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ISPRS Int. J. Geo-Inf. 2017, 6(6), 179; doi:10.3390/ijgi6060179

Retrieval and Comparison of Forest Leaf Area Index Based on Remote Sensing Data from AVNIR-2, Landsat-5 TM, MODIS, and PALSAR Sensors

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
Biosphere Informatics Laboratory, Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 24 March 2017 / Revised: 6 June 2017 / Accepted: 18 June 2017 / Published: 21 June 2017
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Abstract

Remote sensing data from multi-source optical and SAR (Synthetic Aperture Radar) sensors have been widely utilized to detect forest dynamics under a variety of conditions. Due to different temporal coverage, spatial resolution, and spectral characteristics, these sensors usually perform differently from one another. To conduct statistical modeling accuracies evaluation and comparison among several sensors, a linear statistical model was applied in this study for retrieval and comparative analysis based on remote-sensing indices from optical sensors of ALOS AVNIR-2 (Advanced Land Observing Satellite Advanced Visible and Near Infrared Radiometer type 2), Landsat-5 TM (Thematic Mapper), MODIS NBAR (Moderate Resolution Imaging Spectroradiometer Nadir BRDF-Adjusted Reflectance), and the SAR sensor of ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), respectively. This modeling used the forest leaf area index (LAI) as the field measured variable. During modeling, six optical vegetation indices were selected for evaluation and comparison between the three optical sensors, while simultaneously, two radar indices were calculated for the comparison between ALOS AVNIR-2 and PALSAR sensors. The gap between the spatial resolution of remote-sensing data and field plot size can account for the different accuracies found in this study. This study provides a reference for the selection of remote-sensing data types and spatial resolution in specific forest monitoring applications with different data acquisition costs and accuracy needs. Normally, at regional and national scales, remote sensing data with 30 m spatial resolution (e.g., Landsat) could provide significant results in the statistical modelling and retrieval of LAI while the MODIS cannot always meet the requirements. View Full-Text
Keywords: optical sensors; forest monitoring; leaf area index; vegetation index; PLS regression optical sensors; forest monitoring; leaf area index; vegetation index; PLS regression
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Chen, W.; Yin, H.; Moriya, K.; Sakai, T.; Cao, C. Retrieval and Comparison of Forest Leaf Area Index Based on Remote Sensing Data from AVNIR-2, Landsat-5 TM, MODIS, and PALSAR Sensors. ISPRS Int. J. Geo-Inf. 2017, 6, 179.

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