scholarly journals A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data

2019 ◽  
Vol 11 (2) ◽  
pp. 147 ◽  
Author(s):  
Peter Scarth ◽  
John Armston ◽  
Richard Lucas ◽  
Peter Bunting
Keyword(s):  
National Level ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Sensor Data ◽  
Vegetation Density ◽  
Structural Classification ◽  
Alos Palsar ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Vegetation Height

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.

Contribution of L-band SAR to systematic global mangrove monitoring

2014 ◽  
Vol 65 (7) ◽  
pp. 589 ◽  
Author(s):  
Richard Lucas ◽  
Lisa-Maria Rebelo ◽  
Lola Fatoyinbo ◽  
Ake Rosenqvist ◽  
Takuya Itoh ◽  
...  
Keyword(s):  
Policy Development ◽  
Sensor Data ◽  
Global Changes ◽  
Alos Palsar ◽  
Laser Altimeter ◽  
Mangrove Species ◽  
Shuttle Radar Topographic Mission ◽  
International Policies ◽  
The Status ◽  
L Band

Information on the status of and changes in mangroves is required for national and international policy development, implementation and evaluation. To support these requirements, a component of the Japan Aerospace Exploration Agency’s (JAXA) Kyoto and Carbon (K&C) initiative has been to design and develop capability for a Global Mangrove Watch (GMW) that routinely monitors and reports on local to global changes in the extent of mangroves, primarily on the basis of observations by Japanese L-band synthetic aperture radar (SAR). The GMW aims are as follows: (1) to map progression of change within or from existing (e.g. Landsat-derived) global baselines of the extent of mangroves by comparing advanced land-observing satellite 2 (ALOS-2) phased array L-band SAR 2 (PALSAR-2) data from 2014 with that acquired by the Japanese earth resources satellite (JERS-1) SAR (1992–1998) and ALOS PALSAR (2006–2011); (2) to quantify changes in the structure and associated losses and gains of carbon on the basis of canopy height and above-ground biomass (AGB) estimated from the shuttle radar topographic mission (SRTM; acquired 2000), the ice, cloud and land-elevation satellite (ICESAT) geoscience laser altimeter system (GLAS; 2003–2010) and L-band backscatter data; (3) to determine likely losses and gains of tree species diversity through reference to International Union for the Conservation of Nature (IUCN) global thematic layers on the distribution of mangrove species; and (4) to validate maps of changes in the extent of mangroves, primarily through comparison with dense time-series of Landsat sensor data and to use these same data to describe the causes and consequences of change. The paper outlines and justifies the techniques being implemented and the role that the GMW might play in supporting national and international policies that relate specifically to the long-term conservation of mangrove ecosystems and the services they provide to society.

scholarly journals Assessing Potential Safety Zone Suitability Using a New Online Mapping Tool

Fire ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Michael J. Campbell ◽  
Philip E. Dennison ◽  
Matthew P. Thompson ◽  
Bret W. Butler
Keyword(s):  
Burn Injury ◽  
Wildland Fire ◽  
Satellite Image ◽  
Safety Information ◽  
Separation Distance ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Lidar Data ◽  
Landsat 8 ◽  
Vegetation Height

Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation of SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use.

scholarly journals Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain

2020 ◽  
Vol 12 (13) ◽  
pp. 2084
Author(s):  
Yanqiu Xing ◽  
Jiapeng Huang ◽  
Armin Gruen ◽  
Lei Qin
Keyword(s):  
Mean Squared Error ◽  
Photon Counting ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Coefficient Of Determination ◽  
Laser Altimeter ◽  
Terrain Model ◽  
Vegetation Height ◽  
Counting Approach ◽  
Digital Terrain

As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R2 = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error.

scholarly journals Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

2021 ◽  
Vol 13 (5) ◽  
pp. 948
Author(s):  
Lei Cui ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Mei Sun ◽  
Yadong Dong ◽  
...  
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Alternative Methods ◽  
High Signal ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Forest Park ◽  
Clumping Index ◽  
The Impact

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.

scholarly journals Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1691 ◽  
Author(s):  
Xuebo Yang ◽  
Cheng Wang ◽  
Sheng Nie ◽  
Xiaohuan Xi ◽  
Zhenyue Hu ◽  
...  
Keyword(s):  
Surface Characteristics ◽  
Airborne Lidar ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Large Area ◽  
Waveform Data ◽  
Terrain Slope ◽  
Lidar Measurements ◽  
Slope Estimation ◽  
Study Sites

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

scholarly journals REFINED SIMULATION OF SATELLITE LASER ALTIMETER FULL ECHO WAVEFORM

2018 ◽  
Vol XLII-3 ◽  
pp. 1267-1273
Author(s):  
H. Men ◽  
Y. Xing ◽  
G. Li ◽  
X. Gao ◽  
Y. Zhao ◽  
...  
Keyword(s):  
Laser Energy ◽  
Vital Role ◽  
Data Simulation ◽  
Laser Altimeter ◽  
Validation Data ◽  
Simulation Accuracy ◽  
Waveform Data ◽  
Full Waveform ◽  
Ground Target ◽  
Echo Simulation

The return waveform of satellite laser altimeter plays vital role in the satellite parameters designation, data processing and application. In this paper, a method of refined full waveform simulation is proposed based on the reflectivity of the ground target, the true emission waveform and the Laser Profile Array (LPA). The ICESat/GLAS data is used as the validation data. Finally, we evaluated the simulation accuracy with the correlation coefficient. It was found that the accuracy of echo simulation could be significantly improved by considering the reflectivity of the ground target and the emission waveform. However, the laser intensity distribution recorded by the LPA has little effect on the echo simulation accuracy when compared with the distribution of the simulated laser energy. At last, we proposed a refinement idea by analyzing the experimental results, in the hope of providing references for the waveform data simulation and processing of GF-7 satellite in the future.

scholarly journals Component-level selection and qualification for the Global Ecosystem Dynamics Investigation (GEDI) laser altimeter transmitter

2018 ◽  
Author(s):  
Aleksey A. Vasilyev ◽  
Paul R. Stysley ◽  
D. Barry Coyle ◽  
Erich Frese ◽  
Furqan Chiragh ◽  
...  
Keyword(s):  
Ecosystem Dynamics ◽  
Component Level ◽  
Laser Altimeter

scholarly journals Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters

2020 ◽  
Vol 12 (23) ◽  
pp. 3948
Author(s):  
Markus Adam ◽  
Mikhail Urbazaev ◽  
Clémence Dubois ◽  
Christiane Schmullius
Keyword(s):  
Laser Scanning ◽  
Temperate Forests ◽  
Accuracy Assessment ◽  
Canopy Cover ◽  
Global Scale ◽  
Canopy Height ◽  
Ecosystem Dynamics ◽  
Vegetation Height ◽  
Height Estimates ◽  
Terrain Elevation

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

scholarly journals Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

2018 ◽  
Vol 10 (8) ◽  
pp. 1277 ◽  
Author(s):  
Mikhail Urbazaev ◽  
Felix Cremer ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Christiane Schmullius ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Test Data ◽  
Predictive Performance ◽  
Airborne Lidar ◽  
Height Estimation ◽  
Vegetation Height ◽  
Training Samples ◽  
Multi Temporal ◽  
L Band ◽  
And Training

Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

scholarly journals Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1832 ◽  
Author(s):  
Svetlana Saarela ◽  
Sören Holm ◽  
Sean Healey ◽  
Hans-Erik Andersen ◽  
Hans Petersson ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Aboveground Biomass ◽  
Field Data ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Alternative Methods ◽  
Optical Data ◽  
Strongly Correlated ◽  
Landsat Data ◽  
Model Based

Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.

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