Billion Tree Tsunami Forests Classification Using Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2 and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan

Abstract

In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the utilization of remote sensing techniques and satellite image classification has not yet been done. Recently, the Sentinel-2 (S2) satellite has found much utilization in remote sensing and land cover classification. Sentinel-2 (S2) sensors provide freely available images with a spatial resolution of 10, 20 and 60 m. The higher classification accuracy is directly dependent on the higher spatial resolution of the images. This research aims to classify the land cover changes as a result of the Billion Tree plantation drive in the areas of our interest using Random Forest Classifier (RFA) and image fusion techniques applied to Sentinel-2 and Landsat-8 satellite images. A state-of-the-art, model-based image-sharpening technique was used to sharpen the lower resolution Sentinel-2 bands to 10 m. Then the RFA classifier was used to classify the sharpened images and an accuracy assessment was performed for the classified images of the years 2016, 2018, 2020 and 2022. Finally, ground data samples were collected using an unmanned aerial vehicle (UAV) drone and the classified image samples were compared with the real data collected for the year 2022. The real data ground samples were matched by more than 90% with the classified image samples. The overall classification accuracies [%] for the classified images were recorded as 92.87%, 90.79%, 90.27% and 93.02% for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Similarly, an overall Kappa hat classification was calculated as 0.87, 0.86, 0.83 and 0.84 for the sample data of the years 2016, 2018, 2020 and 2022, respectively.