A spectral-temporal constrained deep learning method for tree species mapping of plantation forests using time series Sentinel-2 imagery

Huang, Zehua; Zhong, Liheng; Zhao, Feng; Wu, Jin; Tang, Hao; Lv, Zhengang; Xu, Binyuan; Zhou, Longfei; Sun, Rui; Meng, Ran

doi:10.1016/j.isprsjprs.2023.09.009

Cited by 11 publications

(1 citation statement)

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“…Hemmerling J. et al [27] utilized Sentinel-2 time series data combined with texture features and environmental data to produce a high-precision map of 17 temperate tree species, which demonstrated the significant contribution of time series images for tree species classification. Huang Z. et al [28] extracted the spectrum-temporal features of the Sentinel-2-data-based deep learning approach and applied them to the classification of tree species in plantation. The final results confirmed that the time series data could effectively utilize phenological information to improve the classification accuracy of tree species.…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age

Yang,

Wu,

Liu

et al. 2024

Forests

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Accurate classification of forest tree species holds great significance in the context of forest biodiversity assessment and the management of forest resources. In this study, we utilized Sentinel-2 time series data with high temporal and spatial resolution for tree species classification. To address potential classification errors stemming from spectral differences due to tree age variations, we implemented the Continuous Change Detection and Classification (CCDC) algorithm to estimate tree ages, which were integrated as additional features into our classification models. Four different combinations of classification features were created for both the random forest (RF) algorithm and extreme gradient boosting (XGB) algorithm: spectral band (Spec), spectral band combined with tree age feature (SpecAge), spectral band combined with spectral index (SpecVI), and spectral band combined with spectral index and tree age feature (SpecVIAge). The results demonstrated that the XGB-based models outperformed the RF-based ones, with the SpecVIAge model achieving the highest accuracy at 78.8%. The incorporation of tree age as a classification feature led to an improvement in accuracy by 2% to 3%. The improvement effect on classification accuracy varies across tree species, due to the varying uniformity of tree age among different tree species. These results also showed it is feasible to accurately map regional tree species based on a time-series multi-feature tree species classification model which takes into account tree age.

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Section: Introductionmentioning

confidence: 99%