Broadleaf is a geospatial and remote sensing consultancy.

We create scalable tools to wrangle, clean, and analyze geospatial and environmental datasets.

Contact us at broadleaf.dev@gmail.com.

Visualizing ~7 years of changing green vegetation (GV) cover in Los Angeles, California.

Data:
- ~250 Landsat 8 scenes
- LARIAC airborne imagery
- ancillary data

Tools:
- MESMA
- Rasterio, GeoPandas, CartoPy, Matplotlib, etc.

Publications:
- Paper
- Talk at the 2019 AMS Annual Meeting

Tools for scalable automated Landsat pre-processing via the USGS LPDAAC AppEEARS API

Tools:
- Rasterio, NumPy, GeoPandas, etc.

Code:
- github

Notes:
- The above example is a mosaic of all cloud/aerosol-masked summertime Landsat 5 images from 2009-10.
- Left RGB: 543, Right RGB: 321

Vegetation and Income Inequality in Los Angeles, California, USA

Tools:
- Rasterio, NumPy, Rasterstats, Geopandas, Folium, etc.

Notes:
- Polygons (and Median Household Income) are from the 2016 US Census.
- Fractional tree cover was calculated zonally from a 10m fractional cover map derived from the LARIAC classification.

Mapping the walls "seen" by satellite remote sensors

Data:
- ECOSTRESS Level 1B Geometry Rasters
- NYC DoITT LiDAR-derived DSMs

Tools:
- Rasterio, NumPy, Pandas, affine, etc.

Code:
- github

Notes:
- In (C) shades of blue indicate "seen" wall, red areas are occluded by objects on the surface.
- (D) is aggregated to image scale, with colors representing the fraction of "seen" wall in each pixel.