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. There 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 captures 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,which is Its 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 een calculated and compared to the ground truth data collected in the field, which showed the possiility 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.

• Cedric Kiplimo ,Samuel Gachana,Viola Orina ,Ciira wa Maina,Arthur Sichangi    AGU
• Using Drone Imagery and Deep Learning to Monitor a Reforested Stand in Kenya
• AGU24

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.