Multiple Digital Elevation Model Research Articles

A high-quality global elevation control point dataset from ICESat-2 altimeter data

ABSTRACT The ICESat-2 satellite equipped with a new photon-counting laser altimeter has received much attention as a source of accurate elevation observations. However, in this research field, there is a lack of an open-source high-accuracy elevation control point dataset with the specific quality requirements at a global scale. To this end, using ICESat-2 altimeter data as the main data source, we constructed and organized a dataset as a useful supplement for this research field. The dataset was generated by a methodology based on detection environment evaluation, photon spatial analysis, and the redundant observation statistics. The dataset includes more than 600 million elevation control points and covers the global land areas, except for Greenland and Antarctica. The dataset has been validated by multiple digital elevation models (DEMs) from around the world (sourced from airborne LiDAR data). The results show that the dataset has high-accuracy elevation control points. The overall root-mean-square error (RMSE) of the original elevations of ICESat-2 is about 1.384–4.820 m, but the overall RMSE of the elevation control points in the new dataset is about 0.279–0.642 m. Moreover, the results obtained in this study show that the dataset is suitable for application within high vegetation cover areas.

Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

With accelerating global sea level rise driven by climate change, accurate estimates of potential population exposure (PPE) within the low-elevation coastal zones (LECZ) are critical for coastal planning and assessing the benefits of climate mitigation. Multiple digital elevation models (DEM) and population grid datasets have been used for the PPE assessment of coastal lowlands. However, the uncertainty arising from differences in data sources and production methods results in poorly guided estimates. In this study, four global DEM and five population datasets were used to estimate the PPE in the LECZ of China and to assess the uncertainty of PPE estimation. Based on the DEM and population grid with the best accuracy, we found that more than 13.82% of China’s residents lived in the LECZ in 2010. Different DEM-population combinations yielded significantly different PPE estimates, ranging between 3.59–24.61 million and 31.56–112.24 million people in the LECZ below 1 m and 4 m elevation, respectively. The satellite Lidar-based DEM improves the estimates of the LECZ and obtains the PPE within LECZ below 4 m elevation that far exceeds those of other DEM datasets. The usage of WorldPop and LandScan population datasets leads to an underestimation of PPE within the LECZ of China. In contrast, integrating more geospatial big data helps generate better population grids, thus reducing the uncertainty of coastal PPE estimates. There is still a need to improve the availability and accuracy of coastal geospatial data and to deepen the understanding of coastal vulnerability.

Tracking lake surface elevations with proportional hypsometric relationships, Landsat imagery, and multiple DEMs

AbstractMultidecadal inland surface water dynamics are of increasing interest due to their importance to climate, ecology, and society, yet several key challenges impede long‐term monitoring of inland surface waters globally. This research investigates two novel methods, one addressing subhydroflattened surface estimate uncertainty issues, and a second addressing temporal resolution issues, using 46 water bodies across the western United States. First, low water level estimate uncertainty was reduced using multiple digital elevation models (ALOS, SRTM, and NED) to derive the hypsometric relationship for each lake from the digital elevation model with the lowest hydroflattened water surface. This technique reduced the number of images with subhydroflattened water surfaces by at least 549 over the best individual DEM resulting in higher water surface elevation estimate accuracy. Second, this paper introduces proportional hypsometry which dynamically generates surface area/elevation relationships for every image using clear pixels only by removing contamination from both the image and DEM. Proportional hypsometry was found to be ill‐suited for subhydroflattened water surface levels but produced comparable accuracy to clear images for above hydroflattened water levels. Overall, using the lowest hydroflattened surface along with proportional hypsometry improved temporal resolution enabling analysis of nearly 10,000 additional images while maintaining similar accuracy levels as images with <1% contamination (2.35 m RMSE vs. 2.17 m RMSE). This research increases lower water elevation estimate accuracy and temporal resolution and is scalable enabling regional and global water dynamic analysis.

Web-Based Sea Level Change Simulation System Using PNG Elevation Tiles and Smart Tile Architecture

We have developed a web-based processing system that can simulate positive and negative sea level changes globally by selecting the best Digital Elevation Model (DEM) for a target region from multiple DEMs. A PNG elevation tile format is used as the DEM format, which reduces the DEM data size. The PNG tile format implements client-based processing, and the DEM data are provided from different websites. In addition, the smart tile architecture is adopted, which enables on-demand simulation by adding a tile conversion process (i.e., a DEM selection process) during image drawing by using JavaScript. To demonstrate the system, we have employed three DEMs, i.e., the Geospatial Information Authority of Japan (GSI) map (~10-m resolution), the ASTER Global Digital Elevation Models (ASTER GDEM version 3) as global land area (~30-m resolution), and the General Bathymetric Chart of the Oceans as bathymetric data (~1000-m resolution). The ASTER Global Water Bodies Database is also used in the data selection process. The GSI provides their DEM in a PNG elevation tile format, and the other data are provided by the Geological Survey of Japan in PNG elevation tile format. We assume the current DEM sea level as 0 m, and the sea level can be changed to an arbitrary integer value (−10,000 to 10,000 m). Combining ASTER GDEM for land and GEBCO for sea makes it possible to target DEM of the whole earth. Moreover, it was shown that if a higher resolution DEM is available, it is possible to combine the higher resolution DEM in that area. The combining the PNG elevation tile format with the smart tile architecture demonstrates the possibilities of a client-based web processing service like that of the server-based OGC Web Processing Service.

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km2) were 88.3% distributed at lower elevations compared to larger size glaciers (≥1 km2). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20° and 30°, and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.