Search Results (101)

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

Climatic oscillations in the Quaternary are altering the performance of angiosperms, while the species’ distribution is regarded as a macroscopic view of these spatial and temporal changes. Oaks (Quercus L.) are important tree models for estimating the abiotic impacts on the distribution of forest tree species. In this study, we modeled the past, present, and future suitable habitat for two varieties of deciduous oaks (Quercus serrata and Quercus serrata var. brevipetiolata), which are widely distributed in China and play dominant roles in the local forest ecosystem. We evaluated the importance of environmental factors in shaping the species’ distribution and identified the “wealthy” habitats in harsh conditions for the two varieties. The ecological niche models showed that the suitable areas for these two varieties are mainly concentrated in mountain ranges in central China, while Q. serrata var. brevipetiolata is also widely distributed in the middle-east mountain range. The mean temperature of the coldest quarter was identified as the critical factor in shaping the habitat availability for these two varieties. From the last glacial maximum (LGM) to the present, the potential distribution range of these two sibling species has obviously shifted northward and expanded from the inferred refugia. Under the optimistic (RCP2.6), moderate (RCP 4.5)-, and higher (RCP 6.0)-concentration greenhouse gas emissions scenarios, our simulations suggested that the total area of suitable habitats in the 2050s and 2070s will be wider than it is now for these two varieties of deciduous oaks, as the distribution range is shifting to higher latitudes; thus, low latitudes are more likely to face the risk of habitat losses. This study provides a case study on the response of forest tree species to climate changes in the north temperate and subtropical zones of East Asia and offers a basis for tree species’ protection and management in China. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article

Forest carbon sinks have faced significant challenges with the accelerating warming trend in the 21st century. Net primary productivity (NPP) serves as a critical indicator of the carbon cycle in forest ecosystems and is intricately influenced by both human activities and climate change. This study focuses on the subtropical Southern Forests of China as the research object, using the Wuyi Mountains as a representative study area. The positive and negative contributions of ecologically oriented human activities driven by China’s forestry construction over the past few decades were investigated along with potential extreme climate factors affecting the forest NPP from an altitude gradient perspective and regional-scale forest NPP changes from a novel viewpoint. MODIS NPP, climate, and land use data, along with a vegetation type transfer matrix and statistical methods, were utilized for this purpose. The results are summarized as follows. (1) From 2000 to 2022, NPP in the Wuyi Mountains exhibited a high distribution pattern in the northeastern and southern areas and a low distribution pattern in the central region, with a weak overall increase and an average annual growth increment of only 0.11 gC·m⁻²·year⁻¹. NPP increased with altitude, with a mean growth rate of 5.0 gC·m⁻²·hm⁻¹. Notably, the growth rate of NPP was most pronounced in the altitude range below 298 m in both temporal and vertical dimensions. (2) In the context of China’s long-term Forestry Ecological Engineering Projects and Natural Forest Protection Projects, as well as climate warming, the transformation of vegetation types from relatively low NPP types to high NPP types in the Wuyi Mountains has resulted in a total NPP increase of 211.58 GgC over the past 23 years. Specifically, only the altitude range below 298 m showed negative vegetation type transformation, leading to an NPP decrease of 119.44 GgC. The expansion of urban and built-up lands below 500 m over the 23-year period reduced NPP by 147.92 GgC. (3) The climatic factors inhibiting NPP in the Wuyi Mountains were extreme nighttime high temperatures from June to September, which significantly weakened the NPP of evergreen broadleaf forests above 500 m in elevation. This inhibitory effect still resulted in a reduction of 127.36 GgC in the NPP of evergreen broadleaf forests within this altitude range, despite a cumulative increment in the area of evergreen broadleaf forests above 500 m over the past 23 years. In conclusion, the growth in NPP in the southern inland subtropical regions of China slowed after 2000, primarily due to the significant rise in nighttime extreme high temperatures and the expansion of human-built areas in the region. This study provides valuable data support for the adaptation of subtropical forests to climate change. Full article

Bamboo forest ecosystems are an important component of the Earth’s terrestrial ecosystems and play an important role in addressing the global timber crisis as well as climate change. Bamboo is a typical shallow-rooted, fast-growing clonal plant species whose developed rhizome system and high canopy closure play an important role in soil and water conservation. The function of soil and water conservation services of bamboo forests can intuitively reflect the regional regulation of precipitation, the redistribution function of precipitation, and the function of soil fixation, which is one of the crucial ecological service functions in regional ecosystems. Bamboo forests are divided into monopodial bamboo forests, sympodial bamboo forests, and mixed bamboo forests, which are mainly distributed in tropical and subtropical mountainous areas. The region’s variable climate, abundant precipitation, and high potential risk of soil erosion, in conjunction with the frequent operation of bamboo forests and frequent occurrence of extreme weather events, have the potential to adversely affect the ecosystem function of bamboo forests. Presently, bamboo forests are primarily managed through the cultivation of bamboo, with the objective of enhancing productivity. Extensive research has been conducted on the long-term maintenance of bamboo forest productivity. However, there is a paucity of research on the mechanisms of management measures for ecosystem stability and the development of adaptive management technology systems suitable for soil and water conservation, carbon sequestration and sink enhancement, and biodiversity conservation. This paper is predicated on the biological characteristics of bamboo and, thus, aims to compile the extant research progress on the following subjects: the role of rainfall redistribution in bamboo forest canopies, the role of deadfall interception, and the mechanism of soil fixation mechanics of the root system. It also synthesizes the current status of research on the impact of traditional management measures on the soil and water conservation function of bamboo forests. Finally, it discusses the problems of current research and the direction of future development. Full article

Rapid climate change has significantly impacted species distribution patterns, necessitating a comprehensive understanding of dominant tree dynamics for effective forest resource management and utilization. The Camellia subgenus Camellia, a widely distributed taxon in subtropical China, represents an ecologically and economically important group of woody plants valued for both oil production and ornamental purposes. In this study, we employed the BIOMOD2 ensemble modeling framework to investigate the spatial distribution patterns and range dynamics of the subgenus Camellia under projected climate change scenarios. Our analysis incorporated 1455 georeferenced occurrence records from 15 species, following the filtering of duplicate points, along with seven bioclimatic variables selected after highly correlated factors were eliminated. The ensemble model, which integrates six single species distribution models, demonstrated robust predictive performance, with mean true skil l statistic (TSS) and area under curve (AUC) values exceeding 0.8. Our results identified precipitation of the coldest quarter (Bio19) and temperature seasonality (Bio4) as the primary determinants influencing species distribution patterns. The center of species richness for the subgenus Camellia was located in the Nanling Mountains and eastern Guangxi Zhuang Autonomous Region. The projections indicate an overall expansion of suitable habitats for the subgenus under future climate conditions, with notable scenario-dependent variations: distribution hotspots are predicted to increase by 8.86% under the SSP126 scenario but experience a 2.53% reduction under the SSP585 scenario. Furthermore, a westward shift in the distribution centroid is anticipated. To ensure long-term conservation of Camellia genetic resources, we recommend establishing a germplasm conservation center in the Nanling Mountains region, which represents a critical biodiversity hotspot for this taxon. Full article

As an important component of the global carbon cycle, the variation patterns and driving mechanisms of the productivity and carbon sink capacity of subtropical forest ecosystems urgently need in-depth research. In this study, taking the forest ecosystem in the Ganjiang River Basin as the research object, the Biome-BGC model was used to simulate the forest productivity at different time scales (annual, seasonal, and monthly) from 1970 to 2021, and its spatio-temporal distribution characteristics and responses to climate change were analyzed. The results showed that the interannual net primary productivity (NPP) of evergreen broad-leaved forests was 771.4 g C m⁻² year⁻¹, that of evergreen coniferous forests was 631.6 g C m⁻² year⁻¹, that of deciduous coniferous forests was 610.5 g C m⁻² year⁻¹, and that of shrub forests was 262.8 g C m⁻² year⁻¹. Evergreen broad-leaved forests have greater carbon sink potential under the background of climate change. The forest productivity in the Ganjiang River Basin generally showed an upward trend, but there were obvious differences in spatial distribution, characterized by being higher in the surrounding mountainous areas and lower in the central and northern plains. The methodological framework proposed in this study is beneficial for productivity evaluation and spatio-temporal analysis of carbon balance in subtropical forest ecosystems and provides a scientific reference for model simulation and the application of forest productivity at the regional scale. Full article

The Qinghai–Tibet Plateau is recognized as a biodiversity hotspot, with a wide variety of grasshopper species, including several endemic to the region, which play significant roles in both agricultural and forestry ecosystems. The purpose of this study was to analyze the species diversity and distribution pattern of grasshoppers on the Qinghai–Tibet Plateau. A comprehensive database comprising 390 grasshopper species was established through specimen collection, a literature review, and a geographical distribution data analysis. Diversity analysis showed that the diversity of species under the five vegetation types was relatively average. However, the alpine cold vegetation of Qinghai–Tibet and subtropical evergreen broad-leaved forest still showed a relatively high Shannon index and Simpson index. Grasshopper species are mainly concentrated in the eastern and southern parts of the Qinghai–Tibet Plateau. The richness pattern showed that grasshopper species diversity was particularly high in certain mountain areas, with Bayankala Mountain and Hengduan Mountain being endemic hotspots. The MaxEnt models were used to assess the potential habitats for four dominant genera of grasshoppers under projected climate change scenarios for 2050 and 2070. Altitude was the factor affecting the distribution of Locusta, Chorthippus, and Kingdonella, while precipitation and temperature were the factors affecting the distribution of Leuconemacris. These findings improve our understanding of the distribution patterns of different grasshopper species across various habitat types on the Qinghai–Tibet Plateau and provide valuable insights for developing targeted ecological protection strategies in response to environmental changes. Full article

The semi-humid evergreen broadleaved forest (SEBF) is the zonal vegetation type of western subtropical regions in China. Under human and natural disturbance, the area of SEBFs is severely shrinking, with remaining fragments scattered across mountains of the Central Yunnan Plateau. To explore the mechanisms of community assembly and species maintenance in the severely fragmented SEBFs, we selected three sites—Jinguangsi Provincial Nature Reserve, Huafoshan Scenic Area, and Qiongzhusi Forest Park—across the range of this vegetation type, and sampled a total of 42 plots of forest dominated by Castanopsis orthacantha Franch., the most widely distributed community type of SEBFs. We compared the species richness and composition of the communities of different age classes, employed the net relatedness index to characterize the phylogenetic structure of communities, and used Mantel tests and partial Mantel tests to quantify the impacts of spatial distance, age class, and habitat factors (including climate, topography, and soil) on species turnover across different spatial scales (i.e., intra- and inter-site) for trees, shrubs, and herbs, respectively. The results indicated the following: (1) In the young stage, the C. orthacantha communities exhibited a species richness statistically lower than those in middle-aged and mature communities. Notably, the difference in species richness among age classes was merely significant for shrub and herb species. Moreover, the phylogenetic structure changed towards over-dispersion with increasing community age. (2) The age class of the community played a pivotal role in determining taxonomic β diversity in the tree layer, while climate and soil factors significantly influenced β diversity in the shrub and herb layers of the communities. (3) Environmental filtering emerged as the predominant force shaping community assembly at the intra-site scale, whereas spatial distance was the primary determinant at the inter-site scale. Meanwhile, dispersal limitation versus biological interaction seemed to dominate the community dynamics of the C. orthacantha communities in the early versus middle and old ages, respectively. Our results highlight the variability in community assembly processes across different spatial and temporal scales, providing insights into the priority of the conservation and restoration of severely degraded zonal SEBFs. Expanding research to broader scales and other SEBF types, as well as considering the impacts of climate change and human activities, would provide further insights into understanding the mechanisms of community assembly and effective conservation strategies. Full article

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions. Full article

Atmospheric water vapor, a significant constituent of the atmosphere, affects the energy balance between Earth’s atmosphere and space, and its changes play a crucial role in the greenhouse effect. Layer precipitable water (LPW), which represents the column-integral water vapor within a vertical range, is increasingly recognized as a key indicator of atmospheric water vapor distributions and variations. Due to its capability for layer-wise monitoring, LPW products have the potential to offer valuable insights into the characteristics and evolution of climatic regions through refined atmospheric spatiotemporal information. However, the observational quality and spatiotemporal variations of LPW products across different climate zones, e.g., the diverse climatic regions in China, have not been systematically assessed. In this paper, we aim to evaluate and analyze the climatic and seasonal variations of FY-4A LPW products across five climatic regions in China, contributing to a deeper understanding of water vapor variability and providing valuable data for climate change research. A surface pressure calibration algorithm for ERA5 data is developed to calculate accurate ERA5 LPW products. The results show that all four FY-4A LPWs are consistent with ERA5 LPWs, with an overall root mean square error (RMSE) of 2.58, 0.90, 1.30, and 1.01 mm, respectively. Furthermore, FY-4A LPWs are underestimated in the temperate monsoon area and overestimated in the subtropical and tropical monsoon regions, while FY-4A observations agree well with ERA5 reanalysis in temperate continental and plateau mountain zones. These analyses highlight the remarkable climate dependency of FY-4A LPWs and their potential for climate-related studies. Full article

This study investigates land-cover changes along riparian zones in South Carolina, focusing on intermittent and perennial streams to assess the impact of urbanization, forest loss, and impervious surface expansion on sensitive ecosystems. South Carolina’s diverse geography, ranging from coastal marshes to the Blue Ridge Mountains, and subtropical humid climate, offers a rich context for understanding environmental changes. The research utilizes various geospatial datasets, including the National Land Cover Database (NLCD), National Hydrography Dataset (NHD), and National Agricultural Imagery Program (NAIP) imagery, to evaluate changes in forest cover, urbanization, and impervious surfaces from 2011 to 2021 as a decade of transition. The study areas were divided into buffer zones around intermittent and perennial streams, following South Carolina’s riparian management guidelines. The results indicate significant land-cover transitions, including a total of 3184.56 hectares of non-urban areas converting to forest within the 100 m buffer around intermittent streams. In contrast, 137.43 hectares of forest transitioned to urban land in the same buffer zones, with Spartanburg and Greenville leading the change. Intermittent stream buffers exhibited higher imperviousness (4.6–5.5%) compared to perennial stream buffers (3.3–4.5%), highlighting the increased urban pressure on these sensitive areas. Furthermore, tree canopy loss was significant, with counties such as Greenwood and Chesterfield experiencing substantial reductions in canopy cover. The use of high-resolution NAIP imagery validated the land-cover classifications, ensuring accuracy in the results. The findings emphasize the need for effective land-use management, particularly in the riparian zones, to mitigate the adverse impacts of urban expansion and to safeguard water quality and biodiversity in South Carolina’s streams. Full article

Yunnan is located in the southwestern part of China, with rich tea tree germplasm resources and diversified geomorphological and climatic features, which help us to carry out research related to tea tree chronology and provide scientific and effective support information for enriching the database of tree rings in western Yunnan. This study took the Brown Mountain tea tree in Xishuangbanna as the research object, collected tea tree sample cores through tree growth cone sampling, measured the width of the annual rings, cross-dated them, and established a chronology of the width of the annual rings of the tea tree. The R language was used to analyze the response function of the tea tree’s annual ring chronology with the climatic factors of the study site, discussed the relationship between the radial growth of the tea tree in subtropical regions and climatic factors, and determined the main factors that affected the radial growth of the tea tree. The results of the study showed that the chronology of the tea tree’s whorl width spanned 70 years (1954–2023), with an average annual growth rate of 1.283 mm/year; the average sensitivity was 0.514, which indicated that the chronology contained richer climatic information. The representativeness of the sample group of the whorl width index (EPS) was 0.716, indicating that the consistency of the growth inter-annual variations was better among the different trees. The radial growth was correlated with climatic factors such as temperature and moisture; the radial growth of the tea tree was usually more sensitive to moisture availability, limited by hydrological and climatic factors throughout the rainy season of the year, and positively correlated with the temperature in summer and autumn. In terms of the stability of the radial growth of the tea tree in relation to the climatic response, the growth of the tea tree in the study area may have benefited from future warming of the climate and reduction in precipitation. Full article

Statistical characteristics of atmospheric circulation patterns (ACPs) and associated diurnal variation characteristics (DVCs) of precipitation in summer (June–August) from 2015 to 2019 over the complex terrain in northern Xinjiang (NX), northwestern arid region of China, were investigated based on NCEP FNL reanalysis data and Weather Research and Forecasting model simulation data from Nanjing University (WRF-NJU). The results show that six different ACPs (Type 1–6) were identified based on the Simulated ANealing and Diversified RAndomization (SANDRA), exhibiting significant differences in major-influencing synoptic systems and basic meteorological environments. Types 5, 3, and 2 were the most prevalent three patterns, accounting for 21.6%, 19.7%, and 17.7%, respectively. Type 5 mainly occurred in June and July, while Types 3 and 2 mainly occurred in August and July, respectively. From the perspective of DVCs, Type 1 reached its peak at midnight, while Type 5 was most frequent in the afternoon and morning. The overall DVCs of hourly precipitation intensity and frequency demonstrated a unimodal structure, with a peak occurring at around 16 Local Solar Time (LST). Basic meteorological elements in various terrain regions exhibit significant diurnal variation, with marked differences between mountainous and basin areas under different ACPs. In Types 3 and 6, meteorological elements significantly influence precipitation enhancement by promoting the convergence and uplift of low-level wind fields and maintaining high relative humidity (RH). The Altay Mountains region and Western Mountainous regions experience dominant westerly winds under these conditions, while the Junggar Basin and Ili River Valley regions benefit from counterclockwise water vapor transport associated with the Iranian Subtropical High in Type 6, which increases RH. Collectively, these factors facilitate the formation and development of precipitation. Full article