Monitoring Reforestation efforts Using Deep learning
Background
The Kieni Forest in Kiambu County, Kenya, plays a vital role in the region's ecological health. It
provides habitat for diverse wildlife, regulates water resources, and helps combat climate change.
However, deforestation and degradation have significantly impacted the forest cover. Reforestation
efforts are crucial to restore the Kieni Forest and its ecological benefits. This project aims to
develop a robust and efficient method for tree crown detection in the Kieni Forest using DeepForest, a
machine learning framework. Accurate tree crown detection will support reforestation efforts in several
ways:
Identify Existing Trees:
By automatically detecting existing tree crowns, this
project can create a baseline assessment of the current forest cover. This information is valuable for
planning and prioritizing reforestation areas.
Monitor Progress:Repeated application of the
tree crown detection model can track the success of reforestation initiatives. The model can quantify
the increase in tree cover over time, allowing for informed adjustments to reforestation
strategies.
Drone images taken from a section of the forest provide the data to be used for the
tree detection project. Drones capture multiple images in an RGB (Red, Green, Blue) format which
represents the natural colour we see things through. This makes it easy to see the tree crowns from the
various images. There have been efforts to monitor the reforestation in Kieni by starting out with data
collection, which includes the collection of tree parameter details such as tree height, basal diameter,
crown diameter and tree species identification. We also employed the use of DSAIL's own TreeVision, a low
computation software tool that combines stereoscopic vision and deep learning to automatically estimate
tree biophysical parameters such as diameters at breast height, tree heights, and crown diameters. The
TreeVision software is used together with stereoscopic vision to capture tree images and create a 3D map
of the scene. This is all in the efforts to monitor the growth of trees in the forests.
Accomplishments
Tree crown detection has been done on a section of the forest using drone images. The model's performance has been evaluated using metrics such as F1-score, recall, and precision which provided valuable insights into the model's accuracy and helps identify areas for improvement. Tree crown diameter have been calculated and compared to the ground truth data collected in the field, which showed the possibility of using deep learning in monitoring the reforested sections of Kieni forest. It was a privilege to have an abstract concerning the research on Using Drone Imagery and Deep Learning to Monitor a Reforested Stand in Kenya accepted for AGU Conference. The abstract can be accessed below.
Next Steps
For our next steps, we aim to:
Fine Tuning the model: Further training and adjustments to the
DeepForest model can be conducted to enhance its accuracy and performance. This might involve
incorporating additional training data, exploring different hyperparameter configurations, or trying
alternative deep learning architectures.
Scaling up: This is probably a futuristic step, to
apply the model to larger regions within the Kieni Forest, potentially using multiple drone images to
cover extensive areas. This requires strategies for efficient image processing and model application
across larger datasets.