Geoscience Laser Altimeter System Data Research Articles

New Metrics and the Combinations for Estimating Forest Biomass From GLAS Data

Geoscience laser altimeter system (GLAS) data have been widely used for forest aboveground biomass (AGB) estimation, but there is no consensus on the optimal metrics. To explore whether a few optimal GLAS metrics could generate accurate AGB estimates, we proposed five metrics and explored their combinations with ten existing ones. The importance of these metrics was measured according to their contributions to changes in the cross-validated mean-squared error. The two to eight most important metrics were then selected to develop AGB models, and their performances were evaluated using field AGB. The optimal combination of GLAS metrics was finally used for AGB estimation at GLAS footprints from 2004 to 2007 within a 2°×2° spatial extent in Tahe and Changbai Mountain, China. The results showed that four GLAS metrics, including our proposed metric CRH25 (25th percentile of canopy reflection heights) combined with Lead, quadratic mean canopy height, and H75, yield the best AGB estimates, with an R 2 of 0.61±0.15 and RMSE of 52.20±23.50 Mg/ha, and the inclusion of more GLAS metrics did not improve the results. The estimated forest AGB in Tahe was 89.03±19.16 Mg/ha and 103.07±23.42 Mg/ha in Changbai Mountain. In both regions, the annual average forest AGB estimates for 2005 were higher than the AGB estimates for 2004, 2006, and 2007. The results of this study suggested that a few waveform parameters could enable the accurate estimation of forest AGB. Moreover, this study indicated that GLAS data might be able to monitor forest AGB changes, which require further investigation.

Retrieval of Vertical Foliage Profile and Leaf Area Index Using Transmitted Energy Information Derived from ICESat GLAS Data

The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer–Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R2) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R2 = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.

Geometric Accuracy Improvement Method for High-Resolution Optical Satellite Remote Sensing Imagery Combining Multi-Temporal SAR Imagery and GLAS Data

With the widespread availability of satellite data, a single region can be described using multi-source and multi-temporal remote sensing data, such as high-resolution (HR) optical imagery, synthetic aperture radar (SAR) imagery, and space-borne laser altimetry data. These have become the main source of data for geopositioning. However, due to the limitation of the direct geometric accuracy of HR optical imagery and the effect of the small intersection angle of HR optical imagery in stereo pair orientation, the geometric accuracy of HR optical imagery cannot meet the requirements for geopositioning without ground control points (GCPs), especially in uninhabited areas, such as forests, plateaus, or deserts. Without satellite attitude error, SAR usually provides higher geometric accuracy than optical satellites. Space-borne laser altimetry technology can collect global laser footprints with high altitude accuracy. Therefore, this paper presents a geometric accuracy improvement method for HR optical satellite remote sensing imagery combining multi-temporal SAR Imagery and GLAS data without GCPs. Based on the imaging mechanism, the differences in the weight matrix determination of the HR optical imagery and SAR imagery were analyzed. The laser altimetry data with high altitude accuracy were selected and applied as height control point in combined geopositioning. To validate the combined geopositioning approach, GaoFen2 (GF2) optical imagery, GaoFen6 (GF6) optical imagery, GaoFen3 (GF3) SAR imagery, and the Geoscience Laser Altimeter System (GLAS) footprint were tested. The experimental results show that the proposed model can be effectively applied to combined geopositioning to improve the geometric accuracy of HR optical imagery. Moreover, we found that the distribution and weight matrix determination of SAR images and the distribution of GLAS footprints are the crucial factors influencing geometric accuracy. Combined geopositioning using multi-source remote sensing data can achieve a plane accuracy of 1.587 m and an altitude accuracy of 1.985 m, which is similar to the geometric accuracy of geopositioning of GF2 with GCPs.

Land cover classification method considering the contribution of waveform characteristic parameters and the pooling scale

In view of current land cover classification methods using the full-waveform characteristic parameters from spaceborne lasers, land cover categories are misclassified because the extracted features cannot accurately distinguish various types of features. We propose a land cover classification method that considers the contribution of waveform feature parameters and the pooling scale. First, the waveform after preprocessing and Gaussian decomposition is characteristic extracted and roughly classified, and the importance of each waveform feature parameter is evaluated and sorted. The best feature parameter set is determined by backward selection of the waveform feature parameters. Moreover, the original waveforms are pooled at different scales, and the correlations between ground objects after pooling are compared with the correlations between the original waveforms to ensure the stability of the correlation between various waveforms of ground objects and the peak information of waveforms while reducing the amount of data. Finally, random forest (RF) and support vector machine classifiers are selected to complete the land cover classification experiment based on the optimal waveform parameter set and the optimal pooling scale. We use ICESat (ice, cloud, and land elevation satellite) and Geoscience Laser Altimeter System data from the Beijing–Tianjin–Hebeii region for verification. The results show that, based on the RF classifier, the overall classification accuracy was 86.95%, and the use of spaceborne laser waveform characteristic parameters compared with the classification method greatly improved the accuracy, fully verifying the feasibility of the method in classifying land cover types. These findings can provide a technical basis for the application and promotion of GaoFen 7(GF-7) and other follow-up, satellite-based laser altimetry data in the fine-grained classification of land cover types.

Forest height estimation and change monitoring based on artificial neural network using Geoscience Laser Altimeter System and Landsat data

The continuous forest inventory is critical to forestry monitoring, Earth science applications, and global carbon cycle researching. Traditional field measurements of forest inventory are time-consuming. Targeting the problem, our study proposes an approach for monitoring the forest tree heights by combining geoscience laser altimeter system data with land surface reflectance data from Landsat TM based on artificial neural network (ANN) model in the Nanning research region. The land surface reflectance data are used as inputs of ANN model. The GLAS data are used to derive tree heights for training ANN model. Then the forest height spatial distribution and corresponding height change during 2003 to 2006 are investigated. The validation shows the high precision of modeling tree heights by, respectively, calculating determination coefficient ( R 2) and root-mean-square error (RMSE) of evaluations from 2003 to 2006 ( R 2 = 0.667, mean RMSE = 3.219 m). Finally, the change trends of forest tree heights in Nanning region are obtained by calculating the difference of tree heights from 2003 to 2006.

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R 2). A forest AGB map of the study area was generated using the optimal model.

Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI).

Estimation of Forest Canopy Height in Hilly Areas Using Lidar Waveform Data

Forest canopy height (FCH) is a key parameter in the estimation of forest biomass and productivity. However, areas with hilly or mountainous terrain present a genuine challenge to extract the vertical structural parameters by using the large footprint Lidar full waveform data. In this study, a mathematical method based on the inflection point of Lidar waveform is developed and applied to process geoscience laser altimeter system data. Furthermore, an improved model, the centroid-terrain index model (CTIM), is proposed to estimate FCH of different forest types in hilly areas. The accuracy of the CTIM model is evaluated by using different field measurement data collected from multiple forest districts in China. For conifer and broadleaf forests, the RMSE is 3.8 m in areas with slope angles larger than 5°. Compared to the ground-based Lidar data, the accuracy is satisfactory in hilly areas. The proposed approach makes a significant contribution toward improving the FCH estimation in hilly areas from large footprint full waveform data, and toward the forest ecosystem monitoring at the global scale.

Forest Measurements Using ICESat- Geoscience Laser Altimeter System -A Review

Forests play a vital role in carbon cycle and in maintaining climate balance. The measurement of three dimensional forest structural attributes has a profound impact on the forest ecosystem management. Active remote sensing systems provide the capability to quantify the three dimensional structural of forests by measuring both the vertical as well as horizontal structure of forests. Vertical forest measurements can be directly and accurately estimated by means of the space borne Light Detection and Ranging (LiDAR) and it has been often used to estimate the forest structural attributes. This paper provides an introductory review of application of the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice Cloud and Land Elevation Satellite (ICESat), for the three dimensional structural attribute measurements of forests. The review attempted to briefly explain the characteristics of ICESat/GLAS instruments, data processing and certain applications from the wide range of applications of GLAS data in forestry.

Retrieving leaf area index in discontinuous forest using ICESat/GLAS full-waveform data based on gap fraction model

Leaf area index (LAI) is an important vegetation structure parameter in terrestrial ecosystem modeling. Although the spaceborne Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud and land Elevation Satellite (ICESat) has been proved to have potential for deriving forest LAI, previous methods were only applicable to estimate the effective LAI. In this study, a physical method based on the gap fraction model was proposed to retrieve the LAI correcting the between-crown clumping in discontinuous forest using GLAS full-waveform data. Landsat TM imagery was utilized as auxiliary data for providing crown cover information within the footprint. Using the gap probability from GLAS data and the crown coverage fraction from Landsat imagery, the method corrects the between-crown clumping, which has been proved to contribute most to the total clumping effect, and accurately estimates the LAI in discontinuous forest (R2 = 0.83, RMSE = 0.39, n = 47). Additionally, the forest LAI underestimation caused by between-crown clumping was analyzed in practice and theory. Results show that the between-crown clumping has a nonnegligible influence on forest LAI estimation in cases that the forest area within the footprint is close to the nonforest area, and the average LAI of individual tree is high. This study may shed some light on the development of clumping effect and quantitative LAI inversion models.

A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada

A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest attribute models from field data to intersecting airborne LiDAR data, and then to GLAS footprints. GLAS data were sequentially filtered and submitted to the k-nearest neighbour (k-NN) imputation algorithm to yield regional estimates of stand height and crown closure at a 30 m resolution. Resulting outputs were assessed against independent airborne LiDAR data to evaluate regional estimates of stand height (mean difference = −1 m, RMSE = 5 m) and crown closure (mean difference = −5%, RMSE = 9%). Additional assessments were performed as a function of dominant vegetation type and ecoregion to further evaluate regional products. These attributes form the primary descriptive structure attributes that are typical of forest inventory mapping programs, and provide insight into how they can be derived in northern boreal regions where field information and physical access is often limited.

A Continuous Wavelet Transform Based Method for Ground Elevation Estimation Over Mountainous Vegetated Areas Using Satellite Laser Altimetry

Although a satellite laser altimetry Geoscience Laser Altimeter System (GLAS) can directly measure ground elevation, the measurement accuracy of ground elevation is generally limited over mountainous vegetated areas. Currently, most methods for ground elevation estimations that use GLAS data fail to obtain an accurate ground elevation in sloped areas. Therefore, this study aimed to propose a continuous wavelet transform (CWT) based method for better estimating ground elevation from GLAS data over mountainous vegetated areas. First, the CWT was applied to each GLAS waveform for peak detection. Second, all potential ground peaks were correctly identified using GLAS waveform parameters in conjunction with auxiliary digital elevation model data. Third, the true ground peak was determined from CWT results according to several strict rules, and then the ground elevation was calculated based on the position of the true ground peak. Finally, these ground elevation estimates were validated by the digital terrain model derived from airborne discrete-return LiDAR data in Genhe in the Neimeng province of China. Additionally, the CWT-based method was also compared with previous methods, which estimate ground elevation from GLAS data over mountainous vegetated areas based on a Gaussian decomposition. It was found that our CWT-based method can reduce the root-mean-square error of ground elevation estimates by up to 0.8 m. Overall, this study can provide an available solution for estimating ground elevation from large-footprint waveform LiDAR data over mountainous vegetated areas.

A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat-GLAS Footprints.

We present in this paper a polynomial fitting method applicable to segments of footprints measured by the Geoscience Laser Altimeter System (GLAS) to estimate glacier thickness change. Our modification makes the method applicable to complex topography, such as a large mountain glacier. After a full analysis of the planar fitting method to characterize errors of estimates due to complex topography, we developed an improved fitting method by adjusting a binary polynomial surface to local topography. The improved method and the planar fitting method were tested on the accumulation areas of the Naimona’nyi glacier and Yanong glacier on along-track facets with lengths of 1000 m, 1500 m, 2000 m, and 2500 m, respectively. The results show that the improved method gives more reliable estimates of changes in elevation than planar fitting. The improved method was also tested on Guliya glacier with a large and relatively flat area and the Chasku Muba glacier with very complex topography. The results in these test sites demonstrate that the improved method can give estimates of glacier thickness change on glaciers with a large area and a complex topography. Additionally, the improved method based on GLAS Data and Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) can give estimates of glacier thickness change from 2000 to 2008/2009, since it takes the 2000 SRTM-DEM as a reference, which is a longer period than 2004 to 2008/2009, when using the GLAS data only and the planar fitting method.

Vertical Accuracy Effect Verification for Satellite Imagery With Different GCPs

This letter expands the block adjustment method by, respectively, adopting high-precision global positioning system (GPS) points, geoscience laser altimeter system (GLAS) data, and Shuttle Radar Topography Mission (SRTM)-digital elevation model (DEM) as vertical control data. Block adjustment experiments were conducted with these control data, with 388 ZY-3 satellite stereo image pairs across over 18600 km2 of land in Hubei province of China utilized as experimental images and 180 GPS points as checkpoints. The experimental results obtained show that, with the adoption of 23 GPS points as control points, the horizontal root-mean-square error (RMSE) and vertical RMSE of the ZY-3 image improved from 10.97 to 5.72 m and from 7.12 to 1.65 m, respectively. Furthermore, with the adoption of 326 GLAS data points as vertical control points, the vertical RMSE of the ZY-3 images of the flat terrain areas and mountain terrain areas improved to 1.69 and 3.62 m, respectively. Through block adjustment constrained by SRTM-DEM, the vertical RMSE of the ZY-3 images was 1.44 m for flat terrain areas and 3.05 m for mountain terrain areas, respectively. The vertical accuracy of the satellite images was significantly enhanced, especially on the flat terrain area, when GLAS and STRM-DEM were used for vertical control block adjustment, which is similar to the vertical accuracy of block adjustments adopting GPS points as control.

POINTING ANGLE CALIBRATION OF ZY3-02 SATELLITE LASER ALTIMETER USING TERRAIN MATCHING

Abstract. After GLAS (Geo-science Laser Altimeter System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), satellite laser altimeter attracts more and more attention. ZY3-02 equipped with the Chinese first satellite laser altimeter has been successfully launched on 30th May, 2016. The geometric calibration is an important step for the laser data processing and application. The method to calculate the laser pointing angle error based on existed reference terrain data is proposed in this paper. The public version terrain data, such as 90m-SRTM and 30m-AW3D30, can be used to estimate the pointing angle of laser altimeter. The GLAS data with simulated pointing error and actual ZY3-02 laser altimetry data is experimented to validate the algorithm. The conclusion will be useful for the future domestic satellite laser altimeter.

Monitoring elevation change of glaciers on Geladandong Mountain using TanDEM-X SAR interferometry

Glaciers play an important role in the climate system. The elevation change of a glacier is an important parameter in studies of glacier dynamics. Only a few ground-based measurements of high mountain glaciers are available due to their remoteness, high elevation, and complex topography. The acquisition from the German TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) SAR imaging configuration provides a reliable data sources for studying the elevation change of glaciers. In this study, the bistatic TanDEM-X data that cover the Geladandong Mountain on the Tibetan Plateau were processed with SAR interferometry technique and the elevation changes of the mountain’s glaciers during 2000–2014 were obtained. The results indicated that although distinct positive and negative elevation changes were found for different glacier tongues, the mean elevation change was about -0.14±0.26 m a-1. Geoscience Laser Altimeter System (GLAS) data were obtained for comparison and verification. The investigation using GLAS data demonstrated the efficacy of the proposed method in determining glacier elevation change. Thus, the presented approach is appropriate for monitoring glacier elevation change and it constitutes a valuable tool for studies of glacier dynamics.

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

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 (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 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.

Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas

Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent’s AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km2. Second, the difference between the AGB from the Vieilledent’s AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent’s AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent’s AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R2 = 0.62) to 74.1 t/ha (R2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach).

Differentiating Tree and Shrub LAI in a Mixed Forest With ICESat/GLAS Spaceborne LiDAR

Leaf area index (LAI) is an important descriptor of many biological and physical processes of vegetation. However, the challenges associated with differentiating tree and shrub LAI (tsLAI) have hindered research in mixed forests. Being the first spaceborne LiDAR system, geoscience laser altimeter system (GLAS) has demonstrated its advantage in collecting extensive forest structure information. In this study, we aimed to estimate tsLAI in a mixed forest with GLAS. The refined Levenberg–Marquardt algorithm for Gaussian decomposition was implemented to decompose GLAS data into ground and multiple vegetation signals, within which it is hypothesized that each vegetation signal corresponds to a particular height layer. Subsequently, the height of each layer was extracted through the decomposed GLAS signals, and a height threshold method to distinguish trees from shrubs was developed. Then, a tsLAI-specific ratio defined as ground-to-total energy return of the GLAS signal was calculated, and tsLAI was predicted by a linear regression model established from field measurements and the ratio. Finally, a study site in Ejina, China, where the dominant species are Populus euphratica (tree) and Tamarix ramosissima (shrub) was used to calibrate and validate the methods. Compared with the field measurement LAI, GLAS-predicted LAI presented a high agreement in which R2 , RMSE, and %RMSE of trees were determined to be 0.797, 0.087, and 19.176, respectively. In contrast, R2 , RMSE, and %RMSE of shrubs were found to be 0.676, 0.081, and 21.825, respectively. Overall, our study provided a feasible and effective approach for estimating tsLAI with GLAS over a flat region.

Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150Mg/ha, and >300Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2=0.54, RMSE=48.3Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50Mg/ha and R2=0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.