Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Exploiting Sentinel-1 amplitude data for glacier surface velocity field measurements. Feasibility demonstration on baltoro glacier

The leading idea of this work is to continuously retrieve glaciers surface velocity through SAR imagery, in particular using the amplitude data from the new ESA satellite sensor Sentinel-1 imagery. These imagery key aspects are the free access policy, the very short revisit time (down to 6 days with the launch of the Sentinel-1B satellite) and the high amplitude resolution (up to 5 m). In order to verify the reliability of the proposed approach, a first experiment has been performed using Sentinel-1 imagery acquired over the Karakoram mountain range (North Pakistan) and Baltoro and other three glaciers have been investigated. During this study, a stack of 11 images acquired in the period from October 2014 to September 2015 has been used in order to investigate the potentialities of the Sentinel-1 SAR sensor to retrieve the glacier surface velocity every month. The aim of this test was to measure the glacier surface velocity between each subsequent pair, in order to produce a time series of the surface velocity fields along the investigated period. The necessary co-registration procedure between the images has been performed and subsequently the glaciers areas have been sampled using a regular grid with a 250 × 250 meters posting. Finally the surface velocity field has been estimated, for each image pair, using a template matching procedure, and an outlier filtering procedure based on the signal to noise ratio values has been applied, in order to exclude from the analysis unreliable points. The achieved velocity values range from 10 to 25 meters/month and they are coherent to those obtained in previous studies carried out on the same glaciers and the results highlight that it is possible to have a continuous update of the glacier surface velocity field through free Sentinel-1 imagery, that could be very useful to investigate the seasonal effects on the glaciers fluid-dynamics

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

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

Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge

The digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development

New Enabling Technologies to Observe and Characterise Urban Environments with Big Data from Space – the Urban Thematic Exploitation Platform

Modern Earth Observation (EO) satellite missions provide valuable opportunities to support sustainable urban planning and management by delivering dedicated information on the spatiotemporal development of the built environment and its key morphological and physical characteristics such as imperviousness, greenness, built-up density, building volume, albedo – from global down to local scale. However, the transformation of the raw EO imagery into ready-to-use thematic data and indicators for scientist or planners on the one hand and actionable information for decision makers on the other hand requires detailed technical expert knowledge. Moreover, the imagery collected by satellite missions such as the US Landsat program or the European fleet of Sentinel satellites, but also by airborne systems or drones, rapidly adds up to a multiple of the data volume that can effectively be handled with standard work stations and software solutions. Hence, this contribution introduces the Urban Thematic Exploitation Platform (https://urban-tep.eo.esa.int) that utilizes modern information and communication technology to bridge the gap between the mass data collections of the technology-driven EO sector and the demand of science, planning, and policy for up-to-date information on the status, properties and dynamics of the urban system. Key components of the Urban Thematic Exploitation Platform (U-TEP) are an open, web-based portal that is connected to distributed high-level computing clusters and clouds and that also provides key functionalities for i) high-performance data access, analysis and visualization, ii) customized development and sharing of algorithms, products and services, and iii) networking, communication and exchange of data and information. The overarching objective here is to enable any interested (non-expert) user to easily generate actionable indicators and information for effective sustainable urban development based on a joint analysis of various data sources such as official survey data, EO mission data, socio-economic statistics, and data collected via social media or citizen science. So far more than 3.5 PB of data have been processed and analyzed by means of the U-TEP to finally provide a broad spectrum of urban information products and related services for visualization and analytics that have yet successfully been used by more than 240 institutions (science, planning, NGOs, policy) from 41 countries (i.a. World Bank Group, United Nations, Organisation for Economic Cooperation and Development, World Food Programme, Bill and Melinda Gates Foundation, Group on Earth Observation, Global Platform for Sustainable Cities)

A global product of fine-scale urban building height based on spaceborne lidar

Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences