Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003)

By D. Riaño, E. Chuvieco, J. Salas, I. Aguado. IEEE Transactions on Geoscience and Remote Sensing, 2003

493 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/df5d099f86026436dae7a6eb5a5b80a64df0342b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peter Wolter
2. Ali Shamsoddini
3. John C. Trinder
4. Philip A. Townsend
5. Russell Turner
6. Håkan Olsson
7. Heather Reese
8. William J. Emery

In Ecology

1. Ali Shamsoddini
2. John C. Trinder
3. Russell Turner
4. Peter Wolter
5. Philip A. Townsend
6. B. Cooke
7. Rob S. Skakun
8. Håkan Olsson

In Environmental Engineering

1. Ali Shamsoddini
2. John C. Trinder
3. Russell Turner
4. Philip A. Townsend
5. Peter Wolter
6. B. Cooke
7. Rob S. Skakun
8. Håkan Olsson

Citations in the corpus

• C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter (2011, methodology): “There is no single standard method used to assess the success of a topographic normalization, and a combination of assessments is often recommended (Riaño et al. 2003), but the methods are not agreed upon (e.”
• Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011, methodology): “Riaño et al., 2003) using a 30 m digital elevation model (source: http://ned.usgs.gov/) and scene-specific sun position information.”
• Very High Resolution Multiangle Urban Classification Analysis (2012, methodology): “To the best knowledge of the authors, the approach presented in this paper is unique in its use of single-source very high spatial resolution optical satellite imagery to exploit derived height information for urban classification.”
• Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “For example, different methods for topographic correction of Landsat TM imagery were evaluated and shown to influence the mapping of vegetation types (Riaño et al. 2003).”
• SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain (2005, methodology): “The C-correction has been shown to retain the spectral characteristics of the data and improve overall classification accuracy in areas of rugged terrain, and it can be derived easily [9], [ 23 ].”
• Pine plantation structure mapping using WorldView-2 multispectral image (2013, methodology): “The cosine of the incident angle, cos(i), which is the incident angle between the Sun and a horizontal surface, was calculated according to Riano et al. (2003).”