Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China
Abstract
Key message By integrating vegetation change tracker (VCT), spatial analysis (SA), and random forest regression (RF), the spectral-temporal patterns of forest stand age were mapped for three typical plantations in Southern China. The spectral-temporal distribution of age structure indicated that the plantation stands in the study area were increasingly aging.
Context Plantations play a major role in China for ecosystem restoration and carbon sequestration. Mapping plantation stand age distributions is essential for developing sustainable plantation forest management plans.
Aims The purpose of this study was to propose two new remote sensing based models for mapping plantation ages and to test the model feasibility and accuracy in determining the spectral-temporal patterns of forest ages.
Methods We first integrated vegetation change tracker (VCT) algorithm and spatial analysis (VCT-SA) for the pixels that were disturbed at least once from 1987 to 2017, and integrated VCT and random forest (VCT-RF) for the pixels were not disturbed during the study period. Then the forest age of these two parts were merged separately to generate annual forest age.
Results The spectral-temporal (30-m resolution, from 1987 to 2017) of forest age for the three typical plantations in Lishui were generated. The results indicated that the plantation stands in our large study area were increasingly aging.
Conclusion Our results revealed that it is reasonable to derive the distribution of plantation stand ages from combined remote sensing models. Besides, we confirmed that the stand ages of the plantations in our large study area of Lishui City are on the rise as the result of forest protection policies.
Keywords
Chinese fir, Landsat time series, Oak, Pine, Plantation disturbance, Plantation stand age
Publication
Diao, J., Feng, T., Li, M. et al. Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China. Annals of Forest Science 77, 27 (2020). https://doi.org/10.1007/s13595-020-0924-x
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Data availability
All data generated or analysed during the current study cannot be shared this time because it required to be further analysed by the authors of this study.
