Background: The demand for productive economic plant resources is increasing with the continued growth of the human population. Ancient Pu’er tea trees [Camellia sinensisvar.assamica(J. W. Mast.) Kitam.] are an important ecological resource with high economic value and large interests. The study intends to explore and evaluate critical drivers affecting the species’ productivity, then builds formulas and indexes to make predicting the productivity of such valuable plant resources possible and applicable.Results: Our analysis identified the ideal values of the seven most important environmental variables and their relative contribution (shown in parentheses) to the distribution of ancient Pu’er tea trees: annual precipitation, ca. 1245 mm (28.73%); min temperature of coldest month, ca. 4.2 °C (18.25%); precipitation of driest quarter, ca. 47.5 mm (14.45%); isothermality, 49.9% to 50.4% (14.11%); precipitation seasonality, ca. 89.2 (6.77%); temperature seasonality, ca. 391 (4.46%); and solar radiation, 12,250 to 13,250 kJ m⁻² day⁻¹(3.28%). Productivity was indicated by the total value (viz. fresh leaf harvested multiplied by unit price) of each tree. Environmental suitability, tree growth, and management positively affected productivity; regression weights were 0.325, 0.982, and 0.075, respectively. The degree of productivity was classified as follows: > 0.8, “highly productive”; 0.5–0.8, “productive”; 0.3–0.5, “poorly productive”; and < 0.3, “unproductive”. Overall, 53% of the samples were categorized as “poorly productive” or “unproductive”; thus, the management of these regions require attention.Conclusions: This model improves the accuracy of the predictions of ancient Pu’er tea tree productivity and will aid future analyses of distribution shifts under climate change, as well as the identification of areas suitable for Pu’er tea tree plantations. Our modeling framework provides insights that facilitate the interpretation of abstract concepts and could be applied to other economically valuable plant resources.

The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.

Ancient Pu’er tea trees (Camellia sinensis var. assamica (J. W. Mast.) Kitam.) are an important ecological resource with high economic value. Knowledge of the environmental variables shaping the original distribution and the effects of climate change on the future potential distribution of these trees, as well as the identification of sustainable management approaches, is essential for ensuring their future health and production. Here, we used 28 current environmental variables and the future climate data to model the suitable areas for ancient Pu’er tea trees. We also compared the health of these ancient trees in areas under different local management strategies. The results suggested the general distribution is likely to remain stable, but there are environmentally suitable areas outside its current habitats. To achieve more sustainable management, the main areas in which the management of poorly-managed trees can be improved include learning from managers of well-managed trees and following the common technical management regulations stipulated by the local government. The suitable value ranges for environmental factors, potentially suitable areas under climate change, and assessment of management approaches will aid the future cultivation and transplantation of ancient Pu’er tea trees. The methodology includes management-level analysis and provides practical insights that could be applied to regions outside the most suitable areas identified.

Aboveground biomass (AGB) is a key parameter for studying the carbon cycle, evaluating grassland growth, and assessing the grass–livestock balance. In this study, we established an optimal inversion model for alpine grassland AGB and estimated the growing-season (July–September) AGB from 2018 to 2022 based on field survey data and remote sensing data. We aimed to analyze the spatiotemporal dynamics of AGB in alpine grasslands and its response mechanisms to hydrothermal factors, as well as to explore the indirect impacts of changes in human activities during the COVID-19 pandemic on the grassland ecosystem. The results showed the following: (1) Alpine grassland AGB was high in the southwest and low in the northeast of the studied area, initially increasing and then decreasing over time. This pattern was largely consistent with the spatial distribution and interannual variations in precipitation and temperature, with a significant positive correlation being observed between precipitation and AGB, indicating that hydrothermal factors are key drivers of grassland AGB dynamics. (2) The grasslands demonstrated a trend of slight decrease in AGB overall, with some local areas showing a slight increase. Compared with before 2018, grasslands showed a gradual recovery trend, which may be related to grazing policies and conservation management measures. (3) An increase in grazing intensity in local areas decreased grassland AGB and vice versa, indicating that the restrictive measures led to changes in grazing intensity, which indirectly affected grassland AGB during the pandemic. This study reveals the general patterns of hydrothermal factors’ influence on alpine grassland AGB dynamics during the pre-, mid-, and post-COVID-19-pandemic periods, providing a scientific basis for formulating sustainable grassland management strategies.