Yuan Zeng

Tipping points of vegetation transitions represent the thresholds beyond which ecosystems can no longer maintain their stable states. Approaching these critical points may result in declined resilience or irreversible vegetation transitions. Detecting and predicting tipping points remains notably challenging, yet it is essential for guiding the preservation and restoration of terrestrial ecosystems. In this study, lag-1 temporal autocorrelation (AC1) derived from the Kernel Normalized Difference Vegetation Index (kNDVI) was utilized as an early warning signal to monitor resilience dynamics. We developed a new tipping-point detection method by combining land-cover changes, time series segmentations and temporal–spatial filters. We revealed a widespread resilience decline in China, with the dominant transition type as shrub encroachment. Then, two machine learning models coupled with temporal cross-validation were employed to predict the probabilities of abrupt shifts in the near future. The results showed that Random Forest models (accuracy > 70%) demonstrated robustness across lead times. High probabilities of transitions in 2024 were concentrated along the 400 mm annual isohyet, mainly affected by decreased water availability, lower soil acidity and degraded vegetation functions. Our study provides an effective methodology to pinpoint hotspots of vegetation vulnerability and to support the conservation of ecosystems for a sustainable future.

Urban agglomerations have become the primary form of urbanization in China and serve as a principal means of promoting the nation’s high-quality economic development. As a key driving force behind the development of urban agglomerations, human activity intensity plays a critical role in understanding and regulating their sustainable development. In this study, we estimated the human activity intensity of land surface (HAILS) for five major urban agglomerations in China from 1990 to 2020 based on the land cover. Trend analysis and spatial autocorrelation analysis were employed to examine the temporal dynamics and spatial aggregation pattern of HAILS. Furthermore, effectiveness of the HAILS index was validated and the driving factors of HAILS changes was explored. The results indicated that areas with higher HAILS were mainly concentrated in the plains, while high-altitude regions exhibited lower HAILS values. From 1990 to 2010, human activity intensity increased significantly across all five urban agglomerations, followed by divergent trends after 2010. HAILS showed a strong correlation with population density in artificial surfaces, but weak correlation in the natural and artificial vegetation cover. Changes in human activity intensity were found to be largely influenced by national and local policies. These findings underscore the value of quantifying human activity intensity in urban agglomerations, offering insights into their spatiotemporal dynamics and development patterns, as well as providing theoretical and practical support for policy formulation.

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms.

Accurate estimation of plantation aboveground biomass (AGB) is critical for quantifying carbon cycles and informing sustainable forest resource management, but enhancing estimation accuracy remains a key challenge. Although tree height and stand age are recognized as critical predictors for enhancing AGB models in addition to spectral vegetation indices, their individual and combined contributions in regional plantation forests remain insufficiently quantified, especially concerning the potential for leveraging the distinct characteristics of fast-growing plantations to facilitate AGB estimation. This study developed multi-source remote sensing-based Eucalyptus AGB estimation models for Nanning, Guangxi, integrating stand age and tree height to assess their impacts. Stand age was mapped from Landsat time-series imagery, and tree height was derived from UAV-LiDAR data. Plot-level reference AGB was obtained using fused UAV and terrestrial LiDAR point clouds. A random forest model, incorporating these variables with Sentinel-2 spectral information and topography, then achieved regional AGB estimation. The findings demonstrate that (1) tree height serves as the most influential predictor for AGB estimation at the regional scale, yielding a robust model performance (R² = 0.84). (2) Tree height captures the majority of the explanatory power associated with stand age. Once tree height was included as a predictor, the subsequent addition of stand age offered no significant improvement in model accuracy (R² = 0.85). (3) Given the challenges in obtaining precise tree height data and the robust correlation between stand age and tree height in fast-growing plantations, the integration of stand age substantially improved the accuracy of AGB estimations (from the spectral model of R² = 0.54 to R² = 0.74), with performance approaching that of tree height-based models (ΔR² = 0.10). Consequently, in fast-growing plantations, which are often characterized by high stand homogeneity, a hybrid model incorporating stand age can offer a reliable and cost-effective solution for AGB estimation.

Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. Accurate regional-scale AGB estimation is crucial for advancing research on global climate change, ecosystem carbon cycles, and ecological conservation. Traditional methods, whether based on LiDAR or optical remote sensing, estimate AGB using planar density (t/ha) multiplied by pixel area, which fails to account for vertical forest structure variability. This study proposes a novel “stereoscopic (stereo) density × volume” approach, upgrading planar density to stereo density (t/ha/m) by integrating canopy height information, thereby improving estimation accuracy and exploring the feasibility of this new method. In the Daxing’anling region, plot-scale AGB estimation models were developed using stepwise linear regression (SLR) for both “planar density × area” and “stereo density × volume” methods. Results indicated that the stereo model using arithmetic mean height (H_AM) achieved comparable accuracy (R² = 0.83, RMSE = 2.77 t) with the planar model (R² = 0.83, RMSE = 2.52 t). At the regional scale, high-precision AGB estimates derived from airborne LiDAR were combined with vegetation indices from the Landsat Thematic Mapper (TM), and topographic factors from DEM to develop regional-scale AGB estimation models, using SLR and random forest (RF) algorithms. The results of 10-fold cross-validation demonstrated the superiority of the stereo method over the planar method, with RF outperforming SLR. The optimal RF-based stereo model of H_AM (R² = 0.65, rRMSE = 26.05%) significantly improved AGB estimation compared to the planar model (R² = 0.59, rRMSE = 30.41%). Independent accuracy validation using 75 field plots demonstrated that the stereo model achieved a higher validation R² of 0.45 compared to the planar model’s R² of 0.35. These findings suggest that the stereo approach mitigates the underestimation of AGB caused by forest height variability in planar methods, with no significant differences observed across forest types. In conclusion, the use of the stereo method to estimate forest AGB is superior to the planar method in optical remote sensing. This approach offers a scalable solution for forest AGB estimation and carbon stock assessment.

The interactions between human activities and land cover have a significant impact on ecosystems. Therefore, studying human activity intensity based on land use or land cover is crucial for understanding the sustainable development of ecosystems. In this study, we selected Anhui Province as the study area and estimated the human activity intensity of land surface (HAILS) in 2015 and 2020 based on the ChinaCover datasets. We further analyzed the spatial, slope, and hydrological distribution characteristics of HAILS and explored the drivers of HAILS changes. The results show that the areas with higher HAILS were mainly located in the central part of Hefei, as well as along the Yangtze and the Huaihe rivers. The largest changes from 2015 to 2020 happened in the gentle slopes with the HAILS of 20–30%, and the percentage of HAILS > 20% decreased over the slope of 15°. In the riparian zone, the HAILS showed a clear decreasing trend after 2 km, while the HAILS in 2020 were higher than in 2015 in each flow-path distance belt, except for the Huaihe river. The HAILS index was strongly correlated with population density, rural population density, urban population density, average land GDP of primary industry, and nighttime light data. The rapid growth of the urban population and economy, as well as ecological protection policies, were identified as drivers of the above changes. Therefore, the HAILS in 2015 and 2020 of Anhui Province provide effective data support to address regional ecological conservation issues.

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R² than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R² = 0.44 and 0.83 for MZL and R² = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R² = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R² from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

Trait-based approaches, focusing on the functional characteristics of vascular plants in a community, have been increasingly used in plant ecology and biodiversity research. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the two approaches at changing grain and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by imaging spectroscopy data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC- based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness measures also decreased with increasing pixel grain, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, at high resolution, pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage.