innovative Burned Area Index (BAIS2) offers superior accuracy in delineating the burned areas compared to traditional Normalized Burn Ratio Indices 1 and 2. It particularly excels in identifying severely scorched fields situated just uphill from the city.
+
+The second dataset utilized is a three-band HLS composite image generated from the shortwave infrared, narrow near-infrared, and red bands of pre and post-fire HLS data. These scenes were acquired on August 8 and 13, 2023. The SWIR false color composite provides a visually compelling representation of the extent of the burned area resulting from the Lahaina Fire.
+
+The third dataset utilized is thermal infrared imagery collected from Landsat-8 on the night of August 8, 2023, offers essential insights into the intensity of the fire and the identification of hotspots within the affected area.
+
+These datasets support ongoing scientific research and analysis of the Lahaina Fire and its aftermath, including assessing the fire's impact on the local vegetation cover, the monitoring of long-term recovery of the local environment and ecosystem, and even evaluation of the effectiveness of fire mitigation and suppression efforts.
+
+
+
+
+
+
+
+ BAIS2 burned area calculations show the most highly impacted and highest probability of scorched areas along Front Street in Lahaina, HI after the wildfire (2023 August 13).
+
+
+
+
+## Interpreting the Data
+
+These three datasets concerning the Lahaina Fire should be interpreted with a special consideration of the temporal, spatial, and environmental aspects.
+
+Temporal Aspects: The HLS SWIR FalseColor composite images were taken at 10:30 AM LST on August 8 and 13, 2023, which correspond to pre and post-fire times. The Landsat8 Thermal imagery was taken at 10:30 PM LST on August 8, 2023 as the wildfire was ongoing in the city of Lahaiana. The BAIS2 calculation was taken from the red band of the August 13, 2023 HLS imagery.
+
+Spatial Aspects: All three datasets are at 30 meter resolution, so as to ensure ease of comparison between the datasets.
+
+Environmental Aspects: When interpreting the data, it is essential to consider the local topography and land cover. Lahaina lies along the west coast of Maui, with rapidly rising terrain just to its east.
+
+
+
+
+
+
+
+## Additional Resources
+
+* [Harmonized Landsat-Sentinel](https://hls.gsfc.nasa.gov/)
+
+* [Landsat8](https://landsat.gsfc.nasa.gov/satellites/landsat-8/)
+
+* [Landsat8 Nighttime Data Acquisition](https://www.usgs.gov/faqs/how-do-i-search-and-download-ascending-nighttime-landsat-scenes)
+
+* [PDC/FEMA Report on the Lahaina Fire](https://www.mauicounty.gov/CivicAlerts.aspx?AID=12683)
+
+
+
+
+
+
+
+## Data Story
+
+* [The Devastating August 8th, 2023 Lahaina, Hawai'i Wildfire](https://www.earthdata.nasa.gov/dashboard/stories/lahaina-fire)
+
+
+
\ No newline at end of file
diff --git a/datasets/urban-heating.data.mdx b/datasets/urban-heating.data.mdx
index 1c390f125..ad0f99b21 100644
--- a/datasets/urban-heating.data.mdx
+++ b/datasets/urban-heating.data.mdx
@@ -229,3 +229,11 @@ layers:
Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges.
+
+
+
+## Conclusion
+
+Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges.
+
+
\ No newline at end of file
diff --git a/package.json b/package.json
index 1e3e3a8cd..891805b15 100644
--- a/package.json
+++ b/package.json
@@ -1,7 +1,7 @@
{
"name": "veda-config",
"description": "Configuration for Veda",
- "version": "0.13.1",
+ "version": "0.14.0",
"source": "./.veda/ui/app/index.html",
"license": "Apache-2.0",
"scripts": {
diff --git a/stories/camp-fire-albedo.csv b/stories/camp-fire-albedo.csv
new file mode 100644
index 000000000..10f1259aa
--- /dev/null
+++ b/stories/camp-fire-albedo.csv
@@ -0,0 +1,252 @@
+Date,Albedo,Sources
+1/31/2002,0.09932,Albedo
+2/28/2002,0.10492,Albedo
+3/31/2002,0.10808,Albedo
+4/30/2002,0.11575,Albedo
+5/31/2002,0.11971,Albedo
+6/30/2002,0.12359,Albedo
+7/31/2002,0.12573,Albedo
+8/31/2002,0.12773,Albedo
+9/30/2002,0.1285,Albedo
+10/31/2002,0.12956,Albedo
+11/30/2002,0.12325,Albedo
+12/31/2002,0.11515,Albedo
+1/31/2003,0.10628,Albedo
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diff --git a/stories/camp-fire-background.jpg b/stories/camp-fire-background.jpg
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+++ b/stories/camp-fire-burn-scar.stories.mdx
@@ -0,0 +1,367 @@
+---
+id: burn-scar
+name: Wildfires Affect Local Weather, Climate, and Hydrology
+description: "A study on the lasting impacts of the November 2018 Camp Fire in Northern California"
+media:
+ src: ::file ./camp-fire-background.jpg
+ alt: Engulfed hillside in California, 2021.
+ author:
+ name: Mike Newbry
+ url: https://unsplash.com/photos/DwtX9mMHBJ0
+pubDate: 2023-08-14
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+---
+
+
+
+
+ Authors: Andrew Blackford1, Trent Cowan1, Udaysankar Nair1\
+ 1 The University of Alabama in Huntsville
+
+
+
+
+
+
+
+ ## Introduction: The 2018 Camp Fire
+ 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧
+ Wildfires burn thousands of acres of land every year, resulting in drastic changes in land use and land cover (LULC). The burn scars left behind by these wildfires have the potential to alter local weather, climate, and hydrology. A typical example of the drastic change in LULC is the burn scar that resulted from the November 2018 Camp Fire that devastated Paradise, California.
+
+ The Camp Fire occurred from November 8 to 25, 2018, burning over 153,000 acres and causing $16.65 billion (2018 USD) in damages. The fire was initiated by a faulty transmission line maintained by Pacific Gas and Electric (PG&E), and resulted in 85 fatalities and 17 injuries. The Camp Fire was the most expensive natural disaster in the world in 2018 and remains the seventh deadliest wildfire in U.S. history as of October 2023. Among several communities impacted by the fire, the city of Paradise was the most severely impacted, with 95% of the city burned and 18,804 of the city’s buildings destroyed.
+
+
+
+
+
+ Harmonized Landsat and Sentinel-2 (HLS) true-color imagery from November 11, 2018, of the Camp Fire impacting the Paradise, CA, area. The thick black outline is the extent of the burn scar on November 25, 2018, once the fire was 100% contained.
+
+
+
+
+
+
+ A dominant pathway through which wildfires affect local weather, climate, and hydrology is via alteration of land-atmosphere interactions. Removal of vegetation by wildfires causes surface albedo (proportion of sunlight reaching the surface to that which is reflected) to increase, which reduces the amount of energy deposited by sunlight at the surface. The emissivity of the surface (efficiency for emitting/absorbing infrared radiation) can decrease following a wildfire, leading to a reduction in loss of energy from the surface in the form of infrared radiation. The net radiative energy deposited at the surface is transported as heat and moisture (through evaporation and transpiration) into the atmosphere and the rest as heat flow into deeper layers of the surface. The presence of vegetation at the surface influences how the radiative energy deposited at the surface is partitioned into heat and moisture fluxes into the atmosphere. When vegetation is present at the surface, it increases resistance to airflow and increases the efficiency of heat and moisture transport to the atmosphere. Further, vegetation roots can access water from over a deeper layer of the soil. Thus, removal of vegetation by wildfires alters the amount of heat and moisture removed from the surface, as well as land surface temperature (LST).
+
+ After wildfires, soil water repellency (soil hydrophobicity) is altered and surface debris is removed, leading to a reduction in water infiltration and an increase in runoff. In addition, vegetation removal causes an increase in soil erosion. Immediately following the fires, runoff can transport ash deposits in addition to soil. If water bodies are present near fire-damaged locations, then an increase in sediments and ash in runoff can lead to higher turbidity or cloudiness in water bodies and cause water pollution.
+
+
+
+
+
+ HLS false-color composite imagery from November 11, 2018, of the same scene shown in Figure 1. False color composite imagery is useful in fire analysis in that it makes it easier to see the contrast of healthy vegetation (reds) and burned, bare ground (browns).
+
+
+
+
+
+
+ Wildfires affect local weather, climate, and water bodies by modifying how the land surface interacts with the atmosphere. Removal of dark vegetation leads to more sunlight reaching the surface being reflected back to the atmosphere. However, since plants provide a cooling effect through conversion of water to water vapor, the main impact of vegetation loss is an increase in air temperature. Since rainwater flows more easily over bare ground, rains following a fire more easily transport soil and ash to nearby streams and lakes, making them less clear and more polluted. Below, it will be shown how data from NASA and other sources can be used to detect these changes and identify impacts from these rapid changes in land cover.
+
+
+
+
+
+
+
+
+ The expansion of the perimeter of the 2018 Camp Fire from its inception on November 8 to 100% containment on November 25. Note the rapid expansion of the burned area within the first couple of days that the fire was active. This product is satellite-derived from Visible Infrared Imaging Radiometer Suite (VIIRS) thermal sensors using the Frequency Envelope Distribution (FEDs) algorithm, and is considered experimental. The solid black outline is the official Monitoring Trends in Burn Severity (MTBS) burn scar extent from the Camp Fire.
+
+
+
+
+
+
+
+ ## Satellite Data Analysis of Changes in Surface Properties caused by the Camp Fire
+ The [National Land Cover Dataset](https://www.earthdata.nasa.gov/dashboard/data-catalog/campfire-nlcd) (NLCD) provides land cover classification at 30-meter spatial resolution over the contiguous United States (CONUS) and can be used to assess the impacts of the Camp Fire. The NLCD is derived from Landsat satellite sensor data and is available at approximately three-year time intervals. With the Camp Fire occurring in 2018, NLCD maps for the years 2016 and 2019 were utilized to examine changes in land cover. The dataset shows the dominant pre-wildfire vegetation cover type was evergreen forest and shrub/scrub cover, while the post-wildfire cover type is predominantly grasslands and herbaceous vegetation (Figure 4).
+
+
+
+
+
+
+
+ NLCD land use-land cover classifications over the burn scar domain for 2016 (pre-fire) and 2019 (post-fire). Note the large area of dense vegetation pre-fire that is now grassland/barren land post-fire.
+
+
+
+
+
+
+ To examine how the fire affected the changes in surface properties, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products were acquired for a six-year period (2015 to 2022) over the Camp Fire burn scar extent, temporally centered on the period that the fire was active. First, some definitions from the American Meteorological Society Glossary of Meteorology and NASA’s Earth Observatory are provided:
+
+ [Normalized Difference Vegetation Index](https://www.earthdata.nasa.gov/topics/land-surface/land-use-land-cover/land-use-land-cover-classification/vegetation-index-0): A numerical value used to predict or assess vegetative characteristics such as plant leaf area, total biomass, and general health and vigor of the surface vegetation.
+
+ [Albedo](https://www.earthdata.nasa.gov/topics/land-surface/surface-radiative-properties/albedo): The ratio of sunlight reflected from the land surface to sunlight reaching the land surface.
+
+ [Land Surface Temperature](https://www.earthdata.nasa.gov/topics/land-surface/surface-thermal-properties/land-surface-temperature): How hot the “surface” of Earth would feel to the touch in a particular location.
+
+ These products are available at 16-day intervals and were temporally averaged to compute monthly averaged NDVI, albedo, and LST.
+
+ Monthly mean NDVI was clipped to the region affected by Camp Fire, and the spatially averaged NDVI decreased from 0.65 to 0.45 following the event. Note that NDVI indicates green vegetation cover over a given location, with the values of 0 and 1 indicative of bare ground and complete green vegetation cover, respectively. This change in NDVI shows a 30% decrease in green vegetation cover over the region burned by the Camp Fire.
+
+
+
+
+
+
+
+ Monthly-averaged MODIS-derived NDVI values over the Camp Fire domain from 2002 to 2022. The highlighted portion of the time series indicates when the Camp Fire was active.
+
+
+
+
+
+
+ The reduction in green vegetation cover caused by the wildfire leads to exposure of brighter underlying bare soil. In order to quantify this increase in surface brightness, the monthly mean value of MODIS-derived albedo for the area affected by the Camp Fire was examined.
+
+ The monthly mean albedo increased from 0.123 to 0.125 following the fire, or an increase of 0.02. Note that albedo values of 0 and 1 indicate none or all of the incoming sunlight is being reflected back to the atmosphere, respectively. Thus, the alteration of the surface by the wildfire resulted in a nearly 2% reduction of energy deposited by sunlight onto the land surface. Even though the amount of energy deposited by the sunlight is reduced after the fire, the reduction in green vegetation cover affects how the deposited energy is utilized.
+
+
+
+
+
+
+
+ Monthly-averaged MODIS-derived albedo values over the Camp Fire domain from 2002 to 2022. The highlighted portion of the time series indicates when the Camp Fire was active.
+
+
+
+
+
+
+ Before the Camp Fire, part of the energy deposited is utilized by green vegetation cover for transpiration. However, after the fire, more of the energy deposited at the surface goes into heating and raising the temperature of the land surface. Thus, during the daytime, loss of vegetation caused by the fire results in an increase in land surface temperature and a reduction of moisture flux to the atmosphere. The MODIS-derived LST Day and Night products were utilized to determine whether such changes in LST were observed following the fire. Indeed, the mean daytime LST over the fire-affected areas increased by 2.5 °C (4.5 °F) following the Camp Fire.
+
+ However, during nighttime, when sunlight is not warming the surface, the change in surface emissivity (efficiency in emitting infrared radiation) caused by the wildfire is the major factor that influences nighttime LST. Generally, green vegetation cover has similar emissivity in comparison to bare ground, depending on soil wetness. Removal of vegetation can lead to either an increase or reduction in the loss of energy to the atmosphere in the form of infrared radiation and an increase in nighttime LST, depending upon the nature of surface emissivity. The monthly mean value of emissivity and nighttime LST over the fire scar region for periods before and after the fire were examined. A very slight increase in the mean emissivity, from 97.66% to 97.96% with a corresponding decrease of 0.34 °C (0.64 °F) in nighttime LST was observed over the fire scar.
+
+
+
+
+
+
+
+ Monthly-averaged MODIS-derived LST Day and Night values over the Camp Fire domain from 2002 to 2022. The highlighted portion of the time series indicates when the Camp Fire was active.
+
+
+
+
+
+
+ The analysis of the satellite data described above implies the Camp Fire caused significant alterations in the interactions between the land and the atmosphere. As explained earlier, the modifications in vegetation cover resulted in increased heat and decreased moisture movement from the land into the atmosphere. These alterations within the fire-affected area created a noticeable distinction in the land-atmosphere energy exchanges when compared to the adjacent unaffected regions. This particular response in airflow could bring about variations in wind patterns and cloud formation, a phenomenon akin to the well-known sea breeze effect commonly observed along land-ocean boundaries. Further exploration using computer models and additional data analysis is necessary to fully understand these processes.
+
+
+
+
+
+ ## MODIS NDVI Difference
+ ## (2019-2022) - (2015-2018)
+
+ The average Normalized Difference Vegetation Index (NDVI) over the region decreased from 0.65 to 0.45 following the Camp Fire, signifying a 30% reduction in green vegetation cover.
+
+
+
+ ## MODIS LST Day Difference
+ ## (2019-2022) - (2015-2018)
+
+ On average, satellite observations show that daytime land surface temperature (LST) increased by 2.5 °C over the area affected by the Camp Fire. Satellite observations also show that local changes in daytime LST within the burnscar region can increase by as much as 6 °C, which implies correspondingly large increases in maximum air temperatures as well.
+
+
+
+
+ ## MODIS LST Night Difference
+ ## (2019-2022) - (2015-2018)
+
+ Satellite observations show that loss of vegetation caused by the Camp Fire leads to an average decrease in night time LST of 0.35 °C (0.64 °F). Locally, decreases in night time LST can be as much as 1.5 °C. Satellite observations also show that decreases in nighttime LST is due to increase in efficiency of loss of energy from the surface in the form of infrared radiation.
+
+
+
+
+ ## MODIS Albedo WSA Difference
+ ## (2019-2022) - (2015-2018)
+
+ Satellite observations show that there is a nearly 2% increase in average albedo following the wildfire and thus a greater proportion of incoming sunlight reflected back by the surface.
+
+
+
+
+
+
+
+
+ ## Satellite Data Analysis of Fire Impacts on Surface Hydrology
+ Wildfires can impact surface hydrology through multiple mechanisms. Vegetation adds organic material to the soil, which increases the pore space within which water can reside, thereby enhancing infiltration. Roots of vegetation in the soil also enhance infiltration and reduce soil erosion. Plant litter also increases infiltration by storing water and allowing increments to be released to the soil layer over extended time periods. The litter layer also buffers the soil layer from direct impact via precipitation. Thus, vegetation removal by fire leads to reduced infiltration, increased runoff, and soil erosion. Further, fires can also impact soil properties, such as altering soil water repellency or hydrophobic tendency, which can also contribute to decreases in infiltration and enhancement in runoff.
+
+ An increase in runoff and soil erosion following a wildfire can cause enhancement in sediment flows to water bodies present within or surrounding the region affected by the wildfire. Turbidity or cloudiness of a water body can be affected by sediment flows into it from the surrounding regions. Lake Oroville is a prominent water body located downstream of the location where the Camp Fire occurred and was potentially affected by subsequent changes in sediment flows.
+
+
+
+
+
+
+
+ Location of Lake Oroville compared to the Camp Fire burn scar (black outline), from a regional to local view.
+
+
+
+
+
+
+ The normalized difference turbidity index (NDTI) was utilized to examine if the turbidity of Lake Oroville was affected following the Camp Fire. NDTI was computed using the NASA Harmonized Landsat Sentinel-2 (HLS) dataset using surface reflectance at a geographical location estimated from satellite sensor observations at red and green wavelengths. Denoting the reflectance values at red and green wavelengths as r and g, the NDTI is given by the ratio of (r-g) to (r+g). The reflectance of clear water is higher in green than red wavelengths; however, when the water is turbid, the reflectance of water in the red wavelengths becomes higher than the green wavelengths. Thus NDTI has negative values when the water is clear and becomes positive when it is turbid. It is important to note that the above-described interpretation of NDTI is valid only for water bodies, and thus it needs to be determined if water is indeed present at a location.
+
+ The normalized difference water index (NDWI) was then used to determine if water is present at a given location in an HLS scene. The NDWI is computed using reflectance at the green (g) and near-infrared (nir) wavelengths as the ratio of (g-nir) and (g+nir). The near-infrared reflectance of water is lower compared to that at the green wavelength while the opposite is true for vegetation and bare ground. Thus, the NDWI values are positive for water bodies, and locations in the satellite imagery with values greater than 0.03 were identified as water for this analysis. The NDTI for surface water locations in the cloud-free HLS scenes was computed over the study area between the years 2017 to 2022. A time-series of mean NDTI for Lake Oroville was derived for this time period, which shows a noticeable increase in NDTI following the Camp Fire before returning to a more typical variability after approximately one year.
+
+
+
+
+
+
+
+ Spatially-averaged Normalized Difference Turbidity Index (NDTI) values over Lake Oroville from 2017 to 2022. Days are given as the day of year (1-365).
+
+
+
+
+
+
+
+
+ HLS computed Normalized Difference Turbidity Index (NDTI) values over Lake Oroville from 2017 to 2022.
+
+
+
+
+
+
+
+
+ ## Conclusion
+ Our satellite data analysis shows a drastic change in land surface characteristics following the Camp Fire. Associated alterations in land-atmosphere interactions have the potential to impact local circulations and cloud and precipitation formation. Our analysis also shows noteworthy changes in the turbidity of Lake Oroville following the Camp Fire. This could be an indication of changes in surface hydrology and increased sediment flow. Since the combustion of organic matter during wildfires releases both nutrients (phosphorus, nitrogen, carbon) and a variety of heavy metals such as mercury, arsenic, and lead, nutrients and heavy metal inputs into Lake Oroville may also have increased following the Camp Fire. Enhanced nutrient input can promote algal growth while mercury pollution can contaminate fish consumed by both humans and wildlife. Since the occurrence of wildfires is expected to increase in the future, atmospheric and hydrological effects must be considered when investigating post-fire impacts as well as when conducting pre-fire mitigation strategies.
+
+
+
+
+
+ ## Additional Resources
+ [Dataset landing page for MODIS LST](https://modis.gsfc.nasa.gov/data/dataprod/mod11.php)
+
+ [Dataset landing page for MODIS NDVI](https://modis.gsfc.nasa.gov/data/dataprod/mod13.php)
+
+ [Dataset landing page for MODIS Albedo](https://lpdaac.usgs.gov/products/mcd43c3v006/)
+
+ [Dataset landing page for Harmonized Landsat Sentinel-2 data](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/)
+
+
+
+
+
+ ## References
+ Anthes, R.A. 1984. Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. Journal of Climate and Applied Meteorology, 23, 541-554. doi:10.1175/1520-0450(1984)023%3C0541:EOCPBM%3E2.0.CO;2
+
+ McCumber, M.C. 1980. A numerical simulation of the influence of heat and moisture fluxes upon mesoscale circulations. Ph.D. dissertation, University of Virginia. 255pp.
+
+ Pielke, R., and R. Avissar. 1990. Influence of landscape structure on local and regional climate. Landscape Ecology, 4, 133-155. doi:10.1007/BF00132857
+
+ Santanello Jr., J. A. et al. 2017: Land-Atmosphere Interactions: The LoCo Perspective. Bulletin of the American Meteorological Society, 99, 1253–1272. doi:10.1175/BAMS-D-17-0001.1
+
+ Wittenberg, L., et al. 2007: Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt Carmel Israel. Catena, 71, 76-83. doi:10.1016/j.catena.2006.10.007
+
+
+
+
diff --git a/stories/camp-fire-lst-celsius.csv b/stories/camp-fire-lst-celsius.csv
new file mode 100644
index 000000000..419f0d4fd
--- /dev/null
+++ b/stories/camp-fire-lst-celsius.csv
@@ -0,0 +1,503 @@
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diff --git a/stories/camp-fire-ndvi.csv b/stories/camp-fire-ndvi.csv
new file mode 100644
index 000000000..fe830981c
--- /dev/null
+++ b/stories/camp-fire-ndvi.csv
@@ -0,0 +1,252 @@
+Date,NDVI,Source
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diff --git a/stories/camp-fire-oroville-location.mp4 b/stories/camp-fire-oroville-location.mp4
new file mode 100644
index 000000000..1cd5f667e
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diff --git a/stories/camp-fire-oroville-ndti.csv b/stories/camp-fire-oroville-ndti.csv
new file mode 100644
index 000000000..d1f4ad196
--- /dev/null
+++ b/stories/camp-fire-oroville-ndti.csv
@@ -0,0 +1,256 @@
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diff --git a/stories/camp-fire-perimeter-progression.mp4 b/stories/camp-fire-perimeter-progression.mp4
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diff --git a/stories/crystal-lake-aod-trend.csv b/stories/crystal-lake-aod-trend.csv
new file mode 100644
index 000000000..7cf2290a4
--- /dev/null
+++ b/stories/crystal-lake-aod-trend.csv
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\ No newline at end of file
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diff --git a/stories/crystal-lake-hms-year.csv b/stories/crystal-lake-hms-year.csv
new file mode 100644
index 000000000..b08e448d1
--- /dev/null
+++ b/stories/crystal-lake-hms-year.csv
@@ -0,0 +1,19 @@
+Date,Count,Source
+2005,0,Days
+2006,11,Days
+2007,12,Days
+2008,15,Days
+2009,22,Days
+2010,2,Days
+2011,3,Days
+2012,8,Days
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+2015,9,Days
+2016,31,Days
+2017,20,Days
+2018,24,Days
+2019,4,Days
+2020,72,Days
+2021,65,Days
+2022,19,Days
diff --git a/stories/crystal-lake-location.mp4 b/stories/crystal-lake-location.mp4
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diff --git a/stories/crystal-lake-mercury.stories.mdx b/stories/crystal-lake-mercury.stories.mdx
new file mode 100644
index 000000000..8e86bc9ce
--- /dev/null
+++ b/stories/crystal-lake-mercury.stories.mdx
@@ -0,0 +1,140 @@
+---
+id: crystal-lake-mercury
+name: Wildfires Affect Water Quality
+description: "A Mercury Deposition Analysis of Crystal Lake in Southern California"
+media:
+ src: ::file ./crystal-lake-background-sat.jpg
+ alt: GOES-West Imagery from 9 September, 2020.
+ author:
+ name: NOAA-NASA
+ url: https://www.goes-r.gov/multimedia/dataAndImageryImagesGoes-17.html
+pubDate: 2023-08-21
+taxonomy:
+ - name: Topics
+ values:
+ - Wildfire
+ - Water Quality
+---
+
+
+
+ Authors: Trent Cowan[1], Andrew Blackford[1], Udaysankar Nair[1]\
+ [1] University of Alabama in Huntsville(UAH)
+
+ ## Introduction
+ Over the last five decades, the land area burned by forest fires in the western United States has increased tenfold. This trend is expected to continue and even accelerate under the influence of climate change. Given this scenario, the societal impacts of wildfires are of interest. While the impact of wildfires on the loss of human life, property, and air pollution is obvious, less understood are secondary environmental impacts that affect regions remote from the location of the fires. Recent studies show that smoke from wildfires is expected to become the major air pollutant in the United States. However, the potential impact of wildfires on water quality, especially mercury contamination, is not well studied.
+
+
+
+
+
+ Location of Crystal Lake in Southern California, from a regional to local view.
+
+
+
+
+
+
+
+
+ ## Wildfires Lead to Deposition
+ As organic matter is consumed during wildfires, nutrients (phosphorus, nitrogen, carbon) and a variety of heavy metals such as mercury, arsenic, and lead are released into the soil and air. Following wildfires, subsurface infiltration of water is often reduced due to the removal of surface leaf litter and organic matter from the topsoil layer. This leads to an increase in surface runoff. Further, the removal of leaf litter exposes the soil layer to the direct impacts of rainfall, which coupled with the loss of vegetation root structure to keep soil in place, leads to a higher potential for soil erosion after the occurrence of wildfires. A combination of these factors can lead to higher nutrient and heavy metal inputs into nearby water bodies.
+
+ Similarly, nutrients and heavy metals transported in smoke plumes can also affect remotely-located water bodies. Wet removal or the washout of particulate and soluble gasses by rainfall can deposit nutrients and heavy metals onto soil and water surfaces. Particulate and gaseous emissions from fires can also reach soil and water surfaces through dry deposition, which is their removal from the atmosphere through the action of gravitational settling and diffusion.
+
+ Pollution of water bodies by nutrients removed from the atmosphere by wet and dry deposition can have significant consequences. Increased cloudiness in water can block sunlight needed by aquatic plants and harm their growth. Higher concentrations of phosphorus and nitrogen in water bodies can fertilize and stimulate algal growth. The overabundance of algae can deplete dissolved oxygen leading to the death of aquatic animals. Fertilization can also lead to harmful algal blooms, which release toxins and can cause death.
+
+
+
+
+
+ ## Consequences of Mercury Deposition
+ Heavy metal pollution in water bodies caused by wet and dry deposition can have serious consequences. Once the mercury present in smoke emissions enters water bodies, the mercury is absorbed by plankton in the water. As the plankton is consumed by aquatic animals, mercury accumulates in concentrations proportionate to the level of hierarchy that the animal occupies in the food chain. Thus, mercury concentrations are high in fish and other aquatic animals consumed by humans such as marlin and tuna which occupy the top levels of the aquatic food chain. Mercury is especially toxic to humans during the gestational period and can cause serious disabilities during the early stages of development. For this reason, mercury contamination of fish is a serious concern.
+
+ Water quality monitoring in the U.S. has occasionally detected elevated levels of mercury in distant water bodies, which cannot be solely attributed to human sources like coal-fired power plants, a significant emitter of mercury. Transport of mercury by smoke plumes from wildfires and prescribed burning has been suspected to be the source of heavy metal pollution on such occasions.
+
+ Crystal Lake, located in Los Angeles County, California, is one such water body that is affected by mercury transported by wildfires. Analysis of 150 years of sediment deposit from the lake bed shows episodic spikes in mercury concentration. Since there are no known sources of mercury emissions in the vicinity of the lake, wildfire emission is thought to be the primary source of mercury contamination.
+
+
+
+
+
+ ## Satellite Analysis of Crystal Lake, Los Angeles County, CA
+ We utilized the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product over the time period of 2000-2023 to examine if Crystal Lake is, indeed, affected by smoke plumes from wildfires. AOD is an indicator of the total amount of particulate matter present in the atmosphere over a given location observed by the satellite sensor. Higher values of AOD are indicative of the occurrence of particulate matter in the region. The time series of AOD averaged over the Crystal Lake region for the 2000-2023 time period (Figure 1) shows seasonally varying values with the maximum occurring during summertime. While the long-term trend of AOD shows a statistically insignificant decreasing trend, anomalously high values have occurred in recent years.
+
+
+
+
+
+
+
+ Figure 1. Time series of monthly and spatially averaged MODIS AOD over Crystal Lake, LA County, CA.
+
+
+
+
+
+
+ While the summertime increase in atmospheric particulate matter is indicative of smoke pollution, the role of human-induced particulate pollution cannot be ruled out. Therefore, we utilized smoke plumes tracked by sensors aboard the Geostationary Operational Environmental Satellite (GOES) to confirm if the high particulate matter loading observed from NASA MODIS sensors coincides with occurrences of smoke plumes over the lake. Using the smoke plumes detected by GOES and made available through the Hazard Mapping System (HMS) product, we plotted the monthly occurrences of smoke plumes over the lake (Figure 2). The time series of smoke occurrences available since 2005 shows the frequent occurrence of smoke plumes over the lake during summer months when the particulate matter load is maximized. The time series of smoke occurrences shows a statistically significant increasing trend with anomalously high values of particulate matter loading occurring over the lake in recent years.
+
+
+
+
+
+
+
+ Figure 2. Time series of total number of days with smoke over Crystal Lake, LA County, CA by year.
+
+
+
+
+
+
+ September 2020 was a particularly anomalous month for smoke occurrence over Crystal Lake, with 27 days being impacted. Additionally, the monthly average AOD was at its highest value during any point of the 22-year time series, with a mean value of 0.413. Below is GOES-West imagery from September 9th, 2020 of California's Central Valley. This imagery shows thick, heavy smoke cover over much of the scene as a result of a large wildfire outbreak that would continue for several days across the United States West Coast.
+
+
+
+
+
+
+
+
+ GOES-17 TrueColor imagery the morning of September 9, 2020 of ongoing wildfires in California's Central Valley. These fires led to extensive smoke cover in the vicinity of Crystal Lake.
+
+
+
+
+
+
+ ## Conclusion
+ A major implication of this discovery is that waterbodies in the western U.S. could be subject to increased mercury pollution if the increasing frequency and intensity of wildfire smoke plumes continues to occur. High average values of AOD associated with the smoke plumes from the fires in recent years are reflective of increases in fire intensity and smoke emissions.
+
+
diff --git a/stories/crystal-lake-sept2020.mp4 b/stories/crystal-lake-sept2020.mp4
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diff --git a/stories/lahaina-fire-background.jpg b/stories/lahaina-fire-background.jpg
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+---
+id: lahaina-fire
+name: The Devastating August 8th, 2023 Lahaina, Hawai'i Wildfire
+description: "A Satellite-Based Overview"
+media:
+ src: ::file ./lahaina-fire-background.jpg
+ alt: Fire erupting over Lahaina, HI.
+ author:
+ name: Matthew Thayer/AP
+ url: https://www.sfchronicle.com/travel/article/hawaii-fire-maui-lahaina-18289213.php
+pubDate: 2023-08-21
+taxonomy:
+ - name: Topics
+ values:
+ - EIS
+---
+
+
+
+ ## Introduction
+ On August 8, 2023, a devastating wildfire roared through the city of Lahaina, Hawai’i, located on the island of Maui and home to over 13,000 residents. This wildfire was reportedly sparked by a downed powerline on Lahainaluna Road. Though initially extinguished, the fire was reinvigorated by intense wind gusts that prevailed throughout the day. The National Weather Service reported wind gusts as high as 67 mph in the area, which helped to quickly spread the wildfire across much of Lahaina during the afternoon hours of August 8.
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+ A primary driver for the Lahaina Fire was a strong [gap wind](https://glossary.ametsoc.org/wiki/Gap_wind) that developed between the islands of Maui and Molokai. A gap wind is “a strong, low-level wind through either a relatively level channel between two mountain ranges or a gap in a mountain barrier” (reference to Glossary of meteorology). The intense winds funneling between the two islands were aided by a sharp pressure gradient caused by [Hurricane Dora](https://www.google.com/url?q=https://www.earthdata.nasa.gov/worldview/worldview-image-archive/hurricane-dora-6-aug-2023&sa=D&source=docs&ust=1698703849605505&usg=AOvVaw2v_a0o1c-R2PBY6AQEbNrB), a Category 4 hurricane that was approximately 500 miles south of the islands when the fire ignited. The strong gap wind persisted from August 7 to 9 and led to a rapid expansion of the fire front. What started as a small brush fire grew quickly and subsequently burned much of Lahaina.
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+ According to the Pacific Disaster Center (PDC) and Federal Emergency Management Agency (FEMA), 2,170 acres were burned by the Lahaina Fire, destroying 2,207 structures—86% of which were residential buildings. The fire is estimated to have caused $5.52 billion in damages, and 97 fatalities have been confirmed as a direct result of the fire. The Lahaina Fire now ranks as the deadliest U.S. wildfire since 1918. Severe drought conditions on Maui were present leading up to the wildfire—the worst drought conditions in the entire Hawaiian Island chain. Additionally, Maui is more prone to experiencing drought conditions than the rest of the Hawaiian Islands, in part due to the mountainous portion of the island blocking the passage of westward-propagating precipitation events. These drought conditions supported the amount of dry fuel the Lahaina Fire had access to when it raged across fields and into the city.
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+ US Drought Monitor Index on August 8th, 2023 over the Hawaiian Islands, with increasingly darker colors indicating worse drought conditions. The island of Maui is experiencing the worst conditions in the hours leading up to the fire.
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+ Harmonized Landsat-Sentinel satellite falsecolor composites (Sentinel 30 meter (S30) product) of the Lahaina fire domain before and after the fire.
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+ ## Satellite Analysis of the Lahaina Wildfire
+ Thermal imagery acquired by the Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) aboard the joint NASA/USGS Landsat-8 satellite detected ongoing fires across much of the city of Lahaina during its overpass at 10:35 p.m., Local Standard Time, on August 8. TIRS and OLI take observations from several different wavelengths that can be used to better understand changes to land and vegetation from natural disasters. With a simple calculation using two wavelength bands, scientists can derive burned area from satellite observations. Burned area derived using NASA’s Harmonized Landsat and Sentinel-2 (HLS) products on August 13, 2023, clearly show areas most impacted by the fire. The areas identified with the highest probability of having been burned were located across the severely scorched fields just uphill from the city.
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+ Another technique scientists use to identify the impacts of natural disasters are false-color composite images. False-color composite imagery replaces the traditional red, green, and blue wavelength bands that correspond to how our eyes see with other wavelength bands. Fires are particularly sensitive to the near infrared (IR) and shortwave IR wavelengths. When the red, green, and blue bands are replaced with the shortwave IR, near IR, and red bands, respectively, land areas most impacted by the fire are much darker in the image than unaffected areas.
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+ Landsat-8 nighttime thermal imagery from August 8, 2023 compared to computed BAIS2 burned area from the August 13, 2023 HLS scene.
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