Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran

Pourrahmati, Manizheh Rajab; Baghdadi, Nicolas; Darvishsefat, A A; Namiranian, M; Fayad, Ibrahim; Bailly, Jean-Stéphane; Gond, Valéry

doi:10.1109/jstars.2015.2478478

Cited by 24 publications

(24 citation statements)

References 45 publications

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“…In addition, MLR models are easy to apply using GIS platforms to quickly produce maps, whereas implementation of RF models can be time-intensive for large geographical areas. The better performances of MLR than RF are also observed in other studies (e.g., [78,79]). …”

Section: Discussionsupporting

confidence: 82%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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Section: Discussionsupporting

confidence: 82%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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“…However, both terrestrial and airborne LiDAR data fail to accurately obtain the relevant information about forest ecosystems at large areas due to the limited spatial coverage and high acquisition costs [13][14][15]. In contrast, monitoring forest ecosystems at larger spatial scales can be best achieved by space laser altimetry [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

et al. 2018

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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.

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“…In recent years, some researchers have used wavelet transforms to filter GLAS data [32]. This study compared the denoising effect of the two filters and selected the more effective filtering method.…”

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confidence: 99%

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R 2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R 2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.

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Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran

Cited by 24 publications

References 45 publications

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

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