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Leaf stomata are important structures used for exchanging matter between plants and the environment, and they are very sensitive to environmental changes. The method of efficiently extracting stomata, as well as measuring stomatal density and area, still lacks established techniques. This study focused on the leaves of Fraxinus pennsylvanica Marshall, Ailanthus altissima (Mill.) Swingle, and Sophora japonica (L.) Schott grown on different underlying surfaces and carried out an analysis of stomatal information using multiscale segmentation and classification recognition as well as microscopy images of leaf stomata via eCognition Developer 64 software (Munich, Germany). Using this method, we further analyzed the ecological significance of stomata. The results were as follows: (1) The best parameters of stomatal division and automatic extraction rules were scale parameter 120–125 + shape parameter 0.7 + compactness parameter 0.9 + brightness value 160–220 + red light band >95 + shape–density index 1.5–2.2; the accuracy of stomatal density and stomatal area using this method were 98.2% and 95.4%, respectively. (2) There was a very significant correlation among stomatal density, stomatal area, and stomatal shape index under different growing environments. When the stomatal density increased, the stomatal area lowered remarkably and the stomatal shape tended to be flat, suggesting that the plants had adopted some regulatory behavior at the stomatal level that might be an ecological trade-off strategy for plants to adapt to a particular growing environment. These findings provide a new approach and applicable parameters for stomata extraction, which can further calculate the stomatal density and stomatal area and deepen our understanding of the relationship between stomata and the environment. The study provides useful information for urban planners on the breeding and introduction of high-temperature-resistant urban plants.

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Background: Response and adaptation strategies of plants to the environment have always been the core issues in ecological research. So far, relatively little study exists on its functional traits responses to warming, especially in an urban environment. This information is the key to help understand plant responses and trade-off strategy to urban warming. Results: We chose the common greening trees of mature age in Beijing (Fraxinus pennsylvanica, Koelreuteria paniculata, and Sophora japonica) as the research subjects, and used infrared heaters to simulate warming for three gradients of natural temperature (CK), moderate warming (T1) and severe warming (T2). Results showed that:(1) Leaf dry matter content (LDMC), chlorophyll content (CHL), leaf tissue density (LTD), and stomatal density (SD) all increased with temperature warming. Specific leaf area (SLA), stomatal size (SS), and stomatal aperture (SA) decreased with simulated warming. (2) SLA was extremely significantly negatively correlated with CHL, LDMC, LTD and SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). SA was extremely negatively correlated with SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). There was a significant positive correlation between LDMC and LTD (P < 0.01). This showed that urban greening trees adapted to the environment by coordinating adjustment among leaf functional traits. (3) Under the T1 treatment, the R 2 and slope among the leaf traits were higher than CK, and the significance was also enhanced. The correlation between leaf traits was strengthened in this warming environment. Conversely, it will weaken the correlation between leaf traits under the T2 treatment. Conclusion: Our study demonstrated that there was a strong trade-off between leaf functional traits in the urban warming environment. Plants in the warming environment have adopted relatively consistent trade-offs and adaptation strategies. Moderate warming was more conducive to strengthening their trade-off potential. It is further verified that the global leaf economics spectrum also exists in urban ecosystems, which is generally tend to a quick-investment return type with the characteristics of thick leaves, strong photosynthetic capacity, low transpiration efficiency and long life in urban environments.

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Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R2 of green peak reflectance was the highest (y = 206.98e−10.85x, R2 = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet.

Rapidly determining leaf vein network patterns and vein densities is biologically important and technically challenging. Current methods, however, are limited to vein contour extraction. Further image processing is difficult, and some leaf vein traits of interest therefore cannot be quantified. In this study, we proposed a novel method for the fast and accurate determination of leaf vein network patterns and vein density. Nine tree species with different leaf characteristics and vein types were applied to verify this method. To overcome the image processing difficulties at the microscopic scale, we adopted the remote object-oriented classification method applied comprehensively in the field of remote sensing research. The key to this approach is to determine the universally applicable leaf vein extraction threshold values (scale parameter, shape parameter, compactness parameter, brightness feature, spectral feature and geometric feature). Based on our analysis, the following recommended threshold values were determined: the scale parameter was 250, the shape parameter was 0.7, the compactness parameter was 0.3, the brightness feature value was 230∼280, the spectral feature value was 180∼230, and the geometric feature value was less than 2. With the optimal extraction parameters applied, the extraction precision was above 96.40% on average for the nine species studied. The leaf vein density calculation rate increased by more than 87.3% compared to that of the traditional methods. The results showed that this method is accurate, fast, flexible and complementary to existing technologies. It is an effective tool for the fast extraction of vein networks and the exploration of leaf vein characteristics, particularly for large-scale studies in plant vein physiology.

To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).