## 1 Introduction
Firstly, most of them studied the coupling coordination relationship between economic development and ecological environment from a static perspective, and few studied the relationship from a dynamic or spatial perspective. Secondly, the evaluation index system in most studies was similar, and the relationship between economic development and ecological environment could not be reasonably evaluated. There are two ways to measure the quality of ecological environment. One is to select indicators from pollutant emission, greening rate, and waste treatment and recovery according to the pressure-state-response (PSR) model framework [27; 28; 29; 30; 31]. The second is to evaluate ecosystem service value through land-use change; in particular, the monetary evaluation method of ecosystem service value (ESV) has been widely used in the ecological field [32; 33; 34; 35; 36; 37; 38; 39]. However, there are some limitations in the selection of quantitative indicators. First of all, sewage and waste emissions, energy consumption, carbon emissions, and other indicators are obtained through artificial statistical surveys, leading to statistical errors and other problems, with data often missing in small-scale research. Secondly, the change in land use/land cover is affected by physical and chemical properties, use mode, and other factors, and the impact on ecosystem service value is difficult to measure. In addition, the timeliness and accuracy of existing land-use datasets cannot meet the needs of timely and refined research. With the development of remote sensing technology, multisource data fusion of remote sensing data, ecological data, and social and economic data should be strengthened [40; 41; 42]. Remote sensing observation technology has the advantages of wide observation range, short monitoring period, and large amount of information acquisition, and it is widely used in ecological environment monitoring [43; 44]. Many scholars have constructed ecological indicators from the perspective of remote sensing [45; 46; 47]. The normalized difference vegetation Index (NDVI) [32; 45; 46; 47; 48; 49] is one of the most commonly used ecological remote sensing indicators; it is used to calculate vegetation coverage, which can better reflect vegetation changes. The enhanced vegetation index (EVI) [32; 49] is an improvement of NDVI, and it is superior to NDVI in reducing background and atmospheric effects and saturation problems. The normalized difference water index (NDWI) or modified normalized difference water index (MNDWI) [48; 49; 50; 51] is sensitive to the recognition of surface water, and it is often used to extract surface water. Building and bare land can be identified and extracted using the normalized difference building index (NDBI) and bare soil index (BSI) or a composite index of building and normalized difference bare soil Index (NDBSI) [48; 49; 50; 51]. Due to the influence of various complex factors, it is not enough to use a single ecological indicator to evaluate the status of the ecosystem; instead, comprehensive indicators are needed. This paper selects Wuhan, an important central city in the Yangtze River economic belt, as a case study to analyze the interaction between ecology and regional economic development. In terms of geographical location, Wuhan is located at the intersection of the Yangtze River economic belt and the Beijing-Guangzhou development axis. It is a city in Central China with unique geographical advantages. With the acceleration of urbanization, the conflict between the natural ecosystem and social economic system is becoming more and more serious. Therefore, it is necessary to explore the interaction mechanism and evolution law between ecological environment and economic development. Our work mainly contributes in two ways:  
1. A comprehensive evaluation index system of urban economy and ecological environment is constructed. The comprehensive ecological index system includes the ecological carbon sequestration index and land-cover index.