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Developing a two-step algorithm to estimate the leaf area index of forests with complex structures based on CHRIS/PROBA data
- Lin, Jie, Pan, Ying, Lyu, Heng, Zhu, Xi, Li, Xiao, Dong, Bo, Li, Haidong
- Forest ecology and management 2019 v.441 pp. 57-70
- algorithms, belowground biomass, forests, leaf area index, models, soil erosion, spatial data, terrestrial ecosystems
- Vegetation is a key factor affecting soil erosion, which depends not only on the horizontal structure of vegetation, but also on its vertical structure. The leaf area index (LAI) of plants, as an important structural parameter of the terrestrial ecosystem, can reflect horizontal vegetation coverage, vertical structure of vegetation, litter thickness and underground biomass. All these are the principal aspects of vegetation affecting soil erosion. The multi-angle remote sensing data can provide the stereoscopic and directional information of the surface target, which is helpful to extract the vertical structure parameters of vegetation effectively. Therefore, in order to improve the estimation accuracy of soil erosion, a two-step method was developed in this study to estimate the forest LAI under different vertical structures using the PROSAIL model and multi-angle CHRIS/PROBA data based on the field data. A total of 70 quadrats (10 m × 10 m sized) were deployed in Mount Zijin with undisturbed natural vegetation, considering the vegetation types and accessibility in the middle of August 2017. The pixels were first classified into two categories depending on complexity of vertical structure; then, a specific algorithm was applied to the different classes. For Class 1, the single angle data of 55° had the highest accuracy, indicating that, for forests with a simple vertical structure, the single angle data was sufficient for LAI estimation. For Class 2, the multi-angle data combination of 0°, 55° and −55° had the highest accuracy, indicating that, for forests with complex vertical structure, the multi-angle data can improve the accuracy of LAI estimation. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) were applied to evaluate the accuracy of the LAI estimation algorithm. The results indicated that the two-step method can significantly improve the LAI estimation accuracy of forests with complex vertical structure, with a decrease in MAPE and RMSE of 19.52% and 23.08%, respectively, compared to the traditional empirical algorithm. The developed LAI estimation algorithm can be widely applied to forests with different vertical structures. Lastly, the two-step method was applied to the CHRIS/PROBA image acquired on September 7th, 2015 for mapping the spatial distribution of forest LAI in the western part of Mount Zijin. The result showed that the northern part and the southern part had higher LAI than the middle part of Mount Zijin.