diff --git a/lidar_pipeline/tests/test_visualizations.py b/lidar_pipeline/tests/test_visualizations.py
index 177515d..b412bd0 100644
--- a/lidar_pipeline/tests/test_visualizations.py
+++ b/lidar_pipeline/tests/test_visualizations.py
@@ -198,4 +198,100 @@ class TestFlow:
             data = src.read(1)
             # log1p(x) >= 0 for x >= 0
             valid = data[~np.isnan(data)]
-            assert np.nanmin(valid) >= 0
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+            assert np.nanmin(valid) >= 0
+
+
+class TestRRIM:
+    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
+        from lidar_pipeline.visualizations import generate_rrim
+        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
+        assert result is not None
+        assert result.exists()
+        assert result.suffix == ".tif"
+
+    def test_rrim_is_rgb_3band(self, synthetic_dem, tmp_output_dir):
+        import rasterio
+        from lidar_pipeline.visualizations import generate_rrim
+        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            assert src.count == 3, f"Expected 3 bands, got {src.count}"
+            assert src.dtypes[0] == 'uint8'
+
+    def test_rrim_values_0_255(self, synthetic_dem, tmp_output_dir):
+        import rasterio
+        from lidar_pipeline.visualizations import generate_rrim
+        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            for band in range(1, 4):
+                data = src.read(band)
+                assert data.min() >= 0
+                assert data.max() <= 255
+
+    def test_rrim_no_nan(self, synthetic_dem, tmp_output_dir):
+        """RRIM is uint8 RGB — NaN zones are set to 0 (black)."""
+        import rasterio
+        from lidar_pipeline.visualizations import generate_rrim
+        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            # uint8 bands should not have NaN
+            for band in range(1, 4):
+                data = src.read(band)
+                assert not np.isnan(data).any(), f"Band {band} has NaN values"
+
+
+class TestMultiHillshade:
+    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
+        from lidar_pipeline.visualizations import generate_multi_hillshade
+        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
+        assert result is not None
+        assert result.exists()
+        assert result.suffix == ".tif"
+
+    def test_multi_hillshade_is_rgb_3band(self, synthetic_dem, tmp_output_dir):
+        import rasterio
+        from lidar_pipeline.visualizations import generate_multi_hillshade
+        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            assert src.count == 3, f"Expected 3 bands, got {src.count}"
+            assert src.dtypes[0] == 'uint8'
+
+    def test_multi_hillshade_values_0_255(self, synthetic_dem, tmp_output_dir):
+        import rasterio
+        from lidar_pipeline.visualizations import generate_multi_hillshade
+        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            for band in range(1, 4):
+                data = src.read(band)
+                assert data.min() >= 0
+                assert data.max() <= 255
+
+
+class TestLocalDominance:
+    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
+        from lidar_pipeline.visualizations import generate_local_dominance
+        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
+        assert result is not None
+        assert result.exists()
+        assert result.suffix == ".tif"
+
+    def test_dominance_values_0_1(self, synthetic_dem, tmp_output_dir):
+        import rasterio
+        from lidar_pipeline.visualizations import generate_local_dominance
+        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
+        with rasterio.open(result) as src:
+            data = src.read(1)
+            valid = data[~np.isnan(data)]
+            assert np.nanmin(valid) >= 0, "Local dominance should be >= 0"
+            assert np.nanmax(valid) <= 1, "Local dominance should be <= 1"
+
+    def test_dominance_nan_mask_preserved(self, synthetic_dem, tmp_output_dir):
+        """Check that NaN zones from original DEM are preserved."""
+        import rasterio
+        from lidar_pipeline.visualizations import generate_local_dominance
+        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
+        # The synthetic DEM has no NaN, so this just verifies the output is valid
+        with rasterio.open(result) as src:
+            data = src.read(1)
+            # Shape should match input
+            assert data.shape[0] > 0
+            assert data.shape[1] > 0
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