Finish Aerosol discovery and needed dataset

Co-authored-by: WeathermanTrent <[email protected]>
Co-authored-by: Brian Freitag <[email protected]>

datasets/aerosol-difference.data.mdx

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+---
+id: houston-aod-diff
+name: "Houston Aerosol Optical Depth Difference Over 20 Years"
+description: "The impact of Aerosol over the Houston-metro and the Difference Over 20 Years"
+media:
+  src: ::file ./smog-city.jpg
+  alt: Smog Located In City.
+  author:
+    name: Nick van den Berg
+    url: https://unsplash.com/photos/2vb-_3t6YCM
+thematics:
+  - air-quality
+layers:
+  - id: houston-aod-diff
+    stacCol: houston-aod-diff
+    name: AOD Difference (2010-2019) - (2000-2009)
+    type: raster
+    description: "This figure shows the difference in AOD in the form of a raster when subtracting the two decades from the original AOD Dataset"
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: bwr
+      rescale:
+        - -0.1
+        - 0.1
+      nodata: 0
+    compare:
+      datasetId: houston-urbanization
+      layerId: houston-urbanization
+      mapLabel: |
+        ::js ({dateFns, datetime, compareDatetime}) => {
+          return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
+        }
+    legend:
+      type: gradient
+      min: "-0.1"
+      max: "0.1"
+      stops:
+        - "#4575b4"
+        - "#91bfdb"
+        - "#e0f3f8"
+        - "#ffffff"
+        - "#fee090"
+        - "#fc8d59"
+        - "#d73027"
+
+
+
+---
+
+<Block>
+  <Prose>
+
+  Refer to the "houston-aod" dataset for more information on how AOD Difference is derived. This dataset comes from the two decadal COGs that displayed mean Aerosol Optical Depth for 2000-2009 and for 2010-2019. Those tiffs were subtracted to display the differences between the two decades.
+
+
+  </Prose>
+</Block>
+
+

datasets/modis-aerosol-dataset.data.mdx

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+---
+id: houston-aod
+name: "MODIS MCD19A2 Product"
+description: "Using MODIS MCD19A2 to Analyze impacts of Aerosols in Urban Areas"
+media:
+  src: ::file ./smog-city.png
+  alt: Smog Located In City.
+  author:
+    name: Nick van den Berg
+    url: https://unsplash.com/photos/2vb-_3t6YCM
+thematics:
+  - air-quality
+layers:
+  - id: houston-aod
+    stacCol: houston-aod
+    name: Mean AOD
+    type: raster
+    description: "The average Aerosol Optical Depth in our atmosphere. Note that these are unitless values."
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rdylbu_r
+      rescale:
+        - 0.1
+        - 0.311
+      nodata: 0
+    legend:
+      type: gradient
+      min: "0"
+      max: "0.311"
+      stops:
+        - "#4575b4"
+        - "#91bfdb"
+        - "#e0f3f8"
+        - "#ffffbf"
+        - "#fee090"
+        - "#fc8d59"
+        - "#d73027"
+    compare:
+      datasetId: houston-aod
+      layerId: houston-aod
+---
+
+<Block>
+  <Prose>
+
+  ### About 
+
+  The MCD19A2 product represents a dataset that offers insights into aerosol optical thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations.
+
+  The primary purpose of the MCD19A2 product is to provide a comprehensive set of atmospheric and geometric properties or parameters. These parameters are integral in producing the land surface Bidirectional Reflectance Factor, another important component derived usiung the MAIAC algorithm.
+
+  ### Grid500m Group:
+
+  This segment captures details primarily about aerosol concentrations and characteristics at a 500m resolution. It encompasses:
+  * Aerosol Optical Depth (AOD) at 047 micron and 055 micron, which measures the degree to which aerosol particles prevent the transmission of light, giving an insight into air quality.
+
+  * Uncertainty metrics for AOD at 047 micron to gauge the precision of measurements.
+
+  * Fine-Mode Fraction for Ocean, indicating the proportion of small particles in aerosols over the ocean.
+
+  * The Column Water Vapor in cm liquid water, offering details about atmospheric moisture.
+
+  * AOD QA provides quality assurance metrics.
+
+  * AOD Model shows the regional background model applied.
+
+  * Injection Height provides data on the elevation of smoke introduction over the local surface height.
+
+  ### Grid5km Group:
+
+  This focuses on geometric and solar parameters at a 5km resolution.
+
+  * Cosine of Solar Zenith Angle and View Zenith Angle, which provide information on the solar and observational angles respectively, crucial for understanding light reflection and absorption dynamics.
+
+  * The Relative Azimuth Angle gives the position between the sun and the observing satellite.
+
+  * The Relative Azimuth Angle gives the position between the sun and the observing satellite.
+
+  ### Citing the Data
+
+  Alexi Lyapustin - NASA GSFC, Yujie Wang - Univeristy of Maryland Baltimore County and MODAPS SIPS - NASA. (2015). MCD19A2 MODIS/Terra+Aqua Aerosol Optical Thickness Daily L2G Global 1km SIN Grid. NASA LP DAAC. http://doi.org/10.5067/MODIS/MCD19A2.006
+
+  ### Accessing the Data
+
+  Google Earth Engine (GEE) provides an efficient way to harness the capabilities of the MODIS MCD19A2 dataset.
+
+  1. Initialize Google Earth Engine: Before you can access any datasets on GEE, ensure you've signed up for a Google Earth Engine account and initialized the GEE API in your programming environment.
+
+  2. Search for the Dataset: Navigate to the Google Earth Engine Data Catalog. In the search bar, type "MCD19A2" to locate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset.
+
+  4. Scripting in GEE Code Editor: Open the GEE Code Editor and use the Correct Dataset ID. In this case, use "MODIS/006/MCD19A2/Optical_Depth_047"
+
+  </Prose>
+</Block>
+
+

datasets/nlcd-urbanization.data.mdx

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+---
+id: houston-urbanization
+name: "National Land Cover Database"
+description: "Using the National Land Cover Database (NLCD) to illustrate urbanzation in Houston, TX over a 20-year span."
+media:
+  src: ::file ./smog-city.jpg
+  alt: Smog Located In City.
+  author:
+    name: Galen Crout
+    url: https://unsplash.com/photos/y8mIhkw7ZUI
+thematics:
+  - eis
+layers:
+  - id: houston-urbanization
+    stacCol: houston-urbanization
+    name: Urbanization
+    type: raster
+    description: "This dataset illustrates the growth in the metropolitan area of Houston, TX from 2000-2019. Note that these values are from 0 to 1."
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: reds
+      nodata: 0
+      rescale:
+        - 0
+        - 1
+    legend:
+      type: categorical
+      stops:
+        - color: "#ffffff"
+          label: No Data
+        - color: "#d73027"
+          label: Urbanization
+
+
+---
+
+<Block>
+  <Prose>
+
+  ### About
+
+  The National Land Cover Database (NLCD) stands as a paramount dataset offering an in-depth overview of the land cover characteristics in the United States. Spearheaded by the Earth Resources Observation and Science (EROS) Center, this database is renewed every two to three years to provide updated and accurate data for the nation.
+
+  This is a collective effort between the U.S. Geological Survey (USGS) and the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC, composed of various federal agencies, has a rich legacy spanning over 30 years of generating consistent and pertinent land cover information on a national scale. The NLCD is a testament to their dedication and has emerged as one of the most frequently utilized geospatial datasets within the U.S., catering to an extensive audience ranging from scientists, land managers, city planners, to students.
+
+  As of its latest release, the NLCD showcases land cover data and related changes across nine specific epochs, starting from 2001 and culminating in 2021. These datasets are meticulously crafted, ensuring continuity and consistency with the past releases (from 2001-2019). This methodological consistency ensures that the datasets from the different epochs are directly comparable and well-suited for mult-temporal analyses.
+
+  ### What NLCD Offers
+
+  * Land Cover: This product details the land cover of the Conterminous U.S. at a 30-meter spatial resolution, employing a 16-class legend rooted in the modified Anderson Level II classification system.
+
+  * Land Cover Change Index: This visualization tool portrays the transformations that have transpired across all the NLCD epochs, furnishing users with a holistic view of the evolving landscape.
+
+  * Urban Imperviousness: A crucial dataset for urbanization studies, it highlights impervious surfaces in urban regions, showcasing them as a percentage of the developed surface at every 30-meter pixel.
+
+  * Urban Impervious Descriptor: A more nuanced product that classifies specific urban developments, such as roads, wind tower sites, building locations, and energy production sites. This aids in a more granular analysis of urban features.
+
+  ### Access the Data
+
+  Visit the [Acess Data](https://www.mrlc.gov/data) page to explore all of the options that NLCD offers.
+
+  ### Citing this Dataset
+
+  U.S. Geological Survey (USGS) & Multi-Resolution Land Characteristics (MRLC) Consortium. (2021). National Land Cover Database (NLCD) 2021: Conterminous U.S. Land Cover. Earth Resources Observation and Science (EROS) Center. Retrieved from https://www.mrlc.gov/data
+
+  ### Publications
+
+  * Danielson, Patrick, Postma, Kory, Riegle, J., Dewitz, Jon A., Deep learning artificial intelligence (AI) for improving classification accuracy for the National Land Cover Database (NLCD) [abs.]
+
+  * Wickham, J., Stehman, S.V., Sorenson, D.G., Gass, L., Dewitz, Jon A., Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States: Remote Sensing of Environment, v. 257, at https://doi.org/10.1016/j.rse.2021.112357
+
+  * Rigge, Matthew B., Shi, Hua, Postma, Kory, Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085, v. 12, no. 6, at https://doi.org/10.1002/ecs2.3538
+
+  * Rigge, Matthew B., Homer, Collin G., Shi, Hua, Meyer, Debbie K., Bunde, Brett, Granneman, Brian, Postma, Kory, Danielson, Patrick, Case, Adam, Xian, George Z., Rangeland fractional components across the western United States from 1985 to 2018: Remote Sensing, v. 13, no. 4, at https://doi.org/10.3390/rs13040813
+
+  * Peng, D., Wang, Y. L., Xian, George, Huete, A.R., Huang, W., Shen, M., Wang, F., Yu, L., Liu, L., Xie, Q., Liu, L., Zhang, X., Investigation of land surface phenology detections in shrublands using multiple scale satellite data: Remote Sensing of Environment, v. 252, at https://doi.org/10.1016/j.rse.2020.112133
+
+  * Rigge, Matthew B., Shi, Hua, Meyer, Debbie K., Bunde, Brett, Postma, Kory, Trends of fractional rangeland components across a 1985-2020 time-series [poster], v. Measuring & Monitoring Ecosystems, at http://annualmeeting2021.rangelands.org/presentations-posters/
+
+  * Jin, Suming, Dewitz, Jon A., Sorenson, D., Shogib, Rakibul , Granneman, Brian J., Case, Adam, Li, Congcong, Zhe, Z., Danielson, Patrick, Costello, C., Gass, L., National Land Cover Database 2019—A comprehensive strategy for creating the 1986-2019 Forest Disturbance Date Product [abs.], v. Proceedings, at https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/960755
+
+  </Prose>
+</Block>
+
+