Fix broken links in the NumPy, viewshed and Spatial Ecology tutorials

content/tutorials/external/spatial_ecology_geocomp.qmd

@@ -17,7 +17,7 @@ execute:
 ![](images/spatial_ecology.png){.preview-image}
 
 The following tutorials were developed as part of the
-[Spatial Ecology courses](https://spatial-ecology.net/training/),
+[Spatial Ecology courses](https://spatial-ecology.net/),
 where students are introduced to an array of powerful open-source geocomputation tools and machine learning methodologies under the Linux environment.
 
 ## GRASS Introduction

content/tutorials/numpy_integration/grass_numpy_integration.qmd

@@ -113,7 +113,7 @@ fractal = tools.r_surf_fractal(output=np.array, seed=6)
 
 Directly passing NumPy arrays to GRASS tools and receiving them back is a new feature in GRASS v8.5.
 If you work with older versions of GRASS, you can use
-[grass.script.array](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#script.array.array):
+[grass.script.array](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#grass.script.array.array):
 
 ```{python}
 import grass.script as gs
@@ -242,7 +242,7 @@ For creating a stream network with gullies using GRASS, see the
 :::
 
 Now if we want to store the eroded topography as a native GRASS raster, we can use
-[grass.script.array](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#script.array.array)
+[grass.script.array](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#grass.script.array.array)
 to create a GRASS array object with the dimensions given by the current computational region.
 Then we copy the NumPy array and write the raster map as GRASS native raster.
 
@@ -259,7 +259,7 @@ grass_elevation.write("elevation")
 
 ::: {.callout-note title="What about N-dimensional arrays?"}
 
-For 3D arrays, you can use [grass.script.array3d](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#script.array.array3d) package.
+For 3D arrays, you can use [grass.script.array3d](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#grass.script.array.array3d) package.
 
 For N-dimensional arrays, check out the new [xarray-grass](https://github.com/lrntct/xarray-grass) bridge developed by [Laurent Courty](https://github.com/lrntct) {{< fa rocket >}}
 :::
@@ -287,7 +287,7 @@ elevation_3dmap.show()
 
 ![Resulting elevation with streams in 3D](elevation_3d.webp)
 
-And now let's augment it with [Nano Banana AI](https://nanobanana.ai/):
+You can take it a step further and make the rendering more realistic with an AI image model such as Nano Banana:
 
 ![AI-enhanced landscape rendering for visual realism](thumbnail.webp)
 

content/tutorials/viewshed/visibility_analysis.qmd

@@ -59,7 +59,7 @@ tutorial. Every step also works from the GRASS GUI or command line; use the tool
 The towers are real places, so rather than use a prepackaged sample dataset we
 download the terrain around them from the USGS. We create a new GRASS project in UTM
 zone 17N (EPSG:32617) with
-[create_project](https://grass.osgeo.org/grass-stable/manuals/libpython/script.html#script.core.create_project),
+[create_project](https://grass.osgeo.org/grass-stable/manuals/libpython/grass.script.html#grass.script.create_project),
 then fetch 1 arc-second (about 30 m) elevation from the USGS National Map with
 [r.in.usgs](https://grass.osgeo.org/grass-stable/manuals/addons/r.in.usgs.html), an
 add-on installed with