[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (95)

Search Parameters:
Keywords = Moso bamboo forest

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
15 pages, 3936 KiB  
Article
Altitudinal Effects on Soil Microbial Diversity and Composition in Moso Bamboo Forests of Wuyi Mountain
by Yiming Sun, Xunlong Chen, Jianwei Cai, Yangzhuo Li, Yuhan Zhou, Houxi Zhang and Kehui Zheng
Plants 2024, 13(17), 2471; https://doi.org/10.3390/plants13172471 - 4 Sep 2024
Viewed by 566
Abstract
Moso bamboo (Phyllostachys edulis) forest is a key ecosystem and its soil microbial community plays a crucial role in maintaining the ecosystem’s functions, but it is very vulnerable to climate change. An altitude gradient can positively simulate environmental conditions caused by [...] Read more.
Moso bamboo (Phyllostachys edulis) forest is a key ecosystem and its soil microbial community plays a crucial role in maintaining the ecosystem’s functions, but it is very vulnerable to climate change. An altitude gradient can positively simulate environmental conditions caused by climate change, and hence, it provides an efficient means of investigating the response of soil microorganisms to such climatic changes. However, while previous research has largely concentrated on plant–soil–microorganism interactions across broad altitudinal ranges encompassing multiple vegetation types, studies examining these interactions within a single ecosystem across small altitudinal gradients remain scarce. This study took Moso bamboo forests at different altitudes in Wuyi Mountain, China, as the research object and used high-throughput sequencing technology to analyze the soil microbial community structure, aiming to elucidate the changes in soil microbial communities along the altitude gradient under the same vegetation type and its main environmental driving factors. This study found that the structure of bacterial community was notably different in Moso bamboo forests’ soil at varying altitudes, unlike the fungal community structure, which showed relatively less variance. Bacteria from Alphaproteobacteria phylum were the most dominant (14.71–22.91%), while Agaricomycetes was the most dominating fungus across all altitudinal gradients (18.29–30.80%). Fungal diversity was higher at 530 m and 850 m, while bacterial diversity was mainly concentrated at 850 m and 1100 m. Redundancy analysis showed that soil texture (sand and clay content) and available potassium content were the main environmental factors affecting fungal community structure, while clay content, pH, and available potassium content were the main drivers of bacterial community structure. This study demonstrates that the altitude gradient significantly affects the soil microbial community structure of Moso bamboo forest, and there are differences in the responses of different microbial groups to the altitude gradient. Soil properties are important environmental factors that shape microbial communities. The results of this study contribute to a deeper understanding of the impact of altitude gradient on the soil microbial community structure of Moso bamboo forests, thus providing support for sustainable management of Moso bamboo forests under climate change scenarios. Full article
(This article belongs to the Special Issue Molecular Biology and Bioinformatics of Forest Trees)
Show Figures

Figure 1

Figure 1
<p>Locations of the study area (<b>a</b>,<b>b</b>) and the sampling sites of Moso bamboo forests along the different altitudes in Wuyi Mountain (<b>c</b>).</p>
Full article ">Figure 2
<p>Venn diagram of soil fungi (<b>a</b>) and bacteria (<b>b</b>) in Moso bamboo forests at different altitudes in Wuyi Mountains.</p>
Full article ">Figure 3
<p>Relative abundances of the dominant fungal classes in Moso bamboo forests along the elevational gradient.</p>
Full article ">Figure 4
<p>Relative abundances of the dominant fungal groups in Moso bamboo forests along the elevational gradient.</p>
Full article ">Figure 5
<p>Relative abundances of the dominant bacterial classes in Moso bamboo forests along the elevational gradient.</p>
Full article ">Figure 6
<p>Relative abundances of the dominant bacterial groups in Moso bamboo forests along the elevational gradient.</p>
Full article ">Figure 7
<p>Principal coordinates analysis of soil fungi (<b>a</b>) and bacteria (<b>b</b>) community structure in Moso bamboo forests at different elevations in Wuyi Mountain.</p>
Full article ">Figure 8
<p>Redundant analysis of fungal community structure and environmental factors.</p>
Full article ">Figure 9
<p>Redundant analysis of bacterial community structure and environmental factors.</p>
Full article ">
13 pages, 2429 KiB  
Article
Decreased P Cycling Rate and Increased P-Use Efficiency after Phyllostachys edulis (Carrière) J. Houz. Expansion into Adjacent Secondary Evergreen Broadleaved Forest
by Shuwang Song, Lin Wang, Zacchaeus G. Compson, Tingting Xie, Chuyin Liao, Dongmei Huang, Jun Liu, Qingpei Yang and Qingni Song
Forests 2024, 15(9), 1518; https://doi.org/10.3390/f15091518 - 29 Aug 2024
Viewed by 540
Abstract
(1) Background: Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) expansion has seriously altered the species composition and structure of adjacent forest ecosystems in subtropical regions. However, the shift in phosphorus (P) biogeochemical cycling has yet to be assessed, which is a critical [...] Read more.
(1) Background: Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) expansion has seriously altered the species composition and structure of adjacent forest ecosystems in subtropical regions. However, the shift in phosphorus (P) biogeochemical cycling has yet to be assessed, which is a critical gap considering the great variation in ecophysiological properties between invasive bamboo and the displaced native tree species. (2) Methods: We investigated and compared expansion-induced changes in P pools (plant, litter, and soil) and P fluxes (plant uptake and litterfall return) using paired sampling of the bamboo-dominated forest (BDF) and secondary evergreen broadleaved forest (EBF) at Jiangxi province’s Dagang Mountain National Forest Ecological Station. (3) Results: Both the P storage of the plants and litter were significantly greater by 31.8% and 68.2% in the BDF than in the EBF, respectively. The soil total P and available P storage were 28.9% and 40.4% lower, respectively, in the BDF than in the EBF. Plant P uptake was 15.6% higher in the BDF than in the EBF, and the annual litter P return was 26.1% lower in the BDF than in the EBF due to higher P resorption efficiency for moso bamboo compared with evergreen broadleaved tree species. The ecosystem P cycling rate was reduced by 36.1% in the BDF compared with the EBF. (4) Conclusions: Moso bamboo expansion slowed the broadleaved forest ecosystem’s P cycle rate, likely because moso bamboo has higher P-use efficiency, reserving more P in its tissues rather than returning it to the soil. The results from this study elucidate an understudied element cycle in the context of forest succession, demonstrating the ecosystem consequences related to bamboo invasion. Full article
Show Figures

Figure 1

Figure 1
<p>The P allocation for the plant pool (kg P ha<sup>−1</sup>) of secondary evergreen broadleaved forest (EBF) and bamboo-dominant forest (BDF). (<b>a</b>) spatial allocation patterns of P accumulation; (<b>b</b>) species allocation patterns of P accumulation. The data are given as the mean ± SD, with SD representing the standard deviation (n = 3).</p>
Full article ">Figure 2
<p>P allocation of litter pool (kg P ha<sup>−1</sup>) in the secondary evergreen broadleaved forest (EBF) and bamboo-dominant forest (BDF). The data are given as the mean ± SD, with SD representing the standard deviation (n = 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 3
<p>The P pools (kg ha<sup>−1</sup>) and P fluxes (kg ha<sup>−1</sup> a<sup>−1</sup>) for secondary evergreen broadleaved forest (EBF) and bamboo-dominant forest (BDF). (<b>a</b>) evergreen broadleaved forest, EBF, and (<b>b</b>) bamboo-dominant forest, BDF. P pools included plant biomass (P<sub>B</sub>), standing litter (P<sub>L</sub>), and soil total P (TP) and available P (AP) within 0–20 cm. P fluxes included annual plant P uptake (+P<sub>UP</sub>) and P return by litter (P<sub>AR</sub> is the return by aboveground litterfall, P<sub>UR</sub> is the P return from the underground fine root mortality), and P<sub>OP</sub> is the logging output of BDF.</p>
Full article ">Figure 4
<p>Annual P uptake for secondary evergreen broadleaved forest (EBF) and bamboo-dominant forest (BDF). The data are given as the mean ± SD, with SD representing the standard deviation (n = 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 5
<p>The ecosystem P cycling rate for bamboo-dominant forest (BDF) and secondary evergreen broadleaved forest (EBF) in Dagang Mountain National Forest Ecological Station, Jiangxi Province, China. The data are given as the mean ± SD, with SD representing the standard deviation (n = 3; * <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>P-use efficiency of bamboo-dominant forest (BDF) and secondary evergreen broadleaved forest (EBF). Data are expressed as the mean ± SD; SD is the standard deviation (n = 3; * <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
17 pages, 2569 KiB  
Article
Impact of Nitrogen Fertilizer Application on Soil Organic Carbon and Its Active Fractions in Moso Bamboo Forests
by Haoyu Chu, Wenhui Su, Shaohui Fan, Xianxian He and Zhoubin Huang
Forests 2024, 15(9), 1483; https://doi.org/10.3390/f15091483 - 24 Aug 2024
Viewed by 585
Abstract
Soil organic carbon (SOC) is a crucial indicator of soil quality and fertility. However, excessive nitrogen (N) application, while increasing Moso bamboo yield, may reduce SOC content, potentially leading to soil quality issues. The impact of N on SOC and its active fraction [...] Read more.
Soil organic carbon (SOC) is a crucial indicator of soil quality and fertility. However, excessive nitrogen (N) application, while increasing Moso bamboo yield, may reduce SOC content, potentially leading to soil quality issues. The impact of N on SOC and its active fraction in Moso bamboo forests remains underexplored. Investigating these effects will elucidate the causes of soil quality decline and inform effective N management strategies. Four N application gradients were set: no nitrogen (0 kg·hm−2·yr−1, N0), low nitrogen (242 kg·hm−2·yr−1, N1), medium nitrogen (484 kg·hm−2·yr−1, N2), and high nitrogen (726 kg·hm−2·yr−1, N3), with no fertilizer application as the control (CK). We analyzed the changes in SOC, active organic carbon components, and the Carbon Pool Management Index (CPMI) under different N treatments. The results showed that SOC and its active organic carbon components in the 0~10 cm soil layer were more susceptible to N treatments. The N0 treatment significantly increased microbial biomass carbon (MBC) content but had no significant effect on SOC, particulate organic carbon (POC), dissolved organic carbon (DOC), and readily oxidizable organic carbon (ROC) contents. The N1, N2, and N3 treatments reduced SOC content by 29.36%, 21.85%, and 8.67%, respectively. Except for POC, N1,N2 and N3 treatments reduced MBC, DOC, and ROC contents by 46.29% to 71.69%, 13.98% to 40.4%, and 18.64% to 48.55%, respectively. The MBC/SOC ratio can reflect the turnover rate of SOC, and N treatments lowered the MBC/SOC ratio, with N1 < N2 < N3, indicating the slowest SOC turnover under the N1 treatment. Changes in the Carbon Pool Management Index (CPMI) illustrate the impact of N treatments on soil quality and SOC sequestration capacity. The N1 treatment increased the CPMI, indicating an improvement in soil quality and SOC sequestration capacity. The comprehensive evaluation index of carbon sequestration capacity showed N3 (−0.69) < N0 (−0.13) < CK (−0.05) < N2 (0.24) < N1 (0.63), with the highest carbon sequestration capacity under the N1 treatment and a gradual decrease with increasing N fertilizer concentration. In summary, although the N1 treatment reduced the SOC content, it increased the soil CPMI and decreased the SOC turnover rate, benefiting soil quality and SOC sequestration capacity. Therefore, the reasonable control of N fertilizer application is key to improving soil quality and organic carbon storage in Moso bamboo forests. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>–<b>E</b>) indicates the characteristics of the changes in SOC, POC, MBC, DOC, and ROC under different N application treatments, respectively. Different lowercase letters indicate differences between treatments in the same soil layer (<span class="html-italic">p</span> &lt; 0.05). Note: Values are means ± standard error (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 2
<p>(<b>A</b>–<b>D</b>) indicate the characteristics of the changes in L, LI, CPI, and CPMI under different N application treatments, respectively. Different lowercase letters indicate differences between treatments in the same soil layer (<span class="html-italic">p</span> &lt; 0.05). Note: Values are means ± standard error (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 3
<p>(<b>A</b>–<b>C</b>) represents the correlation between soil organic carbon, active organic carbon, and carbon pool management index in the 0~10 cm, 10~20 cm, and 20~30 cm soil layers, respectively. * <span class="html-italic">p</span> &lt; 0.01; ** <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4
<p>(<b>A</b>,<b>B</b>) represents the principal component analysis and comprehensive evaluation of soil organic carbon, reactive organic carbon, and soil carbon pool management index of Moso bamboo forest under different N application treatments, respectively.</p>
Full article ">
23 pages, 15514 KiB  
Article
Expansion of Naturally Grown Phyllostachys edulis (Carrière) J. Houzeau Forests into Diverse Habitats: Rates and Driving Factors
by Juan Wei, Yongde Zhong, Dali Li, Jinyang Deng, Zejie Liu, Shuangquan Zhang and Zhao Chen
Forests 2024, 15(9), 1482; https://doi.org/10.3390/f15091482 - 23 Aug 2024
Viewed by 495
Abstract
Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the [...] Read more.
Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the absence of human intervention, it remains poorly understood how indigenous moso bamboo forests naturally expand into surrounding areas over the long term, and whether these patterns vary with environmental changes. Using multi-year forest resource inventory data, we extracted moso bamboo patches that emerged from 2010 to 2020 and proposed a bamboo expansion index to calculate the average rate of patch expansion during this period. Using the first global 30 m land-cover dynamic monitoring product with a fine classification system, we assessed the expansion speeds of moso bamboo into various areas, particularly forests with different canopy closures and categories. Using parameter-optimized geographic detectors, we explored the significance of multi-factors in the expansion process. The results indicate that the average expansion rate of moso bamboo forests in China is 1.36 m/y, with evergreen broadleaved forests being the primary area for invasion. Moso bamboo expands faster into open forest types (0.15 < canopy closure < 0.4), shrublands, and grasslands. The importance of factors influencing the expansion rate is ranked as follows: temperature > chemical properties of soil > light > physical properties of soil > moisture > atmosphere > terrain. When considering interactions, the primary factors contributing to expansion rates include various climate factors and the combined effect of climate factors and soil factors. Our work underscores the importance of improving the quality and density of native vegetation, such as evergreen broadleaved forests. Effective management strategies, including systematic monitoring of environmental variables, as well as targeted interventions like bamboo removal and soil moisture control, are essential for mitigating the invasion of moso bamboo. Full article
Show Figures

Figure 1

Figure 1
<p>Distribution of moso bamboo in the study area in 2010 and additional naturally grown moso bamboo forests from 2010 to 2020.</p>
Full article ">Figure 2
<p>Mechanism and procedure of Moso Bamboo Expansion Index.</p>
Full article ">Figure 3
<p>Sample normal distribution curves and box plots of MBEI for mosaic bamboo expansion into different habitats from 2010 to 2020.</p>
Full article ">Figure 4
<p>On the <b>left</b>, the circular bar chart illustrates the contribution values (q values) of individual factors for the bamboo area. The calculation process adopts a t-test with a significance level of 0.05, The <span class="html-italic">p</span>-values for all factors are &lt; 0.05. Indicators with very low contribution rates are not labeled in the figure, as they are sorted from highest to lowest contribution rate.(a1: ≥10 °C accumulated temperature; a2: annual mean temperature; a3: annual mean minimum temperature; a4: annual mean maximum temperature; b1: annual precipitation; b2: relative humidity; b3: potential mean evapotranspiration; c: near-surface wind speed; d1: surface solar radiation; d2: sunshine duration; e1: dominant soil unit; e2: texture class; e3: reference bulk density; e4: AWC for rootable soil depth; f1: organic carbon content; f2: total nitrogen content; f3: total phosphorus content; f4: total potassium content; f5: pH in water; f6: case saturation as the percentage of CECsoil; f7: cation exchange capacity of the soil; g1: elevation; and g2: slope). On the <b>right</b>, the 3D histogram shows the average contribution rate q values of the seven major factors for the expansion of bamboo into different habitats. (T: all expansion areas of moso bamboo; A: rainfed cropland, herbaceous cover, and irrigated cropland; B: open evergreen broadleaved forest (0.15 &lt; fc &lt; 0.4); C: closed evergreen broadleaved forest (fc &gt; 0.4); D: open evergreen needle-leaved forest (0.15 &lt; fc &lt; 0.4); E: closed evergreen needle-leaved forest (fc &gt; 0.4); F: open deciduous broadleaved forest (0.15 &lt; fc &lt; 0.4); G: closed deciduous broadleaved forest (fc &gt; 0.4); H: shrubland; I: evergreen shrubland; J: grassland; K: water, wetlands; L: impervious surfaces. a: temperature; b: moisture; c: atmosphere; d: light; e: physical properties of soil; f: chemical properties of soil; and g: terrain).</p>
Full article ">Figure 5
<p>The circular bar chart shows the q values of the driving factors for the expansion of bamboo into 12 different habitats. The calculation process adopts a <span class="html-italic">t</span>-test with a significance level of 0.05. Except for f5 in type open evergreen broadleaved forests (0.15 &lt; fc &lt; 0.4) and e2 in type open evergreen needle-leaved forests (0.15 &lt; fc &lt; 0.4), the <span class="html-italic">p</span>-values of others are &lt;0.05. Indicators with very low contribution rates are not labeled in the figure, as they are sorted from highest to lowest contribution rate. (a1: ≥10 °C accumulated temperature; a2: annual mean temperature; a3: annual mean minimum temperature; a4: annual mean maximum temperature; b1: annual precipitation; b2: relative humidity; b3: potential mean evapotranspiration; c: near-surface wind speed; d1: surface solar radiation; d2: sunshine duration; e1: dominant soil unit; e2: texture class; e3: reference bulk density; e4: AWC for rootable soil depth; f1: organic carbon content; f2: total nitrogen content; f3: total phosphorus content; f4: total potassium content; f5: pH in water; f6: base saturation as the percentage of CECsoil; f7: cation exchange capacity of the soil; g1: elevation; and g2: slope).</p>
Full article ">Figure 6
<p>A magnitude and heat map of interaction values between 23 drivers across the expansion region of moso bamboo. (a1: ≥10 °C accumulated temperature; a2: annual mean temperature; a3: annual mean minimum temperature; a4: annual mean maximum temperature; b1: annual precipitation; b2: relative humidity; b3: potential mean evapotranspiration; c: near-surface wind speed; d1: surface solar radiation; d2: sunshine duration; e1: dominant soil unit; e2: texture class; e3: reference bulk density; e4: AWC for rootable soil depth; f1: organic carbon content; f2: total nitrogen content; f3: total phosphorus content; f4: total potassium content; f5: pH in water; f6: base saturation as the percentage of CECsoil; f7: cation exchange capacity of the soil; g1: elevation; and g2: slope).</p>
Full article ">Figure A1
<p>The interaction values and heatmap of the 23 driving factors during the expansion of bamboo into habitats A: (rainfed cropland, herbaceous cover, and irrigated cropland), B: open evergreen broadleaved forest (0.15 &lt; fc &lt; 0.4)), C: (closed evergreen broadleaved forest (fc &gt; 0.4)), D: (open evergreen needle-leaved forest (0.15 &lt; fc &lt; 0.4)), E: (closed evergreen needle-leaved forest (fc &gt; 0.4)), and F: (open deciduous broadleaved forest (0.15 &lt; fc &lt; 0.4).</p>
Full article ">Figure A2
<p>The interaction values and heatmap of the 23 driving factors during the expansion of bamboo into habitats G: (closed deciduous broadleaved forest (fc &gt; 0.4)), H: (shrubland), I: (evergreen shrubland), J: (grassland), K: (water, wetlands), and L: (impervious surfaces).</p>
Full article ">
12 pages, 6504 KiB  
Article
Abandonment Leads to Changes in Forest Structural and Soil Organic Carbon Stocks in Moso Bamboo Forests
by Yaowen Xu, Jiejie Jiao, Chuping Wu, Ziqing Zhao, Xiaogai Ge, Ge Gao, Yonghui Cao and Benzhi Zhou
Plants 2024, 13(16), 2301; https://doi.org/10.3390/plants13162301 - 19 Aug 2024
Cited by 1 | Viewed by 643
Abstract
The important role of soil carbon pools in coping with climate change has become widely recognized. Moso bamboo (Phyllostachys pubescens) is an economically important bamboo species in South China; however, owing to factors such as rising labor costs and increasingly stringent [...] Read more.
The important role of soil carbon pools in coping with climate change has become widely recognized. Moso bamboo (Phyllostachys pubescens) is an economically important bamboo species in South China; however, owing to factors such as rising labor costs and increasingly stringent environmental policies, Moso bamboo forests have recently been abandoned. The present study aimed to investigate the effects of abandonment on structural factors and soil organic carbon (SOC) stocks in Moso bamboo forests. We investigated Moso bamboo forests subjected to intensive management or abandonment for different durations and measured forest structural characteristics, mineral properties, soil nutrients, and other soil properties. Although abandonment did not significantly affect the height and diameter at breast height, it increased culm densities, biomass, and SOC stocks. The drivers of SOC stocks depended on soil depth and were mainly controlled by carbon decomposition mediated by soil properties. In the topsoil, mineral protection and soil total nitrogen (TN) exerted significant effects on SOC stocks; in the subsoil, soil TN was the main driver of SOC stocks. As the controlling factors of SOC stocks differed between the subsoil and topsoil, more attention should be paid to the subsoil. Overall, these findings refine our understanding of the structural characteristics and SOC stocks associated with Moso bamboo forest abandonment, serving as a reference for the follow-up management of these forests. Full article
Show Figures

Figure 1

Figure 1
<p>The location of Hangzhou and Deqing of Zhejiang Province, southeast China.</p>
Full article ">Figure 2
<p>(<b>A</b>) DBH, (<b>B</b>) height, (<b>C</b>) culm densities, and (<b>D</b>) biomass in the different groups of Moso bamboo forests. DBH, diameter at breast height; CK, intensive management; AM-1, 2–5 years abandonment; AM-2, 7–10 years abandonment; AM-3, 11–14 years abandonment. Different lowercase letters indicate significant differences between different groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Soil organic carbon (SOC) stocks in the topsoil (<b>A</b>) and subsoil (<b>B</b>). CK, intensive management; AM-1, 2–5 years abandonment; AM-2, 7–10 years abandonment; AM-3, 11–14 years abandonment. Different lowercase letters indicate significant differences between different groups (<span class="html-italic">p</span> &lt; 0.05). log10SOC stock (Mg ha−1).</p>
Full article ">Figure 4
<p>Relationship between soil organic carbon (SOC) stocks and biomass in the topsoil (<b>A</b>) and subsoil (<b>B</b>). The shaded area represents the 95% confidence interval and the solid line represents the fitted regression.</p>
Full article ">Figure 5
<p>Relationship between soil organic carbon (SOC) stocks and (<b>A</b>) pH, (<b>B</b>) TN, (<b>C</b>) TP, (<b>D</b>) soil moisture, and (<b>E</b>) bulk density in the topsoil. Relationship between SOC stocks and (<b>F</b>) pH, (<b>G</b>) TN, (<b>H</b>) TP, (<b>I</b>) soil moisture, and (<b>J</b>) bulk density in the subsoil. TN: total nitrogen; TP: total phosphorus. The shaded area represents the 95% confidence interval, and the solid line represents the fitted regression.</p>
Full article ">Figure 6
<p>Relationship between soil organic carbon (SOC) stocks and (<b>A</b>) Fe<sub>d</sub> + Al<sub>d</sub>, (<b>B</b>) Fe<sub>p</sub> + Al<sub>p</sub>, (<b>C</b>) Fe<sub>o</sub> + Al<sub>o</sub>, (<b>D</b>) CEC, and (<b>E</b>) clay content in the topsoil. Relationship between SOC stocks and (<b>F</b>) Fe<sub>d</sub> + Al<sub>d</sub>, (<b>G</b>) Fe<sub>p</sub> + Al<sub>p</sub>, (<b>H</b>) Fe<sub>o</sub> + Al<sub>o</sub>, (<b>I</b>) CEC, and (<b>J</b>) clay content in the subsoil. Fe<sub>d</sub> + Al<sub>d</sub>: sum of free Fe and Al oxides; Fe<sub>p</sub> + Al<sub>p</sub>: sum of complexed Fe and Al oxides; Fe<sub>o</sub> + Al<sub>o</sub>: sum of poorly crystalline Fe and Al oxides; CEC: cation exchange capacity. The shaded area represents the 95% confidence interval, and the solid line represents the fitted regression.</p>
Full article ">Figure 7
<p>Structural equation model of the effects of environmental factors on soil organic carbon (SOC) stocks in the topsoil (<b>A</b>) and subsoil (<b>B</b>). TN: total nitrogen. Mineral properties: sum of poorly crystalline Fe and Al oxides, sum of complexed Fe and Al oxides, clay (topsoil); sum of poorly crystalline Fe and Al oxides, sum of free Fe and Al oxides, clay (subsoil). The solid and dotted arrows indicate significant and non-significant pathways, respectively. Numbers adjoining the arrows indicate standardized path coefficients. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. Model fitting index: χ<sup>2</sup>/df = 1.309, <span class="html-italic">p</span> = 0.269, GFI = 0.945, CFI = 0.989 (topsoil); χ<sup>2</sup>/df = 1.838, <span class="html-italic">p</span> = 0.159, GFI = 0.941, CFI = 0.982 (subsoil).</p>
Full article ">
20 pages, 7610 KiB  
Article
The Expansion of Moso Bamboo (Phyllostachys edulis) Forests into Diverse Types of Forests in China from 2010 to 2020
by Dali Li, Juan Wei, Jiangzhou Wu, Yongde Zhong, Zhao Chen, Jianghua He, Shuangquan Zhang and Lushan Yu
Forests 2024, 15(8), 1418; https://doi.org/10.3390/f15081418 - 13 Aug 2024
Cited by 1 | Viewed by 755
Abstract
Moso bamboo (Phyllostachys edulis) forests, characterized by their rapid growth and clonal reproduction, have emerged as a significant threat to adjacent forest ecosystems. However, in China, the area, speed, and spatial distribution of moso bamboo forest expansion into other types of [...] Read more.
Moso bamboo (Phyllostachys edulis) forests, characterized by their rapid growth and clonal reproduction, have emerged as a significant threat to adjacent forest ecosystems. However, in China, the area, speed, and spatial distribution of moso bamboo forest expansion into other types of forests remains poorly understood. In this study, we present a case analysis of moso bamboo forests, employing a decade-long dataset from the forest second type inventory (FSTI) that utilizes transition matrices, neighboring ratio analysis, and spatio-temporal autocorrelation. This comprehensive investigation focuses on the spatio-temporal expansion of moso bamboo forests into diverse types of forests, with the aim of providing science-based recommendations for effective moso bamboo forest management. Our findings reveal that areas of moso bamboo forests have been expanding at an approximate annual rate of 2%, with an average expansion speed (including moso bamboo forests manually planted) of approximately 8 m per year. The length of moso bamboo–woodland ecotones (BWEs) increases as a consequence of moso bamboo forest expansion, indicating a sustained escalation in the extent of this expansion. Coniferous forests and evergreen broad-leaved forests are mainly invaded, accounting for around 58% of all invaded forests. The rate of moso bamboo forest expansion into different types of forests varies, although the rate remains fairly consistent within the same forest type. Moso bamboo forest expansion exhibits significant spatial heterogeneity. Furthermore, the area of moso bamboo forest intrusion into various types of forests in different provinces is notably influenced by the presence of moso bamboo forests and the proportional distribution of different forest types. The factors contributing to bamboo forest expansion encompass stand characteristics, soil attributes, light intensity, moso bamboo afforestation, forestry practices, and human disturbances. Full article
Show Figures

Figure 1

Figure 1
<p>Distribution map of all species of bamboo stands in China [<a href="#B48-forests-15-01418" class="html-bibr">48</a>].</p>
Full article ">Figure 2
<p>Diagram of data processing workflow.</p>
Full article ">Figure 3
<p>The area of moso Bamboo forests from 2010 to 2020.</p>
Full article ">Figure 4
<p>The transfer area of moso bamboo forests into other forests during the 2010–2015 and 2015–2020 periods.</p>
Full article ">Figure 5
<p>The average rate of bamboo expansion across different forest types and the overall average rate of expansion.</p>
Full article ">Figure 6
<p>The neighboring rate of moso bamboo forest in every province.</p>
Full article ">Figure 7
<p>(<b>a</b>) The correlation between the area of the moso bamboo expanding and the existing extent in every province and the spatial auto-correlation map of the moso bamboo expanding in 2010–2015. (<b>b</b>) The correlation between the area of the moso bamboo expanding and the existing extent in every province and the spatial auto-correlation map of the moso bamboo expanding in 2015–2020. *** stands for the significance level &gt;0.99.</p>
Full article ">Figure 8
<p>The area of moso bamboo forest expanding into the following areas: (<b>a</b>) evergreen broad-leaved forest (2010–2015); (<b>b</b>) evergreen broad-leaved forest (2015–2020); (<b>c</b>) deciduous broad-leaved forest (2010–2015); (<b>d</b>) deciduous broad-leaved forest (2015–2020); (<b>e</b>) non-timber forest (2010–2015); (<b>f</b>) non-timber forest (2015–2020); (<b>g</b>) other bamboo forest (2010–2015); (<b>h</b>) other bamboo forest (2015–2020); (<b>i</b>) shrubbery (2010–2015); (<b>j</b>) shrubbery (2015–2020); (<b>k</b>) coniferous forest (2010–2015); (<b>l</b>) coniferous forest (2015–2020); (<b>m</b>) other forest (2010–2015); (<b>n</b>) other forest (2015–2020); (<b>o</b>) non-forest (2010–2015); (<b>p</b>) non-forest (2015–2020).</p>
Full article ">Figure 8 Cont.
<p>The area of moso bamboo forest expanding into the following areas: (<b>a</b>) evergreen broad-leaved forest (2010–2015); (<b>b</b>) evergreen broad-leaved forest (2015–2020); (<b>c</b>) deciduous broad-leaved forest (2010–2015); (<b>d</b>) deciduous broad-leaved forest (2015–2020); (<b>e</b>) non-timber forest (2010–2015); (<b>f</b>) non-timber forest (2015–2020); (<b>g</b>) other bamboo forest (2010–2015); (<b>h</b>) other bamboo forest (2015–2020); (<b>i</b>) shrubbery (2010–2015); (<b>j</b>) shrubbery (2015–2020); (<b>k</b>) coniferous forest (2010–2015); (<b>l</b>) coniferous forest (2015–2020); (<b>m</b>) other forest (2010–2015); (<b>n</b>) other forest (2015–2020); (<b>o</b>) non-forest (2010–2015); (<b>p</b>) non-forest (2015–2020).</p>
Full article ">
23 pages, 12514 KiB  
Article
Comparison of Algorithms and Optimal Feature Combinations for Identifying Forest Type in Subtropical Forests Using GF-2 and UAV Multispectral Images
by Guowei He, Shun Li, Chao Huang, Shi Xu, Yang Li, Zijun Jiang, Jiashuang Xu, Funian Yang, Wei Wan, Qin Zou, Mi Zhang, Yan Feng and Guoqing He
Forests 2024, 15(8), 1327; https://doi.org/10.3390/f15081327 - 30 Jul 2024
Viewed by 756
Abstract
The composition and spatial distribution of tree species are pivotal for biodiversity conservation, ecosystem productivity, and carbon sequestration. However, the accurate classification of tree species in subtropical forests remains a formidable challenge due to their complex canopy structures and dense vegetation. This study [...] Read more.
The composition and spatial distribution of tree species are pivotal for biodiversity conservation, ecosystem productivity, and carbon sequestration. However, the accurate classification of tree species in subtropical forests remains a formidable challenge due to their complex canopy structures and dense vegetation. This study addresses these challenges within the Jiangxi Lushan National Nature Reserve by leveraging high-resolution GF-2 remote sensing imagery and UAV multispectral images collected in 2018 and 2022. We extracted spectral, texture, vegetation indices, geometric, and topographic features to devise 12 classification schemes. Utilizing an object-oriented approach, we employed three machine learning algorithms—Random Forest (RF), k-Nearest Neighbor (KNN), and Classification and Regression Tree (CART)—to identify 12 forest types in these regions. Our findings indicate that all three algorithms were effective in identifying forest type in subtropical forests, and the optimal overall accuracy (OA) was more than 72%; RF outperformed KNN and CART; S12 based on feature selection was the optimal feature combination scheme; and the combination of RF and Scheme S12 (S12) yielded the highest classification accuracy, with OA and Kappa coefficients for 2018-RF-S12 of 90.33% and 0.82 and OA and Kappa coefficients for 2022-RF-S12 of 89.59% and 0.81. This study underscores the utility of combining multiple feature types and feature selection for enhanced forest type recognition, noting that topographic features significantly improved accuracy, whereas geometric features detracted from it. Altitude emerged as the most influential characteristic, alongside significant variables such as the Normalized Difference Greenness Index (NDVI) and the mean value of reflectance in the blue band of the GF-2 image (Mean_B). Species such as Masson pine, shrub, and moso bamboo were accurately classified, with the optimal F1-Scores surpassing 89.50%. Notably, a shift from single-species to mixed-species stands was observed over the study period, enhancing ecological diversity and stability. These results highlight the effectiveness of GF-2 imagery for refined, large-scale forest-type identification and dynamic diversity monitoring in complex subtropical forests. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Show Figures

Figure 1

Figure 1
<p>Location of study area. (<b>a</b>,<b>b</b>) Jiangxi Province, China; (<b>c</b>) Spatial distribution of field survey plots, and UAV monitoring sites.</p>
Full article ">Figure 2
<p>(<b>a</b>) 2018 GF-2 image; (<b>b</b>) 2022 GF-2 image; (<b>c</b>,<b>d</b>) 2023 UAV multispectral images.</p>
Full article ">Figure 3
<p>The estimation of the scales using the ESP2 tool. (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 4
<p>Overall accuracy of classification schemes. (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 5
<p>Accuracy comparison of algorithms. (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 6
<p>Comparison of F1-Scores of different forest types. (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 7
<p>F1-Scores of different schemes for different forest types.</p>
Full article ">Figure 8
<p>Feature importance ranking results. (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 9
<p>Classification results of the optimal accuracy classification combination in 2018 and 2022 (<b>a</b>) 2018; (<b>b</b>) 2022.</p>
Full article ">Figure 10
<p>Dynamic changes in forest type from 2018 to 2022.</p>
Full article ">
18 pages, 9265 KiB  
Article
Study on Fast Liquefaction and Characterization of Produced Polyurethane Foam Materials from Moso Bamboo
by Go Masuda, Satoshi Akuta, Weiqian Wang, Miho Suzuki, Yu Honda and Qingyue Wang
Materials 2024, 17(15), 3751; https://doi.org/10.3390/ma17153751 - 29 Jul 2024
Viewed by 568
Abstract
Although bamboo is widely distributed in Japan, its applications are very limited due to its poor combustion efficiency for fuel. In recent years, the expansion of abandoned bamboo forests has become a social issue. In this research, the possibility of a liquefaction process [...] Read more.
Although bamboo is widely distributed in Japan, its applications are very limited due to its poor combustion efficiency for fuel. In recent years, the expansion of abandoned bamboo forests has become a social issue. In this research, the possibility of a liquefaction process with fast and efficient liquefaction conditions using moso bamboo as raw material was examined. Adding 20 wt% ethylene carbonates to the conventional polyethylene glycol/glycerol mixed solvent system, the liquefaction time was successfully shortened from 120 to 60 min. At the same time, the amount of sulfuric acid used as a catalyst was reduced from 3 wt% to 2 wt%. Furthermore, polyurethane foam was prepared from the liquefied product under these conditions, and its physical properties were evaluated. In addition, the filler effects of rice husk biochar and moso bamboo fine meals for the polyurethane foams were characterized by using scanning electron microscopy (SEM) and thermogravimetry and differential thermal analysis (TG-DTA), and the water absorption and physical density were measured. As a result, the water absorption rate of bamboo fine meal-added foam and the thermal stability of rice husk biochar-added foam were improved. These results suggested that moso bamboo meals were made more hydrophilic, and the carbon content of rice husk biochar was increased. Full article
Show Figures

Figure 1

Figure 1
<p>Changes in the amount of bamboo use in Japan [<a href="#B6-materials-17-03751" class="html-bibr">6</a>].</p>
Full article ">Figure 2
<p>Liquefaction equipment settings.</p>
Full article ">Figure 3
<p>FT-IR spectrum of moso bamboo meals.</p>
Full article ">Figure 4
<p>Comparison of residue content by amount of sulfuric acid.</p>
Full article ">Figure 5
<p>Comparison of residue content among different amounts of ethylene carbonate with 1 wt% sulfuric acid.</p>
Full article ">Figure 6
<p>Comparison of residue content among different amounts of ethylene carbonate with 2 wt% sulfuric acid.</p>
Full article ">Figure 7
<p>GPC chromatography of the liquefied products without ethylene carbonate.</p>
Full article ">Figure 8
<p>GPC chromatography of the liquefied products with ethylene carbonate.</p>
Full article ">Figure 9
<p>FT-IR spectra of the liquefied products with ethylene carbonate.</p>
Full article ">Figure 10
<p>FT-IR spectra of polyurethane foams with different amounts of STABiO.</p>
Full article ">Figure 11
<p>Appearance of polyurethane foams of different cyanate/polyol ratios after foaming: (<b>a</b>) 0.6, (<b>b</b>) 0.8, (<b>c</b>) 1, (<b>d</b>) 1.2.</p>
Full article ">Figure 12
<p>SEM images of polyurethane foams with different cyanate/polyol ratios. (<b>a</b>) 0.6, (<b>b</b>) 0.8, (<b>c</b>) 1, (<b>d</b>) 1.2.</p>
Full article ">Figure 13
<p>TG-DTA data of polyurethane foams with bamboo meals.</p>
Full article ">Figure 14
<p>TG-DTA data of polyurethane foams with rice husk filler.</p>
Full article ">Figure 15
<p>SEM images of polyurethane foams (<b>left</b>) without residue and (<b>right</b>) with residue.</p>
Full article ">Figure 16
<p>SEM images of polyurethane foams with different fillers: (<b>a</b>) foam with 7% bamboo filler, (<b>b</b>) foam with 18% bamboo filler, (<b>c</b>) foam with 7% rice husk filler, (<b>d</b>) foam with 18% rice husk filler.</p>
Full article ">Figure 17
<p>The foam density with bamboo filler.</p>
Full article ">Figure 18
<p>The foam water absorption with bamboo filler.</p>
Full article ">
23 pages, 6746 KiB  
Article
Effects of Fertilizer Application Intensity on Carbon Accumulation and Greenhouse Gas Emissions in Moso Bamboo Forest–Polygonatum cyrtonema Hua Agroforestry Systems
by Huiying Chen, Xuekun Cheng, Xingfa Zhang, Haitao Shi, Jiahua Chen, Ruizhi Xu, Yangen Chen, Jianping Ying, Yixin Wu, Yufeng Zhou and Yongjun Shi
Plants 2024, 13(14), 1941; https://doi.org/10.3390/plants13141941 - 15 Jul 2024
Viewed by 653
Abstract
Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer [...] Read more.
Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer rates on soil greenhouse gas (GHG) emissions, vegetation and soil organic carbon (SOC) sinks, and to illustrate the differences in agroforestry systems’ carbon sinks through a one-year positioning test across 12 plots, applying different compound fertilizer application rates (0 (CK), 400 (A1), 800 (A2), and 1600 (A3) kg ha−1). The study demonstrated that, after fertilization, the total GHG emissions of A1 decreased by 4.41%, whereas A2 and A3 increased their total GHG emissions by 17.13% and 72.23%, respectively. The vegetation carbon sequestration of A1, A2, and A3 increased by 18.04%, 26.75%, and 28.65%, respectively, and the soil organic carbon sequestration rose by 32.57%, 42.27% and 43.29%, respectively. To sum up, in contrast with CK, the ecosystem carbon sequestration climbed by 54.41%, 51.67%, and 0.90%, respectively. Our study suggests that rational fertilization can improve the carbon sink of the ecosystem and effectively ameliorate climate change. Full article
Show Figures

Figure 1

Figure 1
<p>Monthly average ± standard deviation (<span class="html-italic">n</span> = 3) at different fertilizer application rates: (<b>a</b>) soil temperature, (<b>b</b>) soil mass water content, and (<b>c</b>) soil pH. The deviations are indicated by error bars.</p>
Full article ">Figure 2
<p>Monthly average ± standard deviation (<span class="html-italic">n</span> = 3) at different fertilizer application rates, including (<b>a</b>) soil microbial biomass carbon and (<b>b</b>) soil water-soluble organic carbon. The deviations are indicated by error bars.</p>
Full article ">Figure 3
<p>Monthly mean ± standard deviation (<span class="html-italic">n</span> = 3) at different fertilizer application rates for (<b>a</b>) soil microbial nitrogen, (<b>b</b>) soil water-soluble organic nitrogen, (<b>c</b>) soil nitrate nitrogen, and (<b>d</b>) soil ammonium nitrogen. The deviations are indicated by error bars.</p>
Full article ">Figure 4
<p>Monthly mean ± standard deviation (<span class="html-italic">n</span> = 3) under different fertilization rates: (<b>a</b>) soil CO<sub>2</sub> emission flux, (<b>c</b>) soil N<sub>2</sub>O emission flux, (<b>e</b>) soil CH<sub>4</sub> uptake flux, (<b>b</b>) soil cumulative CO<sub>2</sub> emissions, (<b>d</b>) soil cumulative N<sub>2</sub>O emissions, and (<b>f</b>) soil cumulative CH<sub>4</sub> uptake. The deviations are represented by error bars, and distinct lowercase letters represent significant variations among different treatments (<span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 5
<p>The impacts of compound fertilizer application, soil MBC, WSOC, MBN, WSON, NO<sub>3</sub><sup>−</sup>-N, and NH<sub>4</sub><sup>+</sup>-N on the soil: (<b>a</b>) CO<sub>2</sub>, (<b>b</b>) N<sub>2</sub>O emission flux, and (<b>c</b>) CH<sub>4</sub> absorption flux are illustrated by the structural equation model (SEM), either directly or indirectly. CK, A1, A2, and A3 signify compound fertilizer application rates of 0, 400, 800, and 1600 kg ha<sup>−1</sup>, respectively. The picture on the right presents the standardized total effect diagram corresponding to greenhouse gases, describing the overall impact of different factors on greenhouse gas emissions, where FC denotes compound fertilizer application. The numbers next to the arrows in the structural equation model represent the standardized path coefficients and significance levels. The symbols *, **, and *** signify <span class="html-italic">p</span>-values of &lt;0.05, &lt;0.01, and &lt;0.001, respectively. Black and red arrows represent positive as well as negative correlations, whereas solid and dashed arrows indicate significant and non-significant relationships. R<sup>2</sup> represents the model interpretation rate. The goodness-of-fit index is signified by GFI, the comparative fit index by CFI, the normative fit index by NFI, and the standardized root mean square residual by SRMR.</p>
Full article ">Figure 5 Cont.
<p>The impacts of compound fertilizer application, soil MBC, WSOC, MBN, WSON, NO<sub>3</sub><sup>−</sup>-N, and NH<sub>4</sub><sup>+</sup>-N on the soil: (<b>a</b>) CO<sub>2</sub>, (<b>b</b>) N<sub>2</sub>O emission flux, and (<b>c</b>) CH<sub>4</sub> absorption flux are illustrated by the structural equation model (SEM), either directly or indirectly. CK, A1, A2, and A3 signify compound fertilizer application rates of 0, 400, 800, and 1600 kg ha<sup>−1</sup>, respectively. The picture on the right presents the standardized total effect diagram corresponding to greenhouse gases, describing the overall impact of different factors on greenhouse gas emissions, where FC denotes compound fertilizer application. The numbers next to the arrows in the structural equation model represent the standardized path coefficients and significance levels. The symbols *, **, and *** signify <span class="html-italic">p</span>-values of &lt;0.05, &lt;0.01, and &lt;0.001, respectively. Black and red arrows represent positive as well as negative correlations, whereas solid and dashed arrows indicate significant and non-significant relationships. R<sup>2</sup> represents the model interpretation rate. The goodness-of-fit index is signified by GFI, the comparative fit index by CFI, the normative fit index by NFI, and the standardized root mean square residual by SRMR.</p>
Full article ">Figure 6
<p>The violin box plot describes the carbon content distribution of 15 <span class="html-italic">Polygonatum cyrtonema</span> Hua samples in each treatment in the form of a curve. The horizontal line inside the box denotes the mean value, the upper and lower lines of the box plot indicate the data’s maximum and minimum values, and the upper and lower bounds of the box signify the upper and lower quartiles of data. The acronym “ns” means there are no appreciable differences among the four treatments (<span class="html-italic">p</span> &gt; 0.05).</p>
Full article ">Figure 7
<p>Location and design of experiments (CK, A1, A2, and A3 were applied with 0, 400, 800, and 1600 kg ha<sup>−1</sup> of compound fertilizer, respectively).</p>
Full article ">Figure 8
<p>Mean monthly temperature and mean monthly precipitation throughout the experiment period.</p>
Full article ">
13 pages, 2552 KiB  
Article
Stoichiometric Homeostasis of N and P in the Leaves of Different-Aged Phyllostachys edulis after Bamboo Forest Expansion in Subtropical China
by Jingxin Shen, Shaohui Fan, Jiapeng Zhang and Guanglu Liu
Forests 2024, 15(7), 1181; https://doi.org/10.3390/f15071181 - 8 Jul 2024
Viewed by 583
Abstract
Stoichiometric homeostasis is an important mechanism in maintaining ecosystem structure, function, and stability. Phyllostachys edulis (moso bamboo) is a typical clone plant, forming pure bamboo forests or bamboo–wood mixed forests by expanding rhizomes around. Studying the stoichiometric homeostasis characteristics of moso bamboo at [...] Read more.
Stoichiometric homeostasis is an important mechanism in maintaining ecosystem structure, function, and stability. Phyllostachys edulis (moso bamboo) is a typical clone plant, forming pure bamboo forests or bamboo–wood mixed forests by expanding rhizomes around. Studying the stoichiometric homeostasis characteristics of moso bamboo at different ages after expansion contributes to a deeper understanding of the stability of bamboo forest ecosystems, and is of great significance for expanding the research scope of ecological stoichiometry. Based on the stoichiometric internal stability theory, the nitrogen (N) and phosphorus (P) elements in the soil and plants of typical moso bamboo forests in Tianbaoyan National Nature Reserve of Fujian Province were determined, and the internal stability index (H) of bamboo leaves of different ages (I-du, II-du, III-du, and IV-du bamboos) was calculated. The results showed that the dependence of moso bamboo on soil nutrients and the ability of moso bamboo to regulate nutrient elements were both significantly affected by the plant’s age. Under the condition of the same soil nutrients (N, P), the content of N and P in bamboo leaves decreased significantly with the increase in bamboo age. The limiting effect of phosphorus on the growth and development of moso bamboo was greater than that of nitrogen, and the limiting effect of phosphorus on aged bamboo was greater than that of young bamboo. The stoichiometric internal stability index of N and P in bamboo leaves is HN:P > HN > HP, which means that the internal stability of moso bamboo is closely related to the limiting elements. Therefore, the regulation ability of the internal stability of moso bamboo of different ages makes it grow well in the changeable environment, has stronger adaptability and competitiveness, and the leaf internal stability of I-du bamboo was higher than that of other ages, which may be one of the reasons for its successful expansion to form a stable bamboo stand structure. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the study area and the layout of the experimental plots.</p>
Full article ">Figure 2
<p>Leaf N (<b>a</b>), P (<b>b</b>), N:P (<b>c</b>), and their coefficient in variation (CV) (<b>d</b>) of different-aged moso bamboo. I, II, III, and IV represent 1, 2–3, 4–5, and &gt;6 years, respectively. Different lowercase letters indicate significant differences among the means of the different ages (at the <span class="html-italic">p</span> &lt; 0.05 level).</p>
Full article ">Figure 3
<p>Nitrogen (N), phosphorus (P), available nitrogen (HN), and available phosphorus (AP) content and N:P ratio in the soil of Moso bamboo at different ages. I, II, III, and IV represent 1, 2–3, 4–5, and &gt;6 years. (<b>a</b>–<b>f</b>) respectively represent soil organic carbon content, soil nitrogen (N) content, soil phosphorus (P) content, soil available nitrogen (HN) content, soil available phosphorus (AP) content, and the soil nitrogen to phosphorus ratio (N:P).</p>
Full article ">Figure 4
<p>The correlation coefficients between soil and plants in their nitrogen (N) and phosphorus (P) concentrations. Red represents a positive correlation and green represents a negative correlation. * Indicates a significant correlation at the <span class="html-italic">p</span> &lt; 0.05 level; ** indicates a significant correlation at the <span class="html-italic">p</span> &lt; 0.01 level.</p>
Full article ">Figure 5
<p>The homeostatic index (<span class="html-italic">H</span>) of N, P, and N:P values of Moso bamboo leaves. (<b>a</b>–<b>c</b>) Represent the fitting relationship between soil N, P, N/P ratio and N, P, N/P ratio of bamboo leaves. (<b>d</b>) represents the H values of N, P and N:P ratio of moso bamboo leaves. The pink area is the fitting function and its confidence interval values. The solid red line shows the fit of the homeostatic index of Moso bamboo leaves.</p>
Full article ">Figure 6
<p>Fitting of the nutrient content of bamboo leaves at different ages to soil nutrient content. The pink area is the fitting function and its confidence interval values. The solid red line shows the fit of the homeostatic index of moso bamboo leaves. I, II, III, and IV represent 1, 2–3, 4–5, and &gt;6 years. (<b>a</b>–<b>l</b>) Represent the fitting relationship between soil N, P, N/P ratio and N, P, N/P ratio of bam-boo leaves.</p>
Full article ">Figure 7
<p>The homeostatic index (<span class="html-italic">H</span>) for different element and ages. I, II, III, and IV represent 1, 2–3, 4–5, and &gt;6 years. The ‘–‘ above column diagrams denotes <span class="html-italic">H</span> values &lt; 0. (<b>a</b>) different element (<b>b</b>) different ages.</p>
Full article ">
13 pages, 3285 KiB  
Article
Minor Effects of Canopy and Understory Nitrogen Addition on Soil Organic Carbon Turnover Time in Moso Bamboo Forests
by Changli Zeng, Shurui He, Boyin Long, Zhihang Zhou, Jie Hong, Huan Cao, Zhihan Yang and Xiaolu Tang
Forests 2024, 15(7), 1144; https://doi.org/10.3390/f15071144 - 1 Jul 2024
Viewed by 780
Abstract
Increased atmospheric nitrogen (N) deposition has greatly influenced soil organic carbon (SOC) dynamics. Currently, the response of SOC to atmospheric N deposition is generally detected through understory N addition, while canopy processes have been largely ignored. In the present study, canopy N addition [...] Read more.
Increased atmospheric nitrogen (N) deposition has greatly influenced soil organic carbon (SOC) dynamics. Currently, the response of SOC to atmospheric N deposition is generally detected through understory N addition, while canopy processes have been largely ignored. In the present study, canopy N addition (CN) and understory N addition (UN, 50 and 100 kg N ha−1 year−1) were performed in a Moso bamboo forest to compare whether CN and UN addition have consistent effects on SOC and SOC turnover times (τsoil: defined as the ratio of SOC stock and soil heterotrophic respiration) with a local NHx:NOy ratio of 2.08:1. The experimental results showed that after five years, the SOC content of canopy water addition without N addition (CN0) was 82.9 g C kg−1, while it was 79.3, 70.7, 79.5 and 74.5 g C kg−1 for CN50, CN100, UN50 and UN100, respectively, and no significant difference was found for the SOC content between CN and UN. Five-year N addition did not significantly change τsoil, which was 34.5 ± 7.4 (mean ± standard error) for CN0, and it was 24.9 ± 4.8, 22.4 ± 4.9, 30.5 ± 4.0 and 22.1 ± 6.5 years for CN0, CN50, CN100, UN50 and UN100, respectively. Partial least squares structural equation modeling explained 93% of the variance in τsoil, and the results showed that soil enzyme activity was the most important positive factor controlling τsoil. These findings contradicted the previous assumption that UN may overestimate the impacts of N deposition on SOC. Our findings were mainly related to the high N deposition background in the study area, the special forest type of Moso bamboo and the short duration of the experiment. Therefore, our study had significant implications for modeling SOC dynamics to N deposition for high N deposition areas. Full article
(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)
Show Figures

Figure 1

Figure 1
<p>Geographic location of the study area and map of the sample plots. CN0: canopy water addition without N addition; CN50 and CN100: canopy N addition with 50 and 100 kg N ha<sup>−1</sup> year<sup>−1</sup>; UN50 and UN100: understory N addition with 50 and 100 kg N ha<sup>−1</sup> year<sup>−1</sup>. The same below.</p>
Full article ">Figure 2
<p>Changes in SOC content to canopy and understory N addition. The error bars indicate the standard error (n = 3). The abbreviations can be found in <a href="#sec2dot2-forests-15-01144" class="html-sec">Section 2.2</a>.</p>
Full article ">Figure 3
<p>Changes in topsoil (0–10 cm) (<b>a</b>) recalcitrant organic carbon (ROC, mg kg<sup>−1</sup>), (<b>b</b>) easily oxidized carbon (EOC, mg kg<sup>−1</sup>), (<b>c</b>) dissolved organic carbon (DOC, mg kg<sup>−1</sup>) and (<b>d</b>) microbial biomass carbon (MBC, mg kg<sup>−1</sup>) contents to canopy and understory N addition. The error bars indicate the standard error (n = 3). ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 4
<p>Changes in (<b>a</b>) SOC stocks (Mg C ha<sup>−1</sup>) and (<b>b</b>) SOC turnover time (τ<sub>soil</sub>, years) of canopy and understory N addition. The error bars indicate the standard error (n = 3).</p>
Full article ">Figure 5
<p>The PLS-SEM model diagram shows the relationship between each environment variable and mean turnover time. SPCP: soil chemical properties; SWC: soil water content; BD: bulk density; HTP: hair tube porosity; NCP: non-capillary porosity; SWS: soil water storage; MWHC: maximum water-holding capacity; HTWHC: hair tube water-holding capacity; SVWC: soil volume water content. Lines and arrows represent relationships between variables. The blue and red lines represent positive and negative effects, respectively. The number represents the path coefficient. *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">
17 pages, 2783 KiB  
Article
Enzyme Activity Stoichiometry Suggests That Fertilization, Especially Nitrogen Fertilization, Alleviates Nutrient Limitation of Soil Microorganisms in Moso Bamboo Forests
by Haoyu Chu, Wenhui Su, Yaqi Zhou, Ziye Wang, Yongmei Long, Yutong Sun and Shaohui Fan
Forests 2024, 15(6), 1040; https://doi.org/10.3390/f15061040 - 16 Jun 2024
Cited by 2 | Viewed by 1065
Abstract
Rational application of N fertilizer is essential for maintaining the long-term productivity of Moso bamboo forests. Microbial activity is a crucial indicator of soil quality. Changes in soil nutrient resources due to N addition can lead to microbial nutrient limitations, thereby impeding the [...] Read more.
Rational application of N fertilizer is essential for maintaining the long-term productivity of Moso bamboo forests. Microbial activity is a crucial indicator of soil quality. Changes in soil nutrient resources due to N addition can lead to microbial nutrient limitations, thereby impeding the maintenance of soil quality. Currently, there is limited research on the effects of N application on microbial nutrient limitations in Moso bamboo forest soils. To examine the changes in extracellular enzyme activity and microbial nutrient limitations in Moso bamboo forest soils following N application, we conducted an N application experiment in northern Guizhou. The findings revealed that the N3 treatment (726 kg·N·hm−2·yr−1) significantly reduced β-glucosidase (BG) activity by 27.61% compared to the control group (no fertilization). The N1 (242 kg·N·hm−2·yr−1), N2 (484 kg·N·hm−2·yr−1), and N3 treatments notably increased the activities of leucine aminopeptidase (LAP) and N-acetyl-β-D-glucosidase (NAG) by 11.45% to 15.79%. Acid phosphatase (ACP) activity remained unaffected by fertilization. N application treatments significantly decreased the C:Ne and C:Pe ratios, while the N:Pe ratio was less influenced by N fertilizer application. Scatter plots and vector characteristics of enzyme activity stoichiometry suggested that microorganisms in the study area were limited by C and N, and N fertilizer application reduced the vector length and increased the vector angle, indicating that N application alleviated the C and N limitation of microorganisms in Moso bamboo forests. Redundancy Analysis (RDA) demonstrated that microbial biomass phosphorus (MBP) was the most critical factor affecting extracellular enzyme activity and stoichiometry. Furthermore, Random Forest Regression analysis identified MBP and the N:Pm ratio as the most significant factors influencing microbial C and N limitation, respectively. The study demonstrated that N application modulates the microbial nutrient acquisition strategy by altering soil nutrient resources in Moso bamboo forests. Formulating fertilizer application strategies based on microbial nutrient requirements is more beneficial for maintaining soil quality and sustainably managing Moso bamboo forests. Additionally, our study offers a theoretical reference for understanding carbon cycling in bamboo forest ecosystems in the context of substantial N inputs. Full article
(This article belongs to the Special Issue How Does Forest Management Affect Soil Dynamics?)
Show Figures

Figure 1

Figure 1
<p>Location of the study area. Red star indicates the geographical location of the study area and red areas indicate fertilized sample plots.</p>
Full article ">Figure 2
<p>Characteristics of changes in soil extracellular enzyme activities (<b>A</b>–<b>C</b>) and enzyme activity stoichiometry (<b>D</b>–<b>F</b>) under different fertilization treatments. BG: β-Glucosidase; LAP: leucine aminopeptidase; NAG: N-Acetyl-β-D-glucosidase; ACP: Acid phosphatases; C:Ne: lnBG/ln(LAP + NAG); C:Pe: lnBG/lnACP; N:Pe: ln(LAP + NAG)/lnACP. Lowercase letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between the different fertilization treatments. Values are means ± standard error (n = 3).</p>
Full article ">Figure 3
<p>Chemical stoichiometric characterization of enzyme activity under different fertilization treatments (<b>A</b>), variation characteristics of vector length and vector angle (<b>B</b>,<b>C</b>), and correlation between vector length and vector angle (<b>D</b>) in soil microbial nutrient limitation. Lowercase letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between the different fertilization treatments. Note: Values are means ± standard error (n = 3).</p>
Full article ">Figure 4
<p>Correlation between extracellular enzyme activities, enzyme activity stoichiometry, and vector characteristics and soil properties under different fertilization treatments. * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01, *** indicates <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 5
<p>RDA analysis between extracellular enzyme activities and stoichiometry and soil properties under different fertilization treatments (<b>A</b>), environmental factor explanatory rate (<b>B</b>), random forest regression analysis between vector length and soil properties (<b>C</b>), and random forest regression analysis between vector angle and soil properties (<b>D</b>). In <a href="#forests-15-01040-f005" class="html-fig">Figure 5</a>A, the blue arrows represent response variables, and the red arrows represent explanatory variables. ** indicates <span class="html-italic">p</span> &lt; 0.01 and * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
13 pages, 1376 KiB  
Article
The Phenotypic Variation in Moso Bamboo and the Selection of Key Traits
by Shihui Zheng, Songpo Wei, Jiarui Li, Jingsheng Wang, Ziyun Deng, Rui Gu, Shaohui Fan and Guanglu Liu
Plants 2024, 13(12), 1625; https://doi.org/10.3390/plants13121625 - 12 Jun 2024
Viewed by 730
Abstract
This research aimed to explore the diverse phenotypic characteristics of moso bamboo in China and pinpoint essential characteristics of moso bamboo. In this study, 63 grids were selected using the grid method to investigate 28 phenotypic traits of moso bamboo across the entire [...] Read more.
This research aimed to explore the diverse phenotypic characteristics of moso bamboo in China and pinpoint essential characteristics of moso bamboo. In this study, 63 grids were selected using the grid method to investigate 28 phenotypic traits of moso bamboo across the entire distribution area of China. The results suggest that the phenotypic traits of moso bamboo exhibit rich diversity, with coefficients of variation ranging from 5.87% to 36.57%. The phenotypic traits of moso bamboo showed varying degrees of correlation. A principal component analysis was used to identify seven main phenotypic trait indicators: diameter at breast height (DBH), leaf area (LA), leaf weight (LW), branch-to-leaf ratio (BLr), leaf moisture content (Lmc), wall-to-cavity ratio (WCr), and node length at breast height (LN), which accounted for 81.64% of the total information. A random forest model was used, which gave good results to validate the results. The average combined phenotypic trait value (D-value) of most germplasm was 0.563. The highest D-value was found in Wuyi 1 moso in Fujian (0.803), while the lowest D-value was observed in Pingle 2 moso in Guangxi (0.317). The clustering analysis of phenotypic traits classified China’s moso bamboo germplasm into four groups. Group I had the highest D-value and is an important candidate germplasm for excellent germplasm screening. Full article
(This article belongs to the Special Issue Biodiversity Informatics and Plant Conservation)
Show Figures

Figure 1

Figure 1
<p>Correlation between phenotypic traits of moso bamboo. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 2
<p>113 moso bamboo germplasm resource clusters: (<b>a</b>) cluster map of 113 moso bamboo germplasm resources; (<b>b</b>) distribution of different taxa of moso bamboo in China.</p>
Full article ">Figure 3
<p>Degree of importance of phenotypic traits in moso bamboo.</p>
Full article ">
15 pages, 4539 KiB  
Article
Introducing Native Tree Species Alter the Soil Organic Carbon, Nitrogen, Phosphorus, and Fine Roots in Moso Bamboo Plantations
by Yilin Ning, Zedong Chen, Hongdi Gao, Chuanbao Yang, Xu Zhang, Zijie Wang, Anke Wang, Xuhua Du, Lan Lan and Yufang Bi
Forests 2024, 15(6), 971; https://doi.org/10.3390/f15060971 - 31 May 2024
Viewed by 934
Abstract
Bamboo and wood-mixed forests are management models that remarkably enhance the balance and productivity of bamboo ecosystems. However, the effects of this model on soil nutrients and enzyme activities remain largely unknown. This study compared the soil organic carbon, nitrogen, phosphorus, and enzyme [...] Read more.
Bamboo and wood-mixed forests are management models that remarkably enhance the balance and productivity of bamboo ecosystems. However, the effects of this model on soil nutrients and enzyme activities remain largely unknown. This study compared the soil organic carbon, nitrogen, phosphorus, and enzyme activity, along with the characteristics of fine roots in pure Moso bamboo plantations (CK) and those mixed with Liriodendron chinense (ML), Sassafras tzumu (MS), Cunninghamia lanceolata (MC), and Pseudolarix amabilis (MP). The results showed that mixed forests improve carbon pools in 0–40 cm soil layers, increasing the total organic C(TOC), free particulate organic C (fPOC), occluded particulate organic C (oPOC), hot-water-extractable organic C (DOC), and mineral-associated organic C (MOC). They also increase soil total N, total P, available N, available P, NH4+-N, NO3−-N, inorganic P, organic P, and microbial biomass N. Bacterial and fungal abundances, along with enzyme activities (urease, acid phosphatase, polyphenol oxidase, peroxidase, and β-glucosidase), also improved. MP and MS were the most effective. Moreover, MS and MP supported a higher biomass and length of fine root and increased the nitrogen and phosphorus uptake of Moso bamboo. In conclusion, Sassafras tzumu and Pseudolarix amabilis are optimal for mixed planting, offering substantial benefits to soil nutrient dynamics and preventing soil quality decline in Moso bamboo forests, thereby supporting better nutrient cycling and carbon sequestration. This research offers insights into enhancing soil quality through diversified Moso bamboo forestry. Full article
(This article belongs to the Section Forest Ecology and Management)
Show Figures

Figure 1

Figure 1
<p>Overview of the study site: pure stands of Moso bamboo (CK), mixed stands of Moso bamboo and <span class="html-italic">Liriodendron chinense</span> (ML), mixed stands of Moso bamboo and <span class="html-italic">Sassafras tzumu</span> (MS), mixed stands of moso bamboo and <span class="html-italic">Cunninghamia lanceolata</span> (MC), and mixed stands of Moso bamboo and <span class="html-italic">Pseudolarix amabilis</span> (MP).</p>
Full article ">Figure 2
<p>Contents and proportions of soil organic carbon pools. (<b>a</b>,<b>c</b>) represent 0–20 cm soil; (<b>b</b>,<b>d</b>) represent 20–40 cm soil. Different lowercase letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between treatments. Error bars indicate standard deviations (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 3
<p>Effects of different treatments on soil nitrogen pool content in 0–20 cm and 20–40 cm soil. Different lowercase letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between treatments. Error bars indicate standard deviations (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 4
<p>Contents and proportions of soil phosphorus pools. (<b>a</b>,<b>c</b>) represent 0–20 cm soil; (<b>b</b>,<b>d</b>) represent 20–40 cm soil. Different lowercase letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between treatments. Error bars indicate standard deviations (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 5
<p>Relationship between soil and fine root. Orange and blue colors indicate positive and negative correlations, and a cross in the box indicates an insignificant correlation.</p>
Full article ">
19 pages, 3215 KiB  
Article
Responses of Soil Carbon and Microbial Residues to Degradation in Moso Bamboo Forest
by Shuhan Liu, Xuekun Cheng, Yulong Lv, Yufeng Zhou, Guomo Zhou and Yongjun Shi
Plants 2024, 13(11), 1526; https://doi.org/10.3390/plants13111526 - 31 May 2024
Viewed by 661
Abstract
Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is known for its high capacity to sequester atmospheric carbon (C), which has a unique role to play in the fight against global warming. However, due to rising labor costs and falling bamboo prices, many [...] Read more.
Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is known for its high capacity to sequester atmospheric carbon (C), which has a unique role to play in the fight against global warming. However, due to rising labor costs and falling bamboo prices, many Moso bamboo forests are shifting to an extensive management model without fertilization, resulting in gradual degradation of Moso bamboo forests. However, many Moso bamboo forests are being degraded due to rising labor costs and declining bamboo timber prices. To delineate the effect of degradation on soil microbial carbon sequestration, we instituted a rigorous analysis of Moso bamboo forests subjected to different degradation durations, namely: continuous management (CK), 5 years of degradation (D-5), and 10 years of degradation (D-10). Our inquiry encompassed soil strata at 0–20 cm and 20–40 cm, scrutinizing alterations in soil organic carbon(SOC), water-soluble carbon(WSOC), microbial carbon(MBC)and microbial residues. We discerned a positive correlation between degradation and augmented levels of SOC, WSOC, and MBC across both strata. Furthermore, degradation escalated concentrations of specific soil amino sugars and microbial residues. Intriguingly, extended degradation diminished the proportional contribution of microbial residuals to SOC, implying a possible decline in microbial activity longitudinally. These findings offer a detailed insight into microbial C processes within degraded bamboo ecosystems. Full article
Show Figures

Figure 1

Figure 1
<p>The content of soil organic carbon (SOC), microbial carbon (MBC) and water-soluble carbon (WSOC) in CK, D-5 and D-10. <span class="html-italic">p</span> represents a significant difference between groups. *, ** and *** respectively represent <span class="html-italic">p &lt;</span> 0.05, <span class="html-italic">p &lt;</span> 0.01 and <span class="html-italic">p &lt;</span> 0.001. The letters represent differences between different degradation time in the same group. (<b>a</b>–<b>c</b>) are WSOC, MBC and SOC Pearson correlation analysis at 0–20 cm soil depth, (<b>d</b>–<b>f</b>) are WSOC, MBC and SOC Pearson correlation analysis at 20–40 cm soil depth.</p>
Full article ">Figure 2
<p>The content of glucosamine, galactosamine, muramic acid and epichitosamine in CK, D-5 and D-10. <span class="html-italic">p</span> represents a significant difference between groups. *, and *** respectively represent <span class="html-italic">p &lt;</span> 0.05 and <span class="html-italic">p &lt;</span> 0.001. The letters represent differences between different degradation time in the same group. (<b>a</b>–<b>d</b>) are WSOC, MBC and SOC Pearson correlation analysis at 0–20 cm soil depth, (<b>e</b>–<b>h</b>) are WSOC, MBC and SOC Pearson correlation analysis at 20–40 cm soil depth.</p>
Full article ">Figure 3
<p>Changes in the influences of degradation on microbial residues (<b>a</b>,<b>b</b>) and the contribution of microbial residues to Moso bamboo soil (<b>c</b>,<b>d</b>). MR means microbial residues; FR means fungal residues; BR means bacterial residues. MR/SOC, FR/SOC and BR/SOC mean their contribution to soil organic C, and show their contribution to soil organic C. The letters represent differences between different degradation time in the same group.</p>
Full article ">Figure 4
<p>Pearson correlation analysis of soil carbon and microbial residues. *, ** and *** respectively represent <span class="html-italic">p &lt;</span> 0.05, <span class="html-italic">p &lt;</span> 0.01 and <span class="html-italic">p &lt;</span> 0.001, represent the correlation significance. (<b>a</b>–<b>c</b>) are WSOC, MBC and SOC Pearson correlation analysis at 0–20 cm soil depth, (<b>d</b>–<b>f</b>) are WSOC, MBC and SOC Pearson correlation analysis at 20–40 cm soil depth.</p>
Full article ">Figure 5
<p>Effect of degradation time on contribution of microbial residues to SOC (the whole circle represents the SOC).</p>
Full article ">Figure 6
<p>Location of the studied Moso bamboo forest. The base image is from National Geographic Information Public Service Platform (<a href="https://www.tianditu.gov.cn" target="_blank">https://www.tianditu.gov.cn</a> (accessed on 12 April 2023)). Image processing using ArcMap (10.8).</p>
Full article ">
Back to TopTop