[Content] NLCD Dataset Landing Page

datasets/aerosol-difference.data.mdx

@@ -32,8 +32,8 @@ layers:
  - 0.1
  nodata: 0
  compare:
- datasetId: houston-urbanization
- layerId: houston-urbanization
+ datasetId: nlcd-new-urbanization
+ layerId: nlcd-new-urbanization
  mapLabel: |
  ::js ({dateFns, datetime, compareDatetime}) => {
  return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;

datasets/nlcd-urbanization.data.mdx

@@ -1,51 +1,86 @@
 ---
-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."
+id: nlcd-new-urbanization
+name: "National Land Cover Database New Urbanization"
+description: "Using the National Land Cover Database (NLCD) to illustrate new urbanization from 2001-2021."
+
 media:
- src: ::file ./smog-city.png
- alt: Smog Located In City.
- author:
- name: Galen Crout
- url: https://unsplash.com/photos/y8mIhkw7ZUI
+ src: ::file ./nlcd-cover.png
+ alt: Boston, MA skyline.
+ author: 
+ name: Eric Kilby
+ url: https://openverse.org/image/a9e174e6-d4e1-4377-81e2-6e71bf1a9602?q=skyline
 taxonomy:
  - name: Topics
  values:
- - EIS
+ - Environmental Justice
+ - Land Cover
+ - name: Source
+ values:
+ - MRLC
 infoDescription: |
  ::markdown
  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.
 layers:
- - id: houston-urbanization
- stacCol: houston-urbanization
+ - id: nlcd-new-urbanization
+ stacApiEndpoint: https://staging.openveda.cloud/api/stac
+ tileApiEndpoint: https://staging.openveda.cloud/api/raster
+ stacCol: nlcd-new-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."
+ description: "This is a binary dataset derived from the National Land Cover Database (NLCD) to illustrate new urbanization from 2001-2021, where 0 is no new urbanization and 1 is new urbanization."
  initialDatetime: newest
  zoomExtent:
  - 0
  - 20
  sourceParams:
  colormap_name: reds
  nodata: 0
+ assets: landcover
  rescale:
  - 0
  - 1
  legend:
  type: categorical
  stops:
- - color: "#ffffff"
- label: No Data
  - color: "#d73027"
- label: Urbanization
+ label: New Urbanization
  info:
  source: EROS
- spatialExtent: Houston
- temporalResolution: Annual 
+ spatialExtent: CONUS
+ temporalResolution: 20 Year Difference 
  unit: Binary
 
 ---
 
+<Block>
+ <Prose>
+ ## Dataset Details
+ - **Temporal Extent:** 2001-2021 
+ - **Temporal Resolution:** 20 Years
+ - **Spatial Extent:** CONUS
+ - **Spatial Resolution:** 30 m
+ - **Data Units:** N/A
+ - **Data Type:** Research
+ - **Data Latency:** N/A
+ </Prose>
+<Figure>
+ <Map
+ datasetId='nlcd-new-urbanization'
+ layerId='nlcd-new-urbanization'
+ dateTime='2001-01-01'
+ zoom={8.65}
+ center={[-86.2,39.8]}
+ />
+ <Caption
+ attrAuthor='NASA'
+ attrUrl='https://nasa.gov/'
+ >
+ Newly urbanized areas per NLCD classifications in the Indianapolis, IN metropolitan area between 2001 and 2021.
+ </Caption>
+</Figure>
+</Block>
+
+
 <Block>
  <Prose>
 
@@ -56,6 +91,14 @@ layers:
  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.
+
+ This dataset landing page is specific to a binary raster dataset of new urbanization seen from the NLCD classifications between the years 2001 and 2021.
+
+ </Prose>
+</Block>
+
+<Block>
+ <Prose>
 
  ### What NLCD Offers
 
@@ -66,32 +109,86 @@ layers:
  * 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.
+
+ </Prose>
+</Block>
 
+<Block>
+ <Prose>
+
  ### Access the Data
 
- Visit the [Acess Data](https://www.mrlc.gov/data) page to explore all of the options that NLCD offers.
+ Visit the [Access Data](https://www.mrlc.gov/data) page to explore all of the options that NLCD offers.
+
+ </Prose>
+</Block>
+
+<Block>
+ <Prose>
 
  ### 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
+
+ </Prose>
+</Block>
 
- ### Publications
+<Block>
+ <Prose>
+
+ ## Disclaimer
+
+ All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly. 
+
+ </Prose>
+</Block>
+
+<Block>
+ <Prose>
+
+ ### Key Publications
+
+ Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., et al. (2007). Completion of the 2001 National Land Cover Database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 73(4), 337–341.
+
+ Homer, C., Fry, J. A., & Barnes, C. A. (2012). The national land cover database. US geological survey fact sheet, 3020(4), 1–4.
+
+ Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., et al. (2015). Completion of the 2011 national land cover database for the conterminous United States – Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, 81(5), 345–354. https://doi.org/10.14358/PERS.81.5.345
+
+ </Prose>
+</Block>
 
- * 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.]
+<Block>
+ <Prose>
 
- * 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
+ ### Other Publications
 
- * 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
+ 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.]
 
- * 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
+ 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
 
- * 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., 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
 
- * 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/
+ 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
 
- * 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>
+
+<Block>
+ <Prose>
+
+ ## Data Stories Using This Dataset
+
+ **<Link to={"/stories/houston-aod"}>Aerosols and Their Impacts on Houston, TX</Link>**
 
  </Prose>
 </Block>
 
+<Block>
+ <Prose>
+
+ ## License
+
+ [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
 
+ </Prose>
+</Block>