Carbon sequestration

We used  Sentinel-2 images to create the dataset In order to estimate sequestered carbon in the above-ground forest Biomass.  Moreover, fieldwork was completed to gather related forest biomass volume. The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared. These bands were selected for two reasons: they have the highest spatial resolution, and they are representative of the crops’ photosynthesis process.  We created different datasets that consisted of tiled sub-images with three different sizes of 32 × 32 (1050 images), 64 × 64 (270 images), and 128 × 128 (72 images) and three bands representing different spectrums (green, red, and near-infrared). The other datasets consisted of the same size and number of tiles but only represent carbon sequestration with six classes (no carbon, very low, low, moderate, high, and very high). A script was written in the Python language to classify the Sentinel-2 sub-images into six classes based on the computed canopy density statistics.

In brief, each dataset has different number of images and a different size. Each dataset consists of raw Sentinel sub-images and categorized training data.