Ankita Mitra

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 lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth.

Planted forests are critical to climate change mitigation and constitute a major supplier of timber/non-timber products and other ecosystem services. Globally, approximately 36% of planted forest area is located in East Asia. However, reliable records of the geographic distribution and tree species composition of these planted forests remain very limited. Here, based on extensive in situ and remote sensing data, as well as an ensemble modeling approach, we present the first spatial database of planted forests for East Asia, which consists of maps of the geographic distribution of planted forests and associated dominant tree genera. Of the predicted planted forest areas in East Asia (948,863 km²), China contributed 87%, most of which is located in the lowland tropical/subtropical regions, and Sichuan Basin. With 95% accuracy and an F1 score of 0.77, our spatially-continuous maps of planted forests enable accurate quantification of the role of planted forests in climate change mitigation. Our findings inform effective decision-making in forest conservation, management, and global restoration projects.