Artifacts in Remote Sensing Data Analysis: An Ecological Perspective
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 2228
Special Issue Editor
Interests: biodiversity estimate; computational ecology; ecological informatics; remote sensing; spatial ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing data are nowadays useful tools to build ecological models, since they allow gathering information on ecosystem properties over wide areas in short time: from biodiversity modelling, to productivity estimates, to land use variability in space and time. Properly processing remote sensing data has been acknowledged as a fundamental step to gather reliable information on Earth changes in space and time in a rapid manner.
While most of the work has been done in considering remote sensing data correction problems, little has been done until now to explicitly consider artifacts related to ecological modelling per se. As an example, multicollinearity in remote sensing bands, mixed pixels, and scale issue might present problems when aiming at building proper ecological models, relying on remote sensing.
The aim of this special issue is to provide theoretical basis and empirical examples, with both case studies and review papers, about artifacts coming out from remote sensing data modelling, related, but not limited, to:
(1) biodiversity estimate distortion with an improper use of remote sensing data,
(2) misconceptions about multi-collinearity of remote sensing data in linear modelling,
(3) scales issue,
(4) remote sensing data uncertainty, etc.
Prof. Duccio Rocchini
Guest Editor
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Keywords
- Atmospheric Correction
- Biodiversity
- Ecological Modelling
- Ecology
- Ecosystem
- Geometric Correction
- Generalized Linear Models
- Generalized Additive Models
- Mixed Pixels
- Remote Sensing Data Quality
- Scale
- Sensors
- Spatial Ecology
- Spatial Resolution
- Spectral Resolution
- Vegetation Indices
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