Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets

A study was conducted to determine the feasibility of obtaining estimates of vegetation canopy height from digital elevation data collected during the 2000 Shuttle Radar Topography Mission (SRTM). The SRTM sensor mapped 80% of the Earth's land mass with a C-band Interferometric Synthetic Aperture Radar (InSAR) instrument, producing the most complete digital surface map of Earth. Due to the relatively short wavelength (5.6 cm) of the SRTM instrument, the majority of incoming electromagnetic energy is reflected by scatterers located within the vegetation canopy at heights well above the “bald-Earth” surface. Interferometric SAR theory provides a basis for properly identifying and accounting for the dependence of this scattering phase center height on both instrument and target characteristics, including relative and absolute vertical error and vegetation structural attributes.An investigation to quantify the magnitude of the vertical error component was conducted using SRTM data from two vegetation-free areas in Iowa and North Dakota, revealing absolute errors of −4.0 and −1.1 m, respectively. It was also shown that the relative vertical error due to phase noise can be reduced significantly through sample averaging. The relative error range for the Iowa site was reduced from 13 to 4 m and for the North Dakota site from 7 to 3 m after averaging of 50 samples. Following error reduction, it was demonstrated that the SRTM elevation data can be successfully correlated via linear regression models with ground-measured canopy heights acquired during the general mission timeframe from test sites located in Georgia and California. Prior to outlier removal and phase noise reduction, initial adjusted r² values for the Georgia and California sites were 0.15 and 0.20, respectively. Following outlier analysis and averaging of at least 20 SRTM pixels per observation, adjusted r² values for the Georgia and California sites improved to 0.79 (rmse=1.1 m) and 0.75 (rmse=4.5 m), respectively. An independent validation of a novel bin-based modeling strategy designed for reducing phase noise in sample plot data confirmed both the robustness of the California model (adjusted r²=0.74) as well as the capacity of the binning strategy to produce stable models suitable for inversion (validated rmse=4.1 m). The results suggest that a minimum mapping unit of approximately 1.8 ha is appropriate for SRTM-based vegetation canopy height mapping.     

Comparative analysis of SRTM–NED vegetation canopy height to LIDAR‐derived vegetation canopy metrics

Vegetation canopy heights derived from the SRTM 30 m grid DEM minus USGS National Elevation Data (NED) DTM were compared to three vegetation metrics derived from a medium footprint LIDAR data (LVIS) for the US Sierra Nevada forest in California. Generally the SRTM minus NED was found to underestimate the vegetation canopy height. Comparing the SRTM–NED‐derived heights as a function of the canopy percentile height (shape/vertical structure) derived from LVIS, the SRTM SAR signal was found to penetrate, on average, into about 44% of the canopy and 85% after adjustment of the data. On the canopy type analysis, it was found that the SRTM phase scattering centres occurred at 60% for red fir, 53% for Sierra mixed conifer, 50% for ponderosa pine and 50% for montane hardwood‐conifer. Whereas analysing the residual errors of the SRTM–NED minus the LVIS‐derived canopy height as a function of LVIS canopy height and cover it was observed that the residuals generally increase with increasing canopy height and cover. Likewise, the behaviour of the RMSE as a function of canopy height and cover was observed to initially increase with canopy height and cover but saturates at 50 m canopy height and 60% canopy cover. On the other hand, the behaviour of the correlation coefficient as a function of canopy height and cover was found to be high at lower canopy height (<15 m) and cover (<20%) and decrease rapidly making a depression at medium canopy heights (>15 m and <50 m) and cover (>20% and <50%). It then increases with increasing canopy height and cover yielding a plateau at canopies higher than 50 m and cover above 70%.     

SRTM vs ASTER elevation products. Comparison for two regions in Crete,Greece

The Shuttle Radar Topography Mission (SRTM) collected elevation data over 80% of earth's land area during an 11‐day Space Shuttle mission. With a horizontal resolution of 3 arc sec, SRTM represents the best quality, freely available digital elevation models (DEMs) worldwide. Since the SRTM elevation data are unedited, they contain occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water‐covered surface. In contrast to the short duration of the SRTM mission, the ongoing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is continuously collecting elevation information with a horizontal resolution of 15 m. In this paper we compared DEM products created from SRTM data with respective products created from ASTER stereo‐pairs. The study areas were located in Crete, Greece. Absolute DEMs produced photogrammetricaly from ASTER using differentially corrected GPS measurements provided the benchmark to infer vertical and planimetric accuracy of the 3 arc sec finished SRTM product. Spatial filters were used to detect and remove the voids, as well as to interpolate the missing values in DEMs. Comparison between SRTM‐ and ASTER‐derived DEMs allowed a qualitative assessment of the horizontal and vertical component of the error, while statistical measures were used to estimate their vertical accuracy. Elevation difference between SRTM and ASTER products was evaluated using the root mean square error (RMSE), which was found to be less than 50 m.     

Validation of surface height from shuttle radar topography mission using shuttle laser altimeter

Spaceborne Interferometric SAR (InSAR) technology used in the Shuttle Radar Topography Mission (SRTM) and spaceborne lidar such as Shuttle Laser Altimeter-02 (SLA-02) are two promising technologies for providing global scale digital elevation models (DEMs). Each type of these systems has limitations that affect the accuracy or extent of coverage. These systems are complementary in developing DEM data. In this study, surface height measured independently by SRTM and SLA-02 was cross-validated. SLA data was first verified by field observations, and examinations of individual lidar waveforms. The geolocation accuracy of the SLA height data sets was examined by checking the correlation between the SLA surface height with SRTM height at 90 m resolution, while shifting the SLA ground track within its specified horizontal errors. It was found that the heights from the two instruments were highly correlated along the SLA ground track, and shifting the positions did not improve the correlation significantly. Absolute surface heights from SRTM and SLA referenced to the same horizontal and vertical datum (World Geodetic System (WGS) 84 Ellipsoid) were compared. The effects of forest cover and surface slope on the height difference were also examined. After removing the forest effect on SRTM height, the mean height difference with SLA-02 was near zero. It can be further inferred from the standard deviation of the height differences that the absolute accuracy of SRTM height at low vegetation area is better than the SRTM mission specifications (16 m). The SRTM height bias caused by forest cover needs to be further examined using future spaceborne lidar (e.g. GLAS) data.     

Evaluation of terrain and canopy height products in central African tropical forests

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.     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia

Mangrove forests are found within the intertropical zone and are one of the most biodiverse and productive wetlands on Earth. We focus on the Ciénaga Grande de Santa Marta (CGSM) in Colombia, the largest coastal lagoon–delta ecosystem in the Caribbean area with an extension of 1280 km², where one of the largest mangrove rehabilitation projects in Latin America is currently underway. Extensive man-made hydrological modifications in the region caused hypersaline soil (> 90 g kg^− 1) conditions since the 1960s triggering a large dieback of mangrove wetlands (~ 247 km²). In this paper, we describe a new systematic methodology to measure mangrove height and aboveground biomass by remote sensing. The method is based on SRTM (Shuttle Radar Topography Mission) elevation data, ICEsat/GLAS waveforms (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System) and field data. Since the locations of the ICEsat and field datasets do not coincide, they are used independently to calibrate SRTM elevation and produce a map of mangrove canopy height. We compared height estimation methods based on waveform centroids and the canopy height profile (CHP). Linear relationships between ICEsat height estimates and SRTM elevation were derived. We found the centroid of the canopy waveform contribution (CWC) to be the best height estimator. The field data was used to estimate a SRTM canopy height bias (− 1.3 m) and estimation error (rms = 1.9 m). The relationship was applied to the SRTM elevation data to produce a mangrove canopy height map. Finally, we used field data and published allometric equations to derive an empirical relationship between canopy height and biomass. This relationship was used to scale the mangrove height map and estimate aboveground biomass distribution for the entire CGSM. The mean mangrove canopy height in CGSM is 7.7 m and most of the biomass is concentrated in forests around 9 m in height. Our biomass maps will enable estimation of regeneration rates of mangrove forests under hydrological rehabilitation at large spatial scales over the next decades. They will also be used to assess how highly disturbed mangrove forests respond to increasing sea level rise under current global climate change scenarios.     

Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM

The accurate quantification of the three-dimensional (3-D) structure of mangrove forests is of great importance, particularly in Africa where deforestation rates are high and the lack of background data is a major problem. The objectives of this study are to estimate (1) the total area, (2) canopy height distributions, and (3) above-ground biomass (AGB) of mangrove forests in Africa. To derive the 3-D structure and biomass maps of mangroves, we used a combination of mangrove maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+), lidar canopy height estimates from ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), and elevation data from SRTM (Shuttle Radar Topography Mission) for the African continent. The lidar measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the interferometric synthetic aperture radar (InSAR) data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate AGB from the canopy height product. The total mangrove area of Africa was estimated to be 25,960 km² with 83% accuracy. The largest mangrove areas and the greatest total biomass were found in Nigeria covering 8573 km² with 132 × 10⁶ Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error includes the impact of using sensors with different resolutions and geolocation error. This study provides the first systematic estimates of mangrove area, height, and biomass in Africa.     

Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.     

The impact of misregistration on SRTM and DEM image differences

Image differences between Shuttle Radar Topography Mission (SRTM) data and other Digital Elevation Models (DEMs) are often performed for either accuracy assessment or for estimating vegetation height across the landscape. It has been widely assumed that the effect of sub-pixel misregistration between the two models on resultant image differences is negligible, yet this has not previously been tested in detail. The aim of this study was to determine the impact that various levels of misregistration have on image differences between SRTM and DEMs. First, very accurate image co-registration was performed at two study sites between higher resolution DEMs and SRTM data, and then image differences (SRTM–DEM) were performed after various levels of misregistration were systematically introduced into the SRTM data. It was found that: (1) misregistration caused an erroneous and dominant correlation between elevation difference and aspect across the landscape; (2) the direction of the misregistration defined the direction of this erroneous and systematic elevation difference; (3) for sub-pixel misregistration the error due solely to misregistration was greater than, or equal to the true difference between the two models for substantial proportions of the landscape (e.g., greater than 33% of the area for a half-pixel misregistration); and (4) the strength of the erroneous relationship with aspect was enhanced by steeper terrain. Spatial comparisons of DEMs were found to be sensitive to even sub-pixel misregistration between the two models, which resulted in a strong erroneous correlation with aspect. This misregistration induced correlation with aspect is not likely specific to SRTM data only; we expect it to be a generic relationship present in any DEM image difference analysis.     

Vertical accuracy assessment of global digital elevation models and validation of gravity database heights in Niger

In this study, we assessed the vertical accuracy of ASTER GDEM (Advanced Space-borne Thermal Emission and Reflection Radiometer – Global Digital Elevation Model) version 2, AW3D30 (ALOS World 3D – 30m) and the 1 and 3 arc-seconds versions of SRTM (Shuttle Radar Topography Mission) in Niger Republic. We explored the GDEMs to evaluate large void and erroneous pixel areas. GDEMs were then compared to three kinds of ground control data located on several sites and all merged data after vertical datum matching. We also analysed the vertical accuracy by land cover and compared GDEMs to each other. We finally validated the gravity database heights by using the relatively most accurate GDEM. All GDEMs still contain void pixels except for SRTM3 CGIAR, it was then retained for the assessment with 1 arc-second GDEMs. The vertical accuracies in terms of RMS (Root Mean Square) and in m are: ASTER (6.2, 8.0, 9.8 and 9.2), AW3D30 (2.2, 2.1, 1.8 and 1.6), SRTM1 (3.8, 4.3, 2.5 and 2.9) and SRTM3 (3.7, 4.1, 2.4 and 2.7) compared to levelling data, local DEM of Imouraren, GPS (Global Positioning System) data and all merged data. Absolute height differences are less than 10 m at 74.00%, 99.99%, 99.91% and 99.98% for ASTER, AW3D30, SRTM1 and SRTM3, respectively. AW3D30 is the most accurate and ASTER is the least accurate. For all GDEMs, different accuracies were found depending on land cover classes that could be caused by the random spatial distribution of validation data. Small differences were observed between SRTM and AW3D30 and large values between the two models and ASTER similarly. The gravity database was validated using AW3D30, large values of height differences were found in the northern part in agreement with the database specifications and in the southern part indicating erroneous elevations.     

Evaluation of ASTER GDEM using GPS benchmarks and SRTM in China

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) has generated one of the most complete high-resolution digital topographic data sets of the world to date. The ASTER GDEM covers land surfaces between 83° N and 83° S at a spatial resolution of 1 arc-second (approximately 30 m at the equator). As an improvement over Shuttle Radar Topography Mission (SRTM) coverage, the ASTER GDEM will be a very useful product for many applications, such as relief analysis, hydrological studies, and radar interferometry. In this article, its absolute vertical accuracy in China was assessed at five study sites using ground control points (GCPs) from high-accuracy GPS benchmarks and also using a DEM-to-DEM comparison with the Consultative Group on International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) SRTM DEM Version 4.1. It is demonstrated that the vertical accuracy of ASTER GDEM is 26 m (root mean square error (RMSE)) against GPS-GCPs, while for the SRTM DEM it is 23 m. Furthermore, height differences in the GDEM-SRTM comparison appear to be overestimated in the areas with a south or southwest aspect in the five study areas. To a certain extent, the error can be attributed to variations in heights due to land-cover effects and undefined inland waterbodies. But the ASTER GDEM needs further error-mitigating improvements to meet the expected accuracy specification. However, as for its unprecedented detail, it is believed that the ASTER GDEM offers a major alternative in accessibility to high-quality elevation data.     

SRTM DEM and its application advances

The Shuttle Radar Topography Mission (SRTM) provides for the first time a near-global high-resolution digital elevation model (DEM) with great advantages of homogeneous quality and free availability. The last 10 years or so have seen rapid advances in the data processing and applications of SRTM DEM. From the perspective of SRTM, we present in this article a brief overview of the principles, datasets, void filling and accuracy of SRTM DEM first. Special emphasis is on its application advances in various research fields including, but not limited to, geology, geomorphology, water resources and hydrology, glaciology, evaluation of natural hazards and vegetation surveys. Problems that arose from the applications and the future research interests are also addressed. We hope this study will greatly facilitate the ease of use of SRTM DEM in extensive fields.     

Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry

Forest canopy height is a critical parameter in better quantifying the terrestrial carbon cycle. It can be used to estimate aboveground biomass and carbon pools stored in the vegetation, and predict timber yield for forest management. Polarimetric SAR interferometry (PolInSAR) uses polarimetric separation of scattering phase centers derived from interferometry to estimate canopy height. A limitation of PolInSAR is that it relies on sufficient scattering phase center separation at each pixel to be able to derive accurate forest canopy height estimates. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to a better height separation than relying on polarization combinations at one wavelength alone. Here we present a new method for canopy height mapping using dual-wavelength SAR interferometry (InSAR) at X- and L-band. The method is based on the scattering phase center separation at different wavelengths. It involves the generation of a smoothed interpolated terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data. The terrain model is then used to remove the terrain component from the single-pass X-band interferometric surface height to estimate forest canopy height. The ability of L-band to map terrain height under vegetation relies on sufficient spatial heterogeneity of the density of scattering elements that scatter L-band electromagnetic waves within each resolution cell. The method is demonstrated with airborne X-band VV polarized single-pass and L-band HH polarized repeat-pass SAR interferometry using data acquired by the E-SAR sensor over Monks Wood National Nature Reserve, UK. This is one of the first radar studies of a semi-natural deciduous woodland that exhibits considerable spatial heterogeneity of vegetation type and density. The canopy height model is validated using airborne imaging LIDAR data acquired by the Environment Agency. The rmse of the LIDAR canopy height estimates compared to theodolite data is 2.15 m (relative error 17.6%). The rmse of the dual-wavelength InSAR-derived canopy height model compared to LIDAR is 3.49 m (relative error 28.5%). From the canopy height maps carbon pools are estimated using allometric equations. The results are compared to a field survey of carbon pools and rmse values are presented. The dual-wavelength InSAR method could potentially be delivered from a spaceborne constellation similar to the TerraSAR system.     

Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data

The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) is the first spaceborne lidar instrument for continuous global observation of the Earth. GLAS records a vertical profile of the returned laser energy from its footprint. To help understand the application of the data for forest spatial structure studies in our regional projects, an evaluation of the GLAS data was conducted using NASA's Laser Vegetation Imaging Sensor (LVIS) data in an area near NASA's Goddard Space Flight Center in Greenbelt, Maryland, USA. The tree height indices from airborne large-footprint lidars such as LVIS have been successfully used for estimation of forest structural parameters in many previous studies and served as truth in this study.The location accuracy of the GLAS footprints was evaluated by matching the elevation profile from GLAS with the Shuttle Radar Topography Mission (SRTM) DEM. The results confirmed the location accuracy of the GLAS geolocation, and showed a high correlation between the height of the scattering phase center from SRTM and the top tree height from GLAS data. The comparisons between LVIS and GLAS data showed that the GLAS waveform is similar to the aggregation of the LVIS waveforms within the GLAS footprint, and the tree height indices derived from the GLAS and LVIS waveforms were highly correlated. The best correlations were found between the 75% waveform energy quartiles of LVIS and GLAS (r² = 0.82 for October 2003 GLAS data, and r² = 0.65 for June 2005 GLAS data). The correlations between the 50% waveform energy quartiles of LVIS and GLAS were also high (0.77 and 0.66 respectively). The comparisons of the top tree height and total length of waveform of the GLAS data acquired in fall of 2003 and early summer of 2005 showed a several meter bias. Because the GLAS footprints from these two orbits did not exactly overlap, several other factors may have caused this observed difference, including difference of forest structures, seasonal difference of canopy structures and errors in identifying the ground peak of waveforms.     

GEDI地面高程和森林冠层高度的精度评价与影响分析

精确地提取地面高程和植被冠层高度,对于地形地貌、生态学等方面的研究具有重要意义。2018年12月发射的新一代全球生态系统动力学调查雷达（GEDI）为地面高程和植被冠层高度大范围精确提取提供了前所未有的机会。研究旨在利用机载激光雷达数据验证GEDI提取的地面高程和冠层高度精度,并探讨地理定位误差、地形坡度、坡向、植被覆盖度、方位角、采集时间、光束类型和不同森林类型因素对其精度的影响。结果表明：通过校正GEDI数据地理定位误差,可以明显提高其提取的地面高程和冠层高度精度;影响冠层高度提取精度最主要的因素是植被覆盖度,其次是坡度;影响地面高程提取精度的主要因素为坡向、坡度。植被覆盖度大于25%时,数据精度更高;坡度为0°—5°的缓坡地区地面高程和冠层高度精度最高。该研究结果将为GEDI数据筛选与应用提供依据。     

SRTM DEM accuracy assessment over vegetated areas in Norway

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C‐band and X‐band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man‐made features in Norway. High‐resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man‐made gravel pits.     

Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data

Topographic correction is a crucial and challenging step in interpreting optical remote-sensing images of extremely complex terrain environments due to the lack of universally suitable correction algorithms and digital elevation models (DEMs) of adequate resolution and quality. The free availability of open source global DEMs provides an unprecedented opportunity to remove topographic effects associated with remote-sensing data in remote and rugged mountain terrains. This study evaluated the performances of seven topographic correction methods including C-correction (C), Cosine C-correction (CC), Minnaert correction (M), Sun–canopy–sensor (SCS) correction (S), SCS+C-correction (SC), Teillet regression correction (TR), and the Terrain illumination correction model (TI) based on multi-source DEMs (local topographic map, Shuttle Radar Topography Mission (SRTM) DEM filled-finished A/B and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data sets) and Landsat-8 Operational Land Imager (OLI) data using visual and statistical evaluation strategies. Overall, these investigated topographic correction methods removed topographic effects associated with Landsat-8 OLI data to varying degrees. However, the performances of these methods generally depend on the use of different DEMs and evaluation strategies. Among these correction methods, the SCS+C-correction performed best and was less sensitive to the use of different DEMs. The performances of topographic corrections based on free and open-access DEMs were generally better than or comparable to those based on local topographic maps. In particular, the topographic correction performance was greatly improved using the SRTM filled-finished B (FFB) data set with a resampling scheme based on the average value within a 3 × 3 pixel window. Nevertheless, further quantitative investigation is needed to determine the relative importance of DEMs and evaluation strategies used to select topographic correction methods.     

Waterline mapping in flooded vegetation from airborne SAR imagery

Multifrequency, polarimetric airborne synthetic aperture radar (SAR) survey of a salt marsh on the east coast of the UK is used to investigate the radar backscattering properties of emergent salt marsh vegetation. Two characteristics of flooded vegetation are observed: backscatter enhanced by approximately 1.2 dB at C-band, and 180° HH-VV phase differences at L-band. Both are indicative of a double bounce backscattering mechanism between the horizontal water surface and upright emergent vegetation. The mapping of inundated vegetation is demonstrated for both these signatures, using a statistical active contour model for the C-band enhanced backscatter, and median filtering and thresholding for the L-band HH-VV phase difference. The two techniques are validated against the waterline derived from tidal elevation measured at the time of overpass intersected with an intertidal DEM derived from airborne laser altimetry. The inclusion of flooded vegetation is found to reduce errors in waterline location by a factor of approximately 2, equivalent to a reduction in waterline location error from 120 to 70 m. The DEM is also used to derive SAR waterline heights, which are observed to underpredict the tidal elevation due to the effects of vegetation. The underprediction can be corrected for vegetation effects using canopy height maps derived from the laser altimetry. This third technique is found to improve the systematic error in waterline heights from 20 to 4 cm, but little improvement in random error is evident, chiefly due to significant noise in the vegetation height map.     

Quality analysis of SRTM and HYDRO1K: a case study of flood inundation in Mozambique

Many countries still lack national digital elevation models (DEMs) and have to rely on global datasets, which can negatively influence the reliability of flood model results. Mozambique is considered the most risk prone country for floods in Southern Africa. In this study a quality and accuracy assessment of two global DEMs (Shuttle Radar Topography Mission (SRTM) and HYDRO1K) is presented for a simple static flood inundation model of lower Limpopo Basin. This is accomplished with a local fit and vertical accuracy assessment of global datasets on a local scale as well as simulations of flood extent in the floodplain carried out by filling the DEMs with water according to the 2000 flood event. The results from the vertical accuracy assessment show that global DEMs can be used on a local scale. However, flood simulations performed on original DEMs contain inadequacies and are misleading with both under- and overestimation of the flooded area, while simulation performed on locally fitted DEMs shows a better agreement with the actual event. This study clearly shows that DEMs with questionable accuracy and resolution should be used with great caution in flood inundation modelling because they could result in deceptive model predictions, and lead to devastating after-effects in risk prone areas.