Adding S3 explorer

Author: nprovost-ctrees

datasets/ctrees-amazon-canopy-height.yaml

@@ -25,6 +25,8 @@ Resources:
     ARN: arn:aws:s3:::ctrees-amazon-canopy-height
     Region: us-west-2
     Type: S3 Bucket
+    Explore:
+      - "[Browse CTrees Bucket](https://ctrees-amazon-canopy-height.s3.us-west-2.amazonaws.com/index.html)"
 DataAtWork:
   Tutorials:
   Tools & Applications:

datasets/ctrees-california-vhr-tree-height.yaml

@@ -1,6 +1,6 @@
 Name: Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org
 Description: |
-  Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA). 
+  Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA).
 Documentation: "[Project overview](https://ctrees.org/products/tree-level)"
 Contact: [email protected]
 ManagedBy: "[CTrees](https://ctrees.org/)"
@@ -18,13 +18,14 @@ Tags:
   - geospatial
 License: |
   https://creativecommons.org/licenses/by/4.0/
-Citation:
-  CTrees.org - 2024. Sub-Meter Canopy Tree Height of California. Accessed DAY MONTH YEAR.
+Citation: CTrees.org - 2024. Sub-Meter Canopy Tree Height of California. Accessed DAY MONTH YEAR.
 Resources:
   - Description: Cloud-optimized GeoTIFF files with names corresponding to image of California for the year 2020 from the National Agriculture Imagery Program (NAIP) - United States Department of Agriculture (USDA) [NAIP](s3://naip-analytic/).
     ARN: arn:aws:s3:::ctrees-tree-height-ca-2020/
     Region: us-west-2
     Type: S3 Bucket
+    Explore:
+      - "[Browse CTrees Bucket](https://ctrees-tree-height-ca-2020.s3.us-west-2.amazonaws.com/index.html)"
 DataAtWork:
   Tutorials:
   Tools & Applications:
@@ -35,4 +36,3 @@ DataAtWork:
     - Title: Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model
       URL: https://doi.org/10.1016/j.rse.2024.114099
       AuthorName: Fabien H Wagner, Sophia Roberts, Alison L Ritz, Griffin Carter, Ricardo Dalagnol, Samuel Favrichon, Mayumi CM Hirye, Martin Brandt, Philippe Ciais and Sassan Saatchi
-