Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats.

Introduction: Mango is a major tropical economic tree species in China, along with being a vital source of livelihood for farmers and an important maintainer of ecosystem services in southern China. Identifying the potential suitable habitats for mango under current and future climate scenarios, along with key influencing factors, can inform mango plantation. However, little is known about these.Methods: Using Maxent, we modeled the current and future potential suitable habitats for mango, evaluated the impact of environmental variables on their distribution, and identified shifts related to climate change in their distribution.Results: The results showed that the current potential suitable habitats for mango were primarily located in southern China, within the tropical and subtropical regions. Under climate scenarios of both SSP585 and SSP126, the potential suitable habitats not only encompassed the southern provinces of China that were already covered but also expanded northward to include central provinces, particularly Sichuan and Chongqing municipalities. Mango exhibited a clear tendency to migrate toward higher altitudes and latitudes under SSP585 scenario, whereas the trend of mango migration to such areas was less pronounced under SSP126 scenario. Mean Temperature of Coldest Quarter, Annual accumulated temperature (≥10°C), Precipitation of Coldest Quarter, and UV-B Seasonality were identified as the main factors shaping the distribution of the potential suitable habitats for mango.Discussion: Our recommendation to adapt to climate change is to expand mango cultivation to high-latitude/altitude areas, particularly Sichuan-Chongqing in central China, along with water-saving irrigation, shade management, development of drought- and disease-resistant cultivars, and mapping of the potential suitable habitats for different varieties.

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

Karst rocky desertification (KRD) profoundly impacts the ecological health and sustainable development of karst areas. However, few studies have examined its evolution and driving factors on a large spatial scale. This research analyzed the KRD’s evolution and driving factors in southwestern China from 1990 to 2020 using Google Earth Engine (GEE). The main results were as follows: 1) Over the past three decades, KRD evolution demonstrated a trend of general improvement but localized deterioration. 2) Area transitions mainly occurred between non-rocky desertification (NRD) and light rocky desertification (LRD) in all periods, with the most significant degradation manifesting as the shift from NRD to LRD. 3) Among the single factors, precipitation consistently had the greatest contribution. Key interacting factors included combinations of precipitation and slope, precipitation and land use, slope and soil texture, and slope and wind speed. To address these findings, policymakers should implement localized KRD control projects with more focus on preventing NRD deterioration and conduct more in-depth research on the impacts of precipitation, geology, and human interference on KRD.

This study aimed to systematize the Zhuang medicinal herbs of Ardisia (ZMHA) in China, to clarify the traditional use in Zhuang medicine and the dynamics of international research on phytochemistry, pharmacology, clinical application, and toxicity. There are 25 species of ZMHA, approximately 938 compounds from the different part, including triterpenoids, phenolics, volatile oils, etc. Pharmacological activity studies have also shown that this genus has anti-tumour, anti-inflammatory, anti-oxidant, anti-bacterial, anti-microbial, etc., and significant effects on respiratory, digestive, urinary, and musculoskeletal system diseases without toxic side effects. The Ardisia has a medicinal history of nearly a thousand years, mainly for treating diseases of the injuries, musculoskeletal, and symptomatic system in Zhuang medicine. Some plants, such as A. crenata, A. gigantifolia, and A. japonica, are also commonly used in folk Zhuang medicine formulas, to treat musculoskeletal, injury, respiratory, and urinate systems disease. These diseases are related to inflammation. These could provide a new direction for future new drug development research. Therefore, species identification and resource investigation should be strengthened, and conducted in vitro mechanism, in vivo pharmacology, clinical efficacy, and toxicology studies and establish a perfect quality standard system.