DEM and bathymetry estimation for mapping a tide‐coordinated shoreline from fine spatial resolution satellite sensor imagery

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

Continuing degradation of urban environments of developing economies results from failure to make decisions based on scientific methods that employ accurate digital elevation models (DEM) in modelling environmental phenomena such as flooding and erosion. Data dumping and wastage occur if fresh DEM are required for every new project ignoring existing data. Every DEM to be used for modelling may be accepted as is, updated or rejected for a new one based on outlier fitness tests. High cost DEM aged only a few months should also be tested since elevation changes could occur in very short periods. Older DEMs might still be useful if changes are only in limited areas which could be mapped to update the DEM, so saving cost. A DEM of Owerri, South East Nigeria created from a 1977 1/2500 topographic map series was evaluated using elevations of random Global Navigation Satellite Systems (GNSS) points resulting in a &plusmn;1.290 m root mean square error (RMSE). Outliers of the 99% certainty were traced to a heavily cut construction site and a topographical survey of the site was used to update the DEM improving RMSE to below the meter mark. &nbsp; Key words: Digital elevation model (DEM), geographic information systems (GIS), outlier, DEM updating, root mean square error (RMSE).

The accuracy of digital elevation models (DEMs) plays an important role in many terrain-related applications, particular in high northern latitudes where there is uncertainty in DEMs. Using the interferometric synthetic aperture radar techniques, this study examined how different RADARSAT-2 beam modes can be used to generate DEMs with high accuracy. Using a conventional interferometry method, the Spotlight DEM shows the highest accuracy among all studied DEM products, with the root-mean-square error (RMSE) ranging from 13.9 to 17.4 m, followed by the F0W3 DEM and U26W2 DEM. The error sources in DEM generation due to uncertainty in perpendicular baseline and atmospheric delay are likely more important than the random phase noise caused by volume scattering and environmental changes during synthetic aperture radar (SAR) acquisitions. The small baselines subset (SBAS) method did not significantly improve DEM quality due to the limitation of the number of SAR images in this study. The integration of both Spotlight conventional DEMs and SBAS DEM considerably improved results yielding high-quality DEMs for the study area, with an RMSE of 9.7 m. Further studies are necessary to quantitatively evaluate the effects of surface motion as well as the orbital and atmospheric errors on the DEM accuracy. The Slave River Delta in the Northwest Territories of Canada was used as a test case.

Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches

Digital terrain models (DTMs) are a fundamental source of information in Earth sciences. DTM-based studies, however, can contain remarkable biases if limitations and inaccuracies in these models are disregarded. In this work, four freely available datasets, including Shuttle Radar Topography Mission C-Band Synthetic Aperture Radar (SRTM C-SAR V3 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM V2), and two nationwide airborne light detection and ranging (LiDAR)-derived DTMs (at 5-m and 1-m spatial resolution, respectively) were analysed in three geomorphologically contrasting, small (3–5 km2) catchments located in Mediterranean landscapes under intensive human influence (Mallorca Island, Spain). Vertical accuracy as well as the influence of each dataset’s characteristics on hydrological and geomorphological modelling applicability were assessed by using ground-truth data, classic geometric and morphometric parameters, and a recently proposed index of sediment connectivity. Overall vertical accuracy—expressed as the root mean squared error (RMSE) and normalised median deviation (NMAD)—revealed the highest accuracy for the 1-m (RMSE = 1.55 m; NMAD = 0.44 m) and 5-m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of the SRTM data was lower (RMSE = 6.98 m; NMAD = 5.27 m), but considerably higher than for the ASTER data (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. Propagation of these errors and coarse horizontal resolution caused negative impacts on flow routing, stream network, and catchment delineation, and to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for catchment hydrogeomorphological modelling.

Abstract. The Antarctic Peninsula (AP) is one of the widely studied polar regions because of its sensitivity to climate change and potential contribution of its glaciers to global sea level rise. Precise digital elevation models (DEMs) at a high spatial resolution are much demanded for investigating the complex glacier system of the AP at fine scales. However, the two most recent circum-Antarctic DEMs, the 12 m TanDEM-X DEM (TDM DEM) from bistatic interferometric synthetic aperture radar (InSAR) data acquired between 2013 and 2014 and the Reference Elevation Model of Antarctica mosaic (REMA mosaic) at an 8 m spatial resolution derived from optical data acquired between 2011 and 2017 have specific individual limitations in this area. The TDM DEM has the advantage of good data consistency and few data voids (approx. 0.85 %), but there exist residual systematic elevation errors such as phase-unwrapping errors in the non-edited DEM version. The REMA mosaic has high absolute vertical accuracy, but on the AP it suffers from extended areas with data voids (approx. 8 %). To generate a consistent, gapless and high-resolution topography product of the AP, we fill the data voids in the TDM DEM with newly processed TDM raw DEM data acquired in austral winters of 2013 and 2014 and detect and correct the residual systematic elevation errors (i.e., elevation biases) in the TDM DEM with the support of the accurately calibrated REMA mosaic. Instead of a pixelwise replacement with REMA mosaic elevations, these provide reference values to correct the TDM elevation biases over entire regions detected through a path propagation algorithm. The procedure is applied iteratively to gradually correct the errors in the TDM DEM from a large to small scale. The proposed method maintains the characteristics of an InSAR-generated DEM and is minimally influenced by temporal or penetration differences between the TDM DEM and REMA mosaic. The performance of the correction is evaluated with laser altimetry data from Operation IceBridge and ICESat-2 missions. The overall root mean square error (RMSE) of the corrected TDM DEM has been reduced from more than 30 m to about 10 m which together with the improved absolute elevation accuracy indicates comparable values to the REMA mosaic. The generated high-resolution DEM depicts the up-to-date topography of the AP in detail and can be widely used for interferometric applications as well as for glaciological studies on individual glaciers or at regional scales.

Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data

수문학, 기상학 및 기후학 등에서 필수적인 자료중의 하나인 지상기온 자료는 최근 보건, 생물, 환경 등의 다양한 분야로까지 활용영역이 확대되고 있어 그 중요성이 커지고 있으나 지상관측을 통한 지상기온자료의 취득은 시공간적인 제약이 크기 때문에 실측된 기온자료는 시공간 해상도가 낮아 높은 해상도가 요구되는 연구 분야에서는 활용성에 큰 제약을 갖게 된다. 이를 극복하기 위한 하나의 대안으로 상대적으로 높은 시공간 해상도를 가지고 있는 위성영상자료에서 얻을 수 있는 지표면온도 자료를 이용하여 지상기온을 추정하는 많은 연구들이 수행되어 왔다. 본 연구는 이러한 연구의 일환으로써 기상청에서 제공하고 있는 AWS(Automatic Weather Station)에서 취득된 2010년 지상 온도 자료(AWS data)를 바탕으로 대표적인 지표면 온도자료인 MODIS Land Surface temperature(LST data:MOD11A1)와 지상기온에 영향을 미칠 수 있는 Land Cover Data, DEM(digital elevation model) 등의 보조 자료와 함께 다양한 지구통계 기법들을 이용하여 남한 지역의 지상기온을 추정하였다. 추정 전 2010년 전체(365일) LST자료와 AWS자료와의 차이에 대한 RMSE(Root Mean Square Error)값의 계절별 피복별 분석결과 계절에 따른 RMSE값의 변동계수는 0.86으로 나타났으나 피복에 따른 변동계수는 0.00746으로 나타나 계절별 차이가 피복별 차이보다 큰 것으로 분석 되었다. 계절별 RMSE 값은 겨울철이 가장 낮은 것으로 나타났으며 AWS자료와 LST자료와 보조자료를 이용한 선형 회귀분석결과에서도 겨울철의 결정 계수가 가장 높은 0.818로 나타났으며, 여름철의 경우에는 0.078로 나타나 계절별 차이가 매우 크게 나타났다. 이러한 결과를 바탕으로 지구통계 기법들의 대표적인 방법론인 크리깅 방법 중 일반적으로 많이 사용되고 있는 정규 크리깅, 일반 크리깅, 공동 크리킹, 회귀 크리깅을 이용하여 지상기온을 추정한 후 모델의 정확도를 판단할 수 있는 교차 검증을 실시한 결과 정규 크리깅과 일반 크리깅에 의한 RMSE 값은 1.71, 공동 크리깅과 회귀 크리깅에 의한 RMSE 값은 각각 1.848, 1.63으로 나타나 회귀 크리깅 방법에 의한 추정의 정확도가 가장 높은 것으로 분석되었다. Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

Accurate and precise topographic information from Digital Elevation Models (DEM) is of fundamental requirement for many geoscience and engineering applications. With a goal of providing accurate global topographic products, German Aerospace Centre (DLR) and Airbus Defence and Space developed X-band TanDEM-X mission. The TanDEM-X Global DEM is generated with a pixel spacing of 12×12 m from interferometric data acquired in strip map mode with a resolution of 3.3 m [1]. In this study, the vertical accuracy of TanDEM-X Global DEM is assessed by comparing it with various elevation datasets. The elevation datasets include DGPS measurements, SRTM 30m, and 90 m DEM, CartoSat DEM, LiDAR data, photogrammetry data, intermediate and fused DEMs’ derived from TanDEM-X CoSSC [2], ICESat elevations and the newly released TanDEM-X 90 m DEM. The study areas include Bihar (flat terrain), Mumbai (moderate and urban terrain), Godavari (moderate and hilly), Koyna (hilly and vegetated), Manali (rugged Himalayan) and Kartarniaghat Wild Life Sanctuary-KWLS (flat and forest) with different terrain conditions. Widely used statistical parameters such as mean, standard deviation (SD) and root mean square error (RMSE) are used for vertical accuracy assessment [3]. The minimum and maximum differences between elevations are also noted, which are useful in exploiting quality of DEM under evaluation. The accuracy of the TanDEM-X Global DEM is found to be less than 1.6 m, 4 m, 5 m, 13 m, 6 m for plain, moderate relief, hilly, Himalayan, vegetated terrains respectively. In vegetated areas (KWLS), we observed an 8 m difference in tree height estimation using TanDEM-X Global DEM and GPS measurement. Elevation and difference profiles were drawn to understand the vertical accuracy of these DEMs’. In all cases, the elevations of TanDEM-X Global DEM closely matched with heights derived from DGPS, ICESAT, LiDAR and photogrammetry data.

Deep-learning based approaches have been proven effective for Digital Elevation Model (DEM) super-resolution (SR) tasks. Previous networks typically treat DEM elevation values as single-channel image for input. However, DEM images alone cannot fully capture spatial and terrain features. Shaded relief images (SRIs), derived from DEMs, serve as crucial visual cues that intuitively convey terrain characteristics, addressing the limitations of DEM images and providing synergistic benefits for training DL models. The primary challenge in utilizing SRIs for guiding DEM SR lies in accurately selecting a consistent structure to extract and effectively integrate features from SRIs and DEMs. In this study, we propose an Attention-based Hierarchical Terrain Fusion (AHTF) framework for guided DEM SR. Specifically, an Attention-based Feature Fusion Module (AFFM) is designed to efficiently fuse relevant information from LR DEM and SRI, which includes a feature enhancement block to select valuable features and a feature recalibration block to fuse diverse terrain features. Additionally, we optimize the loss function from the perspectives of terrain analysis and visual effects. We validate AHTF on our newly constructed real-world Shade-DEM SR dataset and two open-source DEM SR datasets. Compared to the current state-of-the-art methods, our AHTF achieves the best results in terms of root mean square error (RMSE) for elevation, slope, and aspect. Furthermore, the extracted stream networks are closer to real-world conditions. This study offers new insights and methods for further research and application in the field of DEM super-resolution. Our dataset can be obtained at https://doi.org/10.6084/m9.figshare.25590945.

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.