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Keywords = Daxinganling Mountains

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25 pages, 4069 KiB  
Article
Forest Volume Estimation in Secondary Forests of the Southern Daxing’anling Mountains Using Multi-Source Remote Sensing and Machine Learning
by Penghao Ji, Wanlong Pang, Rong Su, Runhong Gao, Pengwu Zhao, Lidong Pang and Huaxia Yao
Forests 2025, 16(8), 1280; https://doi.org/10.3390/f16081280 - 5 Aug 2025
Abstract
Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have [...] Read more.
Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R2), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R2 = 0.75 and RMSE = 8.16 m3/hm2, highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R2 = 0.71 and RMSE = 6.96 m3/hm2; for Quercus mongolica, R2 = 0.74 and RMSE = 6.90 m3/hm2; and for Populus davidiana, R2 = 0.51 and RMSE = 9.29 m3/hm2. The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m3/hm2. (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R2 of 0.86, lowest RMSE of 9.69 m3/hm2, and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article
(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)
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20 pages, 3069 KiB  
Article
Assessing the Synergy of Spring Strip Tillage and Straw Mulching to Mitigate Soil Degradation and Enhance Productivity in Black Soils
by Zhihong Yang, Lanfang Bai, Tianhao Wang, Zhipeng Cheng, Zhen Wang, Yongqiang Wang, Fugui Wang, Fang Luo and Zhigang Wang
Agronomy 2025, 15(6), 1415; https://doi.org/10.3390/agronomy15061415 - 9 Jun 2025
Viewed by 436
Abstract
To address the critical challenges of wind erosion mitigation and sustainable soil management in the fragile agroecosystem of the black soil region in the foothills of the Daxing’anling Mountains, this study evaluated five tillage practices—conventional ridge tillage (CP), no tillage with straw removal [...] Read more.
To address the critical challenges of wind erosion mitigation and sustainable soil management in the fragile agroecosystem of the black soil region in the foothills of the Daxing’anling Mountains, this study evaluated five tillage practices—conventional ridge tillage (CP), no tillage with straw removal (NT), no tillage with straw mulching (R+NT), autumn strip tillage with straw mulching (R+STA), and spring strip tillage with straw mulching (R+STS)—across two landforms: gently sloped uplands and flat depressions. The results demonstrated that R+STS achieved superior performance across both landscapes, exhibiting a 42.99% reduction in the wind erosion rate, a 48.88% decrease in soil sediment discharge, and a 52.26% reduction in the soil creep amount compared to CP. These improvements were mechanistically linked to the enhanced surface microtopography (aerodynamic roughness increased by 1.8–2.3 fold) and optimized straw coverage (68–72%). R+STS also enhanced the topsoil fertility, increasing the total nitrogen (TN), soil organic carbon (SOC), alkaline nitrogen (AN), available phosphorus (AP), and rapidly available potassium (AK) by 22.07%, 12.94%, 14.92%, 32.94%, and 9.52%, respectively. Furthermore, it improved maize emergence and its yield by 10.04% and 9.99% compared to R+NT. Mantel tests and SEM revealed strong negative correlations between erosion and nutrients, identifying nitrogen availability as the key yield driver. R+STS offers a sustainable strategy for erosion control and productivity improvement in the black soil region. Full article
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20 pages, 3025 KiB  
Article
Variations in the Structure and Composition of Soil Microbial Communities of Different Forests in the Daxing’anling Mountains, Northeastern China
by Han Qu, Mingyu Wang, Xiangyu Meng, Youjia Zhang, Xin Gao, Yuhe Zhang, Xin Sui and Maihe Li
Microorganisms 2025, 13(6), 1298; https://doi.org/10.3390/microorganisms13061298 - 3 Jun 2025
Viewed by 549
Abstract
Soil microorganisms are crucial in global biogeochemical cycles, impacting ecosystems’ energy flows and material cycling. This study, via high-throughput sequencing in four forests—the original Larix gmelinii (Rupr.) Kuzen. forest (LG), the conifer–broad-leaved mixed Pinus sylvestris var. mongolica Litv. forest (PS), the original pure [...] Read more.
Soil microorganisms are crucial in global biogeochemical cycles, impacting ecosystems’ energy flows and material cycling. This study, via high-throughput sequencing in four forests—the original Larix gmelinii (Rupr.) Kuzen. forest (LG), the conifer–broad-leaved mixed Pinus sylvestris var. mongolica Litv. forest (PS), the original pure Betula platyphylla Sukaczev forest (BP), and the original pure Populus L. forest (PL) in Shuanghe National Nature Reserve, Daxing’anling mountains—explored soil microbial community structures and diversities. The results indicated that the BP and PL forests had the lowest soil bacterial ACE and Chao1 indices, and the original pure birch forest’s Shannon index was higher than that of the poplar forest. The soil’s fungal Chao1 index of the birch forest was higher than that of the larch forests. Bradyrhizobium and Roseiarcus were the dominant soil bacterial genera; the dominant soil fungal genera were Podila, Russula, and Sebacina. RDA and mantel analyses indicated that soil microbial community structures varied across forest types mainly because of the effective phosphorous and pH levels, soil’s total nitrogen level, and available phosphorus level. This study offers a scientific foundation for cold-temperate-forest ecosystem management regarding soil microbial diversity and community structural changes in different forest types. Full article
(This article belongs to the Special Issue Microbial Mechanisms for Soil Improvement and Plant Growth)
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20 pages, 3141 KiB  
Article
Post-Fire Recovery of Soil Multiple Properties, Plant Diversity, and Community Structure of Boreal Forests in China
by Xiting Zhang, Danqi She, Kai Wang, Yang Yang, Xia Hu, Peng Feng, Xiufeng Yan, Vladimir Gavrikov, Huimei Wang, Shijie Han and Wenjie Wang
Forests 2025, 16(5), 806; https://doi.org/10.3390/f16050806 - 12 May 2025
Viewed by 504
Abstract
Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire [...] Read more.
Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire recordings in the Daxing’anling Mts, 48 pairs of burnt and unburnt controls (1066 plots) were selected for 0–20 cm soil sampling and plant surveys. We recorded 18 plant parameters of the abundance of each tree, shrub, grass, and plant size (height, diameter, and coverage), 7 geo-topographic data parameters, and 2 fire traits (recovery year and burnt area). We measured eight soil properties (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP; alkali-hydrolyzed P, AP; organic P, Po; inorganic P, Pi; total glomalin-related soil protein, T-GRSP; easily-extracted GRSP, EE-GRSP). Paired T-tests revealed that the most significant impact of the fire was a 25%–48% reduction in tree sizes, followed by decline in the plant diversity of arbors and shrubs but increasing plant diversity in herbs. GRSP showed an >18% increase and Po decreased by 17% (p < 0.05). Redundancy ordination showed that the post-fire recovery years and burnt area were the most potent explainer for the variations (p < 0.05), strongly interacting with latitudes and longitudes. Plant richness and tree size were directly affected by fire traits, while the burnt area and recovery times indirectly increased the GRSP via plant richness. A fire/control ratio chronosequence found that forest community traits (tree size and diversity) and soil nutrients could be recovered to the control level after ca. 30 years. This was relatively shorter than in reports on other boreal forests. The possible reasons are the low forest quality from overharvesting in history and the low fire severity from China’s fire prevention policy. This policy reduced the human mistake-related fire incidence to <10% in the 2010s in the studied region. Chinese forest fire incidences were 3% that of the USA. The burnt area/fire averaged 5 hm2 (while the USA averaged 46 hm2, Russia averaged 380 hm2, and Canada averaged 527 hm2). Overharvesting resulted in the forest height declining at a rate of >10 cm/year. Our finding supports forest management and the evaluation of forest succession after wildfires from a holistic view of plant–soil interactions. Full article
(This article belongs to the Section Forest Biodiversity)
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24 pages, 19262 KiB  
Article
Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata
by Maolin Li, Yingda Wu, Yilin Liu, Yu Zhang and Qiang Yu
Forests 2024, 15(11), 1857; https://doi.org/10.3390/f15111857 - 23 Oct 2024
Viewed by 1418
Abstract
Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study [...] Read more.
Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones. Full article
(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)
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18 pages, 9719 KiB  
Article
Detection and Retrieval of Supercooled Water in Stratocumulus Clouds over Northeastern China Using Millimeter-Wave Radar and Microwave Radiometer
by Hao Hu, Yan Yin, Jing Yang, Xinghua Bao, Bo Zhang and Wei Gao
Remote Sens. 2024, 16(17), 3232; https://doi.org/10.3390/rs16173232 - 31 Aug 2024
Viewed by 1351
Abstract
Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm [...] Read more.
Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm for identifying supercooled water based on the multi-spectral peak characteristics in cloud radar power spectra, combined with radar reflectivity factor and mean Doppler velocity. Using microwave radiometer data, we conducted retrieval analyses on two stratocumulus cases in the spring over the northeastern Daxing’anling region, China. The retrieval results show that the supercooled water in the spring stratocumulus clouds over the region is widespread, with liquid water content (LWC) ranging around 0.1 ± 0.05 g/m3, and particle sizes not exceeding 10 μm. The influence of updrafts on supercooled water is evident, with both showing good consistency in spatiotemporal variation trends. Comparing the liquid water path (LWP) variations retrieved from cloud radar and microwave radiometer, both showed good consistency in variation trends and high LWC areas, indicating the reliability of the identification algorithm developed in this study. Full article
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20 pages, 19556 KiB  
Article
A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning
by Jingjing Liu and Yuzhen Zhang
Remote Sens. 2024, 16(7), 1228; https://doi.org/10.3390/rs16071228 - 30 Mar 2024
Cited by 3 | Viewed by 1609
Abstract
The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm [...] Read more.
The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05°, 0.025°, and 0.01°. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1° to 0.05°, 0.025°, and 0.01° resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing’anling Mountains and Xiaoxing’anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing’anling Mountains with complex topography, while both performed comparably in the Xiaoxing’anling Mountains with milder topography, highlighting SBSD’s advantages in high heterogeneity areas. Full article
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15 pages, 3758 KiB  
Article
Study on the Effect of Insulation Materials on the Temperature Field of Piles in Ice-Rich Areas
by Yingjie Sheng, Tianlai Yu, Yuxuan Wu and Xingyu Wang
Appl. Sci. 2022, 12(23), 12235; https://doi.org/10.3390/app122312235 - 29 Nov 2022
Cited by 1 | Viewed by 1680
Abstract
Concrete piles in ice-rich areas exist in negative-temperature environments, which seriously affect the concrete’s strength. In order to maintain the quality of concrete piles in these areas, the temperature during the concrete strength formation period needs to be controlled. In this paper, the [...] Read more.
Concrete piles in ice-rich areas exist in negative-temperature environments, which seriously affect the concrete’s strength. In order to maintain the quality of concrete piles in these areas, the temperature during the concrete strength formation period needs to be controlled. In this paper, the temperature field of the pile body of a test pile with double sheaths filled with polyurethane insulation and one without polyurethane insulation were measured. The temperature disturbance law of the pile base with/without insulation was obtained and comprehensively analyzed. The temperature of the pile body was shown to increase with the thickness of the insulation layer. Analysis of the thermal and physical properties of the insulation materials showed a linear relationship between pile temperature and thermal conductivity, in which a lower thermal conductivity resulted in a higher pile temperature. The effect of applying insulation around the pile perimeter in the ice-rich permafrost region on the concrete strength of the pile foundation was verified. The test pile with insulated double sheaths showed better strength at all ages than the test pile without insulation. The use of insulation maintained the temperature of pile foundations in ice-rich areas and ensured that the pile foundations were in better condition, thus improving the concrete strength at all ages. Adopting a double-sheathing configuration with polyurethane as an insulating layer can improve the concrete strength of the pile. This method is applicable to the ice-rich permafrost area in the Daxinganling Mountains and also has reference value for middle and low-latitude wetland permafrost areas. Full article
(This article belongs to the Section Civil Engineering)
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20 pages, 5805 KiB  
Article
Settlement Characteristic of Warm Permafrost Embankment with Two-Phase Closed Thermosyphons in Daxing’anling Mountains Region
by Guanfu Wang, Jiajun Bi, Youkai Fan, Long Zhu, Feng Zhang and Decheng Feng
Sustainability 2022, 14(19), 12272; https://doi.org/10.3390/su141912272 - 27 Sep 2022
Cited by 8 | Viewed by 1788
Abstract
The Xing’anling Mountains are the second largest permafrost region in China. One of the important issues for highways in these regions is how to control the settlement during the operation period to meet the demand of road stability. This paper selects a typical [...] Read more.
The Xing’anling Mountains are the second largest permafrost region in China. One of the important issues for highways in these regions is how to control the settlement during the operation period to meet the demand of road stability. This paper selects a typical permafrost embankment in the Daxing’anling Mountains permafrost region, presents the finite element models of the embankment, and verifies it using field monitoring data to study the thermal and deformation characteristics within 50 years after construction. Calculation results illustrate that the permafrost under the embankment has degraded significantly during the operation period of the highway and led to serious settlement. To prevent the degradation of permafrost, a series of models with two-phase closed thermosyphons (TPCTs) were established to analyze the cooling effect. The contribution of different factors, including install locations, depth, and shapes of the TPCTs, were assessed on their effects on cooling the permafrost and reducing the embankment settlement. Results show that the TPCTs have an excellent cooling effect on the permafrost embankment. However, as the TPCTs change the temperature distribution of the embankment, they will inevitably cause differential settlement. In order to ensure the cooling effect and reduce the differential settlement of the embankment, it is suggested that L-shaped TPCTs should be adopted in the remedial engineering. Full article
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9 pages, 1416 KiB  
Article
Effects of Forest Types on SOC and DOC in the Permafrost Region of the Daxing’anling Mountains
by Weiwei Du, Di Wang, Xiaodong Wu, Lin Zhao and Shuying Zang
Processes 2022, 10(7), 1293; https://doi.org/10.3390/pr10071293 - 30 Jun 2022
Cited by 1 | Viewed by 1839
Abstract
There is a “symbiotic relationship” between permafrost and the forest ecosystem; the melted permafrost provides sufficient water for forest growth, and the forest ecosystem plays an important role in protecting the permafrost. Aiming to study the effects of different forest types on soil [...] Read more.
There is a “symbiotic relationship” between permafrost and the forest ecosystem; the melted permafrost provides sufficient water for forest growth, and the forest ecosystem plays an important role in protecting the permafrost. Aiming to study the effects of different forest types on soil organic carbon (SOC) and dissolved organic carbon (DOC) in the permafrost region of the Daxing’anling Mountains, this research focuses on the soil of the three forest types of pinus sylvestris var. mongolica forest, larch forest, and birch forest in Beiji Village, Mohe County, Daxing’anling Region, and collected vertical profile soil samples from the three soil layers of 0–10, 10–20, and 20–30 cm at three different sites types (upslope, mesoslope, and downslope) in August 2017. The results show that the forest type is the main influencing factor for the content of SOC and DOC. The site type has a significant effect on the content of SOC and DOC in the three forest types, but the difference varies slightly (p > 0.05). The content of SOC and DOC is negatively correlated with the depth of the soil layer of the vertical profile. The geodetector data analysis shows that there are significant differences (p < 0.05) among the contents of SOC and DOC in the three forest types. In conclusion, this study contributes to an in-depth understanding of carbon storage, the carbon dynamics of SOC, and the effects of different forest types on carbon balance in permafrost regions, and it provides a scientific basis for the study of the carbon cycle mechanism in permafrost regions. Full article
(This article belongs to the Special Issue The Role of Biochar in Soil Remediation Processes)
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14 pages, 1267 KiB  
Article
Livelihood Resilience and Its Influencing Factors of Worker Households in the Face of State-Owned Forest Areas Reform in China
by Siboyu Sun and Yude Geng
Sustainability 2022, 14(3), 1328; https://doi.org/10.3390/su14031328 - 25 Jan 2022
Cited by 11 | Viewed by 2215
Abstract
To promote the sustainable development of state-owned forest areas, the Chinese government announced the reform of state-owned forest areas in 2015. It mainly includes the logging ban of natural forests and the separation of government and enterprises. Timely investigation of the changes in [...] Read more.
To promote the sustainable development of state-owned forest areas, the Chinese government announced the reform of state-owned forest areas in 2015. It mainly includes the logging ban of natural forests and the separation of government and enterprises. Timely investigation of the changes in the livelihood resilience of worker households before and after the reform of state-owned forest areas is of great significance to the sustainable development of state-owned forest areas. With the application of livelihood resilience theory, we established an evaluation index system from three dimensions of buffer capacity, self-organization, and learning capacity. Taking five forest industry enterprises operating state-owned forest areas in Northeast and Inner Mongolia in China as an example, we measured worker households’ livelihood resilience, and identified the key factors of worker households’ livelihood resilience. The results showed: (1) The reform of state-owned forest areas has improved the livelihood resilience of worker households in Longjiang, Daxing’anling, Inner Mongolia, and Jilin forest industry groups, but reduced the livelihood resilience of worker households in Changbai Mountain forest industry groups. (2) With the advancement of the reform of state-owned forest areas, the gap of livelihood resilience of worker households of forest industry groups shows an expanding trend. (3) The influencing factors that affect the worker households’ livelihood resilience of various forest industry groups are similar. Among them, the education of household head, household head health, household size, work experience, and neighborhood relationships are the key factors that affect the resilience of worker households. Full article
(This article belongs to the Section Sustainable Forestry)
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16 pages, 4273 KiB  
Article
A Newly Built Model of an Additive Stem Taper System with Total Disaggregation Model Structure for Dahurian Larch in Northeast China
by Yanli Xu, Lichun Jiang and Muhammad Khurram Shahzad
Forests 2021, 12(10), 1302; https://doi.org/10.3390/f12101302 - 24 Sep 2021
Cited by 3 | Viewed by 1830
Abstract
Stem taper function is an important concept in forest growth and yield modeling, and forest management. However, the additivity of the function and the inherent correlations between stem components (diameter outside bark—dob, diameter inside bark—dib, and double-bark thickness—dbt) are seldom considered. In this [...] Read more.
Stem taper function is an important concept in forest growth and yield modeling, and forest management. However, the additivity of the function and the inherent correlations between stem components (diameter outside bark—dob, diameter inside bark—dib, and double-bark thickness—dbt) are seldom considered. In this paper, a total disaggregation model (TDM) structure was developed based on the well-known Kozak (2004) model to ensure the additivity of the stem components. The reconstructed model was fitted with the data of 1281 felled Dahurian larch trees from three regions of Daxing’anling Mountains in Northeast China. The results from TDM were compared with other additive model structures including adjustment in proportion (AP), non-additive taper models (NAM), and three logical structures of NSUR (AMO, SMI, SMB). The results showed that the difference was significant among the three regions. The performance of TDM was slightly better than those of other model structures. Therefore, TDM was considered as another optimal additive system to estimate stem, bark thickness, and volume predicting for Dahurian larch in Northeast China besides NSUR, a method widely used in calculating additive volume or biomass throughout the world. We believe this work is cutting-edge, and that this methodology can be applied to other tree species. Full article
(This article belongs to the Special Issue Modelling of Forests Structure and Biomass Distribution)
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16 pages, 3167 KiB  
Article
Aboveground Biomass Allocation and Additive Allometric Models for Natural Larix gmelinii in the Western Daxing’anling Mountains, Northeastern China
by Shengwang Meng, Quanquan Jia, Qijing Liu, Guang Zhou, Huimin Wang and Jian Yu
Forests 2019, 10(2), 150; https://doi.org/10.3390/f10020150 - 11 Feb 2019
Cited by 18 | Viewed by 3207
Abstract
Accurate estimates of tree component and aboveground biomass strongly depend on robust and precise allometric equations. However, site-specific and suitable biomass equations are currently scarce for natural Larix gmelinii forests in the western Daxing’anling Mountains, northeastern China. This study aimed to evaluate the [...] Read more.
Accurate estimates of tree component and aboveground biomass strongly depend on robust and precise allometric equations. However, site-specific and suitable biomass equations are currently scarce for natural Larix gmelinii forests in the western Daxing’anling Mountains, northeastern China. This study aimed to evaluate the biomass allocation patterns within tree components and develop additive allometric biomass equations for species of L. gmelinii. A total of 58 trees were destructively sampled and measured for wood (inside bark), bark, branch and leaf biomass. For each component, we assessed the share of biomass allocated to different components by computing its ratio; we also tested two allometric equations based on diameter at breast height (dbh) alone, and dbh fitted with height (h) as independent variables. Seemingly unrelated regression methodology was used to fit an additive system of biomass allometric equations. We performed an independent dataset to evaluate the predictive ability of the best model system. The results revealed that wood biomass accounted for approximately 60% of the aboveground biomass. Wood and branch biomass ratios increased with increasing dbh, while a reverse trend was observed for bark and leaf biomass ratios. All models showed good fitting results with Adj.R2 = 0.958–0.995. Tree dbh provided the lowest estimation errors in the regressions associated with branches and leaves, while dbh2 × h generated the most precise models for stems (wood and bark). We conclude that these allometric equations will accurately predict biomass for Larix trees in the western Daxing’anling Mountains. Full article
(This article belongs to the Section Forest Ecology and Management)
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17 pages, 11032 KiB  
Article
Tree-Ring-Recorded Drought Variability in the Northern Daxing’anling Mountains of Northeastern China
by Jian Yu, Sher Shah, Guang Zhou, Zhenzhao Xu and Qijing Liu
Forests 2018, 9(11), 674; https://doi.org/10.3390/f9110674 - 27 Oct 2018
Cited by 12 | Viewed by 3533
Abstract
We developed two tree-ring width chronologies of Mongolian Scots pine (Pinus sylvestris var. mongolica) from the low elevation forest of the northern Daxing’anling Mountains of Inner Mongolia. Although the two chronologies come from different sampling sites, significant correlations existed among the [...] Read more.
We developed two tree-ring width chronologies of Mongolian Scots pine (Pinus sylvestris var. mongolica) from the low elevation forest of the northern Daxing’anling Mountains of Inner Mongolia. Although the two chronologies come from different sampling sites, significant correlations existed among the chronologies (r = 0.318), and the first principal component (PC1) accounted for 65.9% of total variance over their common period 1792–2016. Climate-growth correlation analysis revealed that the previous June and July Palmer drought severity index (PDSIp6-7) was the main climatic factor controlling tree-ring growth. Using a linear regression model, we reconstructed the PDSIp6-7 for the past 225 years (1792–2016). The reconstruction satisfied required statistical calibration and validation tests, and represented 38.6% of the PDSI variance recorded by instruments over the period 1955–2016. Six wet and five dry periods were revealed during these 225 years. The drought of 1903–1927 was the most severe drought in the study area in the last 225 years. Comparison with other tree-ring-based moisture-sensitive sequences from nearby regions confirmed a high degree of confidence in our reconstruction. The results of a spatial climate correlation analysis with a gridded PDSI dataset revealed that our reconstructions contained strong regional drought signals for the southern Stanovoy Range and the northern Daxing’anling Mountains. The power spectrum revealed the existence of significant frequency cycles, which may be linked to large-scale atmospheric-oceanic variability, such as the El Niño-Southern Oscillation, solar activity, and the North Atlantic Oscillation. Full article
(This article belongs to the Section Forest Ecology and Management)
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20 pages, 6426 KiB  
Article
Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data
by Kaili Liu, Jindi Wang, Weisheng Zeng and Jinling Song
Remote Sens. 2017, 9(4), 341; https://doi.org/10.3390/rs9040341 - 2 Apr 2017
Cited by 63 | Viewed by 7598
Abstract
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind [...] Read more.
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing’anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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