Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa

\ua9 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

Open-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30 m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in data-sparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a ‘must-have’ for any flood model. A high-accuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation

Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

https://www.mdpi.com/2072-4292/12/21/348

Exploring the Relationship between Forest Canopy Height and Canopy Density from Spaceborne LiDAR Observations

Forest structure is a useful proxy for carbon stocks, ecosystem function and species diversity, but it is not well characterised globally. However, Earth observing sensors, operating in various modes, can provide information on different components of forests enabling improved understanding of their structure and variations thereof. The Ice, Cloud and Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS), providing LiDAR footprints from 2003 to 2009 with close to global coverage, can be used to capture elements of forest structure. Here, we evaluate a simple allometric model that relates global forest canopy height (RH100) and canopy density measurements to explain spatial patterns of forest structural properties. The GLA14 data product (version 34) was applied across subdivisions of the World Wildlife Federation ecoregions and their statistical properties were investigated. The allometric model was found to correspond to the ICESat GLAS metrics (median mean squared error, MSE: 0.028; inter-quartile range of MSE: 0.022–0.035). The relationship between canopy height and density was found to vary across biomes, realms and ecoregions, with denser forest regions displaying a greater increase in canopy density values with canopy height, compared to sparser or temperate forests. Furthermore, the single parameter of the allometric model corresponded with the maximum canopy density and maximum height values across the globe. The combination of the single parameter of the allometric model, maximum canopy density and maximum canopy height values have potential application in frameworks that target the retrieval of above-ground biomass and can inform on both species and niche diversity, highlighting areas for conservation, and potentially enabling the characterisation of biophysical drivers of forest structure

Towards quantifying the effects of resource extraction on land cover and topography through remote sensing analysis: Confronting issues of scale and data scarcity

This dissertation focuses on the mapping and monitoring of mineral mining activity using remotely sensed data. More specifically, it explores the challenges and issues associated with remote sensing-based analysis of land use land cover (LULC) and topographic changes in the landscape associated with artisanal and industrial-scale mining. It explores broad themes of image analysis, including evaluation of error in digital elevation models (DEMs), integration of multiple scales and data sources, quantification of change, and remote sensing classification in data-scarce environments. The dissertation comprises three case studies.;The first case study examines the LULC change associated with two scales of mining activity (industrial and artisanal) near Tortiya, Cote d\u27Ivoire. Industrial mining activity was successfully mapped in a regional LULC classification using Landsat multispectral imagery and support vector machines (SVMs). However, mapping artisanal mining required high-resolution imagery to discriminate the small, complex patterns of associated disturbance.;The second case study is an investigation of the potential for quantifying topographic change associated with mountain top removal mining and the associated valley-fill operations for a region in West Virginia, USA, using publicly available DEMs. A 1:24,000 topographic map data, the shuttle radar topography mission (SRTM) DEM, a state-wide photogrammetric DEM, and the Advanced Spaceborne Thermal Emission Radiometer (ASTER) Global DEM (GDEM) were compared to a lidar bare-earth reference DEM. The observed mean error in both the SRTM and GDEM was statistically different than zero and modeled a surface well above the reference DEM surface. Mean error in the other DEMs was lower, and not significantly different than zero. The magnitude of the root mean square error (RMSE) suggests that only topographic change associated with the largest topographic disturbances would be separable from background noise using global DEMS such as the SRTM. Nevertheless, regionally available DEMs from photogrammetric sources allow mapping of mining change and quantification of the total volume of earth removal.;Monitoring topographic change associated with mining is challenging in regions where publicly available DEMs are limited or not available. This challenge is particularly acute for artisanal mining, where the topographic disturbance, though locally important, is unlikely to be detected in global elevation data sets. Therefore, the third and final case study explored the potential for creating fine-spatial resolution bare-earth DEMs from digital surface models (DSMs) using high spatial resolution commercial satellite imagery and subsequent filtering of elevation artifacts using commercial lidar software and other spatial filtering techniques. Leaf-on and leaf-off DSMs were compared to highlight the effect of vegetation on derived bare-earth DEM accuracy. The raw leaf-off DSM was found to have very low error overall, with notably higher error in areas of evergreen vegetation. The raw leaf-on DSM was found to have a RMSE error much higher than the leaf-off data, and similar to that of the SRTM in dense deciduous forest. However, filtering using the commercial techniques developed for lidar notably reduced the error present in the raw DSMs, suggesting that such approaches could help overcome data scarcity in regions where regional or national elevation data sets are not available.;Collectively this research addressed data issues and methodological challenges in the analysis of 3D changes caused by resource extraction. Elevation and optical imagery are key data sets for mapping the disturbance associated with mining. The particular combination required regarding data spatial scale, and for elevation, accuracy, is a function of the type and scale of the mining

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Airborne and spaceborne remote sensing for assessment of forest structural attributes across tropical mosaic landscapes

High-resolution, accurate, and updated forest structure maps are urgently required for the implementation of REDD+, payment of ecosystem services, and other climate change mitigation strategies in tropical countries. The collection of forest inventory data is usually labor intensive and costly, and remote sites can be difficult to access. Remote sensing data, for example airborne laser scanning (ALS), hyperspectral imagery, and Landsat data, complement field-based forest inventories and provide high-resolution, accurate, and spatially explicit data for mapping forest structural attributes. However, issues such as the effect of topography, pulse density, and the single and combined use of various remote sensing data on forest structural attributes prediction warrant further research. The main objective of this thesis was to assess airborne and spaceborne remote sensing techniques for modeling forest structural attributes across a montane forest landscape in the Taita Hills, Kenya. The sub-objectives focused on a) the effect of the topographic normalization of Landsat images on fractional cover (Fcover) prediction, aboveground biomass (AGB), and forest structural heterogeneity modeling using ALS and other remote sensing data and b) the analysis of the maps of forest structural attributes. In Study I, the effect of topographic normalization on ALS-based Fcover modeling was evaluated using common vegetation indices and spectral-temporal metrics based on a Landsat time series (LTS). The results demonstrate that the fit of the Fcover models did not improve after topographic normalization in the case of ratio-based vegetation indices (Normalized Difference Vegetation Index, NDVI; reduced simple ratio, RSR) or tasseled cap (TC) greenness; however, the fit improved in the case of brightness and wetness, particularly in the period of the lowest sun elevation. However, if TC indices are preferred, then topographic normalization using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is recommended. In Study II, field-based AGB estimates are modeled by ALS data and a multiple linear regression. The plot-level AGB was modeled with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 52.9 Mg ha-1. Furthermore, the determinants for AGB spatial distribution are examined using geospatial data and statistical modeling. The AGB patterns are controlled mainly by mean annual precipitation (MAP), the distribution of croplands, and slope, which collectively explained 69.8% of the AGB variation. Study III investigated whether the fusion of ALS with LTS and hyperspectral data, or stratification of the plots to the forest and non-forest classes, improves AGB modeling. According to the results, the prediction model based on ALS data only provides accurate models even without stratification. However, using ALS and HS data together, and employing an additional forest classification for stratification, improves the model accuracy considerably in the studied landscape. Finally, in Study IV, the potential of single and combined ALS and LTS data in modeling forest structural heterogeneity (the Gini coefficient of tree size) was assessed, and the difference between three forest remnants and forest types is evaluated based on predicted maps. If the LTS metrics were included in the models, then ALS data with lower pulse density yield similar accuracy to more expensive, high pulse-density data. Furthermore, the GC map presents forest structural heterogeneity patterns at the landscape scale a

Characterization of Ground Deformation Associated with Shallow Groundwater Processes Using Satellite Radar Interferometry

Shallow groundwater processes maylead to ground deformation and even geohazards. With the features of day-and-night accessibility and large-scale coverage, time-series interferometric synthetic aperture radar (InSAR) has proven a useful tool for mapping the deformation over various landscapes at cm to mm level with weekly to monthly updates. However, it has limitations such as, decorrelation,atmospheric artifacts, topographic errors, andunwrapping errors, in particular for the hilly, vegetated, and complicated deformation patterns. In this dissertation, I focus on characterizing the ground deformation over landslides, aquifer systems, and mine tailings impoundment, using the designed advanced time-series InSAR strategy, as well as theinterdisciplinary knowledge of geodesy, hydrology, geophysics, and geology. Northwestern USA has been exposed to extreme landslide hazards due to steep terrain, high precipitation, and loose root support after wildfire. I characterize the rainfall-triggered movements of Crescent Lake landslide, Washington State. The seasonal deformation at the lobe, with larger magnitudes than the downslope riverbank, suggests an amplified hydrological loading effect due to a thicker unconsolidated zone. High-temporal-resolution InSAR and GPS data reveal dynamic landslide motions. Threshold rainfall intensities and durations wet seasons have been associated with observed movement upon shearing: antecedent rainfall triggered precursory slope-normal subsidence, and the consequent increase in pore pressure at the basal surface reduces friction and instigates downslope slip over the course of less than one month. In addition, a quasi-three-dimensional deformation field is created using multiple spaceborne InSAR observations constrained by the topographical slope, and is further used to invert for the complex geometry of landslide basal surface based on mass conservation. Aquifer skeletons deform in response to hydraulic head changes with various time scales of delay and sensitivity. I investigate the spatio-temporal correlation among deformation, hydrological records and earthquake records over Salt Lake Valley, Utah State. A clear long-term and seasonal correlation exists between surface uplift/subsidence and groundwater recharge/discharge, allowing me to quantify hydrogeological properties. Long-term uplift reflects the net pore pressure increase associated with prolonged water recharge, probably decades ago. The distributions of previously and newly mapped faults suggest that the faultsdisrupt the groundwater flow andpartition hydrological units. Mine tailings gradual settle as the pore pressure dissipates and the terrain subsides, andtailings embankment failures can be extremely hazardous. I investigate the dynamics of consolidation settlement over the tailings impoundment in the vicinity of Great Salt Lake, Utah State, as well as its associated impacts to the surrounding infrastructures. Largest subsidence has been observed around the low-permeable decant pond clay at the northeast corner.The geotechnical consolidation model reveals and predicts the long-term exponentially decaying settlement process. My studies have demonstrated that InSAR methods can advance our understanding about the potential anthropogenic impacts and natural hydrological modulations on various geodynamic settings in geodetic time scale