Abstract
Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.
Highlights
Publisher’s Note: MDPI stays neutralA plateau forest plays an important role in high altitude area carbon circulation and is an important natural solution for mitigating global climate change [1]
In order to validate the proposed structure performance, we validated the results with several state-of-the-art algorithms including normalized difference vegetation index (NDVI), ratio vegetation index (RVI), random forest (RF), support vector machine (SVM)
It can be observed that both the NDVI path loss and the RVI path loss reach a reasonable level after 20 epoch trainings, and the global loss follows the same trend of the individual loss decreasing
Summary
A plateau forest plays an important role in high altitude area carbon circulation and is an important natural solution for mitigating global climate change [1]. Monitoring a plateau forest is able to help us better understand climate change influences at local, regional and global levels [2], and has drawn a lot of attention from governments and companies. High-resolution satellite imagery has recently become available for large-scale highaltitude plateau forest monitoring [4]. Many object detection studies driven by satellite data are utilizing threshold-based vegetation indices [5], such as the normalized difference vegetation index (NDVI) first proposed in the 1990s [6] and ratio vegetation index (RVI) [7]. Wang et al proposed a surface vegetation detection and trend analysis method based on Moderate-resolution Imaging Spectroradiometer with regard to jurisdictional claims in published maps and institutional affiliations
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