Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

Esculetin is an important plant-derived natural product that has multiple bioactivities and applications. Phytophthora capsici is a notorious plant pathogen capable of infecting a broad range of hosts. In this study, we evaluated the antifungal activity of esculetin against P. capsici. The baseline sensitivity of P. capsici to esculetin was established using 108 isolates collected from various geographical regions in the Jiangsu and Shandong Provinces of China. The median effective concentration (EC₅₀) values for esculetin ranged from 2.08 to 16.46 μg/ml (mean, 6.87 ± 2.70 μg/ml) and were normally distributed. Furthermore, both zoospore production and germination were strongly inhibited by esculetin. Importantly, esculetin exhibited protective as well as curative activities against P. capsici on tomato and was capable of restricting the early infection of P. capsici on Nicotiana benthamiana. We found that the esculetin treatment led to cell membrane damage of P. capsici, as revealed by morphological observations and measurements of relative conductivity and malondialdehyde (MDA). Finally, our results also suggested that esculetin may adversely affect P. capsici by inhibiting its DNA and protein synthesis. These findings will contribute to the broader evaluation of the use of esculetin to control diseases caused by P. capsici and toward a better understanding of its mode of action as a potential fungicide.

The spatial resolution of remote sensing images directly affects the accuracy, efficiency, and computational cost of extracting the fractional vegetation cover (FVC). Taking the Liyang woodland region, Jiangsu Province, as the study area, FVCs with varying spatial resolutions were extracted separately from Sentinel-2, Landsat-8, MOD13Q1, and MOD13A1. The variations in FVCs extracted from remote sensing images with varying spatial resolutions were analyzed at one specific time and time series within a year. The results show that (i) the overall mean FVC values of the four spatial resolution images did not differ substantially; however, FVCs with varying spatial resolutions present with a regular pattern of overestimation or underestimation at different vegetation levels. (ii) Taking the 10 m spatial resolution FVC as the reference, the accuracy values of FVC extraction at 30 m, 250 m, and 500 m resolutions were 91.0%, 76.3%, and 76.7%, respectively. The differences in the spatial distribution of FVCs are the most obvious at water–land interfaces and at the edge of each woodland patch. (iii) The highest accuracy of time-series FVC extraction from lower-resolution images is in the range of 0.6~0.7 for FVC. The degree of variation in FVC of time series varying spatial resolutions depends on the season and vegetation cover conditions. In summary, there are considerable differences in the need to monitor high-resolution images depending on the FVC level of the land surface. This study provides a reference for selection and accuracy research of remote sensing images for FVC extraction.

Net primary productivity (NPP) is a key indicator of the health of forest ecosystems that offers important information about the net carbon sequestration capacity of these systems. Precise assessment of NPP is crucial for measuring carbon fixation and assessing the general well-being of forest ecosystems. Due to the distinct ecological characteristics of various forest types, accurately understanding and delineating the distribution of these types is crucial for studying NPP. Therefore, an accurate forest-type classification is necessary prior to NPP calculation to ensure the accuracy and reliability of the research findings. This study introduced deep learning technology and constructed an HRNet-CASA framework that integrates the HRNet deep learning model and the CASA model to achieve accurate estimation of forest NPP in Huangshan District, Huangshan City, Anhui Province. Firstly, based on VHR remote sensing images, we utilized the HRNet to classify the study area into six forest types and obtained the forest type distribution map of the study area. Then, combined with climate data and forest type distribution data, the CASA model was used to estimate the NPP of forest types in the study area, and the comparison with the field data proved that the HRNet-CASA framework simulated the NPP of the study area well. The experimental findings show that the HRNet-CASA framework offers a novel approach to precise forest NPP estimation. Introducing deep learning technology not only enables precise classification of forest types but also allows for accurate estimation of NPP for different types of forests. This provides a more effective tool for forest ecological research and environmental protection.

Global warming poses a serious threat to the alpine grassland on the Tibetan Plateau. Ecological restoration is an effective measure for adapting to climate change and controlling desertification. However, few studies have focused on the relationship between plant growth and the soil properties of valley-slope sand dunes during ecological restoration. We conducted a case study to investigate the changes in the soil properties at various elevations during two stages of ecological restoration. Our results indicated improvements in the plant growth and soil quality during the ecological restoration from 2011 to 2017. The soil particle size changed from predominantly medium-fine sand at stage 1 (2011) to very fine sand at stage 2 (2017). The soil organic matter and total N content ranged from 1.04–1.10 g·kg⁻¹ and 0.06–0.07 g·kg⁻¹ at different elevations in stage 1 and increased to 12.30–14.75 g·kg⁻¹ and 2.82–3.08 g·kg⁻¹, respectively, in stage 2. Higher plants were distributed mainly in the midslope area at stage 2. A significant positive correlation was observed between pH and plant height at stage 2. These findings improve our understanding of the changes in soils and plants on sandy land and their relationships during ecological restoration of alpine valleys.

The ecological restoration of aeolian sandy land has not only improved the function of ecosystem services, such as wind prevention and sand fixation, but has also indirectly reduced the regional economic losses caused by sandstorms. However, the interaction between vegetation and soil properties after natural and artificial restoration of the sandy land in southern Tibetan Plateau has not been sufficiently studied. In the present study, we selected four vegetation types, including artificial forest (A), revegetated shrub (B), natural shrub (C), and natural grassland (D), in the sandy land in the middle reaches of the Yarlung Zangbo River basin, Tibet, China, and investigated the changes in soil particle size and nutrients at depths of 0–20 cm and 20–40 cm, finally examining the potential relationships between soil properties and leaf nutrients. Our results indicated that in the topsoil (0–20 cm), the natural shrub (C) and natural grassland (D) have greater silt content, recorded as 50.77% and 62.16%, respectively, compared to the artificial forest (A) and revegetated shrub (B). Natural grassland (D) had the highest silt content and the lowest soil bulk density (SBD) among the four vegetation types. There was no significant difference in the soil organic matter (SOM) in the topsoil of the different vegetation types. However, at the depth of 20–40 cm, the SOM content of the different vegetation types was in the following order: natural grassland (D) (23.37 g/kg) > natural shrub (C) (17.42 g/kg) > revegetated shrub (B) (14.85 g/kg) > artificial forest (A) (8.43 g/kg). The ammonium nitrogen content (NH₄‐N) in the revegetated shrub (B) was higher compared to the other vegetation types. The SOM content was significantly correlated with the total phosphorus (TP) and available phosphorus (AP) of the sandy land. The leaf total carbon, nitrogen, and phosphorus exhibited a positive correlation with SBD, AP, and available potassium. These findings can provide useful information to optimize the patterns of natural and artificial restoration for controlling desertification in similar eco‐regions.