关键词: 植被分类, 太白山, 时间序列, Sentinel-1A数据, 深度循环网络, 微波遥感, 机器学习

Abstract:

Vegetation classification is the basis and premise of forest resource investigation and dynamic monitoring. Remote sensing techniques have long been important means of forest monitoring with their ability to quickly and efficiently collect the spatial-temporal variability of vegetation. Vegetation classification is a key issue for forest monitoring and is critical to many remote sensing applications in the domain of precision forestry such as vegetation area estimation. Remote sensing applications in vegetation classification have traditionally focused on the use of optical data such as MODIS. However, due to cloud and haze interference, optical images are not always available at phenological stages that are essential to vegetation identification, making it difficult to construct complete time-series vegetation growth and limiting the vegetation classification accuracy. Unlike passive visible and infrared wavelengths which are sensitive to cloud and light, active SAR (Synthetic Aperture Radar) is particularly attractive for vegetation classification due to its all-weather, all-day imaging capabilities. In addition, SAR provides information on the stem and leaf structures of vegetation and is sensitive to soil roughness and moisture content, making it effective in forest applications. In this study, a deep-learning-based time-series analysis method employing multi-temporal SAR data is presented for forest vegetation classification in the Taibai Mountain (the main peak of Qinling Mountains). Firstly, Sentinel-2 optical images and digital elevation data in the study area were used for multi-scale segmentation to produce a precise farmland map. Then pre-processed SAR intensity images were overlaid with the farmland map to construct time-series vegetation growth for each parcel. Finally, a deep-learning-based classifier using the Long Short-Term Memory (LSTM) network was employed to learn time-series features of vegetation and to classify parcels to produce a final classification map. The experimental results show that: (1) Compared with traditional machine learning methods (such as Random Forest and Support Vector Machine), the classification accuracy of the deep-learning-based method proposed in this paper was improved by more than 10%; (2) Among different combinations of Sentinel-1A polarizations, VV+VH performed best, having a similar accuracy with the VV+VH+VV/VH; (3) Time-series classification using all images in the whole year achieved the best performance, with an overall accuracy of 82% using VV+VH. The study shows that the combination LSTM network and time-series Sentinel-1A data can effectively improve the accuracy of vegetation classification and provide new ideas for forest vegetation classification from the perspectives of data source and classification method.

Key words: vegetation classification, Taibai Mountain, time series, Sentinel-1A data, LSTM network, microwave remote sensing, machine learning