Geography
Document Type
Article
Abstract
Conventional methods to analyze a transition matrix do not offer in-depth signals concerning land changes. The land change community needs an effective approach to visualize both the size and intensity of land transitions while considering possible map errors. We propose a framework that integrates error analysis, intensity analysis, and difference components, and then uses the framework to analyze land change in Nanchang, the capital city of Jiangxi province, China. We used remotely sensed data for six categories at four time points: 1989, 2000, 2008, and 2016. We had a confusion matrix for only 2016, which estimated that the map of 2016 had a 12% error, while the temporal difference during 2008–2016 was 22% of the spatial extent. Our tools revealed suspected errors at other years by analyzing the patterns of temporal difference. For example, the largest component of temporal difference was exchange, which could indicate map errors. Our framework identified categories that gained during one time interval then lost during the subsequent time interval, which raised the suspicion of map error. This proposed framework facilitated visualization of the size and intensity of land transitions while illustrating possible map errors that the profession routinely ignores.
Publication Title
Remote Sensing
Publication Date
2020
Volume
12
Issue
20
First Page
1
Last Page
16
ISSN
2072-4292
DOI
10.3390/rs12203323
Keywords
rrror analysis, intensity analysis, land change, Nanchang, transition pattern
Repository Citation
Xie, Zheyu; Pontius, Robert Gilmore; Huang, Jinliang; and Nitivattananon, Vilas, "Enhanced intensity analysis to quantify categorical change and to identify suspicious land transitions: A case study of Nanchang, China" (2020). Geography. 714.
https://commons.clarku.edu/faculty_geography/714
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
