docs: fix no-alt-text (#5154)

.markdownlint.yml

@@ -8,6 +8,5 @@ fix: true
 MD041: false # first-line-h1
 
 # Errors from .html to .md rename (first step in HTML to Markdown conversion)
-no-alt-text: false
 line-length: false
 # The block above this is to be eventually removed.

display/d.rast.arrow/d.rast.arrow.md

@@ -77,7 +77,7 @@ r.mapcalc "direction = atan(U_map, V_map)"
 d.rast.arrow map=direction type=grass magnitude_map=magnitude skip=3 grid=none
 ```
 
-![](d_rast_arrow_wind.png)  
+![Sea wind speed (magnitude) and direction shown in the Tasmanian Sea](d_rast_arrow_wind.png)  
 *Sea wind speed (magnitude) and direction shown in the Tasmanian Sea*
 
 ## SEE ALSO

doc/examples/gui/wxpython/g.gui.example.md

@@ -23,7 +23,7 @@ See README to learn how to get Example Tool to work.
 
 ### EXAMPLE TOOL TOOLBAR
 
-![](icons/layer-raster-add.png)  *Select raster layer*  
+![Select raster layer](icons/layer-raster-add.png)  *Select raster layer*  
 Select raster layer and compute statistics related to this layer.
 
 ## SEE ALSO

doc/gui/wxpython/example/g.gui.example.md

@@ -23,7 +23,7 @@ See README to learn how to get Example Tool to work.
 
 ### EXAMPLE TOOL TOOLBAR
 
-![](icons/layer-raster-add.png)  *Select raster layer*  
+![Select raster layer](icons/layer-raster-add.png)  *Select raster layer*  
 Select raster layer and compute statistics related to this layer.
 
 ## SEE ALSO

general/g.mapsets/g.mapsets.md

@@ -91,7 +91,7 @@ PERMANENT mapset are always selected):
 g.mapsets -s
 ```
 
-![](g_mapsets_gui.png)  
+![g_mapsets_gui](g_mapsets_gui.png)
 
 ### Print available mapsets
 

imagery/i.maxlik/i.maxlik.md

@@ -85,15 +85,14 @@ r.mapcalc "lsat7_2002_cluster_classes_filtered = \
            if(lsat7_2002_cluster_reject <= 12, lsat7_2002_cluster_classes, null())"
 ```
 
-![](i_maxlik_rgb.png)  
-RGB composite of input data
+![RGB composite of input data](i_maxlik_rgb.png)  
+*RGB composite of input data*
 
-![](i_maxlik_classes.png)  
-Output raster map with pixels classified (10 classes)
+![Output raster map with pixels classified (10 classes)](i_maxlik_classes.png)  
+*Output raster map with pixels classified (10 classes)*
 
-![](i_maxlik_rejection.png)  
-Output raster map with rejection probability values (pixel
-classification confidence levels)
+![Output raster map with rejection probability values (pixel classification confidence levels)](i_maxlik_rejection.png)  
+*Output raster map with rejection probability values (pixel classification confidence levels)*
 
 ## SEE ALSO
 

imagery/i.segment/i.segment.md

@@ -154,7 +154,7 @@ Try out a low threshold and check the results.
 i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02
 ```
 
-![](i_segment_ortho_segs_l1.jpg)
+![i_segment_ortho_segs_l1](i_segment_ortho_segs_l1.jpg)
 
 From a visual inspection, it seems this results in too many segments.
 Increasing the threshold, using the previous results as seeds, and
@@ -170,7 +170,7 @@ i.segment group=ortho_group output=ortho_segs_l4 threshold=0.2 seeds=ortho_segs_
 i.segment group=ortho_group output=ortho_segs_l5 threshold=0.3 seeds=ortho_segs_l4
 ```
 
-![](i_segment_ortho_segs_l2_l5.jpg)
+![i_segment_ortho_segs_l2_l5](i_segment_ortho_segs_l2_l5.jpg)
 
 The output `ortho_segs_l4` with **threshold**=0.2 still has too many
 segments, but the output with **threshold**=0.3 has too few segments. A
@@ -191,7 +191,7 @@ Run *i.segment* on the full map:
 i.segment group=ortho_group output=ortho_segs_final threshold=0.25 min=10
 ```
 
-![](i_segment_ortho_segs_final.jpg)
+![i_segment_ortho_segs_final](i_segment_ortho_segs_final.jpg)
 
 Processing the entire ortho image with nearly 10 million pixels took
 about 450 times more then for the final run.
@@ -217,14 +217,14 @@ i.segment group=singleband threshold=0.05 minsize=100
   output=lsat7_2002_80_segmented_min100 goodness=lsat7_2002_80_goodness_min100
 ```
 
-![](i_segment_lsat7_pan.png)  
-Original panchromatic channel of the Landsat7 scene
+![Original panchromatic channel of the Landsat7 scene](i_segment_lsat7_pan.png)  
+*Original panchromatic channel of the Landsat7 scene*
 
-![](i_segment_lsat7_seg_min5.png)  
-Segmented panchromatic channel, minsize=5
+![Segmented panchromatic channel, minsize=5](i_segment_lsat7_seg_min5.png)  
+*Segmented panchromatic channel, minsize=5*
 
-![](i_segment_lsat7_seg_min100.png)  
-Segmented panchromatic channel, minsize=100
+![Segmented panchromatic channel, minsize=100](i_segment_lsat7_seg_min100.png)  
+*Segmented panchromatic channel, minsize=100*
 
 ## TODO
 

ps/ps.map/ps.map.md

@@ -1424,9 +1424,8 @@ Generate map as Postsript file:
 ps.map input=simple_map.txt output=simple_map.ps
 ```
 
-![](ps_map_basic.png)
-*Figure: Result of for the a simple Wake county terrain and roads
-example*
+![Figure: Result of the simple Wake county terrain and roads example](ps_map_basic.png)  
+*Figure: Result of the simple Wake county terrain and roads example*
 
 ### More complicated example
 
@@ -1502,7 +1501,7 @@ g.region raster=elevation
 ps.map input=elevation_map.txt output=elevation.ps
 ```
 
-![](ps_map.png)
+![Figure: Result of for the more complicated Wake county, NC example](ps_map.png)  
 *Figure: Result of for the more complicated Wake county, NC example*
 
 More examples can be found on the [GRASS

raster/r.horizon/r.horizon.md

@@ -184,10 +184,10 @@ r.horizon elevation=elevation direction=0 step=5 bufferzone=200 \
     coordinates=636483.54,222176.25 maxdistance=5000 -d file=horizon.csv
 ```
 
-![](rhorizon_shaded_dem_point.png)  
+![Test point near high way intersection (North Carolina sample dataset)](rhorizon_shaded_dem_point.png)  
 *Test point near high way intersection (North Carolina sample dataset)*
 
-![](rhorizon_singlepoint_plot.png)  
+![Horizon angles for test point (CCW from East)](rhorizon_singlepoint_plot.png)  
 *Horizon angles for test point (CCW from East)*
 
 We can plot horizon in polar coordinates using Matplotlib in Python:
@@ -208,7 +208,7 @@ bars = ax.plot(horizon[:, 0] / 180 * np.pi,
 plt.show()
 ```
 
-![](rhorizon_polar_plot.png)  
+![Horizon plot in polar coordinates.](rhorizon_polar_plot.png)  
 *Horizon plot in polar coordinates.*
 
 ### Raster map mode

raster/r.in.lidar/r.in.lidar.md

@@ -40,7 +40,7 @@ point per cell is counted is sometimes called 2D (two dimensional)
 histogram because a histogram is used in univariate statistics (in one
 dimension) to count the number samples falling into a given bin.
 
-![](r_in_lidar_binning_count.png) ![](r_in_lidar_binning_mean.png)
+![r_in_lidar_binning_count](r_in_lidar_binning_count.png)
 
 *Figure: The binning on left was used to count number of points per
 (sometimes also called 2D histogram). The numbers in cells are examples
@@ -156,7 +156,7 @@ module. The **zrange** parameter is especially powerful when used
 together with the **base_raster** parameter. The **zrange** is applied
 to Z values after the **base_raster** reduction.
 
-![](r_in_lidar_zrange.png)
+![r_in_lidar_zrange](r_in_lidar_zrange.png)
 
 *Figure: This is the principle of zrange filter. Points with the Z
 coordinate value below the lower value in the range (here 180) are
@@ -206,7 +206,7 @@ data often come with precomputed DEMs (quality should be checked in this
 case) and there is often a DEM available for a given area (fit with the
 point cloud, especially vertical, and resolution should be checked).
 
-![](r_in_lidar_base_raster.png)
+![r_in_lidar_base_raster](r_in_lidar_base_raster.png)
 
 *Figure: This is a profile of base raster (in orange) representing
 digital elevation model and selected points, e.g. first return, from
@@ -430,7 +430,7 @@ r.in.lidar input="Serpent Mound Model LAS Data.laz" \
            output=Serpent_Mound_Model_LAS_Data method=mean
 ```
 
-![](r_in_lidar.png)  
+![Figure: Elevation for the whole area of Serpent Mound dataset](r_in_lidar.png)  
 *Figure: Elevation for the whole area of Serpent Mound dataset*
 
 ### Height above ground

raster/r.in.pdal/r.in.pdal.md

@@ -39,7 +39,7 @@ point per cell is counted is sometimes called 2D (two dimensional)
 histogram because a histogram is used in univariate statistics (in one
 dimension) to count the number samples falling into a given bin.
 
-![](r_in_lidar_binning_count.png) ![](r_in_lidar_binning_mean.png)
+![r_in_lidar_binning_count](r_in_lidar_binning_count.png)
 
 *Figure: The binning on left was used to count number of points per
 (sometimes also called 2D histogram). The numbers in cells are examples
@@ -185,7 +185,7 @@ module. The **zrange** parameter is especially powerful when used
 together with the **base_raster** parameter. The **zrange** is applied
 to Z values after the **base_raster** reduction.
 
-![](r_in_lidar_zrange.png)
+![r_in_lidar_zrange](r_in_lidar_zrange.png)
 
 *Figure: This is the principle of zrange filter. Points with the Z
 coordinate value below the lower value in the range (here 180) are
@@ -251,7 +251,7 @@ data often come with precomputed DEMs (quality should be checked in this
 case) and there is often a DEM available for a given area (fit with the
 point cloud, especially vertical, and resolution should be checked).
 
-![](r_in_lidar_base_raster.png)
+![r_in_lidar_base_raster](r_in_lidar_base_raster.png)
 
 *Figure: This is a profile of base raster (in orange) representing
 digital elevation model and selected points, e.g. first return, from

raster/r.li/r.li.padcv/r.li.padcv.md

@@ -6,9 +6,9 @@ in hectares as:
 with:  
 
 - *SD*: standard deviation of patch area size
-  ![](rlipadcv_formula2.png)
+  ![standard deviation of patch area size](rlipadcv_formula2.png)
 - *MPS*: mean patch area size
-- **a_i*: area of patch *i*
+- *a_i*: area of patch *i*
 - *N_patch*: number of patches
 
 ## NOTES

raster3d/r3.flow/r3.flow.md

@@ -83,7 +83,7 @@ r3.flow vector_field=vx,vy,vz flowaccumulation=gw_flowacc
 
 We can visualize the result in 3D view:
 
-![](r3flow_flowlines.png)
+![r3flow_flowlines](r3flow_flowlines.png)
 
 We can store velocity values (and values of the input 3D raster map if
 we use option **input**) for each segment of flow line in an attribute
@@ -97,7 +97,7 @@ v.colors map=flowlines_color@user1 use=attr column=velocity color=bcyr
 Again, we visualize the result in 3D view and we check 'use color for
 thematic rendering' on 3D view vector page.
 
-![](r3flow_flowlines_color.png)
+![r3flow_flowlines_color](r3flow_flowlines_color.png)
 
 ## SEE ALSO
 

raster3d/r3.in.lidar/r3.in.lidar.md

@@ -4,8 +4,7 @@ The *[r.in.lidar](r.in.lidar.md)* module is very similar to the
 *r3.in.lidar* module and many parts of its documentation apply also for
 *r3.in.lidar*.
 
-![](r3_in_lidar.png)
-
+![Proportional count of points per 3D cell](r3_in_lidar.png)
 *Figure: Proportional count of points per 3D cell. When 50% of all
 points in a vertical column fall into a given 3D cell, the value is 0.5.
 Here, the green color was assigned to 0.5, red to 1 and yellow to 0. The

scripts/d.to.rast/d.to.rast.md

@@ -25,10 +25,8 @@ d.to.rast output=composite
 Then uncheck all layers except for elevation and switch to 3D view. In
 Data tab, set color map to the newly created composite map.
 
-![](d_to_rast_3D_example.jpg)
-
-Figure: Raster map created by *d.to.rast* draped over digital elevation
-model.
+![Raster map created by d.to.rast draped over digital elevation model](d_to_rast_3D_example.jpg)
+*Figure: Raster map created by *d.to.rast* draped over digital elevation model.*
 
 ## SEE ALSO
 

scripts/r.rgb/r.rgb.md

@@ -13,7 +13,7 @@ r.rgb input=elevation red=elevation.r green=elevation.g blue=elevation.b
 In this case *r.rgb* produces in the current mapset three new raster
 maps - 'elevation.r', 'elevation.g', 'elevation.b'.
 
-![](r_rgb_elevation.png)  
+![r_rgb_elevation](r_rgb_elevation.png)
 
 ## SEE ALSO
 

scripts/v.dissolve/v.dissolve.md

@@ -4,8 +4,10 @@ The *v.dissolve* module is used to merge adjacent or overlapping
 features in a vector map that share the same category value. The
 resulting merged feature(s) retain this category value.
 
-![](v_dissolve_zipcodes.png) ![](v_dissolve_towns.png)
-
+![Areas with the same attribute value (first image) are merged
+into one (second image)](v_dissolve_zipcodes.png)
+![Areas with the same attribute value (first image) are merged
+into one (second image)](v_dissolve_towns.png)  
 *Figure: Areas with the same attribute value (first image) are merged
 into one (second image).*
 

vector/v.buffer/v.buffer.md

@@ -45,18 +45,18 @@ The following vector line related corners (also called "cap") exist:
 By default *v.buffer* creates rounded buffers (blue color on figure
 below):
 
-![](v_buffer_line.png)
+![v_buffer_line](v_buffer_line.png)
 
 Straight corners with caps are created using the **-s** flag (red color
 on the figure below), while the **-c** flag doesn't make caps at the
 ends of polylines (green color on the figure below):
 
-![](v_buffer_line_s.png) ![](v_buffer_line_c.png)
+![v_buffer_line_s](v_buffer_line_s.png)
 
 With a point vector map as input data, square buffers are created
 instead of round buffers by using the **-s** flag.
 
-![](v_buffer_point_s.png)
+![v_buffer_point_s](v_buffer_point_s.png)
 
 ## EXAMPLES
 

vector/v.cluster/v.cluster.md

@@ -117,8 +117,8 @@ d.mon wx0
 d.vect map=clusters_optics layer=2 icon=basic/point size=10 color=none
 ```
 
-![](v_cluster_4_methods.png)
-
+![Four different methods with default settings applied to 1000
+random points](v_cluster_4_methods.png)  
 *Figure: Four different methods with default settings applied to 1000
 random points generated in the same way as in the example.*
 

vector/v.decimate/v.decimate.md

@@ -72,9 +72,9 @@ region) which has unique categories (e.g. LIDAR classes):
 v.decimate input=points_all output=points_decimated_unique_cats layer=1 -g -c
 ```
 
-![](v_decimate_original.png) ![](v_decimate_count.png)
-![](v_decimate_grid_cat.png)
-
+![original points](v_decimate_original.png)
+![decimation result with every forth point preserved](v_decimate_count.png)
+![grid-based decimation result with points with unique categories in each grid cell](v_decimate_grid_cat.png)  
 *Figure 1: Comparison of original points, decimation result with every
 forth point preserved, and grid-based decimation result with points with
 unique categories in each grid cell*

vector/v.lidar.edgedetection/v.lidar.edgedetection.md

@@ -96,13 +96,11 @@ v.surf.rst input=only_terrain elevation=terrain
 v.extract input=correction layer=2 cats=2,3,4 output=objects
 ```
 
-![](v_lidar_edgedetection.png)
-
+![Example output from complete workflow](v_lidar_edgedetection.png)  
 *Figure 1: Example output from complete workflow (red: objects, green:
 terrain)*
 
-![](v_lidar_edgedetection_objects.png)
-
+![3D visualization of filtered object points](v_lidar_edgedetection_objects.png)  
 *Figure 2: 3D visualization of filtered object points (red) and terrain
 created from terrain points (gray)*
 

vector/v.mkgrid/v.mkgrid.md

@@ -106,8 +106,8 @@ v.mkgrid type=point map=pointpattern2
 v.patch input=pointpattern1,pointpattern2 output=pointpattern3
 ```
 
-![](v_mkgrid_ppattern.png)  
-Different point patterns for sampling design
+![Different point patterns for sampling design](v_mkgrid_ppattern.png)  
+*Different point patterns for sampling design*
 
 ### Creating hexagons in a metric projection
 
@@ -122,8 +122,8 @@ v.mkgrid map=hexagons -h
 d.grid 5000
 ```
 
-![](v_mkgrid_hexagons.png)  
-Hexagon map
+![Hexagon map](v_mkgrid_hexagons.png)  
+*Hexagon map*
 
 ### Using hexagons for point density
 
@@ -160,8 +160,8 @@ considered). The last command sets the vector map color table to
 v.colors map=hexagons use=attr column=count color=viridis
 ```
 
-![](v_mkgrid.png)  
-Point density in a hexagonal grid
+![Point density in a hexagonal grid](v_mkgrid.png)  
+*Point density in a hexagonal grid*
 
 ## SEE ALSO
 

vector/v.net.visibility/v.net.visibility.md

@@ -131,7 +131,7 @@ d.vect archsites col=red
 
 Here is an example with artificial data.
 
-![](v_net_visibility.png)
+![v_net_visibility](v_net_visibility.png)
 
 Load data using here document syntax (Bash and unix-like commands lines
 only):