Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case Study of Gelendost District

Abstract

Determining canopy cover (CC) temporal variation is critical for the sustainable management of natural resources and environmental protection efforts. Remote sensing and data analysis methods are important tools to understand these changes and better adapt to natural systems. In this study, Parcel Identification System (LPIS) database physical blocks were used as field ground data. In the study area, agricultural areas were determined from LPIS data, including classes A0, A1, A3, A4, S1, T0, and T1, and a total of 8424 physical blocks and an area of 14651.9 hectares were evaluated. CC estimates were made using 3-meter spatial resolution Planet Scope multispectral satellite images of July and August 2023, and it was determined that there were significant differences in parcel-based distinctions, especially in parcels A0, A1, T0, and T1 (p