Update episodes/06-raster-intro.md

Co-authored-by: Ou Ku <33962195+rogerkuou@users.noreply.github.com>
carpentries-incubator · Jul 25, 2023 · a478d43 · a478d43
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 -  Visualize single/multi-band raster data.
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-Raster datasets have been introduced in [Episode 1: Introduction to Raster Data]({{site.baseurl}}/01-intro-raster-data). Here, we introduce the fundamental principles, packages and metadata/raster attributes for working with raster data in Python. We will also explore how Python handles missing and bad data values.
+Raster datasets have been introduced in [Episode 1: Introduction to Raster Data](01-intro-raster-data.md). Here, we introduce the fundamental principles, packages and metadata/raster attributes for working with raster data in Python. We will also explore how Python handles missing and bad data values.
 
 [`rioxarray`](https://corteva.github.io/rioxarray/stable/) is the Python package we will use throughout this lesson to work with raster data. It is based on the popular [`rasterio`](https://rasterio.readthedocs.io/en/latest/) package for working with rasters and [`xarray`](https://xarray.pydata.org/en/stable/) for working with multi-dimensional arrays.
 `rioxarray` extends `xarray` by providing top-level functions (e.g. the `open_rasterio` function to open raster datasets) and by adding a set of methods to the main objects of the `xarray` package (the `Dataset` and the `DataArray`). These additional methods are made available via the `rio` accessor and become available from `xarray` objects after importing `rioxarray`.