Next Article in Journal
Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing
Previous Article in Journal
The Added Value of Stratified Topographic Correction of Multispectral Images
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(2), 143; doi:10.3390/rs8020143

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

1
Graduate School of Global Environmental Studies, Kyoto University, Kyoto 606-8501, Japan
2
Department of Geography, University of Leicester, Leicester LE1 7RH, UK
3
School of Geography, University of Leeds, Leeds LS2 9JT, UK
4
Faculty of Agriculture, Bogor Agricultural University, Bogor 16680, Indonesia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 27 November 2015 / Revised: 28 January 2016 / Accepted: 4 February 2016 / Published: 15 February 2016
View Full-Text   |   Download PDF [2845 KB, uploaded 15 February 2016]   |  

Abstract

Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches. View Full-Text
Keywords: impervious surface area; urban expansion; MODIS; random forest impervious surface area; urban expansion; MODIS; random forest
Figures

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

Tsutsumida, N.; Comber, A.; Barrett, K.; Saizen, I.; Rustiadi, E. Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas. Remote Sens. 2016, 8, 143.

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]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top