Abstract
The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p < 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p < 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p < 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.
Highlights
The dominant tree species occurring in the local forest are deciduous forest dominated by Quercus mongolica (Qm), Populus davidiana (Pd), Betula platyphylla (Bp), and Larix gmelinii (Lg), accounting for about 84% of the total forest area in 2018; there is an evergreen forest, which is dominated by Pinus tabulaeformis (Pt)
We focused on the remote-sensing classification results for the four deciduous forest tree species: Betula platyphylla (Bp), Populus davidiana (Pd), Quercus mongolica (Qm), and Larix gmelinii (Lg) based on time-series normalized differential vegetation index (NDVI) data and the combination of NDVI and phenological metrics
For the 10 m time-series data, all deciduous tree species are correctly classified into their respective categories (Figure 8), and their UA and PA can reach more than 83.12%. These results show that using spatial resolution NDVI data combined with temporal phenological characteristics improves the accuracy of the results at each scale from 4 to 30 m (p < 0.05), which indicates that combining the spectral curve and the land-surface phenology (LSP) metrics is a good way to identify and monitor the main forest ecosystem and may improve the precision of spatial mapping in temperate regions from medium to high spatial resolution
Summary
Remote sensing images with medium to high spatial resolution (≤30 m) have been widely used to map and classify dominant tree species. 2021, 13, 2716 in the different regional forest ecosystems, because the viewing field of the image is close to the size of tree species and of tree stands [6,7,8,9]. Given the limitations to a few bands with wide central bandwidths, it remains challenging to map forests finely and accurately by solving the common problem of foreign bodies with spectra similar to that of tree species with multispectral images of differing resolution [10,11,12,13]
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