Deforestation-induced forest loss largely affects both the carbon budget and ecosystem services. Subsequent forest regrowth plays a crucial role in ecosystem restoration and carbon replenishment. However, there is an absence of comprehensive datasets explicitly delineating the forest regrowth following deforestation. Here we employed multiple remotely sensed datasets to generate the first dataset capturing forest structural regrowth, including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR), subsequent to deforestation in globally key deforestation regions at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratios and rates at 5-year intervals spanning primarily from 1985 to 2020. This dataset provides a nuanced understanding of forest regrowth following deforestation across spatial, temporal, and structural scales, thereby facilitating accurate quantification of forest carbon budget and enhancing assessments of forest ecological services.

With the rapid advancement of unmanned aerial vehicles (UAVs) in recent years, UAV-based remote sensing has emerged as a highly efficient and practical tool for environmental monitoring. In vegetation remote sensing, UAVs equipped with hyperspectral sensors can capture detailed spectral information, enabling precise monitoring of plant health and the retrieval of physiological and biochemical parameters. A critical aspect of UAV-based vegetation remote sensing is the accurate acquisition of canopy reflectance. However, due to the mobility of UAVs and the variation in flight altitude, the data are susceptible to scale effects, where changes in spatial resolution can significantly impact the canopy reflectance. This study investigates the spatial scale issue of UAV hyperspectral imaging, focusing on how varying flight altitudes influence atmospheric correction, vegetation viewer geometry, and canopy heterogeneity. Using hyperspectral images captured at different flight altitudes at a Chinese fir forest stand, we propose two atmospheric correction methods: one based on a uniform grey reference panel at the same altitude and another based on altitude-specific grey reference panels. The reflectance spectra and vegetation indices, including NDVI, EVI, PRI, and CIRE, were computed and analyzed across different altitudes. The results show significant variations in vegetation indices at lower altitudes, with NDVI and CIRE demonstrating the largest changes between 50 m and 100 m, due to the heterogeneous forest canopy structure and near-infrared scattering. For instance, NDVI increased by 18% from 50 m to 75 m and stabilized after 100 m, while the standard deviation decreased by 32% from 50 m to 250 m, indicating reduced heterogeneity effects. Similarly, PRI exhibited notable increases at lower altitudes, attributed to changes in viewer geometry, canopy shadowing and soil background proportions, stabilizing above 100 m. Above 100 m, the impact of canopy heterogeneity diminished, and variations in vegetation indices became minimal (<3%), although viewer geometry effects persisted. These findings emphasize that conducting UAV hyperspectral observations at altitudes above at least 100 m minimizes scale effects, ensuring more consistent and reliable data for vegetation monitoring. The study highlights the importance of standardized atmospheric correction protocols and optimal altitude selection to improve the accuracy and comparability of UAV-based hyperspectral data, contributing to advancements in vegetation remote sensing and carbon estimation.

Leaf chlorophyll content (LCC) is an indicator of plant physiological function and is an important parameter in estimating the carbon and water fluxes of terrestrial ecosystems. Spatiotemporally continuous LCC products are therefore needed at scales from the site level to the globe. In this study, we developed a neural network model for LCC retrieval from ENVISAT MERIS data based on radiative transfer model simulations. By considering the influence of canopy non-photosynthetic materials and the co-variations between LCC and biophysical parameters, a synthetic database was generated using the PROSAIL model with a good approximation to the canopy reflectance collection of MERIS data. Using a neural network trained from the synthetic database, we derived more realistic seasonal patterns of LCC than those using neural network models trained from synthetic databases generated without considering the influence of canopy non-photosynthetic materials or the parameter co-variations. A new global LCC product (GLOBMAP MERIS LCC) at 300-m resolution in 2003–2012 was generated using the neural network. It shows an improvement over the previous MERIS LCC product in capturing LCC seasonal variations in different plant functional types, and is potentially useful in improving the integration of physiological information within terrestrial ecosystem modeling and ecological monitoring across a range of spatial and temporal scales.

目的:筛选并验证分析与新生儿急性呼吸窘迫综合征(acute respiratory distress syndrome,ARDS)相关的微小核糖核酸(miRNA),初步研究患儿血浆中miR-6833-3p的表达水平及其诊断效能.方法:选取南京医科大学附属儿童医院25例ARDS患儿(ARDS组)为研究对象,同期选取32例普通新生儿为对照组.采用微流体芯片技术筛选与新生儿ARDS相关的miRNA,对于组间差异表达超过5倍的miRNA进一步进行实时荧光定量聚合酶链式反应(RT-PCR)验证芯片重复性及进行靶基因预测,对两组血浆miR-6833-3p的表达水平进行检测并与急性生理评分做相关性分析,通过绘制受试者工作特征(receiver operat-ing characteristic,ROC)曲线分析miR-6833-3p在患者中的诊断效能并计算诊断敏感度及特异度.结果:通过基因芯片筛选出24个与新生儿ARDS相关的高表达差异基因.表达差异超过5倍的miRNA有8个,4个上调的分别是miR-31-5p、miR-4754、miR-6833-3p和miR-192-3p,4个表达下调的基因分别是miR-362-3p、miR-11a、miR-7a-2-3p和miR-1382.通过验证发现miR-6833-3p在ARDS组血浆中显著上调(P＜0.01),且与APACHEⅡ评分中的急性生理评分呈正相关(r=0.731,P＜0.001).通过靶基因预测分析发现miR-6833-3p与PI3-K/Akt、MAPK信号通路可能密切相关.相关ROC曲线结果也显示,miR-6833-3p预测新生儿ARDS的ROC曲线下面积为0.848,其诊断最佳阈值为1.03,约登指数最大值为0.59,此时miR-6833-3p的诊断灵敏度为84.55％,特异度为75.36％.结论:miR-6833-3p在新生儿ARDS血浆中的表达水平显著升高,可作为新生儿ARDS的特异性标志物.

Comparison and validation of canopy reflectance (CR) models are two important steps to ensure their reliability. Pure forest plantations are an ideal type of forest for validating CR models because of their simple background and the low variance in the crown structures which are usually assumed to be identical in most CR models. A Geometric Optical Model for Forest Plantations (GOFP) was compared using dataset in two radiation transfer model intercomparison exercise (RAMI) stands and validated using in situ dataset of detailed optical and structural data of two forest plantations in the Saihanba Forestry Center, China. The results show that (1) the tree distributions in stands described by the hypergeometric model in GOFP show good consistencies with the dataset in the two RAMI stands and measurements from the two Saihanba forest stands; and (2) the CRs simulated with GOFP are also compared well in the two RAMI stands and validated with measurements collected with unmanned aerial vehicles in the two Saihanba stands. GOFP shows a better consistency with the CR measurements than those from CR models for natual forestsbecause the tree distribution in forest plantations is described more reasonably in GOFP.