The dynamics of Pinus mugo krummholz during concomitant change in pastoral land use and climate in central Italy since the mid-20th century was investigated. Krummholz dynamics were detected using sequential aerial photography and fitted to a logistic regression model with elevation, grazing, proximity to beech forest, and proximity to krummholz as explanatory variables. Dendrochronological series were correlated with temperature and precipitation and fitted to a linear model. During this period krummholz doubled in extent and migrated 35–65 m upslope. Expansion was positively associated with krummholz proximity, residual pastoral grazing, and proximity to beech forest beyond 10 m and negatively associated with elevation and beech forest closer than 10 m. The logistic regression model forecasts krummholz migration by an additional 30 m upslope by 2060. During the 20th century, winter and spring minimum temperatures increased but did not result in increased radial stem growth of P. mugo. The combined evide...

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.

The natural mountain forests in northwest China are recognized as a substantial carbon pool and play an important role in local fragile ecosystems. This study used inventory data and detailed field measurements covering different forest age groups (young, middle-aged, near-mature, mature, old-growth forest), structure of forest (tree, herb, litter and soil layer) and trees (leaves, branches, trunks and root) to estimate biomass, carbon content ratio, carbon density and carbon storage in Altai forest ecosystems. The results showed that the average biomass of the Altai Mountains forest ecosystems was 126.67 t·hm⁻², and the descending order of the value was tree layer (120.84 t·hm⁻²) > herb layer (4.22 t·hm⁻²) > litter layer (1.61 t·hm⁻²). Among the tree parts, trunks, roots, leaves and branches accounted for 50%, 22%, 16% and 12% of the total tree biomass, respectively. The average carbon content ratio was 0.49 (range: 0.41–0.52). The average carbon density of forest ecosystems was 205.72 t·hm⁻², and the carbon storage of the forest ecosystems was 131.35 Tg (standard deviation: 31.01) inside study area. Soil had the highest carbon storage (65.98%), followed by tree (32.81%), herb (0.78%) and litter (0.43%) layers. Forest age has significant effect on biomass, carbon content ratio, carbon density and carbon storage. The carbon density of forest ecosystems in study area was spatially distributed higher in the south and lower in north, which is influenced by climate, topography, soil types and dominant tree species.

The artificial young forest is an important component of ecosystems, and biomass models are important for estimating the carbon storage of ecosystems. However, research on biomass models of the young forest is lacking. In this study, biomass data of 96 saplings of three tree species from the southern foot of the Qilian Mountains were collected. These data, coupled with allometric growth equations and the nonlinear joint estimation method, were used to establish independent, component-additive, and total-control compatible models to estimate the biomass of artificial young wood of Picea crassifolia (Picea crassifolia Kom.), Sabina przewalskii (Sabina przewalskii Kom.), and Pinus tabulaeformis (Pinus tabuliformis Carr.). The distribution characteristics of the biomass components (branch, leaf, trunk, and root biomass) and the goodness of fit of the models were also analyzed. The results showed that (1) the multiple regression models with two independent variables (MRWTIV) were superior to the univariate models for all three tree species. Base diameter was the best-fitting variable of the univariate model for Picea crassifolia and Pinus tabulaeformis, and the addition of base diameter and crown diameter as variables to the MRWTIV can significantly improve model accuracy. Tree height was the best-fitting variable of the univariate model of Sabina przewalskii, and the addition of tree height and crown diameter to the MRWTIV can significantly improve model accuracy; (2) the two independent variable component-additive compatible model was the best-fitting biomass model. The compatible models constructed by the nonlinear joint estimation method were less accurate than the independent models. However, they maintained good compatibility among the biomass components and enabled more robust estimates of regional biomass; and (3) for the young wood of Picea crassifolia, Sabina przewalskii, and Pinus tabulaeformis, the aboveground biomass ratio of each component to total biomass was highest for leaf biomass (26%–68%), followed by branch (10%–46%) and trunk (11%–55%) biomass, and the aboveground biomass was higher than the underground biomass. In conclusion, the optimal biomass model of artificial young forest at the sampling site is a multivariate component-additive compatible biomass model. It can well estimate the biomass of young forest and provide a basis for future research.