Spatial Characterization of Vegetation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 5697
Special Issue Editor
Special Issue Information
Dear Colleagues,
Remotely sensed images are commonly used to derive or generate vegetation maps. Mapped vegetation attributes maps may be discrete, such as the occurrence of dominant species or labels of species assemblages, or continuous, such as percentage of cover of green leaf area, total basal area of tree boles, or fraction of absorbed photosynthetically active radiation. As such, maps are a way to spatially characterize vegetation. Within raster data models most often used in such cases, vegetation attributes may be mapped on a per-raster basis, leading to a wall-to-wall spatial characterization of those attributes. In alternative vector data models, spatial units that are variable in size and shape are associated with their attributes. Further, vegetation may be characterized using spatial statistics such as semivariograms, spatial covariance functions, or size distributions. Papers are invited that deal with methods of remote-sensing-derived spatial characterization and the consequences of those choices for analysis (summary statistics, results of process models, etc.). Possible topics for this Special Issue include:
- Descriptive spatial statistics from data from new remote sensing data collection systems (e.g. lidar, radar, thermal imaging, imaging spectrometers) useful in models of vegetation interactions with the atmosphere and hydrosphere;
- New spatial patterns in vegetation attributes discovered or discoverable via remote sensing;
- Problems in and approaches to challenges in the cross-walking of vegetation classification schema for mapping;
- Ways to describe vegetation spatial patterns for quantifying the abundance of vegetation types, valid habitat analysis and organism movement including invasive species modeling, as well as change detection;
- Problems in comparing inventory data with wall-to-wall, remote-sensing-derived maps;
- Discrepancies between high resolution ("fine scale") and low resolution ("coarse scale") maps and the implications for inference and prediction;
- Operational definitions and applications of spatial scaling of vegetation parameters;
- Visualization methods for very large extent vegetation maps derived from remote sensing;
- Implications of inheriting the spatial characteristics of Earth-observation sensor imagery to describe vegetation;
- Models for change-of-support for vegetation maps made from remotely sensed data.
Review articles are welcome.
Dr. Jennifer L. Dungan
Guest Editor
Manuscript Submission Information
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Keywords
- vegetation maps
- spatial statistics
- inventory data
- scaling
- geographic data models
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