Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data

Wei Chen,Qihui Zheng,Haibing Xiang,Tetsuro Sakai,Xu Chen

doi:10.3390/rs13020174

Abstract

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

Highlights

The canopy height was retrieved from 73–75 image pair and 93–95 image pair based on a differential digital elevation model (DEM) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm, and three-stage RVoG algorithm, respectively
This study explored the forest canopy height estimation algorithm based on PolInSAR
Two PolInSAR image pairs were constructed based on four full-polarized ALOS/PALSAR images

Summary

Introduction

A forest is a terrestrial ecosystem with the most complex structure and abundant functions. It is one of the most abundant natural resource pools. With the global climate change and frequent forest fires, a large number of countries are actively carrying out carbon cycle studies while protecting and monitoring forest ecosystems [1,2]. As one of the major contributors to carbon sinks, are critical to climate change and ecological balance, and countries around the world attach great importance to forest monitoring and management [3,4].

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Conclusion