Abstract
Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.
Highlights
Providing multi-scale spatial distribution information of vegetation types plays a key role in understanding, managing and protecting vegetation ecosystems and their biodiversity
Vegetation information was classified into four levels: the first layer was the vegetation and non-vegetation layer (L1); the second layer was divided into farmland and grassland and forest, called the vegetation type layer (L2); the third layer was divided into secondary forest and original forest, called the forest type (L3) and the fourth layer was canopy and the canopy gaps (L4)
The main reasons are: (1) image objects can be created on multiple specific hierarchical spatial scales; (2) image objects can obtain many attributes; (3) geographic object-based image analysis method (GEOBIA) technique can better imitate people’s perception of real-world objects; (4) image classification reduces the phenomenon of salt-and-pepper effect and
Summary
Providing multi-scale spatial distribution information of vegetation types plays a key role in understanding, managing and protecting vegetation ecosystems and their biodiversity. Remote sensing has become an important source of information for vegetation type information management and monitoring, and it is an effective low-cost technical means for obtaining vegetation types [1,2,3]. It is necessary to study the multi-source remote sensing data to obtain the vegetation structure information of the ecosystem and the multi-scale information of the forest landscape. Most of the research on the structure of ecosystems using remote sensing technology only explores the information on a specific single scale based on the spatial resolution of the remote sensing data used. The extraction of vegetation information at various levels of the landscape from the integrated multi-source remote sensing data is a lack of studies
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