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 sustainable management of natural resources and environmental protection efforts. Data analysis and interpretation methods for remote sensing are important for understanding these changes and adapting to natural systems. In this study used the Parcel Identification System (LPIS) database physical blocks 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-m 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<0.05). According to the study results, it was determined that using the estimated CC data, the A0 (69.27%) and T0 (30.43%) land cover types could be successfully used to determine the changes in the phenological period caused by environmental impact assessment such as climate change. At the same time, this study contributes to the rapid monitoring of agricultural production areas caused by climate change by using physical blocks of agricultural land classes within the LPIS data, the rapid determination of agricultural land management, and support payments with remote sensing data. In this regard, the use of modern technologies and data analysis methods will contribute to increasing agricultural sustainability.