# Using SfM Photogrammetry to Estimate the Volume of a Sediment Deposit on the Ain River

Louis Rey

► To cite this version:

Louis Rey. Using SfM Photogrammetry to Estimate the Volume of a Sediment Deposit on the Ain River. Doctoral. Using SfM Photogrammetry to Estimate the Volume of a Sediment Deposit on the Ain River, France. 2025, pp.26. hal-05047194

HAL Id: hal-05047194

https://hal.science/hal-05047194v1

Submitted on 25 Apr 2025

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# Using SfM Photogrammetry to Estimate the Volume of a Sediment Deposit on the Ain River

Rey Louis

03/02/2025

<figure><img src="image_2.png" /></figure>

EVS

Environnement
Ville Société

cnrs

UMR 5600
Table of contents

<table><tr><td>1. Presentation of the study</td><td>3</td></tr><tr><td>1.1. Work environment</td><td>3</td></tr><tr><td>1.2. Goal of this practical work</td><td>4</td></tr><tr><td>1.3. Data acquisition</td><td>5</td></tr><tr><td>2. Build tie cloud (nuage de points homologues)</td><td>6</td></tr><tr><td>2.1. Import photos</td><td>6</td></tr><tr><td>2.2. Observe metadata</td><td>8</td></tr><tr><td>2.3. Update coordinate system preferences</td><td>9</td></tr><tr><td>2.4. Image quality control</td><td>9</td></tr><tr><td>2.5. Align images</td><td>10</td></tr><tr><td>2.6. Clean tie points</td><td>12</td></tr><tr><td>2.7. Create two chunks (projects)</td><td>12</td></tr><tr><td>3. Create models with or without GCPs</td><td>13</td></tr><tr><td>3.1. Without GCPs</td><td>13</td></tr><tr><td>3.1.1. Optimized camera alignment</td><td>13</td></tr><tr><td>3.1.2. Build dense cloud</td><td>14</td></tr><tr><td>3.1.3. Create dense cloud</td><td>15</td></tr><tr><td>3.1.4. Clean dense cloud</td><td>16</td></tr><tr><td>3.1.5. Class ground points</td><td>18</td></tr><tr><td>3.1.6. Build a DEM (here MNT)</td><td>18</td></tr><tr><td>3.1.7. Build Orthomosaic (optional for the project)</td><td>19</td></tr><tr><td>3.2. With GCPs</td><td>20</td></tr><tr><td>3.2.1. Import GCPs</td><td>20</td></tr><tr><td>3.2.2. Georeferencing GCPs</td><td>20</td></tr><tr><td>3.2.3. Repeat the steps from the part 3.1</td><td>21</td></tr><tr><td>4. Export products</td><td>21</td></tr><tr><td>4.1. Export DEM and orthomosaic</td><td>21</td></tr><tr><td>4.2. Export the report of everything you did in Metashape</td><td>22</td></tr><tr><td>5. QGIS Calculate a volume from a DEM</td><td>23</td></tr><tr><td>5.1. Import your DEM of the terrain you made in last steps</td><td>23</td></tr><tr><td>5.2. Estimate difference of height between model and GNSS points GCP</td><td>23</td></tr><tr><td>5.3. Cut you DEM by the limit of the sediment depot</td><td>23</td></tr><tr><td>5.4. Import water level points format .shp</td><td>23</td></tr><tr><td>5.5. Create a raster from measured water level points with linear interpolation</td><td>23</td></tr></table>
<table><tr><td>5.6.</td><td>Cut the peace of your DEM that you are interested to know the volume</td><td>24</td></tr><tr><td>5.7.</td><td>Subtract MNT with raster</td><td>25</td></tr><tr><td>5.8.</td><td>Calculate the volume of the sediment depot</td><td>25</td></tr><tr><td colspan="2">Bibliography</td><td>26</td></tr></table>

<table><tr><td>Figure 1 : Agisoft Metashape Pro environment</td><td>3</td></tr><tr><td>Figure 2 : Scheme of the study</td><td>4</td></tr><tr><td>Figure 3 : Area of study</td><td>4</td></tr><tr><td>Figure 4 : Differences between DSM and DTM (CDEMA, 2014)</td><td>6</td></tr><tr><td>Figure 5 : On the left-up screen informations from camera acquisition for each image and on the right bottom screen information about each picture</td><td>6</td></tr><tr><td>Figure 6 : Import coordinates and information of IMU (yaw,pitch and Roll)</td><td>7</td></tr><tr><td>Figure 7 : coordinates of your images, taking from your .txt file</td><td>7</td></tr><tr><td>Figure 8 : metadata image</td><td>8</td></tr><tr><td>Figure 9 : Import ground points coordinates</td><td>8</td></tr><tr><td>Figure 10 : Update coordinate system preferences</td><td>9</td></tr><tr><td>Figure 11 : Estimate Image Quality</td><td>10</td></tr><tr><td>Figure 12 : Align Photos tools</td><td>11</td></tr><tr><td>Figure 14 : Tool to select in the main panel</td><td>12</td></tr><tr><td>Figure 14 : Optimized camera alignment</td><td>13</td></tr><tr><td>Figure 15 : Filter Photos by Marker</td><td>14</td></tr><tr><td>Figure 17 : Tool to refresh your model in the main panel</td><td>14</td></tr><tr><td>Figure 19 : Build Dense Cloud</td><td>15</td></tr><tr><td>Figure 21 : Filter points by confidence</td><td>16</td></tr><tr><td>Figure 20 : Button Show Markers</td><td>16</td></tr><tr><td>Figure 22 : Select by hands points</td><td>17</td></tr><tr><td>Figure 23 : Classify ground points</td><td>18</td></tr><tr><td>Figure 24 : Build DEM (en français MNT)</td><td>19</td></tr><tr><td>Figure 25 : Build orthomosaic</td><td>20</td></tr><tr><td>Figure 15 : Red target</td><td>21</td></tr><tr><td>Figure 26 : DEM and Orthomosaic</td><td>22</td></tr><tr><td>Figure 27 : Export the report of your workflow in Metashape</td><td>22</td></tr><tr><td>Figure 28 : Create a new point layer</td><td>24</td></tr><tr><td>Figure 29 : Interpolation of your points layer to raster</td><td>24</td></tr><tr><td>Figure 29 : Cut raster according to a mask</td><td>25</td></tr></table>
# 1. Presentation of the study

## 1.1. Work environment

Metashape = https://www.agisoft.com/downloads/installer/

QGIS = https://qgis.org/fr/site/forusers/download.html

Open Metashape (Figure 1) and go into **Tools>Preferences> GPU** (check your GPU devices to use them) then still in Preferences go to **General > Language** (choose the one you need).

At this point, you should save your project: **File > Save as...** Give your project a name, ensuring that the 'save as type' box is 'PhotoScan project (*.psx)' (or *.psz if using an older version of PhotoScan).

<figure><img src="image_3.png" /><figcaption>Figure 1: Agisoft Metashape Pro environment</figcaption></figure>

### To know:

1. Save your project regularly to prevent data loss.

2. Adjust parameters based on project specifics.

3. Remember that the quality of results depends on the input data quality and appropriate settings in Metashape.

4. Metashape documentation here: https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf
or here in French: https://www.agisoft.com/pdf/manuals_other/metashape_pro_fr_1_5.pdf
## 1.2. Goal of this practical work

The Syndicat Rivière Ain Aval Affluents (SR3A) has deposited several cubic meters of sediment on the banks of the Ain, south of the village of Varambon, in order to compensate for a decrease in sediment inputs into the river. However, it is impossible to get hold of the document where the volume that was deposited was noted...

We ask you to find a way to estimate as accurately as possible the volume of sediments depot for the syndicat (Figure 2). Look at the picture of the terrain (Figure 3).

Look where it is, on Google Earth: https://earth.google.com/earth/d/1oszw_eheioh4IBu_iL-mI504gMGBc7IU?usp=sharing

<figure><img src="image_4.png" /><figcaption>Figure 2 : Scheme of the study</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3 : Area of study</figcaption></figure>
## 1.3. Data acquisition

### Capture Aerial or Ground Photos

Images have been captured with good coverage and sufficient overlap (70% overlap). Usually, we use an overlap of 70% in longitudinal and 70% in lateral.

Before the flight, the height is adjusted according to the camera parameters to get the spatial resolution you need for your project.

### Collect GCP on the field

On-site, well-distributed Ground Controlling Points (GCP) are flashed. Record precise coordinates for each point (here it has been used GNSS Trimble R12). I advise you to import your points coordinates into a GIS as QGIS. Then choose which points will be used as GCP among all points and which will be used as check points.
## 2. Build tie cloud (nuage de points homologues)

Explanation below (Figure 4)(1)

<figure><img src="image_6.png" /><figcaption>Figure 4 :Differences between DSM and DTM (CDEMA, 2014)</figcaption></figure>

### 2.1. Import photos

Go to **Workflow > Add Photos...** or **Add Folder...**. You can choose to either add a selection of photos or add an entire folder. Select the folder where the photos are stored and all of them will be brought together. Alternatively, drag the image files directly into the Workspace pane.

If you have images with **EXIF** information you need to push the button indicates to collect coordinates information for each image (Figure 5). **Even though there is no EXIF information you could still have the coordinates in the image.**

"EXIF stands for 'Exchangeable Image File' or data exchange file. EXIF is a set of information relating to each photo and presented in a standardized format. This data is generated by the camera when shooting, automatically. They are stored in the image file for JPG, TIFF and RAW formats." (https://www.nikonpassion.com/qu-est-ce-que-donnees-exif-comment-lire-utiliser/).

<figure><img src="image_7.png" /><figcaption>Figure 5 :On the left-up screen informations from camera acquisition for each image and on the right bottom screen information about each picture</figcaption></figure>
When you import your images and the coordinates are not found by the software even though you have the coordinates into a file like .txt file there is a way to catch up the coordinates.

First of all, if you have a file with name of each image you need to add the extension format of the image. For instance, if the image is in .tif you need to add the information. Moreover, you need as well to have the separation of column right (Figure 7). **Be careful, to convert your coordinates into coordinates accepted by Metashape.**

Then, when your file imported is ok, you must go to **File>Import>Reference** (Figure 6)

<figure><img src="image_8.png" /><figcaption>Figure 7: coordinates of your images, taking from your .txt file</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6: Import coordinates and information of IMU (yaw,pitch and Roll)</figcaption></figure>
## 2.2. Observe metadata

Right click > Show infos

<figure><img src="image_10.png" /><figcaption>Figure 8 : metadata image</figcaption></figure>

What do you see ? Que voyez-vous ? (Figure 8)

File>Import> Import Reference > GCP (check that your columns are correctly placed)

<figure><img src="image_11.png" /><figcaption>Figure 9 : Import ground points coordinates</figcaption></figure>

<figure><img src="image_12.png" /></figure>

Before continuing, go into reference Settings and put the good coordinate system for camera and marker reference.
The GCPs **allow you to convert your model in the desired coordinate system and/or adjust the altitude.**

## 2.3. Update coordinate system preferences

Now, you have to update coordinate preferences for project, camera and references (Figure 10).

Choose the coordinates system in the panel. If you use one with a geoid applied you may need to import a geoid in Metashape Pro folders. Go at this link to get geoid file adapted for Metashape: https://www.agisoft.com/downloads/geoids/. To know the process to do follow this link : https://agisoft.freshdesk.com/support/solutions/articles/31000148332-how-to-use-height-above-geoid-for-the-coordinate-system.

<figure><img src="image_13.png" /><figcaption>Figure 10 : Update coordinate system preferences</figcaption></figure>

## 2.4. Image quality control

The aim of this step is to **eliminate any blurred, out-of-focus or unwanted photos** from your photoset. These images may obscure some of the features needed to match overlapping imagers and may decrease the accuracy of the final product.

If a photo appears blurred or a major portion of a photo appears blurred, remove it.

On the bottom screen "Photos" right click on it > Estimate Image Quality>All images (Figure 11). If your image quality **is below 0.7 you should remove it**. You can choose the threshold you would prefer according to the quality of data you have.
<figure><img src="image_14.png" /><figcaption>Figure 11 : Estimate Image Quality</figcaption></figure>

2.5. Align images

The first step in the model generation process is to align the photos (Figure 12). This process results in a sparse point cloud, representing the ties points found between overlapping images, and the initial camera locations; both are found using the **SIFT** (Scale-Invariant Feature Transform) algorithm to **find matching point between overlapping images**.

Go to **Workflow > Align Photos** (the virgule means thousand).
<figure><img src="image_15.png" /><figcaption>Figure 12: Align Photos tools</figcaption></figure>

Several parameters are required in the Align photos dialog, which are explained below (Figure 12).

**Accuracy:** Highest – High – [Medium] – Low - Lowest (the photos will not be down sampled - this helps with later steps. lower accuracy setting can be used to get the rough camera positions in the shorter time)

**Generic preselection:** Disabled – [Enabled]. If disabled, processing is faster. Enable the option if you have issues aligning some photos.

**Reference preselection:** Disabled – [Enabled]. If your photos are geotagged, this will use the GPS positions to speed up camera alignment.

**Key point limit:** [40,000] is default. Lower number of key points can speed up processing, but increases the risks of lacking points for correct alignment.

**Tie point limit:** 4000 is default but [10000] is usually used. Sets the minimum number of ties needed to match images.

be set to 0 if images not aligning, but at the expense of lower accuracy.

**Constrain features by mask:** [Disabled] (Enabled in case any area has been masked prior to processing).

You can change the size of the region of interest in going into Resize region
Through this, you will get in the view the photos aligned and you will get the tie points found.

## 2.6. Clean tie points

Tie points created, you can now clean them by hands in removing points which seem wrong, like the very low and high ones (Figure 14).

<figure><img src="image_16.png" /><figcaption>Figure 13 : Tool to select in the main panel</figcaption></figure>

How is your tie points cloud ? Comment est votre nuage de points homologues ? **Figure 8**

<figure><img src="image_17.png" /></figure>

## 2.7. Create two chunks (projects)

You copy your project. Now, you have one named **without** GCPs and one **with** GCPs.

<figure><img src="image_18.png" /></figure>
## 3. Create models with or without GCPs

### 3.1. Without GCPs

#### 3.1.1. Optimized camera alignment

Click on Optimized camera alignment on reference bar (Figure 14).

The depth map will be removed.

This function takes into account:

1. Internal parameters (camera intrinsics):
   - Focal length.
   - Principal point offset (cx, cy).
   - Lens distortion coefficients (e.g., radial and tangential distortion).

2. External parameters (camera extrinsic):
   - Camera position (X, Y, Z).
   - Camera orientation (yaw, pitch, roll or rotation matrix)

<figure><img src="image_19.png" /><figcaption>Figure 14 : Optimized camera alignment</figcaption></figure>
### 3.1.2. Build dense cloud

In order to build the dense cloud, you need to have a depth map. After the step of Optimized Camera, you lose the depth map. That's why when you launch the tool Build dense cloud, the software builds automatically a depth map and then the dense cloud.

<figure><img src="image_20.png" /><figcaption>Figure 15: Filter Photos by Marker</figcaption></figure>

Once at least three markers are set, the software can estimate the location of all the remaining GCPs. Click the Update Transform button in the Reference pane (Figure 17).

<figure><img src="image_21.png" /><figcaption>Figure 16: Tool to refresh your model in the main panel</figcaption></figure>
### 3.1.3. Create dense cloud

Based on the estimated camera positions, the program calculates depth information for each camera to be combined into a single dense point cloud. The dense point cloud operation fills in all the blank or empty areas in the sparse cloud and assigns color values based on these photos. Dense point cloud generation is based on depth maps calculated using **dense stereo matching**. Go to **Workflow > Build Dense Cloud**. (Figure 19).

<figure><img src="image_22.png" /><figcaption>Figure 17: Build Dense Cloud</figcaption></figure>

Set the following recommended values for the parameters in the Build Dense Cloud dialog:

**Quality:** Ultra high – [High] – Medium – Low – Lowest: Uses original photos. Requires a large amount of memory, so recommended only for specific hardware setups. If small number of photos and/or high quality required, select Ultra high. Otherwise, High usually achieves good quality results or Medium if your computer is not adapted for heavy treatments.

* High: Photos are downsampled by a factor of 4. Recommended for smaller photo sets.

* Medium: Photos are downsampled by an additional factor of 4. Recommended for larger photo sets.

* Low: Photos are downsampled by an additional factor of 4. Not recommended.

**Depth filtering:** Disabled – Mild – Moderate – [Aggressive]: (if the geometry of the scene to be reconstructed is complex with numerous small details or untextured surfaces, like roofs, it is recommended to set Mild depth filtering mode, for important features not to be sorted out. If mild and aggressive options do not seem optimal for your data, select Moderate).

**Reuse depth maps:** disable

**Calculate point confidence:** = **important to click here to clean dense cloud then.** Metashape assigns a confidence score to each point in the dense point cloud, based on various criteria such as the redundancy of a point's observations (how many photos it has been seen by).

After this step, a dense point cloud will appear.
### 3.1.4. Clean dense cloud

#### (1) Filter your points cloud by confidence

Before starting the cleaning, click on the button **Show Markers** and deselect the markers (Figure 20).

Confidence value can be used to perform additional filtering of low confidence points. For every point in a point cloud the number of contributing combined depth maps is recorded and stored as a confidence value.(2)

You can now, filter by confidence and remove the points with the lowest confidence.

Go into **Tools>Dense Cloud>Filter by Confidence** (Figure 21).

You have to increase the minimum value of confidence you want and test what is the better in your case. The idea is to remove the points with less confidence.

Then do the same process but between 0 and the minimum value of confidence. Now, all points with confidence you don't want are selected.

Delete them.

**Tool>Dense Cloud>Reset Filter**

Save project.

<figure><img src="image_23.png" /><figcaption>Figure 18: Filter points by confidence</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>Figure 19: Button Show Markers</figcaption></figure>
## (2) Filter incoherence by hands

When you look at your cloud points and if you still have points that are below sur surface or way above, you must remove them.

To do this, go to **the icon Free-from Selection** (Figure 22)

<figure><img src="image_25.png" /><figcaption>Figure 20 : Select by hands points</figcaption></figure>

What does your dense point cloud look like? A quoi ressemble votre nuage de points dense ? **Figure 8**
### 3.1.5. Class ground points

Tools>Dense cloud>Classifying ground points (Figure 23)

**From:** you can choose what class of points

**Max angle (deg):** Determines one of the conditions to be checked while testing a point as a ground one, i.e. sets limitation for an angle between terrain model and the line to connect the point in question with a point from a Editing ground class. For nearly flat terrain it is recommended to use default value of 15 deg for the parameter. It is reasonable to set a higher value, if the terrain contains steep slopes.

**Max distance (m):** Determines one of the conditions to be checked while testing a point as a ground one, i.e. sets limitation for a distance between the point in question and terrain model. In fact, this parameter determines the assumption for the maximum variation of the ground elevation at a time.

**Cell size (m):** Determines the size of the cells for point cloud to be divided into as a preparatory step in ground points classification procedure. Cell size should be indicated with respect to the size of the largest area within the scene that does not contain any ground points, e. g. a building or a dense forest or a bridge.

**Erosion radius (m):** Determines the indentation (in meters) from unclassified points to create an additional area from the object, it is useful when classifying houses and trees to exclude the remaining "stumps" when building DTM

<figure><img src="image_26.png" /><figcaption>Figure 21: Classify ground points</figcaption></figure>

### 3.1.6. Build a DEM (here MNT)

Digital elevation model = DEM

With the points you classed you can build a DEM of ground or all points.

Go to Workflow>Build DEM (Figure 24). A dialog box will appear. Specify the coordinate system, source data, interpolation, and point classes to include. The coordinate system can be modified when the
DEM is exported. Be aware that DEM generation relies on sets of parameters that cannot be set in Metashape. For higher control on DEM creation parameters, it is advised to export model as Point Cloud and use other software (eg. ArcGIS, QGIS, Matlab, R, LASTools, CloudCompare, etc.).

Set the following recommended values for the parameters in the Build DEM dialog:

**Projection type:** [Geographic] – Planar – Cylindrical (if you used a projected or geographic coordinate system, you should select Geographic. Only use Planar if working in local coordinate system).

**Projection:** default (projection set in the Reference Settings is used. The coordinate system must be the same as coordinate system of GCPs).

**Source data:** Sparse cloud – [Dense cloud]

**Interpolation:** [Enabled](default) – Extrapolated.

Points classes: Use only « Ground »

The resolution is automatically defined by the software. You will be able to change this when you export it.

**Think to export your DEM (MNT)**

<figure><img src="image_27.png" /><figcaption>Figure 22 : Build DEM (en français MNT)</figcaption></figure>

### 3.1.7. Build Orthomosaic (optional for the project)

You can build an orthomosaic with the DEM. However, you need a lot of rams Go (Figure 25).

#### Workflow>Build Orthomosaic

A dialog box will appear. A number of parameters may be set :

**Projection type:** Same as DEM (see above).

**Projection:** by default, the projection set in the Reference Settings is used.
**Surface:** DEM: Best for aerial survey data and mesh + texture best for surfaces data as a building to have a smooth and detailed surface

**Blending mode:** [Mosaic] – Average - Disabled (blends specific pixels from photo with best representation of feature with pixels from other images to avoid seams in the texture).

**Enable back-face culling:** Enabled – [Disabled]. (Option mostly important for non-vertical images).

**Enable hole filling:** [Enabled] – Disabled.

**Pixel size (m):** it is advised to choose the same pixel size as the one chosen for DEM generation. If no DEM was built, follow the same rules as above when choosing resolution: aim for orthogonality while keeping highest resolution possible.

**Region:** it is advised to choose the same boundaries as the ones picked for DEM building. If no DEM was built, select boundaries that are coherent with resolution and allow correct model orthogonality.

**Total size (pix):** will adjust automatically with resolution and boundaries.

Click OK button to start Orthomosaic generation.

<figure><img src="image_28.png" /><figcaption>Figure 23 : Build orthomosaic</figcaption></figure>

## 3.2. With GCPs

### 3.2.1. Import GCPs

GCPs should be surveyed prior to image acquisition using a dGNSS. These are necessary to georeferenced your model in a real-world coordinate system, especially if your photos are not geotagged. They can also be used to determine model accuracy, i.e. how confident you can be about the model. The number of GCPs required to generate accurate models depends on the camera used (distortion, resolution, etc.), local topography (high topography difference require more GCPs) and the purpose of the survey (e.g. detection of changes require high accuracy, hence a higher density of GCPs).

Import coordinates of points taken by DGNSS and only select the ones that you want to keep for the process (Figure 9). **Keep only the points: 8;6;5;1;4;12;9. The others will be used later.**

### 3.2.2. Georeferencing GCPs

I advise you to import your GCPs coordinates into a GIS as QGIS. Then choose which points will be used as GCP among all points. The step allows to correct the coordinates of your model and adding the altitude geoid correction (if the GCPs altitude have been corrected).
All points are represented by a **white flag on images randomly**, meaning that either the software or you have not placed the point.

When **the software has placed a point, the flag will be in blue**. When you move manually a point the flag go from white or blue to green. **Green flag means it has been verified by you.**

To associate the points with their locations manually click on each image and if you find the place of a GCP (Figure 15), **you have to move the point into the center of your marker (red square for example)**.

<figure><img src="image_29.png" /><figcaption>Figure 24 : Red target</figcaption></figure>

When a GCP is identified in at least two photographs, the software is able to automatically estimate **the location of that GCP in all aligned photos** (blue flag appears). Right-click on the marker in the Reference pane and choose **Filter Photos by Marker** (Figure 15) in the dropdown menu. Sort through these photos and place the marker accurately in each of them. If the GCP is not in the photo or inaccurately placed, right-click the flag that appears on the photo and choose **Remove Marker** from the dropdown menu that appears.

**You have to use the tool Optimized camera alignment, in order to use the GCPs for corrections of your model.**

#### 3.2.3. Repeat the steps from the part 3.1

Now, you can repeat the steps from 3.1.2(Build dense cloud) to 3.1.6(Build a DEM (here MNT))

# 4. Export products

## 4.1. Export DEM and orthomosaic

Export DEM and Orthomosaic : File>Export>DEM and Orthomosaic (Figure 26).
<figure><img src="image_30.png" /><figcaption>Figure 25 : DEM and Orthomosaic</figcaption></figure>

## 4.2. Export the report of everything you did in Metashape

Export the report of all your treatments in order to know what you have done in Metashape (Figure 27). File>Export>Generate Report

<figure><img src="image_31.png" /><figcaption>Figure 26 : Export the report of your workflow in Metashape</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>Figure 28 : Interpolation of your points layer to raster</figcaption></figure>

If the layer doesn't work for the interpolation create a new point layer and fill the points with the altitude values (Figure 28).

<figure><img src="image_33.png" /><figcaption>Figure 27 : Create a new point layer</figcaption></figure>

## 5.6. Cut the peace of your DEM that you are interested to know the volume

Layer>Create a layer>New shapefile layer (edit your layer and draw the limits of your depot). In the folder input or output there is already a layer available of the limits.
<figure><img src="image_34.png" /><figcaption>Figure 29 : Cut raster according to a mask</figcaption></figure>

## 5.7. Subtract MNT with raster

Calculate Raster:

MNT\_cut – raster interpolated

The new raster has only the difference of altitude. Pixels values will be small (between 0 and some meters). If the no-data are very big, no problem you will be able to precise in the next step.

## 5.8. Calculate the volume of the sediment depot

Tool>Raster Surface Volume

Method: Above base level
Bibliography

1. User S. 3.2 Digital Elevation Models [Internet]. 2014 [cité 29 janv 2024]. Disponible sur: https://www.cdema.org/virtuallibrary/index.php/charim-hbook/data-management-book/3-base-data-collection/3-2-digital-elevation-models

2. Agisoft Metashape: User Manuals [Internet]. [cité 29 janv 2024]. Disponible sur: https://www.agisoft.com/downloads/user-manuals/