Effects of Soil Temperature, Water Content, Species, and Fertilization on Soil Respiration in Bamboo Forest in Subtropical China

Understanding the change pattern of soil respiration (SR) and its drivers under different bamboo species and land management practices is critical for predicting soil CO2 emission and evaluating the carbon budget of bamboo forest ecosystems. A 24-month field study was performed in subtropical China to monitor SR in experimental plots of local bamboo (Phyllostachys glauca) without fertilization (PG) and commercial bamboo (Phyllostachys praecox) with and without fertilization (PPF and PP, respectively). The SR rate and soil properties were measured on a monthly timescale. Results showed that the SR rate ranged from 0.38 to 8.53 µmol CO2 m−2s−1, peaking in June. The PPF treatment had higher SR rates than the PP and PG treatments for most months; however, there were no significant differences among the treatments. The soil temperature (ST) in the surface layer (0–10 cm) was found to be the predominant factor controlling the temporal change pattern of the monthly SR rate in the PG and PP treatments (i.e., those without fertilization). A bivariate model is used to show that a natural factor—comprised of ST and soil water content (SWC)—explained 44.2% of the variation in the monthly SR rate, whereas biological (i.e., bamboo type) and management (i.e., fertilization) factors had a much smaller impact (less than 0.1% of the variation). The annual mean SR showed a significant positive correlation with soil organic matter (SOM; r = 0.51, P < 0.05), total nitrogen (TN; r = 0.47, P < 0.05), total phosphorus (TP; r = 0.60, P < 0.01), clay content (0.72, P < 0.05) and below-ground biomass (r = 0.60*), which altogether explain 69.0% of the variation in the annual SR. Our results indicate that the fertilization effect was not significant in SR rate for most months among the treatments, but was significant in the annual rate. These results may help to improve policy decisions concerning carbon sequestration and the management of bamboo forests in China

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images

Does Internet Use Boost the Sustainable Subjective Well-Being of Rural Residents? Evidence from Rural China

The rapid development of the internet is affecting rural residents’ well-being profoundly in China. To empirically investigate the impacts of internet use on farmers’ subjective well-being, the latest version of the China Family Panel Studies data is utilized and multiple regression methods are employed. The results of the ordered logit model indicate that internet use positively affects farmers’ subjective well-being. Propensity score matching and endogenous switching regression are used to eliminate possible endogeneity and still reveal robust results. The frequencies of online study, online social interaction, and online entertainment are important channels influencing farmers’ subjective well-being. Furthermore, the impacts of internet use are heterogeneous. Internet users from the central and western regions have higher levels of subjective well-being than their counterparts from the eastern region. Young and middle-aged internet users are happier than the elderly ones. Therefore, the government ought to fully cover rural areas with the internet, eliminate the digital division, especially in Central and Western China, and pay more attention to internet use by the elderly

Integrated aggregate turnover and soil organic carbon sequestration using rare earth oxides and 13C isotope as dual tracers

The formation, stabilization and breakdown processes of soil aggregates determine soil organic carbon (SOC) sequestration, in turn, soil aggregate dynamics are mediated by SOC changes. However, the interactions between them remain elusive. Herein, three types of ¹³C-labelled residues were added to two textured soils. Rare earth oxides (REOs) and ¹³C isotope were used as dual tracers to simultaneously track aggregate transfer pathways and SOC sequestration during a 56-day incubation period. Residue-derived CO₂ followed the sequence of Vetch > Maize > Decomposed maize during the first two weeks. Residue-derived CO₂ was significantly negatively correlated with the aggregate turnover time in both investigated soils (P < 0.01), indicating that aggregate turnover was a controlling factor of residue decomposition in addition to its inherent features. Generally, residue addition decreased the aggregate turnover time in the sequence of Vetch < Maize < Decomposed maize. In Red clay soil, macroaggregates attained a higher turnover rate than that of microaggregates, while a similar change pattern was not observed in Sandstone soil with residue application. Aggregates turnover occurred faster in Sandstone soil than in Red clay soil under a given residue application. The aggregate turnover time was significantly reciprocally correlated with the residue-derived C sequestration rate (P < 0.01), suggesting that aggregate turnover was the key factor in C sequestration. A C flow conceptual model was proposed, residue-derived C firstly accumulated in macroaggregates in the formation process, and then relocated from macroaggregates to microaggregates with the breakdown processes at the mid-to-late stage. This study highlights the importance of aggregate turnover in SOC sequestration and demonstrates that these interactions are further affected by residue features and soil texture.ISSN:0016-7061ISSN:1872-625

Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Timely and accurate information on the spatial distribution of urban trees is critical for sustainable urban development, management and planning. Compared with satellite-based remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing has a higher spatial and temporal resolution, which provides a new method for the accurate identification of urban trees. In this study, we aim to establish an efficient and practical method for urban tree identification by combining an object-oriented approach and a random forest algorithm using UAV multispectral images. Firstly, the image was segmented by a multi-scale segmentation algorithm based on the scale determined by the Estimation of Scale Parameter 2 (ESP2) tool and visual discrimination. Secondly, spectral features, index features, texture features and geometric features were combined to form schemes S1–S8, and S9, consisting of features selected by the recursive feature elimination (RFE) method. Finally, the classification of urban trees was performed based on the nine schemes using the random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively. The results show that the RF classifier performs better than SVM and KNN, and the RF achieves the highest accuracy in S9, with an overall accuracy (OA) of 91.89% and a Kappa coefficient (Kappa) of 0.91. This study reveals that geometric features have a negative impact on classification, and the other three types have a positive impact. The feature importance ranking map shows that spectral features are the most important type of features, followed by index features, texture features and geometric features. Most tree species have a high classification accuracy, but the accuracy of Camphor and Cinnamomum Japonicum is much lower than that of other tree species, suggesting that the features selected in this study cannot accurately distinguish these two tree species, so it is necessary to add features such as height in the future to improve the accuracy. This study illustrates that the combination of an object-oriented approach and the RF classifier based on UAV multispectral images provides an efficient and powerful method for urban tree classification

Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A&ndash;D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03&ndash;23.97 percentage points, 7.57&ndash;12.85 percentage points, and 8.15&ndash;14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees

Extraction of Olive Crown Based on UAV Visible Images and the U²-Net Deep Learning Model

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees

Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction

Sustainable restoration of degraded ecosystems is a major environmental concern in several regions of China. Changting is one of the severely affected water- and soil-loss areas in southern China that have been under continuous management for the last 30 years. Taking the typical red soil erosion area in Changting, Fujian, as the research object, an evaluation index system with 30 m resolution was developed based on the Sensitivity–Resilience–Pressure (SRP) model. Spatial principal component analysis, Global Moran’s I, the LISA cluster map, and the CA-Markov model were employed to dynamically evaluate and predict the ecological vulnerability of the red soil erosion area in Changting. The findings revealed that the ecological vulnerability of the red soil erosion area in Changting has obvious spatial differences and topography, meteorological, and economic and social variables are the primary driving factors of ecological vulnerability. The analysis of spatial distribution of ecological vulnerability showed significant sets of contiguous locations of severe and mild ecological vulnerability. The total index of ecological vulnerability in the study area reduced by 9.49% from 2000 to 2020, yet it was still just mildly vulnerable. The proportion of severe and extremely vulnerable areas declined by 4.87% and 5.61%, respectively. The prediction results for the coming ten years showed that the ecological vulnerability of red soil erosion in Changting will tend to improve. In summary, it is found that after years of continuous ecological management in the red soil erosion area of Changting, the ecological restoration effect of the soil erosion area is obvious

A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs

Citrus (Citrus reticulata), which is an important economic crop worldwide, is often managed in a labor-intensive and inefficient manner in developing countries, thereby necessitating more rapid and accurate alternatives to field surveys for improved crop management. In this study, we propose a novel method for individual tree segmentation from unmanned aerial vehicle remote sensing (RS) using a combination of geographic object-based image analysis (GEOBIA) and layer-adaptive Euclidean distance transformation-based watershed segmentation (LAEDT-WS). First, we use a GEOBIA support vector machine classifier that is optimized for features and parameters to identify the boundaries of citrus tree canopies accurately by generating mask images. Thereafter, our LAEDT workflow separates connected canopies and facilitates the accurate segmentation of individual canopies using WS. Our method exhibited an F1-score improvement of 10.75% compared to the traditional WS method based on the canopy height model. Furthermore, it achieved 0.01% and 1.38% higher F1-scores than the state-of-the-art deep learning detection networks YOLOX and YOLACT, respectively, on the test plot. Our method can be extended to detect larger-scale or more complex structured crops or economic plants by introducing more finely detailed and transferable RS images, such as high-resolution or LiDAR-derived images, to improve the mask base map