Abstract
Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.
Highlights
A clear understanding of the spatial distribution of tree species is crucial for afforestation decision-making, carbon cycle estimation, biodiversity assessment [1,2], and further analysis of tree–environment interactions [3,4]
Sensed imagery data with very high spatial resolution have been used for tree species identification, because they can assist in reducing the impact of the occurrence of mixed pixels on tree species classification [5,6,7,8].This is an inherent characteristic determined by remote sensing imaging mechanisms, especially in heterogeneous forests [9,10]
This study explored the use of a non-parametric random forest (RF) classifier built into the Google Earth engine (GEE) cloud computing platform to classify the dominant tree species over a regional area of more than
Summary
A clear understanding of the spatial distribution of tree species is crucial for afforestation decision-making, carbon cycle estimation, biodiversity assessment [1,2], and further analysis of tree–environment interactions [3,4]. Remote sensing technology has greatly improved efficiency, because it is able to capture forest type composition and forest structure information over larger and inaccessible areas through multiband and multimode sensors compared to conventional field works [1] This brings possible solutions to the challenging but promising topic of tree species identification. The processing of hyperspectral imagery is a delicate and time-consuming process due to its large volume [17] and requires a professional background to filter out the optimal bands from the large amount of high-correlation bands characteristic of hyperspectral imagery It is not freely available though it is useful for tree species classification [18].
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