Wa-LiD: A new LiDAR simulator for waters

A simulator (Wa-LiD) was developed to simulate the reflection of LiDAR waveforms from water across visible wavelengths. The specific features of the simulator include (i) a geometrical representation of the water surface properties, (ii) the use of laws of radiative transfer in water adjusted for wavelength and the water’s physical properties, and (iii) modelling of detection noise and signal level due to solar radiation. A set of simulated waveforms was compared with observed LiDAR waveforms acquired by the HawkEye airborne and GLAS satellite systems in the near-infra red or green wavelengths and across inland or coastal waters. Signal-to-noise ratio (SNR) distributions for the water surface and bottom waveform peaks are compared with simulated and observed waveforms. For both systems (GLAS and HawkEye), Wa-LiD simulated SNR conform to the observed SNR distributions

Observation of a local gravity potential isosurface by airborne lidar of Lake Balaton, Hungary

Airborne lidar is a remote sensing method commonly used for mapping surface topography in high resolution. A water surface in hydrostatic equilibrium theoretically represents a gravity potential isosurface. Here we compare lidar-based ellipsoidal water surface height measurements all around the shore of a major lake with a local high-resolution quasi-geoid model. The ellipsoidal heights of the 87 km2 we sampled all around the shore of the 597 km2 lake surface vary by 0.8m and strong spatial correlation with the quasi-geoid undulation was calculated (R2 = 0.91). After subtraction of the local geoid undulation from the measured ellipsoidal water surface heights, their variation was considerably reduced. Based on a network of water gauge measurements, dynamic water surface heights were also successfully corrected for. This demonstrates that the water surface heights of the lake were truly determined by the local gravity potential.We conclude that both the level of hydrostatic equilibrium of the lake and the accuracy of airborne lidar were sufficient for identifying the spatial variations of gravity potential

Structures spatiales en hydrographie continentale

La compréhension de l'effet des hétérogénéités spatio-temporelles de surface sur la réponse hydrologique et l'adaptation par l'homme de ces hétérogénéités représentent un des enjeux majeurs de l'hydrologie tout en dépassant le cadre de la seule recherche académique. Les recherches en géographie quantitative que j'ai réalisées au cours de ces dix dernières années au sein de l'ENGREF puis d'AgroParisTech s'inscrivent dans cette problématique générale et se sont focalisées sur les hétérogénéités spatiales des supports de transfert des flux hydriques de surface concentrés : les réseaux hydrographiques au sens large (cours-d'eau, plans d'eau, ravines, etc). Les approches développées relèvent de modèles de représentation d'hétérogénéités spatiales sur réseau qui combinent des approches métrologiques (télédétection) et de modélisation spatiale stochastique (géostatistique), pour combler les données manquantes et estimer les incertitudes de représentation. Ainsi, comme je tenterai de l'illustrer, ces modèles de représentation spatiale stochastiques contribuent soit à mesurer l'effet de la précision de la représentation des réseaux sur la réponse hydrologique (finalité méthodologique) soit à explorer numériquement l'effet des hétérogénéités spatiales de réseaux sur la réponse hydrologique ainsi que leurs lois d'échelle (finalités cognitives et prédictives). Parce que les capacités d’adaptation des hétérogénéités spatio-temporelles de ces réseaux dans les paysages cultivés sont un enjeu fort agro-environnemental, j'envisage pour mon projet d'étendre ces modèles à la dynamique des propriétés des réseaux hydrographiques des agrosystèmes

The relevance of GLAS/ICESat elevation data for the monitoring of river networks

Abstract: The Ice, Cloud and Land Elevation Satellite (ICESat) laser altimetry mission from 2003 to 2008 provided an important dataset for elevation measurements. The quality of GLAS/ICESat (Geoscience Laser Altimeter System) data was investigated for Lake Leman in Switzerland and France by comparing laser data to hydrological gauge water levels. The correction of GLAS/ICESat waveform saturation successfully improved the quality of water elevation data. First, the ICESat elevations and waveforms corresponding to water footprints across the transition from the land to water were analyzed. Water elevations (2 to 10 measurements) following the land-water transition are often of lesser quality. The computed accuracy for the ICESat elevation measurements is approximately 5 cm, excluding transitions footprints, and 15 cm, including these footprints. Second, the accuracy of ICESat elevation was studied using data acquired on French rivers with a width greater than the size of the ICESat footprint. The obtained root mean square error (RMSE) for ICESat elevations in regard to French rivers was 1.14 m (bias = 0.07 m; standard deviation = 1.15 m), which indicates that small rivers could not be monitored using ICESat with acceptable accuracy due to land-water transition sensor inertia

Performance evaluation of different satellite radar altimetry missions for monitoring inland water bodies

Inland water bodies, e.g. lakes and rivers, play vital roles in society and in nature. Moreover, these water bodies can be considered as integrators of environmental change to study climate effects and hydrological cycle at global and regional scales. Because changes in the water level of lakes and rivers indicate changes in climatic parameters, such as precipitation and evaporation, it is necessary to monitor water level variation of inland water bodies continuously to understand long term changes. Traditional methods, e.g. using in-situ gauges, provide precise water level determination. But they can not monitor these water bodies in a way that today’s human needs are to be satisfied, because in-situ gauge networks do not cover all inland water bodies and their data are not publicly available. Furthermore, they are expensive to install and to maintain, especially in remote areas. In-situ gauge networks follow national policy and there is not a unified data base of their measurements. Satellite altimetry as a space-borne technology helps us to partially solve the issue of water level monitoring. This technique was originally designed to observe ocean water surface. But due to advances in satellite radar systems and in data processing methodologies, the application of satellite altimetry has been extended to monitor small lakes and narrow rivers over the past 20 years. So far, studying water level variations of inland water bodies has been a challenge for satellite altimeters in terms of spatial and temporal resolution as well as accuracy of water level determination. Due to a relatively large radar footprint, the illuminated area inside the footprint can be inhomogeneous, i.e. consisting of water, land and vegetation. Therefore, responses to the radar pulses from such a surface are complex and lead to multi-peak waveforms (corrupted waveforms). Seriously corrupted waveforms need to be analyzed to extract optimal ranges. Retracking is an effective method to improve the accuracy of the range measurement from contaminated waveforms and, consequently, to determine a more accurate water level. The design of an optimal retracking algorithm appropriate for a specific inland water body is very important in this respect. The quality of retracked water level depends on the type of altimeters and on the algorithm that is used in the retracking process. Moreover, the shape and size of the inland water bodies can affect the quality of the water level determination. In this thesis, we analyzed the waveforms in two different ways: full-waveform and sub-waveform retracking. For this purpose, different physical and empirical retracking algorithms have been employed to retrack the waveforms. In full-waveform retracking, for a given waveform one retracked range correction is estimated. But in sub-waveform retracking more than one retracked range correction can be calculated. We analyze all sub-waveforms in a given waveform and select the optimal one to retrack and consequently to determine water level variations. Three different analyses have been performed to select the optimal sub-waveform. In the first analysis we retracked only the first sub-waveform for all of the waveforms. In the second analysis all detected sub-waveforms in a given waveform are retracked to calculate the mean retracked range correction. In the last analysis we retrack the sub-waveform that provides the water level with minimum RMS with respect to model fits. For a given satellite, first we determine the water level according to on-board retrackers. The results of the on-board retrackers have been validated against available in-situ gauge data to find the best on-board retracker. Then, the full and sub-waveforms have been processed by different retracking algorithms to define the retracked water level. The retracked water level derived from different retracking scenarios have been compared with in-situ gauge data to evaluate the accuracy of each scenario. Finally, the results of the best on-board retracker were compared with the results from post-processing the waveforms to find the most accurate water level estimator. Radar characteristics and geometry of the satellite orbit, that affect on the altimeter’s performance, are designed based on main objectives of a given mission. Monitoring inland water bodies have not been the main objectives for the altimetry missions till now. We therefore do our analysis over data from different altimeters and evaluate their performance in water level monitoring of different inland water bodies. To complete our analysis, a comparison between different satellite altimeters has been performed to assess the performance of each altimeter in continental water level determination. We selected challenging objects with different shapes and sizes in different continents. For a given object, two or three satellite altimetry data sets have been analyzed to study water level variations. We used different satellite altimetry missions in our study, divided into pulse-limited and beam-limited altimeters. For the pulse-limited altimeters we selected Envisat, Jason-2, SARAL and CryoSat-2 LRM and for the beam-limited ones we used CryoSat-2 SAR and SARI n modes and I CES at satellite altimeters. GDR and SGDR data of these altimeters have been analyzed over four lakes: Neagh (Northern Ireland), Nasser (Egypt), Urmia (Iran) and Qinghai (China). We also analyzed the same data type of Envisat, Jason-2 and SARAL missions over different sections of the Danube river. We have found that over inland water bodies it is necessary to retrack the waveforms to achieve a qualified water level determination. Comparing the results from the on-board retrackers with those of the post-processed waveforms indicates that there tracked water level is more accurate. Our numerical results of the waveform retracking show that the sub-waveform outperforms the full-waveform especially over small lakes and complex shape (even large) lakes as well as over narrow rivers, e.g. Danube river. Over lakes Neagh and Nasser the beam-limited altimeters show better performance than the pulse-limited altimeters. In the case of Urmia lake, we analyzed only pulse-limited altimeters. Envisat provides the water level more accurately than CryoSat-2 LRM . Over Qinghai lake, covered by beam- and pulse-limited altimeters, both Envisat and CryoSat-2 LRM have the same performance. They show better performance than I CES at. Over Danube river, Envisat and SARAL show the same performance which is better than that of Jason-2. If we compare the results of all retracking scenarios for all missions, we can conclude that the mean sub-waveform retracked with the threshold retracker is the best retracking scenario to monitor small and complex shape inland water bodies. The first sub-waveform retracked with this retracker is an alternative scenario for the inland water bodies

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date