Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt

Abstract

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

“…Comparing the obtained results with the results available in the literature, attention can be paid to the type of plant associations, the number of classes distinguished, the applied machine learning algorithms, and the satellite or airborne imagery used (Table 8). The obtained overall accuracy (90-98%) is quite comparable to that obtained by other authors; an example is the analyses by Zhang et al [2] of nine classes of mountain belts based on RF and multitemporal high-resolution multispectral satellites Gaofen-1, Gaofen-2, and Ziyuan 3-01. The results were as follows: deciduous (oak and birch) forest 75-93% (PA); conifer forest types (fir, pine, and larch) 89-95% (PA); and subalpine shrubs and meadows 75% (PA).…”

“…The adaptations are a consequence of highly differentiated vegetation belts, e.g., temperature, sunlight, exposure to high-energy UV radiation, strong, drying winds, water vapor, soil nutrients, and water content. These factors influence the survival strategies of individual species, visible in the plant physiology and morphology [2,3]. When the winters are relatively warm, plants have a chance to survive in harsher conditions, beginning to occupy higher-located habitats, which under normal circumstances would not be available to them, and during colder winters or when the snow cover decreases, plants are exposed to frost, which initiates fungal and insect-related diseases, causing plant dieback [4][5][6].…”

Tracing hotspots of soil erosion in high mountain environments: how forensic science based on plant eDNA can lead the way. An opinion

“…High mountain environments are characterised by biogeographic units along altitudinal gradients that show a strong differentiation in vegetation communities. Highresolution vegetation maps of these units are increasingly available (e.g., Dirnböck et al 2003;Dobrowski et al 2008;Zhang et al 2020), and usually include typical indicator species per vegetation community. Furthermore, topographically-controlled micro-climatic conditions are associated with local plant species distribution (Scherrer and Körner 2011).…”

Spatial and temporal patterns of upland vegetation over the last 200 years in the northern pyrenees: Example from the Bassiès valley, Ariège, France

“…However, aerial images might underestimate the increase in tree and shrub cover sensed by the LOVE-based estimates. Mapping historical mountain vegetation at a high spatial resolution is challenging as the classification of aerial images is often hindered by i) the lack of spectral discernibility between vegetation types, ii) the difficulty of delineating the boundaries of different vegetation types in a heterogeneous landscape mosaic, iii) the fact that a given vegetation formation may have a different phenology due to seasonal or composite classes of vegetation; iv) the shadow effect from nearby trees or cliffs (Cots-Folch et al, 2007;Dirnböck et al, 2003;Dobrowski et al, 2008;Li and Shao, 2014;Zhang Id et al, 2020). The overall accuracy of the 2008 classified map was assessed at 82.5% when comparing the classified land-cover types with their corresponding ground truth data (Haunold, 2015).…”