Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future

DataSheet_1_Full phenology cycle carbon flux dynamics and driving mechanism of Moso bamboo forest.docx

IntroductionMoso bamboo forests, widely distributed in subtropical regions, are increasingly valued for their strong carbon sequestration capacity. However, the carbon flux variations and the driving mechanisms of Moso bamboo forest ecosystems of each phenology period have not been adequately explained.MethodsHence, this study utilizes comprehensive observational data from a Moso bamboo forest eddy covariance observation for the full phenological cycle (2011-2015), fitting a light response equation to elucidate the evolving dynamics of carbon fluxes and photosynthetic characteristics throughout the entire phenological cycle, and employing correlation and path analysis to reveal the response mechanisms of carbon fluxes to both biotic and abiotic factors.ResultsThe results showed that, First, the net ecosystem exchange (NEE) of Moso bamboo forest exhibits significant variations across six phenological periods, with LSOFF demonstrating the highest NEE at -23.85 ± 12.61 gC·m-2·5day-1, followed by LSON at -19.04 ± 11.77 gC·m-2·5day-1 and FGON at -17.30 ± 9.58 gC·m-2·5day-1, while NFOFF have the lowest value with 3.37 ± 8.24 gC·m-2·5day-1. Second, the maximum net photosynthetic rate (Pmax) and apparent quantum efficiency (α) fluctuated from 0.42 ± 0.20 (FGON) to 0.75 ± 0.24 mg·m-2·s-1 (NFOFF) and from 2.3 ± 1.3 (NFOFF) to 3.3 ± 1.8 μg·μmol-1 (LSOFF), respectively. Third, based on the path analysis, soil temperature was the most important driving factor of photosynthetic rate and NEE variation, with path coefficient 0.81 and 0.55, respectively, followed by leaf area index (LAI), air temperature, and vapor pressure difference, and precipitation. Finally, interannually, increased LAI demonstrated the potential to enhance the carbon sequestration capability of Moso bamboo forests, particularly in off-years, with the highest correlation coefficient with NEE (-0.59) among the six factors.DiscussionThe results provide a scientific basis for carbon sink assessment of Moso bamboo forests and provide a reference for developing Moso bamboo forest management strategies.</p

Intelligent Estimating the Tree Height in Urban Forests Based on Deep Learning Combined with a Smartphone and a Comparison with UAV-LiDAR

In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person–tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person–tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m–0.98 m, and the range of the relative error was 0.20–10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the R2 above 0.93. The person–tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources