Introduction to Raster Data
Figure 1
![raster concept](../fig/E01/raster_concept.png)
Raster Concept (Source: National Ecological
Observatory Network (NEON))
Figure 2
![elevation Harvard forest](../fig/E01/continuous-elevation-HARV-plot-01.png)
Continuous Elevation Map: HARV Field Site
Figure 3
![USA landcover classification](../fig/E01/USA_landcover_classification.png)
USA landcover classification
Figure 4
![spatial extent objects](../fig/E01/spatial_extent.png)
Spatial extent image (Image Source: National
Ecological Observatory Network (NEON))
Figure 5
![resolution image](../fig/E01/raster_resolution.png)
Resolution image (Source: National Ecological
Observatory Network (NEON))
Figure 6
![multi-band raster](../fig/E01/RGBSTack_1.jpg)
RGB multi-band raster image (Source: National
Ecological Observatory Network (NEON).)
Introduction to Vector Data
Figure 1
![vector data types](../fig/E02/pnt_line_poly.png)
Types of vector objects (Image Source: National
Ecological Observatory Network (NEON))
Figure 2
![vector type examples](../fig/E02/vector_types_examples.png)
Vector Type Examples
Coordinate Reference Systems
Figure 1
![US difference projections](https://media.opennews.org/cache/06/37/0637aa2541b31f526ad44f7cb2db7b6c.jpg)
Maps of the United States in different
projections (Source: opennews.org)
Figure 2
![datum fruit example](../fig/E03/citrus.jpg)
Datum Fruit Example (Image
source)
Figure 3
![projection citrus peel](../fig/E03/orange-peel-earth.jpg)
Projection Citrus Peel Example (Image from Prof
Drika Geografia, Projeções Cartográficas)
Figure 4
The UTM zones across the continental United
States (Chrismurf at English Wikipedia, via Wikimedia
Commons (CC-BY))
The Geospatial Landscape
Access satellite imagery using Python
Figure 1
![STAC browser screenshots](../fig/E05/STAC-browser.jpg)
Views of the STAC browser
Figure 2
![earth-search stac catalog views](../fig/E05/STAC-browser-exercise.jpg)
Views of the Earth Search STAC endpoint
Figure 3
![thumbnail of the sentinel-2 scene](../fig/E05/STAC-s2-preview.jpg)
Overview of the true-color image
(“thumbnail”)
Figure 4
![thumbnail of the landsat-8 scene](../fig/E05/STAC-l8-preview.jpg)
Thumbnail of the Landsat-8 scene
Read and visualize raster data
Figure 1
![raster plot with defualt setting](../fig/E06/overview-plot-B09.png)
Raster plot with rioxarray
Figure 2
![raster plot with robust setting](../fig/E06/overview-plot-B09-robust.png)
Raster plot using the “robust” setting
Figure 3
The UTM zones across the continental United
States (Chrismurf at English Wikipedia, via Wikimedia
Commons (CC-BY))
Figure 4
![raster plot masking missing values](../fig/E06/overview-plot-B09-robust-with-nan.png)
Raster plot after masking out missing
values
Figure 5
![multi-band raster](../fig/E06/single_multi_raster.png)
Sketch of a multi-band raster image
Figure 6
![true-color image overview](../fig/E06/overview-plot-true-color.png)
Overview of the true-color image (multi-band
raster)
Figure 7
![raster plot with correct aspect ratio](../fig/E06/overview-plot-true-color-aspect-equal.png)
Overview of the true-color image with the
correct aspect ratio
Vector data in Python
Figure 1
![Pandas and Geopandas](../fig/E07/pandas_geopandas_relation.png)
Figure 2
![Crop fields inside the AOI](../fig/E07/fields.png)
Figure 3
![all wells in the NL](../fig/E07/wells-nl.png)
Figure 4
![50m buffer around the fields](../fig/E07/fields-buffer.png)
Figure 5
![Wells within 50m buffer of fields](../fig/E07/wells-in-buffer.png)
Figure 6
![fields within 50m buffer of the wells, truncated](../fig/E07/fields-in-buffer-clip.png)
Figure 7
![Fields in 50m buffer of wells, not truncated](../fig/E07/fields-in-buffer-sjoin.png)
Figure 8
![waterways, rotated](../fig/E07/waterways-wrong.png)
Figure 9
![waterways, corrected](../fig/E07/waterways-corrected.png)
Crop raster data with rioxarray and geopandas
Figure 1
![Overview of the raster](../fig/E08/crop-raster-overview-raster-00.png)
Figure 2
![Raster cropped by a bounding box](../fig/E08/crop-raster-crop-by-bb-02.png)
Figure 3
![Ratser cropped by field polygons](../fig/E08/crop-raster-crop-fields.png)
Figure 4
![Raster croped by fields with gewascode 257](../fig/E08/crop-raster-fields-gewascode.png)
Figure 5
![Reproject match big to small](../fig/E08/reprojectmatch-big-to-small.png)
Figure 6
![Reproject match small to big](../fig/E08/reprojectmatch-small-to-big.png)
Raster Calculations in Python
Figure 1
![PONE-NDVI image](../fig/E09/PONE-NDVI.jpg)
Source: Wu C-D, McNeely E, Cedeño-Laurent JG,
Pan W-C, Adamkiewicz G, Dominici F, et al. (2014) Linking Student
Performance in Massachusetts Elementary Schools with the “Greenness” of
School Surroundings Using Remote Sensing. PLoS ONE 9(10): e108548. https://doi.org/10.1371/journal.pone.0108548
Figure 2
![red band image](../fig/E09/red-band.png)
Figure 3
![near infra-red band image](../fig/E09/NIR-band.png)
Figure 4
![NDVI map](../fig/E09/NDVI-map.png)
Figure 5
![NDVI histogram](../fig/E09/NDVI-hist.png)
Figure 6
![NDVI histogram with 50 bins](../fig/E09/NDVI-hist-bins.png)
Figure 7
![binned NDVI map](../fig/E09/NDVI-map-binned.png)
Figure 8
![NDVI classes](../fig/E09/NDVI-classes.jpg)
Source: Image created for this lesson (license)
Figure 9
![classified NDVI map](../fig/E09/NDVI-classified.png)
Figure 10
![NDVI map Texel](../fig/E09/NDVI-map-Texel.png)
Figure 11
![NDVI histogram Texel](../fig/E09/NDVI-hist-Texel.png)
Calculating Zonal Statistics on RastersIntroductionMaking vector and raster data compatibleRasterizing the vector dataCalculate zonal statistics
Figure 1
![rasterization results](../fig/E10/rasterization-results.png)
Figure 2
![Rasterization results Xarray](../fig/E10/rasterization-results-xr.png)
Parallel raster computations using Dask
Figure 1
![true color image scene](../fig/E11/true-color-image.png)
Scene’s true-color image
Figure 2
![median filter true color image](../fig/E11/true-color-image_median-filter.png)
True-color image after median filtering
Figure 3
![DataArray with Dask](../fig/E11/xarray-with-dask.png)
Xarray Dask-backed DataArray
Figure 4
![dask graph](../fig/E11/dask-graph.png)
Dask graph