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

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.

Estimation of forest leaf area index using satellite multispectral and synthetic aperture radar data in Iran

“…Both these results are higher with respect to values obtained by Stenberg et al (2004), who used Landsat ETM data in conifer forests (R 2 equal to 0.63); by Zhang et al (2011), who used IRS P6 LISS 3 imagery in a bamboo forest (R 2 equal to 0.68); and by Korhonen et al (2017), who used Sentinel 2 in boreal forests (R 2 equal to 0.73). On the other hand, the results of the present study are slightly less accurate than those obtained by Chen et al (2017), who used AVNIR 2 optical data in a mixed forest mountain area (R 2 equal to 0.93), with higher values possibly thanks to the higher spatial resolution of AVNIR 2 with respect to Sentinel 2.…”

“…In fact, ALOS2 L-band data better penetrate the canopy and report information on vegetation density, with respect to C-band data (Canisius & Fernandes 2012). Chen et al (2017) obtained results similar to those presented here using ALOS PALSAR to estimate forest LAI (R 2 = 0.69), though no cross validation was performed in that study. Manninen et al (2005) using EN-VISAT ASAR data obtained results in the same range of accuracy (R 2 = 0.69) in boreal forests.…”

“…Stankevich et al (2017) developed a method for LAI estimation in temperate deciduous and mixed forests using Sentinel 1 at C-band. Chen et al (2017) compared the performances of various optical sensors with those of ALOS PALSAR for LAI estimation, showing that the addition of SAR brought new information in the opticalbased LAI regression model, and suggested the combined use of optical AVNIR-2 and microwave PALSAR, that are on the same ALOS platform. Manninen et al (2005) developed a method for assessing LAI in boreal forests using ENVISAT C-band SAR data polarization ratio (VV/HH); using the same sensor, Gao et al (2010) evi-denced the sensitivity of SAR backscattering at different polarizations to LAI in poplar and desert date forest plantations in China.…”

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

“…Chen et al [50] found that when using multiple sensors, the ones that had a larger discrepancy between plot size and sensor resolution did not perform as well as the sensors that were closer to the plot size. Middinti et al [51] found that when combining MODIS and OLI data there were a significant number of high LAI values when compared to a solely OLI-derived map.…”

Study on Spatiotemporal Characteristic and Mechanism of Forest Loss in Urban Agglomeration in the Middle Reaches of the Yangtze River

“…Remote sensing data such as AVHRR, MODIS, Landsat, AlOS, SPOT, and ENVISAT have also become mainstream for forest loss analysis, having resolutions ranging from 30 m to 1000 m [40,41]. Land use and landcover (LULC) data provided by the United States Geological Survey (USGS) includes classified vegetation information such as forest, shrub, grassland, and tundra, with a resolution of 100-300 m [42,43].…”