Abstract
Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale.
Highlights
Forest is one of the most important components of the global terrestrial ecosystem, which covers about one third of the earth’s land surface [1]
The dominant tree species are evergreen coniferous forest dominated by Pinus tabulaeformis (Pt), deciduous coniferous forest dominated by Larix gmelinii (Lg), and three deciduous broadleaved forest dominated by Quercus mongolica (Qm), Betula platyphylla (Bp), and Populus davidiana (Pd), which can account for about 95% of the total forest area in 2017 [37]
The kappa coefficient (OA) of forest tree species identification based upon principal component analysis (PCA) transformed patterns derived from 10-day Normalized Differential Vegetation Index (NDVI) time series increased from 0.41 (53.32%) only using the pattern of first component to 0.72 (78.27%), including the first five components in the analysis, and after that with more components included in the analysis the kappa coefficient (OA) kept on a stable condition around 0.76 (80%), and reaching the highest at 22 PCs (0.78 of kappa coefficient and 82.18% of overall accuracies (OA)) (Figure 5b)
Summary
Forest is one of the most important components of the global terrestrial ecosystem, which covers about one third of the earth’s land surface [1]. Sensed data are considered an effective method to identify tree species or forest types at broader scale [4,5], and the higher resolution of satellite images can avoid the spectral distortion of vegetation caused by mixed pixels and is helpful to identify forest patterns [6,7,8]. Compared to airborne hyperspectral and light detection and ranging (LiDAR) data, the multispectral satellite images with higher spatiotemporal scale, larger spatial coverage and lower expense have shown great potential for the identification of tree species at regional scale [8,9]. The advancement in remotely sensed data on spatiotemporal scale over time had been proved to be effective for identifying tree species, and be powerful for delineating the seasonal dynamics of canopy dominant species [13,14]. Many studies suggested that the synthesis of NDVI time series could improve the accuracy of mapping regional tree species instead of using a single image [13,17,18,19]
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