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diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
index 1465e8e..64e6863 100644
--- a/docs/source/introduction.rst
+++ b/docs/source/introduction.rst
@@ -1,6 +1,13 @@
 Introduction
 ============
 
+.. figure:: images/hag.png
+   :alt: Height above ground
+   :align: center
+   :figclass: align-center
+
+   Height Above Ground visualization of LiDAR point cloud data.
+
 `pyforestscan` is a Python package designed for processing and analyzing LiDAR point cloud data. It simplifies complex LiDAR workflows, making it easier to extract and visualize forest structure metrics such as canopy height, plant area density, and more.
 
 Key Features
@@ -13,6 +20,24 @@ Key Features
 - **Outlier Removal and Point Cloud Cleaning**: Efficiently clean data and remove statistical outliers.
 - **Visualization Tools**: Plot 2D and 3D representations of point clouds and vegetation metrics.
 
+.. list-table::
+   :widths: 45 5 45
+   :header-rows: 0
+
+   * - .. figure:: images/chm.png
+         :alt: Canopy Height Model
+         :width: 300
+
+         Canopy Height Model
+
+     -
+
+     - .. figure:: images/pai.png
+         :alt: Plant Area Index
+         :width: 300
+
+         Plant Area Index
+
 Core Modules
 ------------
 
diff --git a/docs/source/metric_calculation.rst b/docs/source/metric_calculation.rst
index 5aa175b..3b185bf 100644
--- a/docs/source/metric_calculation.rst
+++ b/docs/source/metric_calculation.rst
@@ -3,6 +3,13 @@ Forest Structure Metrics Calculations
 
 The core forest structural metrics generated by PyForestScan are based on well-established methods in ecology.
 
+.. figure:: images/voxel_metrics.jpg
+   :alt: Forest structure from voxels.
+   :align: center
+   :figclass: align-center
+
+   Forest structural metrics as calculated from points within voxels. Voxel resolution is given by Δx, Δy, Δz.
+
 Canopy Height
 -------------