Multi-Scale Integrated Perception Ecological Environment Indicator System (MSIP-EEIS) Based on Sensor Web Monitoring in Smart Sustainable Cities
Abstract
To better evaluate smart cities' development progress towards the goal of smartness and sustainability, Key Performance Indicators (KPIs) of sustainable cities have been defined in some international standards, such as the ISO 37120:2018 and the ITU-T Y.4903/L.1603. However, environment related KPIs are scarce or not fully defined, such as the indicators of the sustainable access to an improved water source and the air pollution monitoring system in the standards. Besides, cities with different spatial scales (i.e., urban agglomerations, city, and blocks) have different resource requirements for the calculation of KPIs, i.e., spatio-temporal resolution, timeliness, and accuracy, etc. Therefore, it is of great necessity to define more detailed KPIs for cities with different spatial scales, and provide the query and discovery of the observation resources for the calculation of KPIs.
This study aims to construct the multi-scale integrated perception ecological environment indicator system (MSIP-EEIS) based on sensor web to solve the above problems. The core of the MSIP-EEIS is a proposed three-level association mechanism of the 'Domain-Theme-Element' based on the DPSIR (Drivers, Pressures, States, Impacts, Responses) framework, with the domain level focusing on the ecology, the theme level for the water, air quality, and soil, and the element level including the water quality, PM2.5, soil moisture, etc. The reason why sensor web is used is that it can effectively monitor the observation resources by discovering and interacting web-accessible sensor data with related open standard protocols and interfaces. Compared with existing KPIs studies, the MSIP-EEIS can realize: 1) integrated KPIs perception by the KPIs modeling, including query and calculation; 2) multi-scale monitoring with the in-situ stations, mobile sites, and remote sensing satellites for the data access and release; 3) semantic association by constructing the mapping of 'KPIs-Observation' for the specific application tasks. '1+8' Wuhan Metropolitan Area, Wuhan city, and the Baoxie block in Wuhan city, are selected as the experimental areas, and the results demonstrated that the KPIs and observation resources can be efficiently queried and discovered for the sustainability application tasks of ecological environment decision-making.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN52B..12L
- Keywords:
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- 1640 Remote sensing;
- GLOBAL CHANGE;
- 1942 Machine learning;
- INFORMATICS;
- 4329 Sustainable development;
- NATURAL HAZARDS;
- 6620 Science policy;
- POLICY SCIENCES & PUBLIC ISSUES