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

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

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

Evaluation of riverbank erosion based on mangrove boundary changes identification using multi-temporal satellite imagery

Evaluating riverbank erosion in mangrove forests is dynamic and challenging because of the complex environment that is exposed to tidal and sedimentation factor. Besides, assessing riverbank erosion in this environment requires a technique that reduces dependency on tidal and sedimentation without affecting the quality of the assessment. Hence, this study evaluated riverbank erosion based on mangrove boundary changes using multi-temporal satellite images comprising Quickbird, WorldView-2 and Pleiades-1B. The first objective of this study is to determine mangrove boundary shifting and its long-term impact towards riverbank features followed by validating the mangrove boundary shifting of satellite imagery with field measurement data, which comprise Real Time Kinematic-Global Positioning System (RTK-GPS). Next, the study assessed the rates of changes o f the riverbank erosion and accretion and the final objective developing a riverbank erosion prediction model. In this study, a change detection technique was used to identify the mangrove boundary changes of Kilim River at different timelines. The extracted mangrove boundary from satellite images for the years 2005, 2012 and 2017 were used to identify changes in the riverbank features such as line shifting, river width, erosion, and accretion. Subsequently, a vector image overlay was used to determine the mangrove boundary shifting for the corresponding years and evaluate the erosion and accretion rates using symmetrical difference and erase tool in ArcGIS software. Sequentially, Root Mean Square Error (RMSE) analysis validated the accuracy of image geo- referencing process while residual analysis was employed to validate the accuracy between satellite imagery and field measurement data comprising RTK-GPS and erosion pin data. Then, line buffering and kernel density analysis were used to develop a riverbank erosion prediction model based on three parameters, namely distance of erosion, area of erosion and direction of shifted mangrove boundary. The initial findings of this study showed that the mangrove boundary changes shifted backwards in the opposite direction from the river and the range of shifting was different according to the intensity o f boat traffic. One of the findings showed that the increasing rates of riverbank erosion ranged from 11302.019 square meters in the first epoch to 15674.721 square meters in the second epoch. Another finding illustrated the riverbank erosion prediction model which displayed several areas such as Sections A, B, I and L which are potentially facing serious riverbank erosion problems in the future in comparison to Sections C, D, E, F, G, H and K. The final finding discussed data validation between Pleiades-1B and GPS-RTK which recorded 0.305 of the r-square value whereas 0.477 was recorded as the r-square value for both Pleiades-1B and the erosion pin. The other validation comprised the second epoch of satellite image (WorldView-2 and Pleiades- 1B) and the erosion pin data which revealed the r-square of 0.9347 and showed the strong relationship between both data. As a conclusion, the findings have shown that the evaluation of the riverbank erosion based on mangrove boundary changes using multi-temporal satellite images is capable o f assisting stakeholders including the Langkawi Development Authority (LADA), Department of Irrigation and Drainage Malaysia (DID) and Marine Department Malaysia to have in-depth understanding of riverbank erosion issue that would enable them to prepare a mitigation plan in the future

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796

Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests : A Test Case in Continental Southeast Asia

This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ' self-referencing' normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ArNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e. g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).Peer reviewe

towards disruptions in earth observation new earth observation systems and markets evolution possible scenarios and impacts

Abstract This paper reviews the trends in Earth observation (EO) and the possible impacts on markets of the new initiatives, launched either by existing providers of EO data or by new players, privately funded. After a presentation of the existing models, the paper discusses the new approaches, addressing both commercial and institutional markets. New concepts for the very high resolution markets, in Europe and in the US, are the main focus of this analysis. Two complementary perspectives are summarised: on the one hand, the type of system and its operational performance and, on the other, the related business models, concepts of operation and ownership schemes. Until now, Earth observation systems for the most critical institutional needs are mainly dedicated assets owned and operated by governments or public organisations, often at national level. Even in the case of dual use missions, the governmental and commercial operations are in general fully segregated for the very high resolution satellites. Recent evolutions could affect this paradigm. Firstly, the increased performance of commercial satellites has a high degree of convergence with defence needs: 25–30 cm resolution is now the benchmark or at least a very short term target for commercial missions. The second evolution is the development of hybrid procurement schemes, combining proprietary missions and data buy framework contracts, partly triggered by the budgetary constraints of public customers, some failures in the execution of large spy satellites contracts and by the willingness to foster the competitiveness of industry on the export market. New space is another trend, which is more disruptive. This trend begun in the Silicon Valley and spread worldwide, arousing our expectations, sometimes excessively. This new model involves not only start-ups but also big web actors with substantial investment capacity. Both aim to transforming space into a commodity, taking benefit from the convergence between Information technology and EO. Beside the massive constellations for broadband Internet access, some initiatives have been launched for Earth observation markets, targeting high resolution and high revisit. Last but not least, more and more countries, the newcomers, invest in their own EO capacity, confirming the soft power dimension of space but also opening new opportunities for international or regional cooperation. As many unpredictable events may occur, even in a short time frame, the last part of the paper has a prospective dimension. Based on market trends and industrial stakes, it discusses the realism and likelihood of possible scenarios and identifies their impacts on the EO landscape and the main stakeholders involved, in particular in Europe: – The governmental and institutional actors, using Earth observation data for their operational missions, with an evolving balance between sovereign assets and external services. – The commercial operators of very high resolution satellites, with the new market opportunities and the possible emergence of worldwide champions. – The satellite manufacturers and their competitiveness. – The role of nations and space agencies, including the non-dependence or national sovereignty and international cooperation dimensions. Based on the comparison of three "radical" scenarios, the conclusion shows that there are opportunities for service providers and satellite manufacturers. Even without clear answer to the future industrial, technical and political structure of EO systems, relevant indicators to be monitored during the next three-five years are identified. The last section focuses on Europe and the role of institutions in order to support European champions and small and medium companies in the new worldwide competition

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

Digital surface modelling and 3D information extraction from spaceborne very high resolution stereo pairs

This report discusses the potentials of VHR stereo imagery for automatic digital surface modelling (DSM) and 3D information extraction on large metropolitan cities. Stereo images acquired by GeoEye-1 on Dakar and Guatemala City and by WorldView-2 on Panama City, Constitucion (Chile), Kabul, Teheran, Kathmandu and San Salvador were processed following a rigorous photogrammetric approach. The work focusing on evaluating the quality of the DSMs in relation to the image and terrain characteristics and, among the possible DSM’s application, present a solution for buildings height estimation. The size of the datasets, the variety of case studies and the complexity of the scenarios allow to critically analyzing the potentials of VHR stereo imagery for 3D landscape modeling for natural hazards assessment.JRC.G.2-Global security and crisis managemen