Introduction to Raster Data
Figure 1
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Raster Concept (Source: National Ecological
Observatory Network (NEON))
Figure 2
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Continuous Elevation Map: HARV Field Site
Figure 3
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USA landcover classification
Figure 4
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Spatial extent image (Image Source: National
Ecological Observatory Network (NEON))
Figure 5
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Resolution image (Source: National Ecological
Observatory Network (NEON))
Figure 6
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RGB multi-band raster image (Source: National
Ecological Observatory Network (NEON).)
Introduction to Vector Data
Figure 1
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Types of vector objects (Image Source: National
Ecological Observatory Network (NEON))
Figure 2
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Vector Type Examples
Coordinate Reference Systems
Figure 1
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Maps of the United States in different
projections (Source: opennews.org)
Figure 2
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Datum Fruit Example (Image
source)
Figure 3
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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
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Views of the STAC browser
Figure 2
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Views of the Earth Search STAC endpoint
Figure 3
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Overview of the true-color image
(“thumbnail”)
Figure 4
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Thumbnail of the Landsat-8 scene
Read and visualize raster data
Figure 1
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Raster plot with rioxarray
Figure 2
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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
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Raster plot after masking out missing
values
Figure 5
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Sketch of a multi-band raster image
Figure 6
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Overview of the true-color image (multi-band
raster)
Figure 7
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Overview of the true-color image with the
correct aspect ratio
Vector data in Python
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Crop raster data with rioxarray and geopandas
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Raster Calculations in Python
Figure 1
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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
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Source: Image created for this lesson (license)
Figure 9
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Figure 10
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Figure 11
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Calculating Zonal Statistics on RastersIntroductionMaking vector and raster data compatibleRasterizing the vector dataCalculate zonal statistics
Figure 1
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Figure 2
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Parallel raster computations using Dask
Figure 1
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Scene’s true-color image
Figure 2
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True-color image after median filtering
Figure 3
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Xarray Dask-backed DataArray
Figure 4
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Dask graph