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Forest aboveground biomass is a key environmental variable needed for constraining models of the global carbon cycle, monitoring stocks and fluxes of carbon in forests, and optimizing forest management toward climate mitigation. To date, limited satellite data have been available that are sensitive to Aboveground Biomass Density (AGBD), and the availability of new satellite lidar data streams from NASA's Global Ecosystem Dynamics Investigation (GEDI) [1] and Ice Cloud and Elevation Satellite (ICESat-2) [2] enable a new generation of AGBD estimates representative of 2018–2022 conditions. Here we explore the transferability of GEDI's AGBD estimation framework to ICESat-2. We compare distribution of Relative Height (RH) metrics from both products between 50 and 52° N, and find that ICESat-2's RH metrics are biased high compared to GEDI. We reprocess ICESat-2 RH metrics to make them more comparable to GEDI height metrics, and present a comparison of biomass estimates based on the original and new ICESat-2 RH metrics in boreal forests.

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched on September 15th, 2018 and this mission offers an extraordinary opportunity to contribute to an assessment of forest resources at multiple spatial scales. This study served to develop a methodology for utilizing ICESat-2 data over vegetated areas. The specific objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data, (2) examine the use of simulated PCL metrics for modelling aboveground biomass (AGB) along ICESat-2 profiles using a simulated ICESat-2 PCL vegetation product and reference AGB estimated from airborne lidar data, and (3) estimate forest canopy cover using simulated PCL canopy product data and airborne lidar-derived canopy cover. Using existing airborne lidar data for Sam Houston National Forest (SHNF) in Texas and known ICESat-2 track locations, PCL simulations were carried out. Three scenarios were analyzed in this study; 1) simulated data without the addition of noise, 2) processed simulated data for daytime, and 3) nighttime scenarios. Segments measuring 100 m along the proposed ICESat-2 tracks were used to extract simulated PCL metrics from each of the three data scenarios and spatially coincident, reference airborne lidar-estimated AGB and airborne lidar canopy cover estimates. Linear regression models were then developed with a subset of the simulated PCL segments to estimate AGB and canopy cover and their performance assessed using separate testing sets. AGB model testing with the simulated dataset without noise, nighttime and daytime scenarios resulted in R2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Predictive models for canopy cover (4.6 m) achieved R2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the simulated dataset without noise, nighttime and daytime scenarios respectively. Findings from this study suggest the potential of ICESat-2 for estimating AGB, given the photon detection rate and background noise anticipated by ICESat-2 under temperate forest settings, and especially in the data format provided by standard ICESat-2 data vegetation products.

After being launched in September 2018, measurements from the ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) will be available to the science community starting in the Spring of 2019. These data offer new possibilities for the mapping of global terrain and vegetation as well as monitoring of the Earth's carbon stocks. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 will utilize photon counting technology for the altimetry observations. Photon counting technology is relatively new to the vegetation mapping community thus requires development of new algorithm approaches for terrain surface and canopy height retrievals. Photon counting systems provide range measurements for individual photons given the instrument's detection sensitivity. The sensitivity enables higher laser repetition rates for improved spatial coverage but is susceptible to solar background noise making the separation of signal and noise challenging and the data volume large. The algorithm developed specifically for the extraction of terrain and canopy heights from the ATLAS point clouds produces the ATL08 geophysical data product. This paper provides a detailed description of the ATL08 methodology, presents the data format, discusses many of the critical parameters likely to be of interest to future ICESat-2 data users, and describes the predicated uncertainties for terrain and canopy heights using two simulated ATLAS data sets. The first critical function in the ATL08 algorithm needs to accurately retrieve the surface is to identify the signal photons apart from the noise photons. Using a series of iterative filters, the ground and top of canopy surfaces are then identified in the signal. Next, individual photons are classified as either noise, canopy, or ground photons based on their distance above (or below) the estimated ground and top of canopy surfaces. The ATL08 algorithm has been tested on several simulated ATLAS data sets, and the results from two different ecosystems are described. The terrain extraction results saw sub-meter RMSE for the Alaska Tundra/Taiga ecotone and

The upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission will offer prospects for mapping and monitoring biomass and carbon of terrestrial ecosystems over large areas using photon counting LiDAR data. In this paper, we aim to develop a methodology to derive terrain elevation and vegetation canopy height from test-bed sensor data and further pre-validate the capacity of the mission to meet its science objectives for the ecosystem community. We investigated a novel methodological framework with two essential steps for characterizing terrain and canopy height using Multiple Altimeter Beam Experimental LiDAR (MABEL) data and simulated ICESat-2 data with various vegetation conditions. Our algorithm first implements a multi-level noise filtering approach to minimize noise photons and subsequently classifies the remaining photons into ground and top of canopy using an overlapping moving window method and cubic spline interpolation. Results of noise filtering show that the design of the multi-level filtering process is effective to identify background noise and preserve signal photons in the raw data. Moreover, calibration results using MABEL and simulated ICESat-2 data share similar trends with the retrieved terrain being more accurate than the retrieved canopy height, and the nighttime results being better than corresponding daytime results. Compared to the results of simulated ICESat-2 data, MABEL data achieve lower accuracy for ground and canopy heights in terms of root mean square error (RMSE), which may partly result from the inconsistency between MABEL and reference data. Specifically, simulated ICESat-2 data using 115 various nighttime and daytime scenarios, yield average RMSE values of 1.83 m and 2.80 m for estimated ground elevation, and 2.70 m and 3.59 m for estimated canopy height. Additionally, the accuracy assessment of percentile heights of simulated ICESat-2 data further substantiates the robustness of the methodology from different perspectives. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available.

The most prevalent global surface models are derived from space-based technologies. The spatial and temporal coverage of radar and imaging systems have significant advantage over other sensors and platforms. However, these systems are often are challenged in certain environmental conditions to produce accurate elevations as compared to what might achieved with laser altimetry. Radar derived DEM (Digital Elevation Model) elevation accuracy is often less in vegetated regions, as the wavelengths associated with radar mapping missions do not fully penetrate the canopy. Elevation errors are also substantial over dynamic topography. Correction models using airborne lidar reference datasets can be effective for localized studies to improve the DEM but often the data is not readily available or seasonally/temporally irrelevant. This work presents an automated method for correcting digital terrain models derived from the Shuttle Radar Topographic Mission (SRTM) elevation products using NASA's newest Earth observing laser altimeter, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2). ICESat-2 elevations, in concert with Landsat 8 (global imagery), create a model based correction strategy for the SRTM derived elevations using geographically correlated canopy cover and surface slope information. The results are validated at the study site using high-resolution, high fidelity airborne lidar datasets as a reference surface. The application of the correction model on the radar measurements results in nearly 50% improvement in elevation accuracy for this region. Additionally this established proof of concept provides a starting point for further research in how this method, with ICESat-2 data, can be extended to other environmental regions, other radar or image derived elevation products and inform future techniques for similar application at the global scale.

A first look at data from NASA’s laser altimeter mission ICESat-2 reveals very high resolution 3-D profiles of ice on land and sea, forests, and shallow bodies of water.

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Since the introduction of space-based altimetry data into the science community, global products associated with elevation and vegetation metrics have been heavily utilized for a variety of ecological applications. Satellite remote sensing enables the collection of global or near-global, standardized data sets, which can be used in their original form or used as inputs along with other data sets in generating new products. Recent effort has focused on using available data to generate maps of tree heights at a global scale in the service of a better understanding of above ground biomass distribution and its effects on global carbon storage. However, global data sets, while validated at a global scale, often display local and regional variations in accuracy which must be quantified before applying those data sets to smaller scale studies. This work addresses the need for a better understanding of the quality of the Shuttle Radar Topography Mission SRTM 90 m digital elevation model and a global 1 km canopy height model in the dense tropical forests of Gabon by using a small-footprint airborne lidar survey and large-footprint, space-based waveform lidar data from teh National Air and Space Administration’s Ice, Cloud, and land Elevation Satellite ICESat for validation. As expected, the study found SRTM elevations to be heavily biased by vegetation in this biome, with elevations consistently located within the canopy volume. In addition, the global canopy height model consistently underestimates maximum canopy height at both local and regional scales.

NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in the fall of 2018 and is collecting vegetation canopy height and terrain measurements globally. In this paper we validate the terrain and canopy heights estimated from 11 months of ICESat-2 data using airborne lidar data collected in southern Finland. Overall, the terrain heights from the ATL08 data product agreed with the airborne lidar with vertical errors less than 75 cm (mean = −0.07 m; MAE = 0.53 m, RMSE = 0.73 m; n observations = 909,467). ATL08 terrain heights had positive bias (33 cm) when permanent snow cover was present compared to the airborne lidar. Canopy heights derived from ICESat-2 varied significantly by the beam selection (i.e. strong versus weak). Use of the weak beams increased the underestimation of canopy height by 1.06 m and increased RMSE% by 8.08%. The lowest canopy height errors were associated with strong beam/night/summer ATL08 acquisitions, which underestimated canopy height by 0.56 m (Bias% = 3.18; RMSE% of 13.75%), followed by strong beam/day/summer acquisitions (Bias% = 2.85%; RMSE% = 14.77%). For this study area with managed, coniferous-dominated boreal forests, we found that ATL08 canopy height errors were lowest for canopy cover ranging from 40 to 85% and within this range, we found that on average, ICESat-2 missed the top 11–13% of canopy heights. At low canopy cover (

NASA’s Ice, Cloud and Land Elevation (ICESat-2) satellite is planned for launch in 2018 with the goal of providing a global distribution of elevation measurements to support many Earth science applications. The primary science mission is focused on the Polar Regions and will provide data to help understand controlling mechanisms of polar ice sheet mass balance, and investigate ice-ocean-atmosphere exchanges of mass, energy and moisture as it relates to sea ice thickness. ICESat-2 will also allow for terrain and canopy height retrievals as it operates continually throughout its orbit. The satellite will utilize a laser altimeter that provides signal detection sensitivities on the photon-level. This instrumentation allows for lower power and weight requirements to support a high repetition rate, multiple-beam configuration for improved spatial coverage as compared to previous missions. In order to develop the geophysical data product algorithms in preparation for launch, simulated data sets have been produced based on the statistical representation of the expected system performance. These data allow for data product quality analysis over specific types of ecosystems. This is of particular interest for vegetated regions, where canopy cover characteristics will directly affect the ability to retrieve terrain heights. This paper will discuss the expected ICESat-2 land/vegetation data product quality a selected ecosystem.

We develop and validate an automated approach to determine canopy height, an important metric for global biomass assessments, from micro-pulse photon-counting lidar data collected over forested ecosystems. Such a lidar system is planned to be launched aboard the National Aeronautics and Space Administration’s follow-on Ice, Cloud and land Elevation Satellite mission (ICESat-2) in 2017. For algorithm development purposes in preparation for the mission, the ICESat-2 project team produced simulated ICESat-2 data sets from airborne observations of a commercial micro-pulse lidar instrument (developed by Sigma Space Corporation) over two forests in the eastern USA. The technique derived in this article is based on a multi-step mathematical and statistical signal extraction process which is applied to the simulated ICESat-2 data set. First, ground and canopy surfaces are approximately extracted using the statistical information derived from the histogram of elevations for accumulated photons in 100 footprints. S...

Light detection and ranging (LIDAR) technology offers the capability to rapidly capture high-resolution, 3-dimensional surface data with centimeter-level accuracy for a large variety of applications. Due to the foliage-penetrating properties of LIDAR systems, these geospatial data sets can detect ground surfaces beneath trees, enabling the production of highfidelity bare earth elevation models. Precise characterization of the ground surface allows for identification of terrain and non-terrain points within the point cloud, and facilitates further discernment between natural and man-made objects based solely on structural aspects and relative neighboring parameterizations. A framework is presented here for automated extraction of natural and man-made features that does not rely on coincident ortho-imagery or point RGB attributes. The TEXAS (Terrain EXtraction And Segmentation) algorithm is used first to generate a bare earth surface from a lidar survey, which is then used to classify points as terrain or non-terrain. Further classifications are assigned at the point level by leveraging local spatial information. Similarly classed points are then clustered together into regions to identify individual features. Descriptions of the spatial attributes of each region are generated, resulting in the identification of individual tree locations, forest extents, building footprints, and 3-dimensional building shapes, among others. Results of the fully-automated feature extraction algorithm are then compared to ground truth to assess completeness and accuracy of the methodology.

The rapid and accurate assessment of above ground biomass (AGB) of woody vegetation is a critical component of climate mitigation strategies, land management practices and process-based models of ecosystem function. This is especially true of semi-arid ecosystems, where the high variability in precipitation and disturbance regimes can have dramatic impacts on the global carbon budget by rapidly transitioning AGB between live and dead pools. Measuring regional AGB requires scaling ground-based measurements using remote sensing, an inherently challenging task in the sparsely-vegetated, spatially-heterogeneous landscapes characteristic of semi-arid regions. Here, we test the ability of canopy segmentation and statistic generation based on aerial LiDAR (light detection and ranging)-derived 3D point clouds to derive AGB in clumps of vegetation in a juniper savanna in central New Mexico. We show that single crown segmentation, often an error-prone and challenging task, is not required to produce accurate estimates of AGB. We leveraged the relationship between the volume of the segmented vegetation clumps and the equivalent stem diameter of the corresponding trees (R2 = 0.83, p < 0.001) to drive the allometry for J. monosperma on a per segment basis. Further, we showed that making use of the full 3D point cloud from LiDAR for the generation of canopy object statistics improved that relationship by including canopy segment point density as a covariate (R2 = 0.91). This work suggests the potential for LiDAR-derived estimates of AGB in spatially-heterogeneous and highly-clumped ecosystems.

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.

The Geoscience Laser Altimeter System (GLAS) is a waveform lidar system carried on board the Ice, Cloud, and Elevation Satellite (ICESat). This study tested the use of GLAS data, from the L3e and L3g campaigns, to estimate total canopy height. GLAS footprint locations were sampled for reference data. The GLAS-derived and field-derived canopy heights portrayed good correlation (R 2 = 0.8354). This study also examined two representative fire fuel models within forests in East-Central Mississippi. GLAS waveforms were compared with field data for fire fuel models 9 and 10 of the fire fuel models described by Anderson (1982). GLAS data intensities were extracted and averaged to create predictive variables. Two variables were applied in Logistic regression to predict the probability of belonging to either fuel model (overall accuracy = 0.6875).

The Ice, Cloud and Land Elevation Satellite (ICESat) with the Geoscience Laser Altimeter System (GLAS) data provides a new potential for extracting information on the vertical structure of vegetation worldwide. Analysis of the returned full-waveform energy over areas of low topography allows for the direct retrieval of canopy heights. The returned laser energy is a complex interaction between surface scattering and the outgoing laser properties. In addition, the return pulse is convolved with the outgoing system response. Laser ranges are determined by computing the laser travel time between the outgoing pulse and the return pulse. This research examines deconvolution and Gaussian decomposition as a methodology for locating ranging locations on the return pulse. Heights retrieved from ICESat/GLAS using both deconvolution and Gaussian decomposition were evaluated against heights derived from a small-footprint, full-waveform dataset. Early results indicate that ranges derived from deconvolution revealed ver...

Focused on the Okavango Delta, Botswana, this research investigates (a) whether ecosystem signals derived from remotely sensed imagery can be decomposed using a harmonic regression, (b) if the deviations from the decomposed signal are correlated with observed flooding and fire regimes, and (c) the impact of explicitly including agriculture, settlement areas, and land management systems on the derived signals.

A series of 98 satellite images was analysed to reconstruct the fire and flood history of a floodplain system in southern Africa (Okavango Delta, Botswana). The data was used to investigate interactions between fire and flooding, and to determine the relevance of rainfall and flood-events for fire occurrences on floodplains and on drylands. The aims of the study are (1) to analyse and compare the fire frequency on floodplains and on adjacent drylands, (2) to investigate the influence of rainfall and flooding on the fire occurrence and (3) to determine correlations between fire frequency and flood frequency. The analyses show higher fire frequencies on floodplains than on drylands because of higher biomass production and fuel loads. The fire occurrence on drylands shows a correlation with annual rainfall events, while the fire frequency on floodplains is in principle determined by the flood frequency. Between floodplain types, clear differences in the susceptibility to fire where shown by analysing flood frequency vs. fire frequency. Here, the highest potential to burn was found for floodplains that get flooded about every second year. By calculating mean fire return intervals, the potential to burn could be specified for the different floodplain types.

High-resolution (0.6m) commercial satellite images contain a wealth of information for mapping earthquake damage. Satellite images of the city of Bam, acquired on 30 September 2003 (pre-earthquake) and 03 January 2004 (post-earthquake), were obtained and used to distinguish damage patterns across the city. Comparisons between pre- and post-earthquake images clearly show structural damage and collapse. Using spectral (color) and textural information from the post-earthquake image, regions of damage were identified using a semi-automated computer-based algorithm. This analysis indicates that the damage within the city of Bam was concentrated in the eastern sections of the city. The extent of damage in some sections of the city reached 100%. The results from this study not only provide information regarding damage patterns for the city of Bam, but they also illustrate the potential for using satellite images to understand and document earthquake effects during future earthquakes.

The Earth Observing‐1 (EO‐1) satellite acquired a sequence of data in 2001 and 2002 that highlighted the annual flooding of the lower Okavango Delta. The data were collected as part of the calibration/validation programme for the Advanced Land Imager (ALI) sensor on the NASA EO‐1 satellite. The primary purpose of this study was to compare the capability of ALI to that of Landsat ETM+ for large‐scale mapping applications in the Okavango Delta. While the extent and inaccessibility of many areas of the Delta make application of remote sensing attractive, the availability of data with adequate spatial and spectral resolution has limited the characterization of the complex patterns of land cover and geomorphology in the Delta. Initial analysis of the ALI data via supervised classification clearly showed macro‐flood features, delineation of downstream channel flow areas, and lateral‐downstream inundation of the floodplain. These patterns and the proportions of flooding of the channel compared to that of the flo...

AIRSAR and TOPSAR data were acquired over the wetlands of Bolivar Peninsula along the Gulf coast of Texas for mapping land cover types and topographic features such as beach ridges, dunes, and relict storm features. Classification of land cover over this wetlands and uplands environment is difficult because of the similarity of spectral signatures of the vegetation types. In addition, because the distribution of vegetation communities in coastal marshes is strongly related to salinity, which in turn is largely dictated by frequency and duration of inundation, surface topography is critical to determination of the vegetation characteristics at any location. The potential advantages of multisensor classification, including, in particular, topographic information from a TOPSAR DEM are investigated. An approach which employs a class dependent feature selection procedure in conjunction with pairwise Bayesian classifiers is proposed and applied to the polarimetric and interferometric SAR data.

Bare earth extraction is an important component to LADAR data analysis in terms of terrain classification. The challenge in providing accurate digital models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. A successful approach provides a flexible methodology (adaptable for topography and/or environment) that is capable of integrating multiple ladar point cloud data attributes. A newly developed approach (TE-SiP) uses a 2nd and 3rd order spatial derivative for each point in the DEM to determine sets of contiguous regions of similar elevation. Specifically, the derivative of the central point represents the curvature of the terrain at that position. Contiguous sets of high (positive or negative) values define sharp edges such as building edges or cliffs. This method is independent of the slope, such that very steep, but continuous topography still have relatively low curvature values and are preserved in the terrain classification. Next, a recursive segmentation method identifies unique features of homogeneity on the surface separated by areas of high curvature. An iterative selection process is used to eliminate regions containing buildings or vegetation from the terrain surface. This technique was tested on a variety of existing LADAR surveys, each with varying levels of topographic complexity. The results shown here include developed and forested regions in the Dominican Republic.

Many of the recent small, low power ladar systems provide detection sensitivities on the photon(s) level for altimetry applications. These "photon-counting" instruments, many times, are the operational solution to high altitude or space based platforms where low signal strength and size limitations must be accommodated. Despite the many existing algorithms for lidar data product generation, there remains a void in techniques available for handling the increased noise level in the photon-counting measurements as the larger analog systems do not exhibit such low SNR. Solar background noise poses a significant challenge to accurately extract surface features from the data. Thus, filtering is required prior to implementation of other post-processing efforts. This paper presents several methodologies for noise filtering photoncounting data. Techniques include modified Canny Edge Detection, PDF-based signal extraction, and localized statistical analysis. The Canny Edge detection identifies features in a rasterized data product using a Gaussian filter and gradient calculation to extract signal photons. PDF-based analysis matches local probability density functions with the aggregate, thereby extracting probable signal points. The localized statistical method assigns thresholding values based on a weighted local mean of angular variances. These approaches have demonstrated the ability to remove noise and subsequently provide accurate surface (ground/canopy) determination. The results presented here are based on analysis of multiple data sets acquired with the high altitude NASA MABEL system and photon-counting data supplied by Sigma Space Inc. configured to simulate the NASA upcoming ICESat-2 mission instrument expected data product.

WOS: 000309496000007, This study aimed to estimate canopy fuel properties relevant for crown fire behavior using ICESat/GLAS satellite LiDAR data. GLAS estimates were compared to canopy fuel products generated from airborne LiDAR data, which had been previously validated against field data. The geolocation accuracy of the data was evaluated by comparing ground elevation on both datasets, showing an offset of 1 pixel (20 m). Canopy cover (CC) was estimated as the ratio of the canopy energy to the total energy of the waveform. Application of a canopy base height threshold (OH) to compute the canopy energy increased the accuracy of CC estimates (R-2=0.89; RMSE = 16.12%) and yielded a linear relationship with airborne LiDAR estimates. In addition, better agreement was obtained when the CC derived from airborne LiDAR data was estimated using the intensity of the returns. An empirical model, based on the CC and the leading edge (LE), was derived to estimate leaf area index (LAI) using stepwise regression providing good agreement with the reference data (R-2=0.9, RMSE=0.15). Canopy bulk density (CBD) was estimated using an approach based on the method developed by Sando and Wick (1972) to derive CBD from field measurements, and adapted to GLAS data. Thus, foliage biomass was distributed vertically throughout the canopy extent based on the distribution of canopy material and CBD was estimated as the maximum 3 m-deep running mean considering layers with a thickness of 15 cm, which is the vertical resolution of the GLAS data. This approach gave a coefficient of determination of 0.78 and an RMSE of 0.02 kg m(-3). (C) 2012 Elsevier Inc. All rights reserved., University of Alcala, This research was carried out during a visit of the first author to the Spatial Sciences Laboratory, Department of Ecosystem Science and Management, Texas A&M University, funded by the University of Alcala. Comments made by the anonymous reviewer are greatly appreciated.

Terrain classification, or bare earth extraction, is an important component to LADAR data analysis. The terrain classification approach presented in this effort utilizes an adaptive lower envelope follower (ALEF) with an adaptive gradient operation for accommodations of local topography and roughness. In order to create a more robust capability, the ALEF was modified to become a strictly data driven process that facilitates a quick production of the data product without the subjective component associated with user inputs. This automated technique was tested on existing LADAR surveys over Wyoming's Powder River Basin and the John Starr Memorial Forest in Mississippi, both locations with dynamic topographic features. The results indicate a useful approach in terms of operational time and accuracy of the final bare earth recovery with the advantage of being fully data driven.

Laser Radar, also referred to as lidar, has become widely available and is an established contributor to the military and intelligence community by providing precise elevation data using 3-dimensional measurements. The utilization of customized algorithms designed for lidar data exploitation provides the capability to determine corridors or gaps in areas of vegetation cover. These capabilities lend themselves as geospatial tools for mobility applications and tactical planning. This effort uses elevations derived from small-footprint (airborne) lidar surveys to create accurate surface models and corresponding canopy characterization maps. The canopy height models are based on elevation voxels above ground level and are used as input into a tree finding algorithm. Corridors under the canopy are then predicted using the obstruction identification technique and neighboring point characteristics. Path determination can also be performed using the obstruction maps and a modified A-star algorithm. A lidar survey over Camp Shelby, MS was chosen as the test case for the obstruction detection utilities as it provides fairly dense vegetation cover and interesting topographic features. The survey was completed using both a full-waveform lidar and a discrete return system which offers a coincident comparison of the obstruction methodology for differing data types. It is determined that the fullwaveform data provides a more complete and accurate assessment of the surface, the canopy and potential obstruction detection than the discrete return system.

In response to the 2010 Haiti earthquake, the ALIRT ladar system was tasked with collecting surveys to support disaster relief efforts. Standard methodologies to classify the ladar data as ground, vegetation, or man-made features failed to produce an accurate representation of the underlying terrain surface. The majority of these methods rely primarily on gradient- based operations that often perform well for areas with low topographic relief, but often fail in areas of high topographic relief or dense urban environments. An alternative approach based on a adaptive lower envelope follower (ALEF) with an adaptive gradient operation for accommodating local slope and roughness was investigated for recovering the ground surface from the ladar data. This technique was successful for classifying terrain in the urban and rural areas of Haiti over which the ALIRT data had been acquired.

Full-waveform laser altimetry has been used in the research community since the mid-1990s and this technology holds great potential for the science and defense communities. Laser waveforms are a digital recording of the entire temporal profile from the reflected laser energy. The shape of the returned laser waveform is a function of both laser and surface properties. Waveform metrics were extracted for each waveform and include peak amplitude, peak standard deviation, integrated canopy energy, integrated ground energy, total waveform energy, ratio between canopy and ground energy, rise time to the first peak, fall time of the last peak, and vegetation height. The utilization of such metrics provides a potential for discriminating and identifying discrete targets on a per-shot basis. Analysis of the entire reflected laser energy profile provides a detailed description of distributed targets/features along the laser line-of-sight. Waveform data collected over Camp Shelby, Mississippi reveal separation of conifer from broadleaf vegetation. Metrics such as integrated canopy energy and fall time were found to be higher in hardwood forest than pine forest. Other landscape features such as the presence of a burn are also detected with full-waveform data, which would otherwise be missed with discrete return elevation data. With new full-waveform systems entering the commercial sector, new possibilities emerge to utilize the lidar data to classify land cover as well as quantify surface parameters.

Innovative algorithm development for full-waveform lidar data processing extends this remote sensing technology's capabilities to even more complicated acquisition scenarios then previously determined, namely success of surveys over obscured areas. Waveform decomposition and the extraction of waveform metrics provide a straightforward approach to identifying vertical structure within each laser measurement. However, there are some limitations in this approach as faint returns within the waveform go undetected in the processing chain. These faint returns are the result of reduced energy levels due to obscurant scattering, attenuation and absorption. Lidar surveys over non-homogeneous wooded regions indicate that there are meaningful ground returns within dense tree coverage if extracted correctly from the data. One difficulty associated with detecting weaker returns is the presence of a hardware induced ring by the Avalanche Photo Diode (APD) detector in the returned waveform. By using a waveform stacking technique with adjacent waveforms in near geospatial proximity to the original, these faint returns can be augmented and detected during data processing without the inclusion of the false ring. In comparison to the traditional approach, the waveform stacking technique provides a 9% increase in faint signal extraction for the particular dataset. These faint signals are low level last returns that correspond to perceived ground reflections under canopy cover. The enhanced capability in the presence of foliage provides a decrease in operational effort associated with data density, dwell or targeting techniques and survey expense.

[1] ICESat/GLAS laser altimetry data have become increasingly utilized for vegetation mapping and canopy characterization. Waveform shapes are dependent upon complex relationships between several factors on the illuminated surface including topography, brightness, clouds, satellite pointing, laser energy, footprint size, shape and orientation, and vegetation height and position within the footprint. However, the understanding of these factors is presently unclear. We first examine the simple case introduced into the GLAS waveform by laser retro-reflectors placed at White Sands, New Mexico, as a proxy for vegetation height detection. We observed that the 1/e2 energy distribution was only an approximation and that strong reflectors contribute to the returned energy beyond the estimated parameters. The precise position of the corner cube reflector within each footprint coupled with laser pointing angle is an important factor on the estimated vegetation height. Next we examine the implications of vegetation structure and surface topography on the waveform shape and derived elevations, compared with data from an airborne lidar system at Freeman Ranch, Texas, which has been targeted by ICESat since 2005. Small-footprint waveforms were combined to synthesize the energy distribution within a GLAS footprint, and they compared well (>90% correlation) to the GLAS waveforms. The GLAS-estimated canopy heights compared well (μ = 0.21 m) to the airborne lidar estimates during leaf-on conditions. However, GLAS-estimated ground elevations are biased by ∼1 m compared to airborne lidar in vegetated regions. In this landscape, the GLAS-energy ratio (canopy-to-ground energy) was a good indicator (R2 = 0.74) of the amount of woody cover within the footprint.

Full-waveform digitizer hardware integration within discrete return LIDAR systems provides an enhanced capability to resolve vertical structure within the laser line of sight and potentially classify specific surfaces or objects. However, the subject of waveform signal processing as it applies to surface classification is fairly underdeveloped. This research includes the examination of LIDAR waveform pulse characteristics for known targets and vegetation types. Using these data, a number of signal processing techniques were investigated as precursors to classification engines without prior knowledge of surface slope, or obscuration density. Relevant waveform features were extracted using both Gaussian decomposition method and raw waveform features revealing surface classification distinctions. Preliminary results from this data set indicate that the total integrated waveform energy provides an efficient and rapid methodology for discrimination of vegetation from built surfaces. These results indicate that metrics derived from the full waveforms can be utilized to characterize and classify (to a limited degree) an environment without prior knowledge.

Small-footprint airborne lidar (Light Detection and Ranging) topographic mapping is widely used in the commercial and research sectors for applications including shoreline monitoring, forestry, flood zone delineation, and urban mapping. A new module developed by Optech, Inc. to digitally record the entire returned lidar pulse has been integrated into, and runs simultaneously with, a conventional small-footprint lidar system operated by the University of Texas (1). Small-footprint airborne waveform lidar data was flown over Freeman Ranch located in Central Texas on August 12, 2005. The objectives for the Freeman Ranch lidar project include 1) the comparison of waveform ranging to the standard discrete return Airborne Laser Terrain Mapping (ALTM) product and 2) the comparison of small-footprint waveform lidar to large-footprint waveform lidar from the Geoscience Laser Altimeter System (GLAS) onboard ICESat. The preliminary analysis of small-footprint waveforms indicates an overall improvement in canopy penetration and subsequent detection of underlying topography compared with the discrete ALTM last return. For the Freeman Ranch site, the small-footprint waveform data were synthesized to simulate a GLAS waveform using the same energy power distribution function as GLAS. Results indicate that synthesized waveforms were a good qualitative fit to the returned GLAS waveforms. However, preliminary analysis indicates an increase of error in the GLAS elevation with increasing amounts of woody cover within the footprint.

The characterization of landscape disturbance is critical for understanding ecosystem structure and function; disturbance both impacts and is impacted by spatial heterogeneity or patchiness via vegetation competition, resource availability, and even herbivory [1, 2]. The research presented here focuses on the distal portions of the Okavango Delta in northern Botswana, where both fire and flooding are prevalent on the landscape. An overarching goal of this research is determining how the interaction of these two disturbances influences the distribution of the plant types that, in turn, potentially alter ecological function. This paper examines the spatial configuration of these disturbances via remotely sensed imagery and assesses whether these patterns are discernible in a 2001 land cover classification. Annual flooding and fire histories from 1989 to 2002 (14-years) were extracted from a multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) time series consisting of 87 images with an average time step of approximately 2 months. These 14-year fire and flooding history maps were combined to map disturbance history (DH) clusters, defined based on the number of times a pixel burned and/or was flooded from 1989 to 2002. Similarly, disturbance return (DR) clusters, or the average return time for an event, were also defined and mapped for the 14-year cycle.

A new, computationally efficient classification methodology was developed and implemented to classify Light Detection and Ranging (LIDAR) data as ground, vegetation, and man-made features (Weed 2001). The new procedure consists of several components that create ground, vegetation, and building surfaces, which are then used to classify the first and last reflection of each laser pulse. Ground and non-ground data are classified by adapting the concept of a lower envelope follower used to recover information in an amplitude modulated (AM) signal to the problem of extracting the ground surface from the LIDAR signal. The detected ground points include bare surface pixels and locations where the laser was able to penetrate the vegetation canopy, but exclude buildings and vegetation. Buildings are then classified by detecting the extended low gradient regions on their roofs. The first return LIDAR data points are used to accurately detect building edges distorted by multi-path errors in the last return LIDAR data. The combined roof and edge surfaces are then employed to threshold the first and last return LIDAR height values and detect the LIDAR points reflecting from buildings. Once the building points are classified, the vegetation points are extracted from the remaining LIDAR points using a mask of the regions where there were significant differences in the first and last return of the laser pulse. The technique is robust for classifying LIDAR data acquired over a range of terrains with different vegetation cover and types and sizes of buildings. It requires minimal user intervention for parameter selection.

Located in northwestern Botswana and fed by the Okavango River originating in Angola's western highlands, the Okavango Delta is the world's largest inland delta. The floodwaters require approximately nine months to flow from the source to the bottom of the Delta due to the extremely low topographic relief. During the peak of flooding, the delta's area can expand to over 16,000 square kilometers, almost doubling the 9,000 square kilometers during the dry season. Investigation of the hydrologic cycle of the Okavango is relevant to studies ranging from fundamental research in climate change to exploration for groundwater to alleviate water shortages faced by local villages. Because the extent and inaccessibility of many areas of the Delta, the application of remote sensing technology is extremely attractive, if it can be shown to effectively map important landcover and geomorphological characteristics (or their surrogates). While results of previous investigations of the capability of multispectral sensors for characterizing drying gradients were promising, the increased dynamic range and additional bands of ALI data potentially provide greater capability for mapping flooding events and characterizing the ecological changes that are rapidly occurring in the Okavango Delta due to climate change and anthropogenic impacts. Further, Hyperion hyperspectral data should provide even greater discrimination of complex vegetation assemblages and improved characterization of seasonal changes in spectral response of vegetation to the annual flooding event. A series of eight near cloud-free EO-1 ALI and Hyperion acquisitions were obtained over Chief's Island (located in the center of the Delta) spanning the 2001 dry and flooding seasons.

Classification of hyperspectral data is challenging because of high dimensionality inputs coupled with possible high dimensional outputs and scarcity of labeled information. Previously, a multiclassifier system was formulated in a binary hierarchical framework to group classes for accurate, rapid discrimination. In order to improve performance for small sample sizes, a new approach was developed that utilizes a feature reduction scheme which adaptively adjusts to the amount of labeled data available, while exploiting the fact that certain adjacent hyperspectral bands are highly correlated. The resulting best-basis binary hierarchical classifier (BB-BHC) family is thus able to address the "small sample size" problem, as evidenced by experimental results obtained from analysis of AVIRIS and Hyperion data acquired over Kennedy Space Center.

Near concurrent airborne data were acquired over the wetlands of the Bolivar Peninsula on the Texas coast by the NASA/JPL AIRSAR (June 28, 1996) and NASA/Stennis Space Center Calibrated Airborne Multispectral Scanner (CAMS) (July 3, 1996), both at 4 m spatial resolution. Several approaches which utilize information from both sensors are investigated for classifying the landcover in these data sets. Differences in statistical characteristics of the data necessitate individual parametric models for observations from each sensor, so data are initially classified separately, then a final classification is obtained by combining results from the statistical models using different multisensor integration techniques. These integrated results are compared to single-sensor classification results, as well as to a multisensor classification based on artificial neural networks.

Topographic information can now be derived from a variety of approaches and instruments, including interferometric SAR, optical and radar stereo, and laser altimetry. Extraction of actual terrain in vegetated areas is a difficult problem for all sensors. The small footprint of the airborne laser terrain (ALTM) data coupled with the potential for acquiring multiple returns for each outgoing pulse now offers new potential for obtaining more accurate digital elevation models (DEMs) which are critical for many applications such as hydrology and shoreline mapping. An Optech 1210 airborne laser terrain mapping instrument was flown over the Kennedy Space Center (KSC) in summer 1999 for the purpose of generating a high resolution DEM of the space complex and the extended wetland environs. Features such as relict dune ridges and swales, impoundment dikes, and man-made features are all clearly visible in the DEM. However, the data exhibited inter-flightline biases and cross track artifacts. New processing techniques are under development to remove anomalies, geometrically correct the data, and extract the vegetation signature to produce the "bald Earth" topography.