Abstract
Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.
Highlights
For extraction of the evergreen forest cover fraction, we assumed that the two endmember components could be replaced with evergreen and non-evergreen forest in the NDVIann-min image, both the evergreen and non-evergreen forest pixels were impacted by spatial heterogeneity across the large study area (Figure 4)
We proposed an FEVC-self-adaptive parameter (SAP) model for estimating the fraction of evergreen forest cover
Without relying on high-resolution images, extraction of the evergreen forest cover fraction was achieved across a large area
Summary
Forests have unique ecological value, depending on their species composition [1]. The spatial distribution pattern of tree species determines the uniqueness of their ecosystem service functions, including carbon sequestration, water and soil conservation, and biodiversity [2,3,4]. As the dominant forest species in most tropical areas and some subtropical areas, evergreen trees exhibit significant differences in seasonal evapotranspiration and carbon and nitrogen cycling compared to deciduous tree [5,6,7].
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