Skip to main content
Log in

Decision support system based on spatial and temporal pattern evolution of ecological environmental quality in the Yellow River Delta from 2000 to 2020

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The Yellow River Delta is the youngest large river delta in the world and is an important part of the national coordinated regional development strategy. In this paper, using the Google Earth Engine (GEE) platform, Landsat remote sensing images in the summer (June–September) of 2000, 2005, 2010, 2015, and 2020 were selected to remove clouds and mask water body information. The median value is taken to synthesize the remote sensing images of each year and extract multiple indices of greenness, wetness, heatness, and dryness. Then integrate the population density data by principal component analysis to quickly construct an improved composite remote sensing ecological index (CRSEI). The results showed that the average contribution of the six indicators of greenness (NDVI and FVC), wetness (NDMI), heatness (LST), dryness (NDBSI), and population density (Pop) to the first principal component (PC1) was above 79%, and the construction of an improved comprehensive remote sensing ecological index (CRSEI) based on PC1 was feasible in the Yellow River Delta. In the past 20 years, the ecological and environmental quality of the Yellow River Delta has shown a continuous trend of improvement. The ecological environment quality has improved by 3570.95 square kilometers, accounting for 48.0% of the total area, while the ecological environment quality has degraded by 712.99 square kilometers, accounting for only 9.6% of the total area. The improved area is 38.4% more than the degraded area, which shows that the ecological environment quality has improved in general. The ecological environment quality in Kenli County has improved the most. The ecological projects have been implemented in this area with the most obvious effect, and the investment in ecological environmental protection in this area should be continued or increased. The use of the GEE platform can obtain timely and accurate spatial and temporal patterns and evolution of ecological environmental quality in the Yellow River Delta, which is of great significance for ecological environmental protection and construction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Caixia S, Fan Y, Jin Hu (2021) New ecological index evaluation based on remote sensing data. Bull Survey Map 11:12–15

    Google Scholar 

  • Chen H, Chen C, Zhang Z, Lu C, Wang L, He X, Chen J (2021) Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: a case study of Zhoushan Island, China. Ocean Coast Manag 213:105842

    Article  Google Scholar 

  • Chen C, Liang J, Xie F, Hu Z, Sun W, Yang G, Zhang Z (2022) Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China. Int J Appl Earth Obs Geoinf 107:102711

    Google Scholar 

  • Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecol Ind 1:3–10

    Article  Google Scholar 

  • de Araujo BCC, Atkinson PM, Dearing JA (2015) Remote sensing of ecosystem services: a systematic review. Ecol Ind 52:430–443

    Article  Google Scholar 

  • Dizdaroglu D, Yigitcanlar T, Dawes L (2012) A micro-level indexing model for assessing urban ecosystem sustainability. Smar Sust Buil Environ 1:291–315

    Article  Google Scholar 

  • Foga S, Scaramuzza PL, Guo S et al (2017) Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens Environ 194:379–390

    Article  Google Scholar 

  • Foley JA, DeFries R, Asner GP et al (2005) Global consequences of land use. Science 309:570–574

    Article  Google Scholar 

  • Goward SN, Xue YK, Czajkowski KP (2002) Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements–an exploration with the simplified simple biosphere model. Remote Sens Environ 79:225–242

    Article  Google Scholar 

  • Hu X, Xu H (2018) A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: a case from Fuzhou City, China. Ecol Ind 89:11–21

    Article  Google Scholar 

  • Hu X, Xu H (2019) A new remote sensing index based on the pressure-state-response framework to assess regional ecological change. Environ Sci Pollut Res 26:5381–5393

    Article  Google Scholar 

  • Indrawati L, Murti BSSH, Rachmawati R (2020) Integrated ecological index (IEI) for urban ecological status based on remote sensing data: a study at Semarang-Indonesia. IOP Conf Ser Earth Environ Sci 500:012074

    Article  Google Scholar 

  • Jaafari S, Sakieh Y, Shabani AA et al (2016) Landscape change assessment of reservation areas using remote sensing and landscape metrics (case study: Jajroud reservation, Iran). Environ Dev Sustain 18:1701–1717

    Article  Google Scholar 

  • Jiang F, Zhang Y, Li J et al (2021) Research on remote sensing ecological environmental assessment method optimized by regional scale. Environ Sci Pollut Res 28:68174–68187

    Article  Google Scholar 

  • Karbalaei Saleh S, Amoushahi S, Gholipour M (2021) Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran. Environ Monit Assess 193:1–20

    Article  Google Scholar 

  • Klemas V, Smart R (1983) The influence of soil salinity, growth form, and leaf moisture on-the spectral radiance of partina alterniflora Canopies. Photogramm Eng Remote Sens 49:77–83

    Google Scholar 

  • Kwok R (2018) Ecology’s remote-sensing revolution. Nature 556:137–138

    Article  Google Scholar 

  • Liao W, Jiang W (2020) Evaluation of the spatiotemporal variations in the eco-environmental quality in China based on the remote sensing ecological index. Remote Sens 12:2462

    Article  Google Scholar 

  • Lin T, Ge R, Huang J et al (2016) A quantitative method to assess the ecological indicator system’s effectiveness: a case study of the ecological province construction indicators of China. Ecol Ind 62:95–100

    Article  Google Scholar 

  • Lloyd CT, Sorichetta A, Tatem AJ (2017) High resolution global gridded data for use in population studies. Sci Data 4:1–17

    Article  Google Scholar 

  • Mc Donald ME (2000) EMAP overview: objectives, approaches, and achievements. Environ Monit Assess 64:1–8

    Article  Google Scholar 

  • Mirzaei M, Shayesteh K (2015) Land use changes analysis using GIS, remote sensing and landscape metrics: a case study of Golpayegan City, Iran. Int J Ecol Environ Sci 41:133–140

    Google Scholar 

  • Ouyang ZY, Wang Q, Zheng H et al (2014) National ecosystem survey and assessment of China (2000–2010). Bull Chin Acad Sci 29:462–466

    Google Scholar 

  • Rikimaru A, Roy PS, Miyatake S (2002) Tropical forest cover density mapping. Trop Ecol 43:39–47

    Google Scholar 

  • Sellers PJ, Tucker CJ, Collatz GJ et al (1996) A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: the generation of global fields of terrestrial biophysical parameters from satellite data. J Clim 9:706–737

    Article  Google Scholar 

  • Song HM, Xue L (2016) Dynamic monitoring and analysis of ecological environment in Weinan City, Northwest China based on RSEI model. J Appl Ecol 27:3913–3919

    Google Scholar 

  • Sun D, Zhang J, Zhu C et al (2012) An assessment of China’s ecological environment quality change and its spatial variation. Acta Geogr Sin 67:1599–1610

    Google Scholar 

  • Wang L, Chen C, Xie F, Hu Z, Zhang Z, Chen H, Chu Y (2021) Estimation of the value of regional ecosystem services of an archipelago using satellite remote sensing technology: a case study of Zhoushan Archipelago, China. Int J Appl Earth Obs Geoinf 105:102616

    Google Scholar 

  • Willis KS (2015) Remote sensing change detection for ecological monitoring in United States protected areas. Biol Cons 182:233–242

    Article  Google Scholar 

  • Wu LX, Sun B, Zhou SL et al (2004) A new fusion technique of remote sensing images for land use/cover. Pedosphere 14:187–194

    Google Scholar 

  • Xu HQ (2008) A new index for delineating built-up land features in satellite imagery. Int J Remote Sens 29:4269–4276

    Article  Google Scholar 

  • Xu HQ (2013) A remote sensing index for assessment of regional ecological changes. China Environ Sci 33:889–897

    Google Scholar 

  • Xu H, Wang M, Shi T et al (2018) Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol Ind 93:730–740

    Article  Google Scholar 

  • Xu H, Wang Y, Guan H et al (2019) Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sens 11:2345

    Article  Google Scholar 

  • Yan L, Tian J, Liu J (2001) Application of landcover change detection based on remote sensing image analysis. Multispect Hyperspect Image Acquis Process SPIE 4548:184–188

    Article  Google Scholar 

  • Yang J, Huang X (2021) 30 m annual land cover and its dynamics in China from 1990 to 2019. Earth System Sci Data Discuss 13:3907–3925

    Article  Google Scholar 

  • Yang C, Zhang C, Li Q et al (2020) Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: a remote sensing perspective. Ecol Ind 115:106373

    Article  Google Scholar 

  • Yue H, Liu Y, Li Y et al (2019) Eco-environmental quality assessment in China’s 35 major cities based on remote sensing ecological index. IEEE Access 7:51295–51311

    Article  Google Scholar 

  • Zhang Y, Jiang F (2021) Developing a remote sensing-based ecological index based on improved biophysical features. J Appl Remote Sens 16:012008

    Article  Google Scholar 

  • Zhang J, Zhu Y, Fan F (2016) Mapping and evaluation of landscape ecological status using geographic indices extracted from remote sensing imagery of the Pearl River Delta, China, between 1998 and 2008. Environ Earth Sci 75:1–16

    Google Scholar 

  • Zhang X, Cao Q, Ji S et al (2022) Quantifying the contributions of climate change and human activities to vegetation dynamic changes in the Yellow River Delta. Acta Sci Circum 42:56–69

    Google Scholar 

  • Zhou G, Chen X, Huang J, et al. (2014) The ecological environment assessment and repairing of Guilin Karst Scenery based on satellite remote sensing. In: 2014 IEEE geoscience and remote sensing symposium. IEEE, pp. 1666–1669

  • Zhu D, Chen T, Wang Z et al (2021) Detecting ecological spatial-temporal changes by remote sensing ecological index with local adaptability. J Environ Manage 299:113655

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and feedback on this article.

Funding

This research received no external funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ping Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

Not applicable.

Informed Consent

Not applicable.

Additional information

Communicated by Shah Nazir.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Wang, P., Yasir, M. et al. Decision support system based on spatial and temporal pattern evolution of ecological environmental quality in the Yellow River Delta from 2000 to 2020. Soft Comput 26, 11033–11044 (2022). https://doi.org/10.1007/s00500-022-07399-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07399-9

Keywords

Navigation