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.
Highlights
With significant developments in remote-sensing technology, a variety of satellites and sensors with different spatial, spectral, and temporal resolutions have been quickly developed
The modeling results based on AVNIR-2 data are shown in Table 3(a) and Figure 5
The regression model based on Difference Vegetation Index (DVI) showed the lowest correlation
Summary
With significant developments in remote-sensing technology, a variety of satellites and sensors with different spatial, spectral, and temporal resolutions have been quickly developed. More than 6000 satellites have been launched, over 60% of which serve military purposes Those for civilian and scientific use mainly include the series of Landsat, meteorological, ocean, geodetic, astronomical observation, and communications satellites. These satellites provide sufficient spatial and temporal coverage of high quality data at various scales. Multi-source optical and radar data, including the widely recognized Landsat records [1], high spatial resolution Quickbird imagery [2], and time-series SPOT (Systeme Probatoire d’Observation de la Terre)-VEGETATION images [3] as well as L-band PALSAR data [4], have been widely utilized to monitor the forest status and its dynamics under various circumstances
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