Skip to content

Commit

Permalink
update classification page with correct information
Browse files Browse the repository at this point in the history
  • Loading branch information
@WeathermanTrent
WeathermanTrent authored Nov 25, 2024
1 parent 047bd39 commit 8428285
Showing 1 changed file with 111 additions and 30 deletions.
141 changes: 111 additions & 30 deletions datasets/marsh-ida.data.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -76,46 +76,127 @@ layers:
label: Gain of Marsh
---

<Block>
<Prose>
## Dataset Details
- **Temporal Extent:** August 23 - September 9, 2021
- **Temporal Resolution:** Inconsistent
- **Spatial Extent:** Southern Louisiana
- **Spatial Resolution:** 3 meters
- **Data Units:** N/A
- **Data Type:** Research
- **Data Latency:** N/A
</Prose>
<Figure>
<Map
datasetId='marsh-ida'
layerId='marsh-ida'
dateTime='2021-08-23'
zoom={13}
compareDateTime='2021-09-09'
/>
<Caption
attrAuthor='NASA'
attrUrl='https://nasa.gov/'
>
Planet satellite imagery classification of salt marshes pre- and post-Ida for southern Louisiana.
</Caption>
</Figure>
</Block>

<Block type='wide'>
<Prose>
Harmonized Landsat Sentinel-2 (HLS) project from NASA is designed to integrate and harmonize data from multiple satellite sources, specifically the Operation Land Imager (OLI) on Landsat-8/9 and the Mult-Spectral Instrument (MSI) on Sentinel-2A/B satellites. This project aims to create a seamless surface reflectance record that is essential for various Earth Observation and monitoring tasks.

- **Temporal Extent:** Landsat-9 2021-10-31; Sentinel-2B 2017-07-06
- **Temporal Resolution:** ~3 days
- **Spatial Extent:** Global with the exception of Antarctica
- **Spatial Resolution:** 30 m x 30 m
- **Data Units:** Surface Reflectance
- **Data Type:** Research
- **Data Latency:** 2 to 3 days
<Block>
<Prose>

### About

**Scientific Details:**
HLS project incorporates several advanced scientific methodologies and technologies to harmonize data from the Landsat and Sentinel-2 satellites such as atmospheric correction, geographic co-registration and common gridding, bidirectional reflectance distribution normalization, and cloud-shadow masking. To calculate the Normalized Difference Vegetation Index (NDVI) from the HLS-2 Dataset, we utilize the red and near-infrared bands to assess vegetation health by applyin the formula NDVI = NIR - Red / NIR + Red, where 'NIR' refers to the near-infrared surface reflectance, and 'Red' denotes the red light surface reflectance, both harmonized from the Landsat and Sentinel satellites.
</Prose>
Planet Labs’ SmallSat imagery, captured by the PlanetScope Dove satellite constellation, is a highly valuable commercial satellite remote sensing product, frequently leveraged for rapid damage assessment and environmental monitoring. Known for its frequent overpasses, with revisit times on the order of a couple of days, and an impressive spatial resolution of 3 meters, this imagery offers exceptional capabilities for monitoring changes in landscapes and infrastructure. The PlanetScope constellation has near-global coverage daily across the visible and near-infrared channels, providing extensive data for timely and precise analysis of up to 140-150 million square kilometers.
</Prose>
</Block>

<Block>
<Prose>
## Source Data Product Citation
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161.
## Disclaimer
All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly.

<Prose>

### What PlanetScope Data Offers

* High-Resolution Imagery: With a 3-meter spatial resolution, the PlanetScope Dove satellite imagery provides detailed views of landscapes, enabling precise assessments of building and vegetation damage.

* Frequent Revisit Times: The satellite constellation’s ability to capture imagery on the order of every couple of days makes it highly effective for monitoring rapid changes, such as those caused by natural disasters.

* Extensive Coverage: Collecting up to 2 million square kilometers of imagery daily, the Dove constellation ensures broad coverage across visible and near-infrared channels.

* Building and Vegetation Damage Assessment: The high-resolution and frequent imagery facilitate quick identification and analysis of damage to infrastructure and vegetation, aiding in damage determination and response efforts.

</Prose>
</Block>

## Key Publications
Su Ye, John Rogan, Zhe Zhu, J. Ronald Eastman, A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection, Remote Sensing of Environment, Volume 252, 2021,112167, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2020.112167)[https://doi.org/10.1016/j.rse.2020.112167].
<Block>
<Prose>

### Access the Data

Visit Planet's [home page](https://www.planet.com) to explore options for data access. This data was made available through the NASA [Commercial Satellite Data Acquisition (CSDA) Program](https://earthdata.nasa.gov/about/csda/vendor-planet). You can access the CDSA data explorer [**HERE**](https://csdap.earthdata.nasa.gov).

Su Ye, Zhe Zhu, Guofeng Cao, Object-based continuous monitoring of land disturbances from dense Landsat time series, Remote Sensing of Environment, Volume 287, 2023, 113462, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2023.113462)[https://doi.org/10.1016/j.rse.2023.113462].
</Prose>
</Block>

### Other Relevant Publications
Ye, S., Zhu, Z., & Suh, J. W. (2024). Leveraging past information and machine learning to accelerate land disturbance monitoring. Remote Sensing of Environment, 305, 114071.
<Block>
<Prose>

## Acknowledgment
This work has been supported by the USGS-NASA Landsat Science Team (LST) Program for Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US (140G0119C0008)
### Citing this Dataset

Image © 2024 Planet Labs PBC. Planet Application Program Interface: In Space for Life on Earth. https://api.planet.com.

</Prose>
</Block>

<Block>
<Prose>

## Disclaimer

## License
[Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
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

Marshall, W and C. Boshuizen, 2013: Planet Labs' Remote Sensing Satellite System. Proc. of the 2013 Small Satellite Conference, Utah State University. https://digitalcommons.usu.edu/smallsat/2013/all2013/7/

</Prose>
</Block>

<Block>
<Prose>

### Other Publications

Molthan, A., L. A. Schultz, K. M. McGrath, J. E. Burks, J. P. Camp, K. Angle, J. R. Bell, and G. J. Jedlovec, 2020: Earth Remote Sensing in NWS Severe Weather Damage Assessments. *Bull. Amer. Meteor. Soc.*, **101**, 221–226. https://www.jstor.org/stable/27028125

</Prose>
</Block>

<Block>
<Prose>

## Data Stories Using This Dataset

**<Link to={"/stories/wetland-impacts"}>How Hurricane Ida’s Impact on Wetlands Endangers Inland Communities</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>
</Prose>
</Block>

0 comments on commit 8428285

Please sign in to comment.