Mapping Planted Forests in the Korean Peninsula Using Artificial Intelligence

作者：Ankita Mitra, César Iván Álvarez, Akane Abbasi, Nancy L. Harris, Guofan Shao, Bryan C. Pijanowski, Mohammad R. Jahanshahi, Javier G. P. Gamarra, Hyun Seok Kim, Taekyung Kim, Daun Ryu, Jingjing Liang · 发表于：Forests · 年份：2024 · DOI：10.3390/f15071216 · 被引用次数：1 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Fire effects on ecosystems

Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model’s predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly...