Very High Resolution Satellite Imagery Research Articles

Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones

AbstractMangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.

Estimating beluga whale abundance from space: using drones to ground‐validate VHR satellite imagery

AbstractRoutine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is a logistically efficient method for providing an instantaneous view of areas spanning hundreds of square kilometers. The objective of this study was to determine two factors required to reliably conduct beluga whale population abundance estimates with VHR satellite imagery: (1) depths that beluga whales are visible in VHR satellite images, which are used to define availability bias correction factors, and (2) a comparison of abundance estimates in VHR satellite imagery to current aerial methods. We submerged beluga whale models to different depths in two different water clarities and determined that beluga whales are distinguished only at the surface in turbid water (Secchi depth: 2.56 m) and at depths of 0–2 m in clear water (Secchi depth: 4.04 m). Based on the proportion of time beluga whales spend at these depths, an availability bias correction factor for Western Hudson Bay beluga whales was defined as 2.40 ± 0.16 for turbid water and 1.89 ± 0.05 for clear water. Synchronous ground‐validation surveys determined availability corrected beluga whale abundance estimates in 0.31 m VHR satellite imagery (n = 173 beluga whales) and imagery that was HD sharpened using a proprietary algorithm to approximate 0.15 m resolution (n = 170) to be comparable to drone imagery (n = 164). VHR satellite imagery has the potential to increase the frequency of beluga whale population surveys, which has become increasingly important as beluga whales face rapid ecosystem changes and increased anthropogenic disturbances.

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection

Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing meteorological satellite imagery. Akin to various vision tasks, ImageNet pre-training is commonly used for pre-training SFD. However, in the context of multi-spectral meteorological satellite imagery, the initial step of deep learning has received limited attention. Recently, pre-training with Very High-Resolution (VHR) satellite imagery has gained increased popularity in remote-sensing vision tasks, showing the potential to replace ImageNet pre-training. However, it is worth noting that the meteorological satellite imagery applied in SFD, despite being an application of computer vision in remote sensing, differs greatly from VHR satellite imagery. To address the limitation of pre-training for SFD, this paper introduces a novel deep-learning paradigm to the meteorological domain driven by Masked Image Modeling (MIM). Our research reveals two key insights: (1) Pre-training with meteorological satellite imagery yields superior SFD performance compared to pre-training with nature imagery and VHR satellite imagery. (2) Incorporating the architectural characteristics of SFD models into a vanilla masked autoencoder (MAE) can augment the effectiveness of meteorological pre-training. To facilitate this research, we curate a pre-training dataset comprising 514,655 temporal multi-spectral meteorological satellite images, covering the Bohai Sea and Yellow Sea regions, which have the most sea fog occurrence. The longitude ranges from 115.00E to 128.75E, and the latitude ranges from 27.60N to 41.35N. Moreover, we introduce SeaMAE, a novel MAE that utilizes a Vision Transformer as the encoder and a convolutional hierarchical decoder, to learn meteorological representations. SeaMAE is pre-trained on this dataset and fine-tuned for SFD, resulting in state-of-the-art performance. For instance, using the ViT-Base as the backbone, SeaMAE pre-training which achieves 64.18% surpasses from-scratch learning, natural imagery pre-training, and VRH satellite imagery pre-training by 5.53%, 2.49%, and 2.21%, respectively, in terms of Intersection over Union of SFD.

Using VHR satellite imagery, OBIA and landscape metrics to improve mosquito surveillance in urban areas

Surveillance is critical to efficiently control and prevent mosquito-borne diseases such as Dengue. Surveillance relies on sampling the target region for arthropod vectors over time. However, in most cases the sampling framework is ad hoc and relies only on expert opinion. We sought to improve the efficiency of mosquito surveillance in Córdoba (Argentina) by designing a spatial sampling scheme within complex urban areas that would optimize ovitrap collections. We classified a very high resolution (VHR) satellite image following an object based (OBIA) approach and estimated several landscape metrics over which we applied a k-means clustering. The objective was to identify an optimal distribution for the ovitrap network characterizing the urban coverage of the city at three types of territorial units: neighbourhoods, census tracts and Thiessen polygons around health care facilities. We distributed 150 ovitraps throughout the city based on the identified environmental groups and compared results with the current strategy used by the Ministry of Health. Stratified ovitrap distributions for census tracts or Thiessen polygons performed best compared to the current strategy in terms of environmental variability covered, i.e., relevant environmental groups are either subsampled or oversampled in the current distribution. Because of the general availability of these environmental data sets and algorithms, the approach could be applied in most urban areas where vector borne disease control is challenging.

A Biologist’s Guide to the Galaxy: Leveraging Artificial Intelligence and Very High-Resolution Satellite Imagery to Monitor Marine Mammals from Space

Monitoring marine mammals is of broad interest to governments and individuals around the globe. Very high-resolution (VHR) satellites hold the promise of reaching remote and challenging locations to fill gaps in our knowledge of marine mammal distribution. The time has come to create an operational platform that leverages the increased resolution of satellite imagery, proof-of-concept research, advances in cloud computing, and machine learning to monitor the world’s oceans. The Geospatial Artificial Intelligence for Animals (GAIA) initiative was formed to address this challenge with collaborative innovation from government agencies, academia, and the private sector. In this paper, we share lessons learned, challenges faced, and our vision for how VHR satellite imagery can enhance our understanding of cetacean distribution in the future.

Object-based classification of urban plant species from very high-resolution satellite imagery

Cities are facing too many challenges. Urban vegetation, in particular trees, are essential as they provide services in terms of air pollution mitigation, freshness, biodiversity, and citizens’ well-being. Accurate data on location, species, and structural characteristics are essential for quantifying their benefits. However, the cost of measuring thousands of individual trees through field campaigns can be prohibitive and reliable information on domestic gardens is lacking due to difficulties in acquiring systematic data. The main objective of this study was to investigate the suitability of very-high resolution satellite imagery, e.g., WorldView-2, for detecting, delineating, and classifying the urban plant species in both public and private areas. The characterization of urban vegetation is difficult due to the complexity of the urban environment (buildings, shadows, open courtyards), the diversity of species and the spatial proximity between trees. To overcome these constraints, an object-based classification was developed with the selection of new relevant spectral and texture-based features for each plant species. Four spectral bands (blue, green, yellow, red) and four texture features (i.e., energy, entropy, inverse difference moment, Haralick correlation) were found to be the most efficient attributes for object-based classification from WV-2 images. Then, a classification of plant species, by using a Random Forest classifier, and ground validation were performed. In the two study areas, Aix-en-Provence (France) and Florence (Italy), 22 and 20 dominant plant species, and grassland, were identified and classified with an overall accuracy of 84% and 83%, respectively. The highest classification accuracy was obtained for Pinus spp. and Platanus acerifolia in Aix-en-Provence, and for Celtis australis and Cupressus sempervirens in Florence. The lowest classification accuracy was obtained for Quercus spp. in Aix-en-Provence, and Magnolia grandiflora in Florence.

Shoreline change rates and land to sea sediment and soil organic carbon transfer in eastern Parry Peninsula from 1965 to 2020 (Amundsen Gulf, Canada)

As the Arctic warms, permafrost coasts are eroding faster, threatening coastal communities, habitats, and altering sediment and nutrient budgets. The western Canadian Arctic is eroding at a rapid pace, however little is known on changes occurring in the Amundsen Gulf area. This study was conducted in the eastern coast of Parry Peninsula, a neglected rock-dominated coastal area. We used orthorectified aerial photos of 1965 and 1993 and very-high resolution satellite imagery of 2020 to manually delineate the shoreline according to backshore and foreshore centered approaches. Shoreline change rates were calculated and sediment and Organic Carbon transfer from land to sea estimated using digital elevation model, the Northern Circumpolar Soil Carbon Database and ground-ice content. The results show a mean erosion rate of 0.12 m/yr for the backshore zone and 0.16 m/yr for the foreshore zone, with increasing erosion in the Paulatuk Peninsula in recent decades. The average sediment transfer from land to sea was 20 m<sup>3</sup>/m/yr and the SOC flux was 7 kg C/m/yr. We highlight the importance of using the cliff-top as shoreline reference to accurately estimate sediment and SOC transfers, an approach neglected in automatic shoreline delineation techniques based on remote sensing imagery using the waterline.

Allometric equations to estimate the dry mass of Sahel woody plants mapped with very-high resolution satellite imagery

Very high-resolution satellite images and deep learning are achieving the mapping of individual trees and shrubs over large areas in Africa. Each woody plant is precisely georeferenced and defined by its crown area and, sometimes, its height. The challenge is to build allometric equations for foliage, wood and root dry masses based either on crown area alone, or the product crown area × tree height as independent variables regardless of species. This was met by reanalyzing existing Sahel woody plant data from destructive sampling. Overall, the foliage (seasonal maximum), wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species respectively. The regression models tested for foliage, wood or root masses were linear or power functions using a range of different approaches: Ordinary least square (OLS) regression after log–log transform with or without Baskerville correction (1972), and non-linear regressions (NLR). All the models outputs were intercompared for fit indicators and prediction uncertainty, as well as the resulting trends in root to wood ratio, and foliage to wood ratio over the range of crown areas. This process selected a set of OLS log–log equations with crown area as independent variable. When the Baskerville correction is applied the approximation errors are 0.4 kg Dry Matter (DM) for foliage, 12.2 for wood and 6.3 for roots, aggregating at 13.8 kg DM per tree. These allometry equations were compared to published equations for tropical trees, most from the more humid tropics that were generally based on stem diameter, tree height and wood density. This paper’s allometry predictions were within the range of these other allometry equations, reinforcing the confidence in their use even beyond the Sahel domain towards sub-humid savannas.

Impact assessment of humanitarian demining using object-based peri-urban land cover classification and morphological building detection from VHR Worldview imagery

This study utilised very high-resolution (VHR) imagery to monitor and evaluate the impact of humanitarian demining activities in a peri-urban environment in Afghanistan. Identifying buildings and mapping the spatial distribution of different types of land cover is of great practical significance for demining organisations, such as the HALO Trust, to assess the impact of their mine-clearance activities, by quantifying change and the growth of a population in a specific area over time. This study had two main objectives: (i) to map the post-clearance land cover, and (ii) to detect and quantify the change in the number and area of buildings. Two independent workflows were implemented and evaluated. To map land cover, this study investigated the implementation of various machine learning algorithms in object-based image classification (OBIA) of VHR satellite imagery (Worldview-1,2,3). Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, before classification was performed based on a machine learning Random Forests (RF) approach. Different parameters and spatial distribution of training samples were tested to analyse the model's performance. Further analysis determined that by using only the segments' mean value per spectral band (Red, Green, Blue), data redundancy in the training stage was eliminated. The final classified map had an overall accuracy of 90.67% and a total built-area of 643,660.28 m2 was detected in the 4.11 km2 study area. To detect and quantify buildings present in the study area, an alternative, automatic, unsupervised approach based on the morphological building index (MBI) was implemented using MATLAB. Two VHR (0.5 m) panchromatic images acquired by WorldView-1 in 2008 and 2018 were processed using a series of multi-scale and multi-directional morphological operators, before a series of post-processing thresholds were applied to refine the output. Parameters were systematically optimised for the datasets and their sensitivity analysed. By comparing the output to manually labelled reference maps, producer's and user's accuracy of 78.63/81.70% and 75.30/77.91% were attained for 2008 and 2018 imagery respectively. The built-area was found to increase from 58,705 to 611,920 m2 over the ten-year period, indicating a significant increase in the number of people resident in the area. Whilst OBIA was found to be more accurate than MBI for the 2018 pansharpened imagery, with producer's and user's accuracy of 91.00/90.60% versus 84.65/79.44% respectively, it must be considered that the MBI, an unsupervised method requiring no training, offers a fast solution if the sole objective is building detection.

Allometric Equations to Estimate the Dry Mass of Sahel Woody Plants from Very-High Resolution Satellite Imagery

Allometric Equations to Estimate the Dry Mass of Sahel Woody Plants from Very-High Resolution Satellite Imagery

Cetacean Strandings From Space: Challenges and Opportunities of Very High Resolution Satellites for the Remote Monitoring of Cetacean Mass Strandings

The study of cetacean strandings was globally recognised as a priority topic at the 2019 World Marine Mammal Conference, in recognition of its importance for understanding the threats to cetacean communities and, more broadly, the threats to ecosystem and human health. Rising multifaceted anthropogenic and environmental threats across the globe, as well as whale population recovery from exploitation in some areas, are likely to coincide with an increase in reported strandings. However, the current methods to monitor strandings are inherently biased towards populated coastlines, highlighting the need for additional surveying tools in remote regions. Very High Resolution (VHR) satellite imagery offers the prospect of upscaling monitoring of mass strandings in minimally populated/unpopulated and inaccessible areas, over broad spatial and temporal scales, supporting and informing intervention on the ground, and can be used to retrospectively analyse historical stranding events. Here we (1) compile global strandings information to identify the current data gaps; (2) discuss the opportunities and challenges of using VHR satellite imagery to monitor strandings using the case study of the largest known baleen whale mass stranding event (3) consider where satellites hold the greatest potential for monitoring strandings remotely and; (4) outline a roadmap for satellite monitoring. To utilise this platform to monitor mass strandings over global scales, considerable technical, practical and environmental challenges need to be addressed and there needs to be inclusivity in opportunity from the onset, through knowledge sharing and equality of access to imagery.

Фотограмметрическая обработка и применение разновременных стереопар космических снимков

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

Remote Sensing for Short-Term Economic Forecasts

Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that arises for instance from the time needed to collect and process the data. To improve the forecasts by reducing the delay, it is of interest to find alternative data sources that provide information on economic activity without significant delays. Among others, satellite images are thought to assist here. This paper addresses the potential of earth observation imagery for short-term economic forecasts. The study is focused on the estimation of investments in the construction sector based on high resolution (HR) (10–20 m) and very high resolution (VHR) (0.3–0.5 m) images as well as on the estimation of investments in agricultural machinery based on orthophotos (0.1 m) simulating VHR satellite imagery. By applying machine learning it is possible to extract the objects of interest to a certain extent. For the detection of construction areas, VHR satellite images are much better suited than HR satellite images. VHR satellite images with a ground resolution of 30–50 cm are able to identify agricultural machinery. These results are promising and provide new and unconventional input for economic forecasting models.

Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery

Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.

A Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery

Road extraction is to automatically label the pixels of roads in satellite imagery with specific semantic categories based on the extraction of the topographical meaningful features. For governments, timely and accurate road mapping is crucial to plan infrastructure development and mobilize relief around the world. Recent advances in deep learning have shown their dominance on road extraction from very high-resolution (VHR) satellite imagery. However, previous road extraction based on deep learning mainly stacked the multiple convolution operators and failed to predict the contextual spatial relationship correctly. Besides, the precision of cross-domain road extraction is limited by an insufficient amount of labeled data and the transferability of the model. To remedy these issues, a Global Context-aware and Batch-independent Network (GCB-Net) is proposed, which is a novel road extraction framework extract complete and continuous road networks. In GCB-Net, the Global Context-Aware (GCA) block is added to the encoder-decoder structure to effectively integrate global context features. The Filter Response Normalization (FRN) layer is used to enhance the original basic network, which eliminates the batch dependency to accelerate learning and further improve the robustness of the model. Experimental results on two diverse road extraction data sets demonstrated that the proposed method outperformed the state-of-the-art methods both quantity and quality. Moreover, to test the robust generalizability of the proposed method, the proposed CHN6-CUG Roads Dataset was used for spatial transfer evaluation, and GCB-Net achieved significantly higher transferability than other methods.

The Potential of Satellite Imagery for Surveying Whales.

The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use.

Smart solutions for smart cities: Urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada

Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using time- and cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's.

Multiscale Integration of High-Resolution Spaceborne and Drone-Based Imagery for a High-Accuracy Digital Elevation Model Over Tristan da Cunha

Very high-resolution (VHR) optical Earth observation (EO) satellites as well as low-altitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both data sets. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides the most detailed model of the island so far, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

A WORLDWIDE 3D GCP DATABASE INHERITED FROM 20 YEARS OF MASSIVE MULTI-SATELLITE OBSERVATIONS

Abstract. High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010.Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy.This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented.