Effects of prescribed burning on understory Quercus species of Pinus yunnanensis forest

IntroductionPositioning studies on prescribed burning in Pinus yunnanensis forests have been conducted for several years, focusing on the effects of fire on the composition and structure, growth, regeneration, relative bark thickness, and bark density of understory oak species in Pinus yunnanensis forests.MethodsThe study was conducted on Zhaobi Mountain, Yi-Dai Autonomous County of Xinping, Yuxi City, Yunnan Province. In the prescribed burn after restoration of full 1 year of the area and did not implement the prescribed burn area were set up 10 m × 10 m sample plots 30 pairs of comparisons, and all the oak trees in the sample plots were recorded, each sample plot in the four apexes and the middle were set up five 2 m × 2 m small sample squares, the shrubs in the small sample squares for each plant survey, comparison, statistics and analysis of all data.ResultsThe study results showed that (1) prescribed burning significantly affected the species composition of the understorey of Pinus yunnanensis forests. In both tree and shrub layers, the important values of Quercus aliena, Quercus serrata, Quercus fabri, and Quercus variabilis were significantly reduced in the burned areas. In contrast, the important values of Quercus acutissima increased somewhat. (2) The under crown height of oak trees in the burned areas was significantly lower than in the burned areas, but the height of oak trees in the burned areas was not significantly different from that in the burned areas. In the shrub layer, the height and cover of oak plants in the prescribed burning areas were significantly lower than in the unprescribed burned areas, effectively reducing the vertical continuity of the forest surface combustible material and reducing the possibility of fire converting from surface to canopy fire along the “ladder fuel.” (3) The regeneration of oak plants in the burned area is mainly by sprout tillers, and very few young sprouts are regenerated by seed germination. Renewed young sprouts are difficult to survive the prescribed burn areas the following year due to their lack of fire tolerance. (4) The relative bark thickness and density of oak plants in prescribed burn areas were significantly higher than those in unprescribed burn areas due to the fire tolerance exhibited by oak plants in long-term prescribed burns.DiscussionPrescribed burning has profoundly altered the structural composition and growth of oak plants in the understory of Pinus yunnanensis forests, and oak plants have shown significant fire-adapted traits to resist fire under long-term fire disturbance. The study can provide a scientific basis for prescribed burning, forest fuels, and forest fire management

Microbiome-derived bile acids contribute to elevated antigenic response and bone erosion in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, disabling and incurable autoimmune disease. It has been widely recognized that gut microbial dysbiosis is an important contributor to the pathogenesis of RA, although distinct alterations in microbiota have been associated with this disease. Yet, the metabolites that mediate the impacts of the gut microbiome on RA are less well understood. Here, with microbial profiling and non-targeted metabolomics, we revealed profound yet diverse perturbation of the gut microbiome and metabolome in RA patients in a discovery set. In the Bacteroides-dominated RA patients, differentiation of gut microbiome resulted in distinct bile acid profiles compared to healthy subjects. Predominated Bacteroides species expressing BSH and 7a-HSDH increased, leading to elevated secondary bile acid production in this subgroup of RA patients. Reduced serum fibroblast growth factor-19 and dysregulated bile acids were evidence of impaired farnesoid X receptor-mediated signaling in the patients. This gut microbiota-bile acid axis was correlated to ACPA. The patients from the validation sets demonstrated that ACPA-positive patients have more abundant bacteria expressing BSH and 7a-HSDH but less Clostridium scindens expressing 7a-dehydroxylation enzymes, together with dysregulated microbial bile acid metabolism and more severe bone erosion than ACPA-negative ones. Mediation analyses revealed putative causal relationships between the gut microbiome, bile acids, and ACPA-positive RA, supporting a potential causal effect of Bacteroides species in increasing levels of ACPA and bone erosion mediated via disturbing bile acid metabolism. These results provide insights into the role of gut dysbiosis in RA in a manifestation-specific manner, as well as the functions of bile acids in this gut-joint axis, which may be a potential intervention target for precisely controlling RA conditions.Comment: 38 pages, 6 figure

A review of building detection from very high resolution optical remote sensing images

Building detection from very high resolution (VHR) optical remote sensing images, which is an essential but challenging task in remote sensing, has attracted increased attention in recent years. However, despite the many methods that have been developed, an in-depth review of the recent literature on building extraction from VHR optical images is still lacking. In this article, we present a comprehensive review of the recent advances (since 2000) in this field. In total, we survey and summarize 417 articles in terms of the building detection method, post-processing, and accuracy assessment. The building detection methods are categorized into physical rule based methods, image segmentation based methods, and traditional and advanced machine learning (i.e. deep learning) methods. Furthermore, four promising related research directions of building polygon delineation, building change detection, building type classification, and height retrieval from monocular optical images are also discussed. Overall, building detection from VHR optical images is a popular research topic that has received extensive attention, due to its great significance. It is hoped that this review will help researchers to have a better understanding of this topic, and thus assist them to conduct related work

A Dual-Generator Translation Network Fusing Texture and Structure Features for SAR and Optical Image Matching

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%

Regionalized classification of stand tree species in mountainous forests by fusing advanced classifiers and ecological niche model

Though many new remote sensing technologies have been introduced to analyze forests, regional-scale species-level mapping products are still rare, especially in large mountainous areas. Tree species abundance, low spectral separability among species and huge computing demand are hindrances for obtaining an accurate stand tree species map. This study addressed these problems by synergizing regionalization, multiple feature fusion, and model fusion and proposed a new machine learning workflow. The whole area, i.e. Yunnan province in China (approximately 390,000 km2), was firstly divided into 8 distinct floristic regions according to the distributions and phylogenetic relationships of native tree species. Thereafter, with Google Earth Engine (GEE) platform, multiple data sets, including Sentinel-2 imagery, SRTM DEM, and WorldClim bioclimatic, were collected to construct a high-dimensional feature pool for each region. Thirdly, the maximum entropy model （MaxEnt）, generally used for predicting ecological niche, and three classifiers, i.e. Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost), were used to pre-classify environmental and remote sensing data, respectively. After that, two types of decision fusion strategies, parallel and serial ensembles, were applied to fuse pre-classification probability maps for each sub-region. Finally, the spatial distribution of 19 forest stand species over the whole Yunnan Province was obtained by mosaicking the best classification results from 8 sub-regions. Our method achieves an overall accuracy of 72.18% on the entire validation dataset. The decision fusion models significantly improve the classification accuracy, with the eight partitioned best fusion models improving the accuracy by 7.33%–25.39% on average compared to base classifiers. This study demonstrates that the spatial partitioning strategy and the decision fusion integrating a proper machine learning algorithm and ecological niche model can significantly improve the classification accuracy of forest stand species in montane forests

Classifying Forest Types over a Mountainous Area in Southwest China with Landsat Data Composites and Multiple Environmental Factors

Accurate information about forest type and distribution is critical for many scientific applications. It is possible to make a forest type map from the satellite data in a cost effective way. However, forest type mapping over a large and mountainous geographic area is still challenging, due to complex forest type compositions, spectral similarity among various forest types, poor quality images with clouds or cloud shadows and difficulties in managing and processing large amount data. Based on the Google Earth Engine (GEE) cloud platform, a method of forest types mapping using Landsat-8 OLI imagery and multiple environmental factors was developed and tested within Yunnan Province (about 390,000 km2) of China. The proposed approach employed a pixel-based seasonal image compositing method to produce two types of seasonal composite images, i.e., four 7-spectral-band composite images and four 5-VI-band composite images associated in spring, summer, autumn, and winter. Then, single-season feature bands and multi-seasonal feature bands were combined with the feature bands of topography, temperature, and precipitation, respectively, and resulting in 17 feature combinations. Finally, using a random forest (RF) classifier, 17 feature combinations were separately experimented to classify the forest type over the study area. The study area was firstly classified into the forest and the non-forest, and then the forest was sub-classified into five forest types (evergreen needleleaf forest, deciduous needleleaf forest, evergreen broadleaf forest, deciduous broadleaf forest, and mixed forest). The results showed that the pixel-based multi-seasonal median composite can produce a cloud-free image for the entire region and is suitable for forest type mapping. Compared with a single-season composite, a multi-seasonal composite can distinguish different forest types more effectively. The environmental factors also improve the accuracy of forest type mapping. With the ground survey samples as reference values, the classification performance of 17 feature combinations was compared, and the optimal feature combination was found out. For the optimal feature combination, its overall accuracy of the forest/non-forest cover map and the forest type map reached 97.57% (Kappa = 0.950) and 70.30% (Kappa = 0.628), respectively. The proposed approach has demonstrated strong potential of high classification accuracy and convenient calculation when mapping forest types over a national or global scale, and its product of 30 m resolution forest type map is capable of contributing to forest resource management

Reduction in Uncertainty in Forest Aboveground Biomass Estimation Using Sentinel-2 Images: A Case Study of Pinus densata Forests in Shangri-La City, China

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R2 (0.602) and the highest RMSE (48.180 Mg/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg/ha, the QRNNb has the highest R2 of 0.961 and the lowest RMSE of 1.733 (Mg/ha). Meanwhile, when AGB is larger than 160 Mg/ha, the QRNNb has the highest R2 of 0.867 and the lowest RMSE of 18.203 Mg/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images

Effects of Prescribed Burning on Surface Dead Fuel and Potential Fire Behavior in <i>Pinus yunnanensis</i> in Central Yunnan Province, China

Prescribed burning is a widely used fuel management employed technique to mitigate the risk of forest fires. The Pinus yunnanensis Franch. forest, which is frequently prone to forest fires in southwestern China, serves as a prime example for investigating the effects of prescribed burning on the flammability of surface dead fuel. This research aims to establish a scientific foundation for managing dead fuel in forests, as well as fire prevention and control strategies. Field data was collected from P. yunnanensis forests located in central Yunnan Province in 2021 and 2022. The study implemented a randomized complete block design with two blocks and three treatments: an unburned control (UB), one year after the prescribed burning (PB1a), and three years after the prescribed burning (PB3a). These treatments were evaluated based on three indices: surface dead-bed structure, physicochemical properties, and potential fire behavior parameters. To analyze the stand characteristics of the sample plots, a paired t-test was conducted. The results indicated no significant differences in the stand characteristics of P. yunnanensis following prescribed burning (p > 0.05). Prescribed burning led to a significant decrease in the average surface dead fuel load from 10.24 t/ha to 3.70 t/ha, representing a reduction of 63.87%. Additionally, the average fire−line intensity decreased from 454 kw/m to 190 kw/m, indicating a decrease of 58.15%. Despite prescribed burning, there were no significant changes observed in the physical and chemical properties of dead fuels (p > 0.05). However, the bed structure of dead fuels and fire behavior parameters exhibited a significant reduction compared with the control sample site. The findings of this study provide essential theoretical support for the scientific implementation of prescribed burning programs and the accurate evaluation of ecological and environmental effects post burning