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A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches-including regional and national scales-confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover data-sets, in this study, we clearly distinguished and differentiated land-use from land cover. By focusing on our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery. Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work. Furthermore, our novel approach in regional scale land-use mapping produced robust results in our study area: the overall internal accuracy of the classifier was 95.2% and the external accuracy of the classifier was measured at 74.8%.

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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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