Geoscience Laser Altimeter System Waveform Research Articles

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99%, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex.

Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

Piecewise Adaptive-Norm Trend Filtering Method for ICESat/GLAS Waveform Data Denoising

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) was able to capture the full waveform of backscattered laser pulse. However, the accuracy of the surface information extracted from the waveform was vulnerable to background noise. In this paper, a piecewise adaptive l q -norm trend filtering method is proposed for the GLAS full waveform denoising on the basis of trend filtering. To minimize the loss of useful signal while removing the noise, the proposed method adaptively assigns different norms to the smooth constraints according to the local signal energy. The filtered results can then be obtained by iteratively minimizing the hybrid-norm loss function. The proposed method is tested on both the simulated waveforms and real GLAS waveform data. In the simulated experiments, the quantitative evaluation is conducted with the filtered waveforms, as well as the results after waveform decomposition. For comparison, the most commonly used waveform filtering methods, i.e. Gaussian filtering, wavelet transform, Empirical model decomposition and l 1 trend filtering, are involved in the experiments. The results show that the proposed method outperforms the mainstream methods on waveform filtering, in terms of removing noise and preserving the shape and energy amplitude of the GLAS waveforms.

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).

Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass

The effect of terrain slope on large footprint lidar waveforms is still a great challenge to the accurate mapping of forest aboveground biomass (AGB), although previous studies have made significant progress for slopes lower than 15°. A new method was proposed to calculate slope-adaptive waveform metrics for forest AGB mapping over mountainous areas. First, a theoretical model was proposed to calculate lidar waveforms of bare ground with known terrain slopes. The forest waveform and its corresponding calculated bare ground waveform were then aligned by the signal ending points. Their energy quartiles were extracted separately by taking the signal ending points as references. The height differences between the corresponding energy quartiles were defined as slope-adaptive waveform metrics, which were then used as independent variables for the estimation of the forest AGB. The proposed method was examined with both simulated lidar waveforms of forest stands and real waveforms acquired by the Geoscience Laser Altimeter System (GLAS). Two typical footprint sizes (i.e., 25 m and 70 m in diameter) and 21 terrain slopes (i.e., from 0° to 40° at an interval of 2°) were used in the simulation of the lidar waveforms. The terrain slopes of the GLAS waveforms were between 5.54° and 34.08°. The results showed that the terrain stretching on the forest waveform could be compensated for by the bare ground waveform. Compared with the Gaussian decomposition and waveform parameter methods, the proposed method gave the best estimation of the forest AGB with R2 = 0.92 and RMSE = 15.95 Mg/ha using simulated waveforms with a footprint size of 70 m and R2 = 0.84 and RMSE = 34.99 Mg/ha using the GLAS waveforms. The results demonstrated that the proposed method had great potential for the estimation of forest AGB using lidar waveforms over mountainous areas.

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.

Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China

Forest aboveground biomass (AGB) assessments are essential for accurate understanding of carbon accounting under forest disturbance effects and climate change. We mapped AGB data (from 1986 to 2016) by combining the forest inventories and multisource remotely sensed data, including the Ice, Cloud, and Land Elevation Satellite data and Landsat dense time series imagery, and L-band Synthetic Aperture Radar (PALSAR) mosaic data in Guangdong, China. We used random forest (RF) and stochastic gradient boosting (SGB) algorithms to determine the optimal variables of statistical models for mapping and validation of the AGB purpose. Our results showed that the Geoscience Laser Altimeter System (GLAS)-based AGB correlated well (R2adj = 0.89, n = 277, p < 0.001, RMSE = 21.24t/ha) with those obtained using the field-based method that used an RF-based approach, although inevitably, there is a saturation problem. The combined remotely sensed optical and radar imagery and ancillary data sets for mapping AGB using the RF algorithm yielded a stronger (R2adj = 0.86, n = 558, p < 0.001, RMSE = 11.35t/ha) linear correlation with those produced using the GLAS waveform data than that produced using the SGB algorithm. The overall accuracy and Kappa coefficient of mapping forests based on the PALSAR-forest/non-forest Landsat-based phenology for AGB masking were approximately 92.1% and 0.83, respectively. Additionally, the total amount of AGB had increased from 1986 to 2016 by 55.9%. The same increasing trend was observed for total AGB in both mid-subtropical (from 42% to 62%) and south-subtropical (from 38% to 57%) evergreen broadleaved forests, whereas a decreasing trend was witnessed in the tropical forest, particularly after 2010. There was an upward trend of total AGB among the four economic zones of Guangdong; the mountainous area had the highest AGB value distribution, accounting for 58%–70%, followed by the Pearl River Delta region (20%–30%), the western coast of Guangdong (3%–9%), and the eastern coast of Guangdong (2%–7%). The resulting provincial continuous forest AGB maps will provide a better evaluation of carbon dynamic in southern China.

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

利用波形匹配实现卫星激光测高脚点精确定位的方法

The footprints of a satellite laser altimeter have an elevation accuracy of the decimeter order, which satisfies the elevation accuracy needs of ground control points (GCP) for mapping. However, the horizontal accuracy of footprints is only few tens of meters, and only the footprints illuminating on flat ground targets can be used as GCPs. In this paper, the waveform model was derived and used to develop a waveform simulator of laser altimeters. Compared with the current method of waveform matching, the new simulator considered more detailed effects arising from the device and target, e.g., time and spatial distribution of lasers, surface profile, and surface reflectivity. The airborne LiDAR point cloud and the Geoscience Laser Altimeter System(GLAS) data were involved to match the best-fit waveform by maximizing the correlation coefficients of the simulated waveform and GLAS waveform, and the precise location of every GLAS footprint could be acquired where the correlation coefficient was the maximum. The results show that, the mean of maximum correlation coefficients is more than 0.9 during the GLAS operating periods with normal received energies, and the horizontal accuracy of footprints is approximately 2 m after the waveform matching. The proposed method can be used to extract the laser GCPs on complex relief of the surface.

A New Wind Speed Retrieval Method for an Ocean Surface Using the Waveform Width of a Laser Altimeter

ABSTRACTThe current retrieval method of sea wind speeds using a space-borne laser device is based on the received energies, which are significantly influenced by the absorption and scatter effect in the atmosphere. A new method is derived to retrieve the wind speed by the path of waveform width—root mean square (RMS) surface height—wind speed, which differs from the classical path of received energy—mean sea slope—wind speed. First, by deriving a more rigorous expression of the waveform model of an ocean surface for a laser altimeter, the theoretical relationship between the surface height RMS and waveform width is established. Second, the surface height RMS is fitted to a new empirical formula that is related to the wind speed using the geoscience laser altimeter system (GLAS) waveform data and the National Centers for Environmental Prediction wind speeds. Then, the JONSWAP spectra with swell effect and the waveform simulator are used to generate sea surface profiles and echo waveforms to verify the new retrieval method. Finally, in a Pacific region by using GLAS waveform data, the retrieval results indicate that the bias was approximately 0.2 m/s, the RMSE was approximately 1.2 m/s, and the ratio of valid data exceeded 20%.

Forest canopy height estimation using satellite laser altimetry: a case study in the Western Ghats, India

Canopy height is a crucial metric required to quantify the aboveground plant biomass accurately. The study explores the data derived using Light Detection and Ranging (LiDAR) technology from GeoScience Laser Altimeter System (GLAS) aboard Ice, Cloud, and Land Elevation satellite (ICESat) to derive canopy height estimate equations in the tropical forests of the Western Ghats, India. The interpretation of LiDAR waveforms for the purpose of estimating canopy heights is not straightforward, especially over sloping terrain where vegetation and ground are found at comparable heights. Canopy height models are developed using GLAS waveform extent and terrain index, derived from ASTER digital elevation, to counter the effect of topographic relief effects in canopy height estimates over steep terrain. The model was applied to calculate tree heights for whole of the Western Ghats. Results showed that the model can estimate tree heights within the specified height range with an accuracy of more than 90% while using percent overestimation/underestimation method of validation. This shows the effectiveness of the model, especially over steep slopes, also revealing that the models were able to successfully account for the pulse broadening effect. The study highlights the development of a LiDAR-based canopy height model for tropical forest and its ability to yield better canopy height estimates especially over steep slopes.

Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model

The Geoscience Laser Altimeter System (GLAS) has the potential to accurately map global vegetation heights and fractional cover metrics using active laser pulse emission/reception. However, large uncertainties in the derivation of data products exist, since multiple physically plausible interpretations of the data are possible. In this study a method is described and evaluated to derive vegetation height and fractional cover from GLAS waveforms by inversion of the FLIGHT radiative transfer model. A lookup-table is constructed giving expected waveforms for a comprehensive set of canopy realisations, and is used to determine the most likely set of biophysical parameters describing the forest structure, consistent with any given GLAS waveform. The parameters retrieved are canopy height, leaf area index (LAI), fractional cover and ground slope. The range of possible parameters consistent with the waveform is used to give a per-retrieval uncertainty estimate for each retrieved parameter. The retrieved estimates were evaluated first using a simulated data set and then validated against airborne laser scanning (ALS) products for three forest sites coincident with GLAS overpasses. Results for height retrieval show mean absolute error (MAE) of 3.71m for a mixed temperate forest site within Forest of Dean (UK), 3.35m for the Southern Old Aspen Site, Saskatchewan, Canada, and 5.13m for a boreal coniferous site in Norunda, Sweden. Fractional cover showed MAE of 0.10 for Forest of Dean and 0.23 for Norunda. Coefficient of determination between ALS and GLAS estimates over the combined dataset gave R2 values of 0.71 for height and 0.48 for fractional cover, with biases of −3.4m and 0.02 respectively. Smallest errors were found where overpass dates for ALS data collection closely matched GLAS overpasses. Explicit instrument parameterisation means the method is readily adapted to future planned spaceborne LiDAR instruments such as GEDI.

ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70&amp;thinsp;m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. &amp;lt;i&amp;gt;MNF1&amp;lt;/i&amp;gt;, &amp;lt;i&amp;gt;MNF2&amp;lt;/i&amp;gt; and &amp;lt;i&amp;gt;MNF3&amp;lt;/i&amp;gt;), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; and RMSE of SVR-AGB models were 0.75 and 4.68&amp;thinsp;t&amp;thinsp;hm&amp;lt;sup&amp;gt;&amp;minus;2&amp;lt;/sup&amp;gt; for broadleaf forests, 0.73 and 5.39&amp;thinsp;t&amp;thinsp;hm&amp;lt;sup&amp;gt;&amp;minus;2&amp;lt;/sup&amp;gt; for coniferous forests and 0.71 and 6.15&amp;thinsp;t&amp;thinsp;hm&amp;lt;sup&amp;gt;&amp;minus;2&amp;lt;/sup&amp;gt; for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11&amp;thinsp;t&amp;thinsp;hm&amp;lt;sup&amp;gt;&amp;minus;2&amp;lt;/sup&amp;gt; of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

Abstract. Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70 m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. MNF1, MNF2 and MNF3), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R2 and RMSE of SVR-AGB models were 0.75 and 4.68 t hm−2 for broadleaf forests, 0.73 and 5.39 t hm−2 for coniferous forests and 0.71 and 6.15 t hm−2 for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11 t hm−2 of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data

Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The Geoscience Laser Altimeter System (GLAS) produced substantial scientific data for detecting the vegetation structure at the footprint level. This study combined GLAS data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass (AGB) in Xishuangbanna, Yunnan Province, China. The GLAS waveform characteristic parameters were extracted using the wavelet method. The ASTER DEM was used to compute the terrain index for reducing the topographic influence on the GLAS canopy height estimation. A neural network method was applied to assimilate the MODIS BRDF data with the canopy heights for estimating continuous forest heights. Forest leaf area indices (LAIs) were derived from Landsat TM imagery. A series of biomass estimation models were developed and validated using regression analyses between field-estimated biomass, canopy height, and LAI. The GLAS-derived canopy heights in Xishuangbanna correlated well with the field-estimated AGB (R2 = 0.61, RMSE = 52.79 Mg/ha). Combining the GLAS estimated canopy heights and LAI yielded a stronger correlation with the field-estimated AGB (R2 = 0.73, RMSE = 38.20 Mg/ha), which indicates that the accuracy of the estimated biomass in complex terrains can be improved significantly by integrating GLAS and optical remote sensing data.

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

Estimates of forest structure parameters from GLAS data and multi-angle imaging spectrometer data

Quantitative estimates of forest vertical and spatial distribution using remote sensing technology play an important role in better understanding forest ecosystem function, forest carbon storage and the global carbon cycle. Although most remote sensing systems can provide horizontal distribution of canopies, information concerning the vertical distribution of canopies cannot be detected. Fortunately, laser radars have become available, such as GLAS (Geoscience laser altimeter system). Because laser radar can penetrate foliage, it is superior to other remote sensing technologies for detecting vertical forest structure and has higher accuracy. GLAS waveform data were used in this study to retrieve average tree height and biomass in a GLAS footprint area in Heilongjiang Province. However, GLAS data are not spatially continuous. To fill the gaps, MISR (multi- angle imaging spectrometer) spectral radiance was chosen to predict the regional continuous tree height by developing a multivariate linear regression model. We compared tree height estimated by the regression model and GLAS data. The results confirmed that estimates of tree height and biomass based on GLAS data are considerably more accurate than estimates based on traditional methods. The accuracy is approximately 90%. MISR can be used to estimate tree height in continuous areas with a robust regression model. The R2, precision and root mean square error of the regression model were 0.8, 83% and 1m, respectively. This study provides an important reference for mapping forest vertical parameters.

Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.

Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and Random Forest Regressions

Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) or on Principal Component Analysis (PCA) of GLAS waveforms. Results show that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m). For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs) and the waveform extent (RMSE = 3.8 m). Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m). Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index). Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF) based on the PCA of waveform samples with waveform extent information is an interesting alternative for canopy height estimation as it does not require several metrics that are difficult to derive from GLAS waveforms in dense forests, such as those in French Guiana.

Quantitative assessment of the impact of typhoon disturbance on a Japanese forest using satellite laser altimetry

Spaceborne light detection and ranging (LiDAR) sensors can accurately observe forest parameters, such as canopy height and forest biomass. In this study, we investigated the potential of spaceborne LiDAR to quantify the effects of forest disturbances caused by typhoons on Hokkaido Island, Japan, after typhoon Songda in 2004. We developed a model to estimate canopy height from the Geoscience Laser Altimeter System (GLAS) of the Ice Cloud and land Elevation Satellite. GLAS waveforms are broadened by slopes (“pulse broadening”), so we used the lead10 and trail10 values (edge extents referring to 10% and 90% of the cumulative energy returned) to correct for this effect, because they were the parameters most strongly correlated with pulse broadening. We developed the estimation model by dividing GLAS data between gentle and steep slopes, using an empirically determined threshold, and obtained a root-mean-square error of 3.5m for canopy height. We then applied the model to GLAS data in the area damaged by typhoon Songda to estimate changes in canopy height caused by the typhoon. We used a wind damage map produced in a previous study to identify the damage severity. The model showed that canopy height decreased by an average of 2.7±1.8m (95% confidence interval) in heavily damaged areas. The canopy height decreased most in coniferous forests, and especially in Japanese larch forests, that they have very shallow roots. We also assessed the geographic factors that most strongly influenced wind damage risk by means of multivariate analysis. We found serious damage in areas with a gentle slope that sustained strong winds. Understory plants and topography type also affected the risk. Our results show that spaceborne LiDAR can be used to quantify the severity of wind damage caused by forest disturbances, thereby providing guidance for forest management.