Abstract
This paper investigates the benefits of integrating multibaseline polarimetric interferometric SAR (PolInSAR) data with LiDAR measurements using a machine-learning approach in order to obtain improved forest canopy height estimates. Multiple interferometric baselines are required to ensure consistent height retrieval performance across a broad range of tree heights. Previous studies have proposed multibaseline merging strategies using metrics extracted from PolInSAR measurements. Here, we introduce the multibaseline merging using a support vector machine trained by sparse LiDAR samples. The novelty of this method lies in the new way of combining the two datasets. Its advantage is that it does not require a complete LiDAR coverage, but only sparse LiDAR samples distributed over the PolInSAR image. LiDAR samples are not used to obtain the best height among a set of height stacks, but rather to train the retrieval algorithm in selecting the best height using the variables derived through PolInSAR processing. This enables more accurate height estimation for a wider scene covered by the SAR with only partial LiDAR coverage. We test our approach on NASA AfriSAR data acquired over tropical forests by the L-band UAVSAR and the LVIS LiDAR instruments. The estimated height from this approach has a higher accuracy (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.81, RMSE = 7.1 m) than previously introduced multibaseline merging approach (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.67, RMSE = 9.2 m). This method is beneficial to future spaceborne missions, such as GEDI and BIOMASS, which will provide a wealth of near-contemporaneous LiDAR samples and PolInSAR measurements for mapping forest structure at global scale.
Highlights
Forest height is one of the most important forest biophysical parameters influencing light competition, stand productivity, carbon sequestration and biodiversity [1]
We first present the results of single baseline PolInSAR height estimation algorithm described in Section IIIA to individual Uninhabited Aerial Vehicle SAR (UAVSAR) interferometric pairs
This paper evaluated the capability of the PolInSAR height estimation using multi-baseline UAVSAR L- Band data over a heterogeneous tropical forest in Gabon
Summary
Forest height is one of the most important forest biophysical parameters influencing light competition, stand productivity, carbon sequestration and biodiversity [1]. Forest height serves as a proxy to key information on forest ecosystems such as aboveground biomass and biomass change. It can be used to constrain allometric models in order to estimate forest aboveground biomass [2]. LiDAR remote sensing, with airborne platforms, has been widely used for estimating forest canopy height from local to regional scales data availability is constrained by cost of acquisition. Persistent cloud coverage, over tropical areas, can limit LiDAR data availability. Spaceborne LiDAR missions like ICESAT-GLAS (2003-2007) and NASA’s forthcoming Global Ecosystem Dynamics Investigation (GEDI) mission aim to overcome these limitations by reducing the spatial density of samples, without producing directly an image, but rather a grid of the world’s land surface
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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