Introduction to Raster Data

Figure 1

raster concept
Raster Concept (Source: National Ecological Observatory Network (NEON))

Figure 2

elevation Harvard forest
Continuous Elevation Map: HARV Field Site

Figure 3

USA landcover classification
USA landcover classification

Figure 4

spatial extent objects
Spatial extent image (Image Source: National Ecological Observatory Network (NEON))

Figure 5

resolution image
Resolution image (Source: National Ecological Observatory Network (NEON))

Figure 6

multi-band raster
RGB multi-band raster image (Source: National Ecological Observatory Network (NEON).)

Introduction to Vector Data

Figure 1

vector data types
Types of vector objects (Image Source: National Ecological Observatory Network (NEON))

Figure 2

vector type examples
Vector Type Examples

Coordinate Reference Systems

Figure 1

US difference projections
Figure 3.1: Maps of the United States in different projections (Source: opennews.org)

Figure 2

datum fruit example
Datum Fruit Example (Image source)

Figure 3

projection citrus peel
Projection Citrus Peel Example (Image from Prof Drika Geografia, Projeções Cartográficas)

Figure 4

UTM zones across the CONUS
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
Views of the STAC browser

Figure 2

earth-search stac catalog views
Views of the Earth Search STAC endpoint

Figure 3

thumbnail of the sentinel-2 scene before the wildfires
Overview of the true-color image (“thumbnail”) before the wildfires on Rhodes

Figure 4

thumbnail of the sentinel-2 scene after the wildfires
Overview of the true-color image (“thumbnail”) after the wildfires on Rhodes

Figure 5

thumbnail of the landsat-8 scene
Thumbnail of the Landsat-8 scene

Read and visualize raster dataResampling the raster image

Figure 1

raster plot with defualt setting
Raster plot with rioxarray

Figure 2

raster plot with defualt setting
Raster plot 80 x 80 meter resolution with rioxarray

Figure 3

raster plot with robust setting
Raster plot using the “robust” setting

Figure 4

raster plot with robust setting
Raster plot using vmin 100 and vmax 2000

Figure 5

UTM zones across the CONUS
The UTM zones across the continental United States (Chrismurf at English Wikipedia, via Wikimedia Commons (CC-BY))

Figure 6

raster plot masking missing values
Raster plot after masking out missing values

Figure 7

multi-band raster
Sketch of a multi-band raster image

Figure 8

true-color image overview
Overview of the true-color image (multi-band raster)

Figure 9

raster plot with correct aspect ratio
Overview of the true-color image with the correct aspect ratio

Vector data in Python

Figure 1

Pandas and Geopandas

Figure 2

greece_administrations

Figure 3

rhodes_administrations

Figure 4

greece_highways

Figure 5

rhodes_highways

Figure 6

rhodes_infra_highways

Figure 7

rhodes_builtup_buffer

Figure 8

rhodes_assets

Crop raster data with rioxarray and geopandas

Figure 1

Large visual raster

Figure 2

Clip box results

Figure 3

Clip results

Figure 4

rhodes_builtup_buffer

Figure 5

DEM

Figure 6

Matched DEM

Raster Calculations in Python

Figure 1

red band image

Figure 2

near infra-red band image

Figure 3

NDVI map

Figure 4

NDVI histogram

Figure 5

NDWI index

Figure 6

custom index

Figure 7

RGB image with burned area in red

Calculating Zonal Statistics on Rasters

Figure 1

Rasterized zones

Parallel raster computations using Dask

Figure 1

DataArray with Dask
Xarray Dask-backed DataArray

Figure 2

dask graph
Dask graph

Data cubes with ODC-STAC

Figure 1

NDVI before the wildfire
NDVI before the wildfire

Figure 2

NDVI after the wildfire
NDVI after the wildfire

Figure 3

NDVI plot with selected point
NDVI plot with selected point

Figure 4

NDVI time series
NDVI time series