Abstract
Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification.
Highlights
Forests are among the most biologically-diverse and largest terrestrial ecosystems on Earth [1]
Because the Charge Coupled Device (CCD) and Hyperspectral Imager (HSI) sensors were carried on the same platform and the images were both taken on 20 December 2012, they have identical spatial reference and similar spectral information and could be matched together in the spatial and spectral domain
We could find that the Segmented Difference Value method (SEGDV) fused image could retain the detailed spatial resolution from the CCD data while preserving the consistent spectral information of the original HSI data
Summary
Forests are among the most biologically-diverse and largest terrestrial ecosystems on Earth [1]. They play an important role in global carbon and hydrological cycles and provide a wide range of valuable ecosystem goods and services, such as food, timber and climate moderation [2,3]. High-accuracy forest mappings including the types, spatial distribution, canopy structure, tree species composition and temporal changes are of great importance to forest management, conservation biology and ecological restoration. Identification of forest types at fine resolution is critical to provide useful information for forest managers, as well as ecological modelers [5]
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