Next Article in Journal
Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces
Next Article in Special Issue
Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data
Previous Article in Journal
Determination of the Optimal Mounting Depth for Calculating Effective Soil Temperature at L-Band: Maqu Case
Previous Article in Special Issue
Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 478; doi:10.3390/rs8060478

Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data

1
Department of Geography and Environment, Rowan University, Glassboro, NJ 08028, USA
2
Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, USA
3
Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610, USA
4
School of Geographical Sciences and Urban Planning, Arizona State University, COOR 5628, Tempe, AZ 85287-0104, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yudong Tian, Ken Harrison, Alfredo R. Huete and Prasad S. Thenkabail
Received: 30 March 2016 / Revised: 26 May 2016 / Accepted: 2 June 2016 / Published: 7 June 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
View Full-Text   |   Download PDF [4124 KB, uploaded 7 June 2016]   |  

Abstract

Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to landscape change assessments. The heterogeneous Lerma-Chapala-Santiago watershed of central Mexico exemplifies both natural and anthropogenic forces enacting variability and change on the landscape. This study employed a time series of Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectoradiometer (MODIS) for 2001–2007 and per-pixel multiple linear regressions in order to model changes in EVI as a function of precipitation, temperature, and elevation. Over the seven-year period, 59.1% of the variability in EVI was explained by variability in the independent variables, with highest model performance among changing and heterogeneous land cover types, while intact forest cover demonstrated the greatest resistance to changes in temperature and precipitation. Model results were compared to an independent change uncertainty assessment, and selected regional samples of change confusion and natural variability give insight to common problems afflicting land change analyses. View Full-Text
Keywords: vegetation; variability; Land Use and Land Cover Change; precipitation; temperature; MODIS; TRMM; EVI; LST vegetation; variability; Land Use and Land Cover Change; precipitation; temperature; MODIS; TRMM; EVI; LST
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

Christman, Z.; Rogan, J.; Eastman, J.R.; Turner, B.L. Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data. Remote Sens. 2016, 8, 478.

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