Terrain Elevation Data Research Articles

Surface Elevation Change Monitoring Based on ICESat-2 Satellite Altimetry Data

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2), launched by NASA on September 18, 2018, offers higher precision in surface elevation monitoring. This study aims to investigate the surface elevation changes over different time periods in the Delong and Bulianta open-pit mines in Wulanmuren Township, Ordos City, using ICESat-2 laser data. The study utilizes the spatial distribution information of each photon from ATL03 (terrain elevation data) and the classification information of each photon from ATL08 (vegetation canopy height and surface elevation data) to extract surface photon elevation data for different periods in the same area. The precision of ICESat-2 satellite elevation data is validated using the on-board radar data from Henan Polytechnic University's Feima unmanned aerial vehicle D2000 in Jiaozuo City, Henan Province.Results indicate that in the No.1 area of the Delong open-pit mine, the surface subsided by approximately 16.3 meters between November 14, 2019, and November 10, 2021, due to soil removal operations. In the No.2 and No.3 areas of the Bulianta open-pit mine, the surface was uplifted by 3.1 meters and 13.6 meters, respectively, between May 16, 2019, and August 13, 2020, due to backfilling after mining. Additionally, the No.4 area was uplifted by about 10.5 meters between November 14, 2019, and November 10, 2021.Furthermore, the accuracy assessment yielded the following results: R2=0.956, RMSE=0.324, and MAE=0.105 meters, indicating that ICESat-2 data can be used as scientific data for inverting surface elevation changes.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Mapping of landslide susceptibility using analytical hierarchy process on the example of the right bank of the Kaniv Reservoir

The study of landslide-prone areas requires the analysis of a large amount of objective data, products of remote sensing data processing (spatial images, digital terrain elevation data and analytical data on calculated indices), analytical maps based on field measurements. All these data make it possible to objectively and more accurately characterize the studied territory. It is important, based on the results of the assessment, to get a conclusion about favorable, unfavorable and dangerous areas. The result is a landslide susceptibility map. In this study, we carried out the procedure of creating a landslide susceptibility map at the regional level for the Rzhyshchiv united territorial community of the Kyiv region. Nine factors that influence the development of landslides or become indicators of landslide processes are selected. A description of the algorithm for creating a landslide susceptibility map using the method of hierarchical analysis is provided. The obtained result gives an idea of the different propensity of the areas of the studied territory to the development of landslide processes, it means the areas of the greatest threats, which contributes to the rational adoption of management decisions.

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

AVALIAÇÃO COMPARATIVA DA QUALIDADE DOS DADOS DE ELEVAÇÃO OBTIDOS COM GNSS, GOOGLE EARTH E SRTM PARA PROJETOS DE ESTRADAS

Obtaining terrain elevation data is essential for numerous engineering applications. However, regardless of the technique used to obtain the elevation data, there will be errors intrinsic to the instruments and to the measurement technique used, as well as depending on the model of Earth representation, which are plane, spherical or ellipsoidal. In this perspective, this work proposes a comparative approach between the elevation data obtained from the geodesic coordinates, provided by Global Navigation Satellite System (GNSS), and those obtained from Google Earth (GE) and Shuttle Radar Topography Mission (SRTM). The data collection was carried out in the urban section between km 125 to km 145,500 of Dom Pedro-I Highway (SP-065), State of São Paulo, Brazil. The quality of the results is evaluated based on the Mean Quadratic Error (RMSE). The main results showed that the elevation data obtained through the SRTM show greater agreement or adjustment to the GNSS data when compared to the higher quality and less variation than those obtained through the GE, since they have smaller RMSE and are closer to the average elevations calculated for the highway segment analyzed. However, it is worth noting that the use of GE can be a good alternative for low-cost preliminary studies.

Correction of ICESat-2 terrain within urban areas using a water pump deployment criterion with the vertical contour of the terrain

Previous studies have shown that the terrain elevation of the ICESat-2's ATL08 product is of good accuracy, overall, but in urban areas under complex surface conditions, the terrain elevation is vulnerable to the influence of the effect of dense buildings, resulting in deviations and even outliers. However, the previous studies have not effectively corrected these deviations and outliers. To this end, we propose a method for correcting the terrain elevation of the ATL08 product in urban areas, which is an auto-refinement method for the terrain elevation. Based on the data characteristics of this type of terrain elevation data and the distribution characteristics of urban buildings, the proposed method innovatively corrects the deviated part of the terrain elevation by a water pump deployment criterion with the vertical contour of the terrain. To increase its applicability, the proposed method uses a strategy of mixing the terrain elevation data that are from the strong- and weak-beam observations, allowing the deviated terrain in one beam with a higher missing data rate to be corrected with the aid of the other beam with a lower missing data rate. We used airborne LiDAR data from several areas (New York, Amsterdam, and Palmerston North) with different city sizes to validate the corrected results. The results show that the root-mean-square error (RMSE) of the terrain elevation in urban areas is reduced from ∼4 m to ∼1 m after correction. The correction results also show that the proposed method can correct the deviated terrain elevation, while ensuring that the high-quality terrain elevation can be effectively retained.

A turbulent orographic form drag scheme accounting for anisotropy and orientation for kilometer‐ to subkilometer‐scale models

AbstractThis paper presents a turbulent orographic form drag (TOFD) parameterization scheme that takes into account the directional effects stemming from the angle between the low‐level wind and the principal axis of small‐scale orography. Suitable for models with both kilometer and subkilometer horizontal resolutions, this scheme builds upon prior theoretical and numerical studies to formulate surface TOFD based on the slope and direction of the sinusoidal hills. In this study, we proposed a straightforward function to calculate the surface TOFD for various orographic aspect ratios and directional parameters. The vertical decay of the drag is modeled with a scale twice the standard deviation of the sub‐grid orography. A comparison with numerous large‐eddy simulations featuring a single ellipsoidal hill demonstrates that our scheme effectively captures the dependence of drag on factors such as maximum slope, aspect ratio, the angle between low‐level wind and the principle axis, and hill height. We recommend calculating the sub‐grid orographic parameters using a well‐established method and digital terrain elevation data with a horizontal resolution less than a 100 m. This will allow for representation on orographic scales of both kilometers and subkilometers. Real‐case numerical weather prediction tests are conducted and verified with dense surface wind observations. The proposed scheme improves surface wind simulation compared to a renowned TOFD scheme, and also effectively exhibits the wind response to orographic anisotropy.

Geometry and topology of a Polish Outer Carpathian digital-elevation-model-interpreted lineament network in the context of regional tectonics

Abstract. The Polish part of the Western Outer Carpathian lineament network was analysed based on the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) digital elevation model. Lineaments were identified in the visual screening of the hillshade model. To the best of our knowledge, no one has studied the geometrical properties of the network in relation to the topological ones. The NetworkGT QGIS toolbox was applied to identify the nodes and branches of the network as well as to calculate the topology parameters. Our aim was to find differences between the western and eastern parts of the Western Outer Carpathians; therefore, the analyses were carried out in six sectors chosen based on the geographical subdivision in the geological context: three in the north, mainly the Silesian unit, and three in the south, mainly the Magura unit. We found general agreement of the identified network with the photo-lineament map; however, some of the photo-lineaments are not confirmed by a digital elevation model (DEM). We found that the topological parameters of the networks change from west to east but not from north to south. There are areas of increased interconnectivity, especially the Nowy Sącz Basin, where the lineament network may reflect a complicated system of cross-cutting, deep-rooted fault zones in the basement.

VERTICAL ACCURACY ASSESSMENT FOR OPEN-SOURCE DIGITAL ELEVATION MODEL: A CASE STUDY OF BASRAH CITY, IRAQ

Abstract. In the diverse domains of earth observation, elevation data are essential for a wide range of applications with various technical requirements and use cases. The Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Shuttle Radar Topography Mission (SRTM), and other projects have made a large number of global Digital Elevation Model (DEM) datasets for environmental modelling and studies freely available. Global DEMs have undergone an accuracy review to measure their inherent vertical uncertainty to show how accurate information should be considered while planning and analysing. Comparing the DEMs with highly accurate geodetic control points as the independent reference data one of the best methods in the evaluation process. SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM are among the worldwide DEMs that were examined. Comparisons are made between 793 geodetic control points values and those from SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM. The statistical analysis of global DEMs from GPS reference elevations gave us that the accuracy of the ALOS World 3D-30m is much better than other models with RMSE and STD values of 1.2497 and 1.235 m, respectively. In contrast, Aster-GDEM exhibited the highest RMSE and residual error of STD values of 5.793 m and 3.394 m, respectively.

Numerical Simulation of Wind Characteristics in Complex Mountains with Focus on Terrain Boundary Transition Curve

In recent years, an increasing number of projects have been developed in complex mountainous areas. The wind environment in mountainous areas, extremely complex due to the undulating terrain and diverse landscapes, is a key factor threatening the structural safety of buildings and their appurtenances in mountainous areas. Therefore, it is important to study the wind environment in complex terrain to clarify the wind resistance of structures in mountainous areas. Computational fluid dynamics (CFD) approaches are commonly used to examine wind fields in complex terrain; however, due to the limited range of terrain considered, direct modeling using terrain elevation data can result in truncated elevation differences, affecting the accuracy of numerical simulations. To address the problem of truncated elevation differences at terrain boundaries, the parameters of the wind tunnel contraction curve are optimized regarding the wind tunnel contraction section design principle. Moreover, several transition curves are analyzed and evaluated by numerical simulation methods, and a transition curve applicable to the terrain boundary transition form is proposed. The proposed terrain transition curves are applied to model the terrain of complex mountainous ski resort areas to be used in CFD numerical simulations. Furthermore, the accuracy of the numerical simulation is verified through a comparison with the field-measured data. Results indicate that the proposed method can accurately and effectively reflect the wind environment characteristics of a ski resort area. The proposed terrain transition curve provides a theoretical basis and case support for designing the terrain model boundary transition section, which can be used as a reference for wind tunnel and numerical simulation studies in complex mountainous areas.

Estimating exposure to extremely low frequency magnetic fields near high-voltage power lines and assessment of possible increased cancer risk among Slovenian children and adolescents.

Some previous research showed that average daily exposure to extremely low frequency (ELF) magnetic fields (MF) of more than 0.3 or 0.4 μT could potentially increase risk of childhood leukaemia. To allow calculations of ELF MF around high voltage (HV) power lines (PL) for the whole Slovenia, a new three-dimensional method including precision terrain elevation data was developed to calculate the long-term average ELF MF. Data on population of Slovenian children and adolescents and on cancer patients with leukaemia's aged 0-19 years, brain tumours at age 0-29, and cancer in general at age 0-14 for a 12-year period 2005-2016 was obtained from the Slovenian Cancer Registry. According to the large-scale calculation for the whole country, only 0.5% of children and adolescents under the age of 19 in Slovenia lived in an area near HV PL with ELF MF density greater than 0.1 μT. The risk of cancer for children and adolescents living in areas with higher ELF MF was not significantly different from the risk of their peers. The new method enables relatively fast calculation of the value of low-frequency magnetic fields for arbitrary loads of the power distribution network, as the value of each source for arbitrary load is calculated by scaling the value for nominal load, which also enables significantly faster adjustment of calculated estimates in the power distribution network.

A Review on Global and Localised Coverage Elevation Data Sources for Topographic Application

As the need for elevation data grows, it is more vital than ever for users to match the data degree of dependability, precision, and spatial resolution to their specific uses to produce a useful and cost-effective product. This article will describe several sources of elevation data, ranging from space-based to aerial-based techniques, and classify the data according to its respective quality and accuracy. The elevation data sources can be classified into two namely localised or can also be referred to as regional, and global coverage. Among the example of localised sources of elevation data are Light Detection and Ranging (LiDAR) and Interferometry Synthetic Aperture Radar (InSAR). The global sources of elevation data are Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER), Advanced Land Observing Satellite (ALOSW3D), Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), TerraSAR-X add on for daily Digital Elevation Measurement (TanDEM-X), The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), Radar Satellite (RADARSAT) Constellation Mission (RCM) and Satellite-Derived Bathymetry (SDB). The characteristics of each elevation data source were discussed in terms of its launch date, period of observation, spatial resolution, horizontal and vertical datum, and coverage. Its reliability was described in detail for future topographic applications.

Accuracy assessment of open-source global digital elevation models (GDEMs) with global navigation satellite system (GNSS) levelling

Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Advanced Land Observing Satellite World-3D (ALOSW3D), and TerraSAR-X Digital Elevation Measurement (TanDEM-X) are open-source Digital Elevation Model (DEM) datasets for environmental modelling and studies. The spatial resolution and vertical accuracy of DEM data sources play a significant role, particularly in dealing with land inundation, periodic flooding, and coastal erosion. In this study, the comparison between orthometric height, H from Global Navigation Satellite System (GNSS) observation, and DEM is performed to evaluate the accuracy of each DEM in terms of their Root Mean Square Error (RMSE) and correlation coefficient, R2 for monitoring the coastline. The result has indicated that TanDEM-X shows the smallest RMSE of 2.574 m compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D with RMSE of 2.968 m, 3.006 m, 3.217 m, 2.975 m, and 2.876 m respectively. Furthermore, TanDEM-X illustrates the largest correlation coefficients, R2 of 0.959 m, compared to SRTM30, SRTM90, ASTER, GMTED10, and ALOSW3D at 0.891 m, 0.899 m, 0.590 m, 0.888 m 0.913 m respectively. Hence, the result has indicated that TanDEM-X is the best option among all DEMs for any topographic applications.

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.

Review of Remote Sensing Technologies for the Acquisition of Very High Vertical Accuracy Elevation Data (DEM) in the Framework of the Precise – Remediation of Industrial Disasters – Part 1

In the last 10 years, the technological developments have changed the paradigm in remote sensing science. Nowadays, very diverse technologies can be employed to capture and/or extract very accurate terrain elevation data and prepare digital elevation models. This article aims at reviewing the existing remote sensing technologies which could support disaster remediation (by excavation of the soil) with very accurate elevation data acquisition. Ground based technologies (like terrestrial laser scanning, InSAR and SfM) and airborne technologies (airborne laser scanning [ALS], UAV photogrammetric approach, UAV with LiDAR) are reviewed. Their capacities are examined according to the following technical criteria: spatial efficiency, point density, accuracy and applicability in disaster situation.

On the accuracy of an emitter localization method based on multipath exploitation in realistic scenarios

This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures systems. Previously, the authors targeted only the theoretical development of the localization method. In fact, this could be a serious concern in practice since there is no evidence regarding its accuracy under the real data gathered from realistic scenarios. Therefore, an accurate ray-tracing algorithm is adapted to enable the implementation of the method in practice. Then, realistic scenarios are determined based on the geographic information system map generated to collect high-resolution digital terrain elevation data, as well as realistic localization problems for radar emitters. Next, simulations are performed to test the localization method. Thus, the performance of the method is verified for practical implementation in the electronic warfare context for the first time. Lastly, the performance bounds of the method are discussed.

The Sierra Nevada de Santa Marta (Colombia) and Nevado de Famatina (Argentina): The effects of tectonic syntaxis on the topography of the Andes

The Sierra Nevada de Santa Marta (Santa Marta) in Colombia and Nevado del Famatina (Famatina) in Argentina are two isolated peaks within the Andes of South America, having elevations of about 5,500 and 6,000 m, respectively. Both massifs are bounded by major wrench faults of opposite kinematics but comparable magnitude in their lateral displacement. Despite the volumes of research available, little has been said in the literature about the tectonics surrounding these particular systems. In this context, digital terrain elevation (DEM), regional geology, satellite imagery and geophysical data were analysed to illustrate how a network of transcurrent faults uplifted the peaks. We propose a model to account for topography by the opposed movement of faults operating in two compartments: internal (acute) and external (obtuse). Fault motion caused uplift of the internal compartments: a) the Santa Marta tectonic syntaxis is generated by the sinistral Santa Marta-Bucaramanga Fault on the west and the dextral Oca-Pilar Fault to the north whilst, b) the Famatina tectonic syntaxis is caused by the Valle Fértil sinistral fault on the western side and the dextral Tucumán lineament to the north. Elevation maps documenting the lateral displacement of modern alluvial fans provide evidence of the existence and kinematics of these faults. Active since the Neogene, the transcurrent faults resulted in displacements in the order of 25–60 km, with velocities of approximately 1–2 cm/year. Seismic records suggest that the syntaxis occurred after reactivation of oblique discontinuities in the basement due to the convergence of the Nazca, Caribbean, and South American plates. This tectonic framework led to the formation of a remarkable positive relief and the development of internal positive compartments subject to constrictive forces. Simultaneously, external distensive and negative compartments controlled the syntectonic deposition of thick sedimentary sequences. The significant topographic difference between the internal positive compartments and their external negative counterparts triggered high rates of erosion, transport, and ultimately sedimentation. Regional subsurface surveys indicate strong anomalies of the underlying bedrock influenced by tectonic syntaxis.

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases.

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3–5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more than 90% under some scenarios, except for deciduous broadleaf forests, where an optimal algorithm could provide low biased estimations. In contrast, the improvements of stacking in R2 and RMSE were not significant. The stacking of MARS, MLP, and SVR provided improved results compared with the optimal base learner, and the average RI in R2 was 3.54% when we used all data without separating forest types. Finally, the optimal stacking model was used to generate global forest AGB maps.

Topographic SLAM Using a Single Terrain Altimeter in GNSS-Restricted Environment

In a Global Navigation Satellite System (GNSS)-restricted area, a mobile robot navigation system exploits surrounding environment information. For an aerial or underwater vehicle, undulating terrain of a land or seabed surface is a valuable information resource that leads to the development of terrain-referenced navigation (TRN) algorithms. However, due to the vast amount of a vehicle’s activity area, surveying all the regions to obtain a high-resolution terrain map is impractical and requires simultaneous localization and mapping (SLAM) as a highly desirable capability. This paper presents a topographic SLAM algorithm using only a single terrain altimeter, which is low-cost, computationally efficient, and sufficiently stable for long-term operation. The proposed rectangular panel map structure and update method enable robust and efficient SLAM. As terrain elevation changes are inherently nonlinear, an extended Kalman filter (EKF)-based SLAM filter is adopted. The feasibility and validity of the proposed algorithm are demonstrated through simulations using terrain elevation data from a real-world undersea environment.