Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index.

Ruiliang Pu,Peng Gong,Qian Yu

doi:10.3390/s8063744

Abstract

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.

Highlights

Three revolutionary imagers: Advanced Land Imager (ALI), Atmospheric Corrector (AC) and Hyperspectral Imager (Hyperion), onboard the EO-1 satellite have been collecting multispectral and hyperspectral scenes in coordination with the Enhanced Thematic Mapper Plus (ETM+) on Landsat 7[1]
ETM+) by the effect of individual bands on estimating forest crown closure (CC) and leaf area index (LAI), we found the Hyperion data consistently outperformed the ALI and ETM+ data while ALI was better than ETM+ [7]
To compare capabilities of the three sensors’ data for mapping forest CC and LAI, we developed six multivariate regression models with inputs of 10 selected spectral features, variables and indices and either CC or LAI measurements

Summary

Introduction

Three revolutionary imagers: Advanced Land Imager (ALI), Atmospheric Corrector (AC) and Hyperspectral Imager (Hyperion), onboard the EO-1 satellite have been collecting multispectral and hyperspectral scenes in coordination with the Enhanced Thematic Mapper Plus (ETM+) on Landsat 7[1]. Three revolutionary imagers: Advanced Land Imager (ALI), Atmospheric Corrector (AC) and Hyperspectral Imager (Hyperion), onboard the EO-1 satellite have been collecting multispectral and hyperspectral scenes in coordination with the Enhanced Thematic Mapper Plus (ETM+) on Landsat 7. A significant part of the EO-1 program is to perform data comparisons among Hyperion, ALI and. Such a comparison is required by the United States Landsat Data Continuity Mission (LDCM, [2]) to advance the legacy of the Landsat program with the intent of serving science and society. The comparisons are ensured, since the EO-1 orbit matches the Landsat 7 orbit with only one minute delay. After comparing the retrieved surface reflectances from ALI with those from ETM+ Since launching EO-1, such comparisons have been conducted by many researchers who focused on either absolute radiometric values [3,4,5] or applicabilities of various sensors’ data [611].

Methods

Results

Conclusion