update figs of episodes 1-4

carpentries-incubator · Jul 26, 2023 · 284087e · 284087e
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diff --git a/episodes/01-intro-raster-data.md b/episodes/01-intro-raster-data.md
@@ -49,7 +49,7 @@ that connects the data to a particular location. This includes the
 raster's extent and cell size, the number of rows and columns, and
 its coordinate reference system (or CRS).
 
-![Raster Concept (Source: National Ecological Observatory Network (NEON))](fig/E01-01-raster_concept.png){alt="raster concept"}
+![Raster Concept (Source: National Ecological Observatory Network (NEON))](fig/E01/raster_concept.png){alt="raster concept"}
 
 Some examples of continuous rasters include:
 
@@ -61,7 +61,7 @@ A map of elevation for Harvard Forest derived from the [NEON AOP LiDAR sensor](h
 is below. Elevation is represented as a continuous numeric variable in this map. The legend
 shows the continuous range of values in the data from around 300 to 420 meters.
 
-![Continuous Elevation Map: HARV Field Site](fig/E01-02-continuous-elevation-HARV-plot-01.png){alt="elevation Harvard forest"}
+![Continuous Elevation Map: HARV Field Site](fig/E01/continuous-elevation-HARV-plot-01.png){alt="elevation Harvard forest"}
 
 Some rasters contain categorical data where each pixel represents a discrete
 class such as a landcover type (e.g., "forest" or "grassland") rather than a
@@ -72,7 +72,7 @@ maps include:
 2. Tree height maps classified as short, medium, and tall trees.
 3. Elevation maps classified as low, medium, and high elevation.
 
-![USA landcover classification](fig/E01-03-USA_landcover_classification.png){alt="USA landcover classification"}
+![USA landcover classification](fig/E01/USA_landcover_classification.png){alt="USA landcover classification"}
 
 The map above shows the contiguous United States with landcover as categorical
 data. Each color is a different landcover category. (Source: Homer, C.G., et
@@ -118,7 +118,7 @@ The spatial extent of an object represents the geographic edge or
 location that is the furthest north, south, east and west. In other words, extent
 represents the overall geographic coverage of the spatial object.
 
-![Spatial extent image (Image Source: National Ecological Observatory Network (NEON))](fig/E01-04-spatial_extent.png){alt="spatial extent objects"}
+![Spatial extent image (Image Source: National Ecological Observatory Network (NEON))](fig/E01/spatial_extent.png){alt="spatial extent objects"}
 
 :::challenge
 ## Extent Challenge
@@ -142,7 +142,7 @@ A resolution of a raster represents the area on the ground that each
 pixel of the raster covers. The image below illustrates the effect
 of changes in resolution.
 
-![Resolution image (Source: National Ecological Observatory Network (NEON))](fig/E01-05-raster_resolution.png){alt="resolution image"}
+![Resolution image (Source: National Ecological Observatory Network (NEON))](fig/E01/raster_resolution.png){alt="resolution image"}
 
 ### Raster Data Format for this Workshop
 
@@ -183,7 +183,7 @@ electromagnetic spectrum. The pixel brightness for each band, when
 composited
 creates the colors that we see in an image.
 
-![RGB multi-band raster image (Source: National Ecological Observatory Network (NEON).)](fig/E01-06-RGBSTack_1.jpg){alt="multi-band raster"}
+![RGB multi-band raster image (Source: National Ecological Observatory Network (NEON).)](fig/E01/RGBSTack_1.jpg){alt="multi-band raster"}
 
 We can plot each band of a multi-band image individually.
 

diff --git a/episodes/02-intro-vector-data.md b/episodes/02-intro-vector-data.md
@@ -21,7 +21,7 @@ locations (x, y values) known as vertices that define the shape of the spatial
 object. The organization of the vertices determines the type of vector that we
 are working with: point, line or polygon.
 
-![Types of vector objects (Image Source: National Ecological Observatory Network (NEON))](fig/E02-01-pnt_line_poly.png){alt="vector data types"}
+![Types of vector objects (Image Source: National Ecological Observatory Network (NEON))](fig/E02/pnt_line_poly.png){alt="vector data types"}
 
 * **Points:** Each point is defined by a single x, y coordinate. There can be
 many points in a vector point file. Examples of point data include: sampling
@@ -51,7 +51,7 @@ The plot below includes examples of two of the three types of vector
 objects. Use the definitions above to identify which features
 are represented by which vector type.
 
-![Vector Type Examples](fig/E02-02-vector_types_examples.png){alt="vector type examples"}
+![Vector Type Examples](fig/E02/vector_types_examples.png){alt="vector type examples"}
 
 ::::solution
 ## Solution

diff --git a/episodes/03-crs.md b/episodes/03-crs.md
@@ -79,11 +79,11 @@ peel is similar to how projections get made.
 * A datum is the choice of fruit to use. Is the Earth an orange, a lemon, a
   lime, a grapefruit?
 
-![Datum Fruit Example ([Image source](https://github.com/MicheleTobias/R-Projections-Workshop))](fig/E03-01-citrus.jpg){alt="datum fruit example"}
+![Datum Fruit Example ([Image source](https://github.com/MicheleTobias/R-Projections-Workshop))](fig/E03/citrus.jpg){alt="datum fruit example"}
 
 A projection is how you peel your orange and then flatten the peel.
 
-![Projection Citrus Peel Example (Image from Prof Drika Geografia, Projeções Cartográficas)](fig/E03-02-orange-peel-earth.jpg){alt="projection citrus peel"}
+![Projection Citrus Peel Example (Image from Prof Drika Geografia, Projeções Cartográficas)](fig/E03/orange-peel-earth.jpg){alt="projection citrus peel"}
 
 * An additional parameter could include a definition of the location of the
 stem of the fruit. What other parameters could be included in this analogy?

diff --git a/episodes/fig/E01-06-RGBSTack_1.jpg → episodes/fig/E01/RGBSTack_1.jpg b/episodes/fig/E01-06-RGBSTack_1.jpg → episodes/fig/E01/RGBSTack_1.jpg
diff --git a/...g/E01-03-USA_landcover_classification.png → .../fig/E01/USA_landcover_classification.png b/...g/E01-03-USA_landcover_classification.png → .../fig/E01/USA_landcover_classification.png
diff --git a/...-02-continuous-elevation-HARV-plot-01.png → ...E01/continuous-elevation-HARV-plot-01.png b/...-02-continuous-elevation-HARV-plot-01.png → ...E01/continuous-elevation-HARV-plot-01.png
diff --git a/episodes/fig/E01-01-raster_concept.png → episodes/fig/E01/raster_concept.png b/episodes/fig/E01-01-raster_concept.png → episodes/fig/E01/raster_concept.png
diff --git a/episodes/fig/E01-05-raster_resolution.png → episodes/fig/E01/raster_resolution.png b/episodes/fig/E01-05-raster_resolution.png → episodes/fig/E01/raster_resolution.png
diff --git a/episodes/fig/E01-04-spatial_extent.png → episodes/fig/E01/spatial_extent.png b/episodes/fig/E01-04-spatial_extent.png → episodes/fig/E01/spatial_extent.png
diff --git a/episodes/fig/E02-01-pnt_line_poly.png → episodes/fig/E02/pnt_line_poly.png b/episodes/fig/E02-01-pnt_line_poly.png → episodes/fig/E02/pnt_line_poly.png
diff --git a/...odes/fig/E02-02-vector_types_examples.png → episodes/fig/E02/vector_types_examples.png b/...odes/fig/E02-02-vector_types_examples.png → episodes/fig/E02/vector_types_examples.png
diff --git a/episodes/fig/E03-01-citrus.jpg → episodes/fig/E03/citrus.jpg b/episodes/fig/E03-01-citrus.jpg → episodes/fig/E03/citrus.jpg
diff --git a/episodes/fig/E03-02-orange-peel-earth.jpg → episodes/fig/E03/orange-peel-earth.jpg b/episodes/fig/E03-02-orange-peel-earth.jpg → episodes/fig/E03/orange-peel-earth.jpg