Geo-spatial Information Science (Jun 2024)

Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana)

  • Jinjin Chen,
  • Xuejian Li,
  • Zihao Huang,
  • Jie Xuan,
  • Chao Chen,
  • Mengchen Hu,
  • Cheng Tan,
  • Yongxia Zhou,
  • Yinyin Zhao,
  • Jiacong Yu,
  • Lei Huang,
  • Meixuan Song,
  • Huaqiang Du

DOI
https://doi.org/10.1080/10095020.2024.2362752

Abstract

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The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

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