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[Display omitted] • Species richness hotspots were identified by multi-species distribution modeling. • The magnitude and direction of future biodiversity hotspots changes were discussed. • The MaxEnt and InVEST models were complementary in biodiversity conservation. • Suggestions for biodiversity conservation were proposed based on gap analysis. Mainstreaming biodiversity into protection planning and management is of great significance for biodiversity conservation and sustainable development. Species potential distribution modeling is an effective way for species diversity evaluation and biodiversity hotspots identification, which are crucial for biodiversity conservation. Taking the Central Urban Area of Chongqing Municipality as the study area, the main objectives of this study were to identify the potentially suitable habitats, species richness and biodiversity of key protected species in current and future, determine the relative contribution of environmental factors and assess the conservation effectiveness of protected areas (PAs) based on MaxEnt model and gap analysis. The results showed that the current potentially suitable habitats of total key protected species were mainly located in "two rivers and four mountains", with a total area of 1610.55 km2, of which forestland accounted for 59.78 %. Species suitable habitats demonstrated clear topographic heterogeneity, and the distribution index decreased at first and then increased with increasing terrain niche index (TNI). Meanwhile, it was observed that key protected plants and birds shared similar suitable habitats in mountainous areas, with an overlapping area of 753.53 km2, and the high species richness covered 182.83 km2. In 2050, the future biodiversity hotspots would remain stable and increase steadily. In terms of the direction of centroid shift, the biodiversity hotspots would migrate to low latitude, low altitude and southeast by 8.34 km. The jackknife tests indicated that the potential distribution of key protected species was mainly determined by land use, mean diurnal range and TNI. Additionally, the problems of high protection gaps and low protection effectiveness coexisted in the existing PAs, with the overlapping area of the comprehensive biodiversity hotspots and the existing PAs was only 446.96 km2. Finally, suggestions for natural PAs system optimization and ecological protection were proposed. This study provides scientific supports for biodiversity conservation and efficient management. [ABSTRACT FROM AUTHOR]

• The time series reconstruction algorithm is applied to RSEI for the first time. • Corrected the correlation between RSEI and ecological components. • Alleviate the instability of RSEI time series. Remote Sensing Ecological Index (RSEI) is one of the most widely used ecological quality assessment indicators. Due to the noise caused by adverse atmoshperic conditions and other factors, the RSEI calculated from the original image usually has the phenomena of lack of information and unstable image quality. Therefore, based on Google Earth Engine (GEE) cloud platform, this study adopts three common data reconstruction algorithms firstly, namely: Savitory-Golay filter (SG), harmonic analysis of time series (HANTS), Whittaker Smoother (WS), which are used to reconstruct the original MODIS time series data in the Yangtze River Basin (YRB) from 2000 to 2020, in order to optimize the calculation process of RSEI. At the same time, three indicators (correlation coefficient (R), standard deviation (STD), root mean square error (RMSE)) are used for the accuracy evaluation. The results show that data reconstruction can fill gaps in RSEI, the reconstruction performance of WS and SG for four parameters is better than HANTS, and the four SG reconstructed sequences have the strongest correlation with the original sequences (R between 0.8 ∼ 1), while the WS reconstruction sequence has the lowest error value (both STD and RMSE are less than 1), both of them can correct the pixel value, which is conducive to maintaining the stability of RSEI in the temporal dimension; the RSEI produced by HANTS has the best accuracy, that is, R, STD, RMSE are respectively 0.898, 0.130, 0.104. As shown by the research, it is necessary to de-noise each parameter before synthesizing RSEI. This study can provide a theoretical basis for applying time-frequency algorithms to optimize the ecological monitoring performance of RSEI. [ABSTRACT FROM AUTHOR]

• The RSEI of the long time series is calculated by GEE. • Apply Theil-Sen-MK trend analysis to judge the long-term trend of RSEI. • Investigate the contribution of environment, human, and topography to RSEI changes. The Yangtze River Basin has a wide range and complicated topography. In recent years, under the background of climate and land cover change, the ecological response of the whole ecological quality of the Yangtze River Basin is still unknown. To reveal the spatiotemporal changes in ecological quality in the Yangtze River Basin from 2001 to 2019 and their relationship with environmental and topographical factors, this study used the Google Earth Engine (GEE) platform to calculate the remote sensing ecological index (RSEI) based on the Moderate-resolution Imaging Spectroradiometer (MODIS) product image set, combined with the digital elevation data set and statistical yearbook. The data evaluated the ecological quality of the Yangtze River Basin and analyzed its causes. The results showed that: 1) the average RSEI of the Yangtze River Basin showed an overall upward trend, the growth rate was 0.027 (year−1), and the variation ranged from 0.5 to 0.568. The overall ecological quality rank was mainly neutral and slightly good. 2) The ecological quality of 85.7% of the Yangtze River Basin remains stable. A total of 11.2% of the regional ecological quality is improving, and 3.1% of the regional ecological quality is declining. Areas with reduced ecological quality are concentrated in the Hengduan Mountains. The dominant LST factor drives the deterioration of its ecological quality at a rate of −1.06 (year−1). The areas with improved ecological quality are concentrated in the upper and middle reaches of the Yangtze River. The dominant WET factor drives its ecological quality to improve at a rate of 0.27 (year−1). 3) From the perspective of topography, the ecological quality of the Yangtze River basin shows a wave-like decline and first rises and then falls in elevation and slope (the elevation is bounded by 2000 m and 6000 m, and the slope is bounded by 15°.). The average RSEI of the Yangtze River Basin is the highest on the northwest slope (0.554), and the ecological quality of sunny slopes is generally higher than that of shady slopes. The research shows that from 2001 to 2019, the overall ecological quality of the Yangtze River Basin has improved and evolved, but the ecological quality of the Hengduan Mountains has declined. Therefore, implementing different ecological protection policies in different regions is an important strategy for enhancing the stability of the ecosystem. [ABSTRACT FROM AUTHOR]