Soil organic carbon primarily control the soil moisture characteristic during forest restoration in subtropical China

Zhao, Xinyu; Zhang, Weiqiang; Feng, Yingjie; Mo, Qifeng; Su, Yi; Njoroge, Brian; Qu, Chao; Gan, Xianhua; Liu, Xiaodong

doi:10.3389/fevo.2022.1003532

Cited by 7 publications

(2 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it prefers to grow in clusters with bedrock fractures (Table 1) and is interspersed among the middle slope. H. heptaphyllum is a native species, more resistant to drought and salt [47]. We find that it is mainly distributed on the northwest of windward side (Figure 6), with the steepest slope (Figure 10b), and closer to the coastline (Figure 10c).…”

Section: Spatial Distribution Of Dominant Species Based On the Identi...mentioning

confidence: 78%

Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models

Wu,

Huang,

Luo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Comprehensive vegetation surveys are crucial for species selection and layout during the restoration of degraded island ecosystems. However, due to the poor accessibility of uninhabited islands, traditional quadrat surveys are time-consuming and labor-intensive, and it is challenging to fully identify the specific species and their spatial distributions. With miniaturized sensors and strong accessibility, high spatial and temporal resolution, Unmanned Aerial Vehicles (UAVs) have been extensively implemented for vegetation surveys. By collecting UAVs multispectral images and conducting field quadrat surveys on Anyu Island, we employ four machine learning models, namely Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Random Forest (RF) and Multiple Classifier Systems (MCS). We aim to identify the dominant species and analyze their spatial distributions according to spectral characteristics, vegetation index, topographic factors, texture features, and canopy heights. The results indicate that SVM model achieves the highest (88.55%) overall accuracy (OA) (kappa coefficient = 0.87), while MCS model does not significantly improve it as expected. Acacia confusa has the highest OA among 7 dominant species, reaching 97.67%. Besides the spectral characteristics, the inclusion of topographic factors and texture features in the SVM model can significantly improve the OA of dominant species. By contrast, the vegetation index, particularly the canopy height even reduces it. The dominant species exhibit significant zonal distributions with distance from the coastline on the Anyu Island (p < 0.001). Our study provides an effective and universal path to identify and map the dominant species and is helpful to manage and restore the degraded vegetation on uninhabited islands.

show abstract

Section: Spatial Distribution Of Dominant Species Based On the Identi...mentioning

confidence: 78%

Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models

Wu,

Huang,

Luo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…At the ecosystem scale, soil carbon and hydrological processes are closely interconnected [88]. Under different vegetation types, the soil texture, litter quality, plant species, microbial community, and microhabitat factors may exhibit significant variations due to their management or rainfed regimes [38,[89][90][91][92][93][94].…”

Section: Discussionmentioning

confidence: 99%

Drought Exerted a Stronger Controlling Effect on Soil Carbon Release than Moisturizing in a Global Meta-Analysis

Xiao,

Lin,

et al. 2023

Forests

View full text Add to dashboard Cite

The carbon cycle within a terrestrial ecosystem is a pivotal functional process that drives ecosystem evolution, and the precipitation pattern variations exert a profound influence on it. To comprehensively assess the response of carbon release in the global terrestrial ecosystem to water variation, we performed a global meta-analysis by extracting data from 144 publications. Additionally, we incorporated various moderators to elucidate the heterogeneity observed in the data. The results showed that soil carbon release was highly sensitive to water variation, with drying and moisturizing treatments responding differently to water variability. Specifically, drought inhibited the soil carbon release of terrestrial ecosystems (24% reduction in effect size), but precipitation promoted it (11% increase in effect size). Moreover, this sensitivity could be affected by other ambient factors, depending on water manipulation (drying or moisturizing treatment). In moisturizing treatment cases, ambient precipitation, altitude, and vegetation type more or less affected the sensitivity of soil carbon release to a water increase. However, in drying treatment cases, these factors had no significant influence on the water sensitivity of soil carbon release. Unlike the above ambient factors, a temperature increase strengthened this sensitivity in both of the treatments. In addition, our study also showed that the response of carbon release to water variation did not depend on the substrate type or the carbon–nitrogen ratio (C/N) of the substrates, revealing that these effect factors on carbon release on the local scale could be overshadowed by water conditions. Overall, water variation positively affected soil carbon release on the global scale. Particularly, drought had a strong controlling effect on carbon release over the other environmental factors. Therefore, the impact of soil water loss on carbon release should be of great concern for the management of ecosystems and the prediction of carbon release models, especially when high temperatures and drought have been occurring more and more frequently on the planet in recent years.

show abstract

Phosphorus addition increases microbial necromass by increasing N availability in China: A meta-analysis

Zhang

Yang

et al. 2023

Applied Soil Ecology

View full text Add to dashboard Cite

Soil organic carbon primarily control the soil moisture characteristic during forest restoration in subtropical China

Cited by 7 publications

References 77 publications

Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models

Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models

Drought Exerted a Stronger Controlling Effect on Soil Carbon Release than Moisturizing in a Global Meta-Analysis

Phosphorus addition increases microbial necromass by increasing N availability in China: A meta-analysis

Contact Info

Product

Resources

About