Data Cookbook: Forest Monitoring

Discover how to utilize  GRUS-1 satellite images to monitor forest health and growth

Download satellite image dataset from our Axelglobe platform.  Need a refresher? Here’s the AxelGlobe User Manual for your reference.

• 1A. For this exercise, we will be using a dataset from Demo Italy. Four tiles are selected (Cell ID: N41200203, N41200204, N41210203, and N41210204) for three dates ( Mar 20, May 21, and July 22). All in all, there should be 12 selected and downloaded GRUS Tiles. We will be utilizing Multispectral Product for this forest monitoring use case.

Learn how to merge or clip your areas of interest. Check out our Getting Started Manual for more details. 

2A. To easily manage and analyze our data, we will merge each of the four-tile dataset per date of acquisition using a GIS software. There should be three image outputs for this process: one merged imagery per date (2020/03/20, 2020/05/21, and 2020/07/22)

Note: The default band rendering of QGIS is: Red Channel – Band 1, Green Channel – Band 2, and Blue Channel – Band 3. The reason why the images are not displayed in their true color form is because the channels are not matched with their correct Band designation yet. 

Visualize your data in their True Color and False Color composites. Our Getting Started Manual has a step-by-step information on how you can achieve this.

3A. True Color Composite. Also called “Natural Color Composite”, this consists of three visual bands: Red, Green, and Blue. The image is displayed in its true color as it resembles closely what can be seen by human eyes. For GRUS-1 Multispectral product, Band 3 corresponds to Red Band, Band 2 to Green Band, and Band 1 to Blue Band.

3B. False Color Composite. Compared to True Color Composite, False Color composite imagery shows changes in area which cannot be easily seen using True Color imagery (Red, Green, & Blue) by visualizing them through different band combinations (Red Edge or Near Infrared). The False Color imagery shown below made use of Bands 5 (NIR), 3(Red), and 2(Green) in the Red, Green, and Blue channel.

Band rendering settings for True Color and False Color Composites are shown below as used in this manual.

Optimize forest operation and track forest  health using indicators derived from our satellite images

4A. Vegetation Indices. Generally, Vegetation Indices (VI) are  indicators of photosynthetic activity which can be linked to growth and health of plants. High values correspond to healthy vegetation, and low values correspond to degradation. For this data cookbook, we will be using two of the most used VIs – Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE).

4B. Raster Calculator Function. To generate NDVI and NDRE image derivatives, we need to use the Raster Calculator  tool in QGIS. To access the tool, just navigate to the Raster Menu and click on the Raster Calculator tab. 

Notice that the raster bands are automatically separated by dataset, as shown below, for direct computation.

4C. NDVI Computation. To compute for NDVI, we need to use Bands NIR (Band 5) and Red (Band 3). The formula for NDVI is: (NIR-Red)/(NIR+Red) or (Band 5 – Band 3)/(Band 5 + Band 3). Ensure that the bands you are using for each NDVI computation are from the same data period. Do this for all three datasets.

The raster formula used for the NDVI computation is shown below:

4D. NDRE Computation. To compute for NDRE, we need to use Bands NIR (Band 5) and Red  Edge (Band 4). The formula for NDRE is:  (NIR-Red Edge)/(NIR+Red Edge) or (Band 5 – Band 4)/(Band 5 + Band 4). Do this for all three datasets. The raster formula used for the NDRE computation is shown below:

4E. Visualization. Navigate to Symbology tool by right clicking the layer > Select Properties > Select Symbology. Set the Render Type to  Singleband Pseudocolor. For Color Ramp, you can select from the choices using the arrow button. In this visualization procedure, Spectral color ramp is used.  Click Classify, then Apply button. To close the dialogue box, click OK.

Final symbology settings and visualization result are shown below:

4F. Time Series Visualization Comparison. To compare the datasets from different time period, we visualize them in a similar min-max NDVI/NDRE value. From their NDVI values, we set -0.28 as minimum and 0.81 as maximum for each NDVI imagery.

From their NRE values, we set -0.38 as minimum and 0.62 as maximum for each NDRE imagery.

Time-series comparisons for NDVI and NDRE Analysis are shown below. NDVI can be used to detect chlorophyll present in the area which can be utilized for reforestation monitoring. The higher the NDVI, the more robust the forest area is in the area. On the other hand, NDRE can detect a decrease in chlorophyll in the early stress stage. This can be especially useful for forest health monitoring.

Last updated: June 2021