Search Results (21)

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Forest aboveground biomass (AGB) serves as a crucial quantitative indicator that reflects the carbon sequestration capacity of forests, and accurately mapping AGB is pivotal for assessing forest ecosystem stability. However, mapping AGB in subtropical evergreen broadleaf forests in southern China presents challenges due to their complex canopy structure, stand heterogeneity, and spectral signal saturation. The phenological features reflecting seasonal vegetation dynamics are conducive to over-coming these challenges. By analyzing differential spectral reflectance patterns during the non-growing (Jan–Mar, Nov–Dec) versus growing (Apr–Oct) seasons, this study established a phenological feature-based methodology for improving AGB estimation in subtropical evergreen broadleaf forests. Subsequently, four time series vegetation indices (VI), namely NDVI, EVI2, NDPI, and IRECI were employed to extract phenological features (PFs) for mapping forest AGB using a multiple linear regression model (MLR), K-nearest neighbor model (KNN), support vector machine model (SVM), and random forest model (RF). The results demonstrated significant differences in Sentinel-2 spectral reflectance (740–1610 nm bands) between the growing and non-growing seasons. The PFs demonstrated the highest distance correlation coefficient (0.57), significantly outperforming other baseline feature types (0.44). Furthermore, seasonal changes in NDVI and NDPI were found to better reflect AGB accumulation in evergreen broadleaf forests compared to EVI2 and IRECI. Incorporating diverse PFs derived from all four VI significantly enhanced the accuracy of AGB mapping by yielding rRMSE values ranging from 21.01% to 25.06% and R² values ranging from 0.40 to 0.58. The results inferred that PFs can be considered a key factor for alleviating spectral signal saturation problems while effectively improving the accuracy of AGB estimation. Full article

Autumn phenology plays a crucial role in shaping the capacity for carbon sequestration. However, understories, a vital yet often neglected ecosystem component, have complicated autumn phenology prediction. We address the challenge of monitoring understory phenological dynamics by using a UVL4 trail camera and selecting appropriate deriving processes and vegetation indices (VIs). We found the understory photoperiod was on average 1.88 h shorter than the canopy’s, while the understory temperature was 2.11 °C higher than the canopy’s open-air temperature. The maximum temperature inside the understories was on average 1.37 °C lower than in open-air conditions. Specifically, the 60% quantile of the daily VI in July and the 15% quantile in November effectively captured the prolonged minimum and the minimum in the VI time series when applying logistic modeling. The excess green vegetation index (ExG) outperformed other VIs in estimating understory greenness change. The cold degree days model (CDD) and low-temperature and photoperiod multiplicative model (TPM) revealed that senescence progressed from the upper crown downwards, causing over 13 days of lag in the understory. These findings offer a new perspective on quantifying autumn phenology in subtropical forests and provide insights into asynchronous changes in vertical microclimatic gradients in Earth system and vegetation models. Full article

Moso bamboo forests (MBFs) are unique subtropical ecosystems characterized by distinct leaf phenology, bamboo shoots, rapid growth, and carbon sequestration capability. Leaf area index (LAI) is an essential metric for evaluating the productivity and ecological quality of MBFs. However, accurate and large-scale methods for remote-sensing-based LAI monitoring during the winter growth stage remain underdeveloped. This study introduces a novel method integrating hyperspectral indices from Zhuhai-1 Orbit Hyperspectral Satellites (OHS) imagery with the particle swarm optimization-support vector machine (PSO-SVM) coupling model to estimate LAI in winter MBFs. Five traditional vegetation indices (VI_Rs) and their red-edge variants (VI_REs) were optimized to build empirical models. Machine learning algorithms, including SVM, Random Forest, extreme gradient boosting, and partial linear regression, were also applied. The PSO-SVM model, integrating three VI_Rs and three VI_REs, achieved the highest accuracy (R² = 0.721, RMSE = 0.490), outperforming traditional approaches. LAI was strongly correlated with indices, such as NDVI_R, RVI_R, EVI_RE, and SAVI_R (R > 0.77). LAI values of MBFs primarily ranged from 2.1 to 5.5 during winter, with values exceeding 4.5 indicating high winter bamboo shoot harvesting. These findings demonstrate the potential of OHS data to improve LAI retrieval models for large-scale LAI mapping, offering new insights into MBFs monitoring and contributing to sustainable forest management practices. Full article

Accurate identification of tea plantation distribution is critical for optimizing agricultural practices, informing land-use policies, and preserving ecological balance. However, challenges persist in mountainous regions with persistent cloud cover and heterogeneous vegetation, where conventional methods relying on single-source remote sensing features face limitations due to spectral confusion and information redundancy. This study proposes a novel framework integrating multisource remote sensing data and feature optimization to address these challenges. Leveraging the Google Earth Engine (GEE) cloud platform, this study synthesized 108 spectral, textural, phenological, and topographic features from Sentinel-1 SAR and Sentinel-2 optical data. SVM-RFE (support vector machine recursive feature elimination) was employed to identify the optimal feature subset, prioritizing spectral indices, radar texture metrics, and terrain parameters. Comparative analysis of three classifiers, namely random forest (RF), support vector machine (SVM), and decision tree (DT), revealed that RF achieved the highest accuracy, with an overall accuracy (OA) of 95.03%, a kappa coefficient of 0.95. The resultant 10 m resolution spatial distribution map of tea plantations in Shiyan City (2023) demonstrates robust performance in distinguishing plantations from forests and farmlands, particularly in cloud-prone mountainous terrain. This methodology not only mitigates dimensionality challenges through feature optimization but also provides a scalable solution for large-scale agricultural monitoring, offering critical insights for sustainable land management and policy formulation in subtropical mountainous regions. Full article

Primary and secondary growth of trees are key components of carbon sequestration in forest ecosystems. However, the temporal relationships between primary and secondary growth as well as their responses to environmental variations are still poorly understood. Herein, we continuously measured the intra-annual leaf, shoot and stem growth of Quercus serrata for two years on Lushan Mountain, southeastern China. Our results showed that shoots were ranked as the first organ to initiate, peak and cease growth, rather than leaves and stems. Moreover, the phenological stages of shoot growth were negatively associated with those of leaves and stems, whereas there was a weak positive correlation in phenological events between leaves and stems. These temporal connections in phenological events between primary and secondary growth suggest a prioritized carbon allocation to shoot growth and a high dependence of stem growth on carbon from newly developing leaves. Although stem growth started earlier in response to the warmer spring in 2018 compared to the colder spring in 2017, no significant difference in annual increment was observed between years, which was related to the more severe drought condition during the dry season in 2018. At the intra-annual scale, different organs generally had a consistent growth response to temperature variables but showed a divergent response to vapor pressure deficit. Despite a relatively short observational period and potential bias in spatial representativeness, our data provide nuanced knowledge on seasonal growth dynamics in primary and secondary of broadleaved species, underlining the importance of jointly considering intra-seasonal variabilities of environmental conditions in order to correctly predict tree growth response to climate change in subtropical regions. Full article

Increased tree species diversity can promote forest production by reducing intra-specific competition and promoting an efficient unitization of resources. However, questions remain on whether and how mixed stands affect the dynamics of intra–annual xylem formation in trees, especially in subtropical forests. In this study, we randomly selected 18 trees from a monoculture of 63-year-old Masson pine (Pinus massoniana) growing in pure stands and mixed them with 39-year-old Castanopsis hystrix in Pinxiang, southern China. A total of 828 microcores were collected biweekly throughout the growing season from 2022 to 2023 to monitor the intra-annual xylem formation. Cell production started in early March and ended in late December and lasted about 281 to 284 days. Xylem phenology was similar between mixed and pure stands. During both seasons, the Masson pine in mixed stands showed higher xylem production and growth rates than those in pure stands. The Masson pine in mixed stands produced 45–51 cells in 2022 (growth rate of 0.22 cells day⁻¹) and 35–41 cells in 2023 (0.17 cells day⁻¹). Growth rate, and not growth seasons, determined the superior xylem growth in the mixed stands. Our study shows that after 39 years of management, Masson pine and C. hystrix unevenly aged mixed stands have a significant positive mixing effect on Masson pine xylem cell production, which demonstrates that monitoring intra-annual xylem growth dynamics can be an important tool to evaluate the effect of species composition and reveal the mechanisms to promote tree growth behind the mixing effect. Full article

Functional traits are pivotal for understanding the functional niche within plant communities. Yet, the relationship between the functional niches of typical forest plant communities across different climatic zones, as defined by functional traits, and their association with community and phylogenetic structures remains elusive. In this study, we examined 215 woody species, incorporating 11 functional traits spanning leaf economy, mechanical support, and reproductive phenology, gathered from forests in four climatic zones from tropical, subtropical, warm-temperate to cold-temperate zones in China and supplemented by the literature. We quantified the functional niche hypervolume (FNH), reflecting the multidimensional functional niche variability. We then probed into the correlation between the FNH and community and phylogenetic structures of forests. Our findings reveal that species richness significantly influences the geographic variance of functional niche space in forest vegetation across different climatic zones. Specifically, a community’s species richness correlates positively with the functional niche breadth occupied by the community species. The FNH of woody plants across diverse forest types shows significant associations with both the mean phylogenetic distance (MPD) and the mean nearest phylogenetic taxon distance (MNTD) of the communities. There is a progressive increase in tropical rainforest (TF), subtropical evergreen deciduous broad-leaved mixed forest (SF), and warm-temperate coniferous broad-leaved mixed forest (WF), followed by a decline in the cold-temperate coniferous forest (CF). This pattern suggests potential environmental filtering in CF, which may constrain the spatial extent of plant functional niches. Our research underscores the substantial variability in the FNH across China’s typical forest vegetation, highlighting the complex interplay between functional traits, community richness, and phylogenetic distance. Full article

Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations of phenological rhythms are important indicators of climatic impacts on forests. Therefore, this study aimed to analyze both a total area and different forest growth environments within the whole (i.e., coastal site areas (II, IV) and inland site areas (I, III)) as to spatiotemporal patterns associated with subtropical forests in Fujian Province, which is located at the boundary between the middle and south subtropical zones. Considering the asymmetric effects of climate and forest growth, this study chose pre-seasonal and cumulative temperature and precipitation factors and utilized the GeoDetector model to analyze the dominant drivers and interactions within phenology differentiation in Fujian Province. The results show the following: (1) All of the phenological parameters were advanced or shortened over the 19-year observation period; those of shrubland and deciduous broadleaf forests fluctuated greatly, and their stability was poor. (2) The phenological parameters were more distinct at the borders of the site areas. Additionally, the dates associated with the end of the growth season (EOS) and the date-position of peak value (POP) in coastal areas (i.e., II and IV) were later than those in inland areas (i.e., I and III). Among the parameters, the length of the growth season (LOS) was most sensitive to altitude. (3) Precipitation was the main driving factor affecting the spatial heterogeneity of the start of the growth season (SOS) and the EOS. The relatively strong effects of preseason and current-month temperatures on the SOS may be influenced by the temperature threshold required to break bud dormancy, and the relationship between the SOS and temperature was related to the lag time and the length of accumulation. The EOS was susceptible to the hydrothermal conditions of the preseason accumulation, and the variation trend was negatively correlated with temperature and precipitation. Spatial attribution was used to analyze the attribution of phenology differentiation from the perspectives of different regions, thus revealing the relationships between forest phenology and meteorological time-lag effects, the result which can contribute to targeted guidance and support for scientific forest management. Full article

Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems. Full article

Seasonal variabilities in hydrological fluxes of dissolved organic carbon (DOC) and their driving factors in the evergreen broad-leaved forest are inadequately understood. To aid this understanding, we conducted a three-year study to examine seasonal changes in DOC concentration and flux in throughfall, stemflow, and litter leachate in an evergreen broad-leaved subtropical forest in central Japan. We specifically addressed (1) how DOC in different hydrological fluxes vary on a monthly to seasonal basis, and (2) how canopy phenology and meteorology shape the DOC concentration and flux of throughfall, stemflow, and litter leachate trends in this evergreen forest. Clear seasonal changes were found in throughfall and stemflow DOC concentration but not in litter leachate DOC concentration; the highest throughfall DOC concentrations were observed in spring (10.03 mg L⁻¹ in 2017 and 9.59 mg L⁻¹ in 2018, respectively) and the highest stemflow DOC concentrations were observed in summer (13.95 mg L⁻¹ in 2017 and 16.50 mg L⁻¹ in 2018, respectively). Correlation analysis revealed the monthly throughfall DOC concentration to be positively related to the dry weight of fallen leaves (r = 0.72, p < 0.05) and flowers (r = 0.91, p < 0.05). In addition, Random Forest models predicted that the dry weight of flowers was a primary driver of throughfall DOC concentration and that the DOC concentrations of stemflow and litter leachate were constrained by the throughfall DOC concentration. DOC fluxes in different hydrological flux were significantly positive related to bulk precipitation amounts and temperature. Moreover, the throughfall DOC concentration had a considerable effect on throughfall and litter leachate DOC fluxes. Over 75% of annual net tree-DOC (throughfall + stemflow) fluxes and more than 70% of the annual litter leachate DOC fluxes were produced in the flowering season. Thus, we speculated that the seasonal phenological canopy changes (leaf emergence, fallen leaves, flowering, and pollen) and the sufficient rainfall had great impacts on the amount and quality of DOC concentrations in the evergreen forest; and, furthermore, that the DOC from different forest hydrological fluxes was a significant fraction of the carbon that accumulates in soils. Full article

The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area. Full article

Forest fires are considered to be an important part of numerous terrestrial ecosystems and vegetation types, being also a significant factor of ecosystem disruption. In this sense, fires play an important role in the structure and function of the ecosystems. Biomes are characterized by a specific type of fire regime, which is a synergy of the climate conditions and the characteristics of the vegetation types dominating each biome. The assessment of burned areas and the identification of the fire regimes can be implemented with freely available low- to high-resolution satellite data as those of Landsat and Sentinel-2. Moreover, the biomes are characterized by the phenology, a useful component for vegetation monitoring, especially when time series of satellite images are used. Both the identification of fire regime by reconstructing the fire history and the monitoring of the post-fire evolution of burned areas were studied with remote sensing methods. Specifically, the present paper is a pilot study implemented in a Mediterranean biome, aimed at establishing the methodological framework to (i) define fire regimes, (ii) characterize the phenological pattern of the vegetation (pre-fire situation) of the fire-affected areas, and (iii) compare the phenology of the recovered fire-affected areas with the corresponding one of the pre-fire situation. At the global level, based on MODIS fire perimeters, we found that fires are occurring at 70% in the tropical and subtropical grasslands, savannas, and shrublands, followed by fires at tropical and subtropical moist broadleaf forests by 7% and by fires at deserts and xeric shrublands by 6.5%. Full article

Knowledge of spatio-temporal variation in vegetation phenology is essential for understanding environmental change in mountainous regions. In recent decades, satellite remote sensing has contributed to the understanding of vegetation phenology across the globe. However, vegetation phenology in subtropical mountains remains poorly understood, despite their important ecosystem functions and services. Here, we aim to characterize the spatio-temporal pattern of the start of the growing season (SOS), a typical spring leaf phenological metric, in subtropical forests across the Nanling Mountains (108–116° E, 24–27° N) in southern China. SOS was estimated from time series of GEOV2 leaf area index (LAI) data at 1 km spatial resolution during the period 1999–2019. We observed a slightly earlier regional mean SOS in the southern of the region (24–25° N) than those in the central and northern regions. We also observed spatially varying elevation gradients of the SOS. The SOS showed a change slope of −0.2 days/year (p = 0.21) at the regional scale over 1999–2019. In addition, approximately 22% of the analyzed forested pixels experienced a significantly earlier SOS (p < 0.1). Partial correlation analysis revealed that preseason air temperature was the most responsible climate factor controlling interannual variation in SOS for this region. Furthermore, impacts of air temperature on the SOS vary with forest types, with mixed forests showing a stronger correlation between the SOS and air temperature in spring and weaker in winter than those of evergreen broadleaf forests and open forests. This suggests the complication of the role of air temperature in regulating spring leaf phenology in subtropical forests. Full article

The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability and quality of remote sensing images (e.g., frequent cloud coverage) pose significant challenges to accurate long-term rice mapping, especially for traditional pixel and phenological methods in subtropical monsoon regions. This study proposed a superpixel and deep-learning-based time series method to analyze Landsat time series data for paddy rice classification in complex landscape regions. First, a superpixel segmentation map was generated using a dynamic-time-warping-based simple non-iterative clustering algorithm with preprocessed spectral indices (SIs) time series data. Second, the SI images were overlaid onto the superpixel map to construct mean SIs time series for each superpixel. Third, a multivariate long short-term memory full convolution neural network (MLSTM-FCN) classifier was employed to learn time series features of rice paddies to produce accurate paddy rice maps. The method was evaluated using Landsat imagery from 2000 to 2020 in Cengong County, Guizhou Province, China. Results indicate that the superpixel MLSTM-FCN achieved a high performance with an overall accuracy varying from 0.9547 to 0.9721, which presents an 0.17–1.23% improvement compared to the random forest method. This study showed that combining spectral, spatial, and temporal features with deep learning methods can generate accurate paddy rice maps in complex landscape regions. Full article