Next Article in Journal
Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data
Next Article in Special Issue
A Prototype Network for Remote Sensing Validation in China
Previous Article in Journal
Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data
Previous Article in Special Issue
Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(4), 4834-4857; doi:10.3390/rs70404834

Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

1
Department of Geography, University of Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany
2
Bayreuth Center of Ecology and Environmental Research (BAYCEER), University of Bayreuth, Dr. Hans-Frisch-Str. 1-3, 95448 Bayreuth, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Xin Li, Yuei-An Liou, Qinhuo Liu, Heiko Balzter and Prasad S. Thenkabail
Received: 28 January 2015 / Revised: 15 April 2015 / Accepted: 15 April 2015 / Published: 20 April 2015
View Full-Text   |   Download PDF [1624 KB, uploaded 22 April 2015]   |  

Abstract

The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality). Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation) scene (MOD15A2) in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28). Green LAI samples were then empirically modeled (LAImodel) with high resolution RapidEye imagery derived Difference Vegetation Index (DVI) using a linear regression (R2 = 0.71). As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1) and a potential adjustment by means of the MODIS Burned Area product (MCD45A1). View Full-Text
Keywords: dry season; savanna; Leaf Area Index; vegetation pattern; RapidEye; MOD15A2; empirical modeling; Namibia dry season; savanna; Leaf Area Index; vegetation pattern; RapidEye; MOD15A2; empirical modeling; Namibia
Figures

Figure 1

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

Mayr, M.J.; Samimi, C. Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna. Remote Sens. 2015, 7, 4834-4857.

Show more citation formats Show less citations formats

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