Next Article in Journal
Proximity-Based Asynchronous Messaging Platform for Location-Based Internet of Things Service
Next Article in Special Issue
Integrating Spatial and Attribute Characteristics of Extended Voronoi Diagrams in Spatial Patterning Research: A Case Study of Wuhan City in China
Previous Article in Journal
A New Approach to Urban Road Extraction Using High-Resolution Aerial Image
Previous Article in Special Issue
A Local Land Use Competition Cellular Automata Model and Its Application
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2016, 5(7), 113; doi:10.3390/ijgi5070113

Integrating Logistic Regression and Geostatistics for User-Oriented and Uncertainty-Informed Accuracy Characterization in Remotely-Sensed Land Cover Change Information

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qiming Zhou, Zhilin Li and Wolfgang Kainz
Received: 12 May 2016 / Revised: 1 July 2016 / Accepted: 8 July 2016 / Published: 14 July 2016
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
View Full-Text   |   Download PDF [3228 KB, uploaded 14 July 2016]   |  

Abstract

Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output. View Full-Text
Keywords: land cover change; accuracy; local; geostatistics; logistic regression; patterns of class occurrences; standard errors; adaptive sampling land cover change; accuracy; local; geostatistics; logistic regression; patterns of class occurrences; standard errors; adaptive sampling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, J.; Mei, Y. Integrating Logistic Regression and Geostatistics for User-Oriented and Uncertainty-Informed Accuracy Characterization in Remotely-Sensed Land Cover Change Information. ISPRS Int. J. Geo-Inf. 2016, 5, 113.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top