KaydedilenlerDerginizi ekleyin

Dergiler / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Evaluation of ecological environmental quality based on Remote Sensing Ecological Index using the Google Earth Engine platform: A case study of Kabul city

Sajaweddin Sadid(Eskisehir Teknik Universitesi)0000-0002-0031-4531
Gordana Kaplan(ESKISEHIR TECHNICAL UNIVERSİTY)0000-0002-0353-7423
Sayfa
—
Yayın Tarihi
2026-10-05
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These findings provide valuable scientific insights for ecological monitoring, urban planning, and the formulation of sustainable development strategies.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Rapid urbanization and population growth have intensified anthropogenic pressures on ecological environments, particularly in developing cities, thereby challenging the balance between urban expansion and environmental sustainability.
📊
Temel Bulgular & Veri:This study aims to assess the ecological environmental quality of Kabul, Afghanistan, over the 1990–2020 period using the Remote Sensing Ecological Index (RSEI) within the Google Earth Engine (GEE) cloud-based platform.
💡
Sonuç & Katkı:These findings provide valuable scientific insights for ecological monitoring, urban planning, and the formulation of sustainable development strategies.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2020 Verileri
⚙️Değişkenler:Kabul city · ecological quality · Remote Sensing Ecological Index (RSEI)

Abstract

Rapid urbanization and population growth have intensified anthropogenic pressures on ecological environments, particularly in developing cities, thereby challenging the balance between urban expansion and environmental sustainability. In this context, the quantitative monitoring of spatiotemporal ecological changes has become essential for sustainable urban planning. This study aims to assess the ecological environmental quality of Kabul, Afghanistan, over the 1990–2020 period using the Remote Sensing Ecological Index (RSEI) within the Google Earth Engine (GEE) cloud-based platform. Long-term Landsat time-series imagery was utilized to derive four key ecological indicators: greenness (NDVI), wetness (WET), dryness (NDBSI), and land surface temperature (LST). Principal Component Analysis (PCA) was applied to integrate these indicators and construct the RSEI model. Satellite images from 1990, 2000, 2010, and 2020 were analyzed at ten-year intervals. The results indicate that ecological environmental quality in Kabul was predominantly concentrated in moderate and high classes throughout the study period, exhibiting a fluctuating yet relatively stable trend. The mean RSEI values were calculated as 0.551, 0.651, 0.547, and 0.506 for the respective years. These findings provide valuable scientific insights for ecological monitoring, urban planning, and the formulation of sustainable development strategies.

🔖

Bu Makaleye Atıf Yap

Sadid, S., Kaplan, G. (2026). Evaluation of ecological environmental quality based on Remote Sensing Ecological Index using the Google Earth Engine platform: A case study of Kabul city. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1864740

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (89)

  1. Alberti, M. (2005). The effects of urban patterns on ecosystem function. International Regional Science Review, 28(2), 168–192. https://doi.org/10.1177/0160017605275160
  2. Alqadhi, S., Mallick, J., Balha, A., Bindajam, A., Singh, C. K., & Hoa, P. V. (2021). Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia. Earth Science Informatics, 14(3), 1547–1562. https://doi.org/10.1007/s12145-021-00633-2
  3. Amiri, B., & Lukumwena, N. (2018). An overview of informal settlement upgrading strategies in Kabul City and the need for an integrated multi-sector upgrading model. Current Urban Studies, 6(3), 348–365. https://doi.org/10.4236/cus.2018.63019
  4. Ayoobi, A. W., Ahmadi, H., Inceoglu, M., & Pekkan, E. (2022). Seasonal impacts of buildings’ energy consumption on the variation and spatial distribution of air pollutant over Kabul City: Application of Sentinel—5P TROPOMI products. Air Quality, Atmosphere & Health, 15(1), 73–83. https://doi.org/10.1007/s11869-021-01085-9
  5. Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423–431. https://doi.org/10.1080/2150704X.2014.915434
  6. Boori, M. S., Choudhary, K., Paringer, R., & Kupriyanov, A. (2021). Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia. Journal of Environmental Management, 285, 112138. https://doi.org/10.1016/j.jenvman.2021.112138
  7. Chen, C., He, X., Lu, Y., & Chu, Y. (2020). Application of Landsat time-series data in island ecological environment monitoring: A case study of Zhoushan Islands, China. Journal of Coastal Research, 108(SI), 193–199. https://doi.org/10.2112/JCR-SI108-038.1
  8. Chen, C., Wang, L., Yang, G., Sun, W., & Song, Y. (2023). Mapping of ecological environment based on Google Earth Engine cloud computing platform and Landsat long-term data: A case study of the Zhoushan Archipelago. Remote Sensing, 15(16), 4072. https://doi.org/10.3390/rs15164072
  9. Chen, J., Shen, M., Zhu, X., & Tang, Y. (2009). Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan Plateau. Ecological Indicators, 9(4), 818–823. https://doi.org/10.1016/j.ecolind.2008.09.009
  10. Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014). Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2094–2107. https://doi.org/10.1109/JSTARS.2014.2329330
  11. Chiabai, A., Quiroga, S., Martinez-Juarez, P., Higgins, S., & Taylor, T. (2018). The nexus between climate change, ecosystem services and human health: Towards a conceptual framework. Science of the Total Environment, 635, 1191–1204. https://doi.org/10.1016/j.scitotenv.2018.03.323
  12. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S. G., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, 637–651. https://doi.org/10.1016/j.rse.2016.09.007
  13. Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6
  14. Cui, R., Han, J., & Hu, Z. (2022). Assessment of spatial temporal changes of ecological environment quality: A case study in Huaibei City, China. Land, 11(6), 944. https://doi.org/10.3390/land11060944
  15. de Araujo Barbosa, C. C., Atkinson, P. M., & Dearing, J. A. (2015). Remote sensing of ecosystem services: A systematic review. Ecological Indicators, 52, 430–443. https://doi.org/10.1016/j.ecolind.2015.01.007
  16. Deng, Y., Wu, C., Li, M., & Chen, R. (2015). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40–48. https://doi.org/10.1016/j.jag.2015.02.010
  17. Dong, T., Liu, J., Shang, J., Qian, B., Ma, B., Kovacs, J. M., Walters, D., Jiao, X., Geng, X., & Shi, Y. (2019). Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sensing of Environment, 222, 133–143. https://doi.org/10.1016/j.rse.2018.12.032
  18. Elkhrachy, I., Alhamami, A., & Alyami, S. H. (2023). Landfill site selection using multi-criteria decision analysis, remote sensing data, and geographic information system tools in Najran City, Saudi Arabia. Remote Sensing, 15(15), 3754. https://doi.org/10.3390/rs15153754
  19. Ellis, E. C., Wang, H., Xiao, H. S., Peng, K., Liu, X. P., Li, S. C., Ouyang, H., Cheng, X., & Yang, L. Z. (2006). Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery. Remote Sensing of Environment, 100(4), 457–473. https://doi.org/10.1016/j.rse.2005.11.002
  20. Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M., & Trigo, I. F. (2020). Google Earth Engine open-source code for land surface temperature estimation from the Landsat series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471
  21. Estornell, J., Martí-Gavilá, J. M., Sebastiá, M. T., & Mengual, J. (2013). Principal component analysis applied to remote sensing. Modelling in Science Education and Learning, 6, 83–89. https://doi.org/10.4995/msel.2013.1905
  22. Fan, X., Nie, G., Xia, C., & Zhou, J. (2021). Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 101, 102339. https://doi.org/10.1016/j.jag.2021.102339
  23. Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Joseph Hughes, M., & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026
  24. Goodhand, J. (2002). Aiding violence or building peace? The role of international aid in Afghanistan. Third World Quarterly, 23(5), 837–859. https://doi.org/10.1080/0143659022000028620
  25. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  26. Gorgij, A. D., & Moayeri, M. M. (2023). Proposing a novel method for the irrigation water quality assessment, using entropy weighted method, entitled: “EIWQI.” Environmental Earth Sciences, 82(20), 462. https://doi.org/10.1007/s12665-023-11150-4
  27. Healey, S. P., Cohen, W. B., Zhiqiang, Y., & Krankina, O. N. (2005). Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3), 301–310. https://doi.org/10.1016/j.rse.2005.05.009
  28. Hekmat, H., Ahmad, T., Singh, S. K., Kanga, S., Meraj, G., & Kumar, P. (2023). Land use and land cover changes in Kabul, Afghanistan focusing on the drivers impacting urban dynamics during five decades 1973–2020. Geomatics, 3(3), 447–464. https://doi.org/10.3390/geomatics3030024
  29. Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A systematic review of Landsat data for change detection applications: 50 years of monitoring the Earth. Remote Sensing, 13(15), 2869. https://doi.org/10.3390/rs13152869
  30. Huang, C., Wylie, B., Yang, L., Homer, C., & Zylstra, G. (2002). Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing, 23(8), 1741–1748. https://doi.org/10.1080/01431160110106113
  31. Huang, H., Chen, W., Zhang, Y., Qiao, L., & Du, Y. (2021). Analysis of ecological quality in Lhasa Metropolitan Area during 1990–2017 based on remote sensing and Google Earth Engine platform. Journal of Geographical Sciences, 31(2), 265–280. https://doi.org/10.1007/s11442-021-1846-8
  32. Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., Shinoda, M., Ma, Z., Guo, W., Li, Z., Zhang, L., Liu, Y., Yu, H., He, Y., Xie, Y., Guan, X., Ji, M., Lin, L., Wang, S., … Wang, G. (2017). Dryland climate change: Recent progress and challenges. Reviews of Geophysics, 55(3), 719–778. https://doi.org/10.1002/2016RG000550
  33. Ibrahimi, M. T., Zaryab, A., Ali, S., & Hasani, M. (2025). Key drivers impacting water resources of Afghanistan under rapid climate change conditions. Water Conservation Science and Engineering, 10(2), 80. https://doi.org/10.1007/s41101-025-00394-9
  34. Ivits, E., Cherlet, M., Mehl, W., & Sommer, S. (2009). Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets. Ecological Indicators, 9(3), 422–431. https://doi.org/10.1016/j.ecolind.2008.05.013
  35. Jamali, A. A., Montazeri Naeeni, M. A., & Zarei, G. (2020). Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 20, 100428. https://doi.org/10.1016/j.rsase.2020.100428
  36. Jia, H., Yan, C., & Xing, X. (2021). Evaluation of eco-environmental quality in Qaidam Basin based on the ecological index (MRSEI) and GEE. Remote Sensing, 13(22), 4543. https://doi.org/10.3390/rs13224543
  37. Jin, W., & Shi, Y. (2022). Dynamic monitoring of ecological environment quality in Gansu Province supported by Google Earth Engine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 887–892. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-887-2022
  38. Kazemi Garajeh, M., Haji, F., Tohidfar, M., Sadeqi, A., Ahmadi, R., & Kariminejad, N. (2024). Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine. Scientific Reports, 14(1), 5469. https://doi.org/10.1038/s41598-024-56160-9
  39. Kennedy, R. E., Andréfouët, S., Cohen, W. B., Gómez, C., Griffiths, P., Hais, M., Healey, S. P., Helmer, E. H., Hostert, P., Lyons, M. B., Meigs, G. W., Pflugmacher, D., Phinn, S. R., Powell, S. L., Scarth, P., Sen, S., Schroeder, T. A., Schneider, A., Sonnenschein, R., … Zhu, Z. (2014). Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment, 12(6), 339–346. https://doi.org/10.1890/130066
  40. Kherif, F., & Latypova, A. (2020). Principal component analysis. In A. Mechelli & S. Vieira (Eds.), Machine learning: Methods and applications to brain disorders (pp. 209–225). Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00012-2
  41. Lakes, T., & Kim, H.-O. (2012). The urban environmental indicator “Biotope Area Ratio”—An enhanced approach to assess and manage the urban ecosystem services using high resolution remote-sensing. Ecological Indicators, 13(1), 93–103. https://doi.org/10.1016/j.ecolind.2011.05.016
  42. Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., & Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing, 12(7), 1130. https://doi.org/10.3390/rs12071130
  43. Liu, Y., Meng, Q., Zhang, L., & Wu, C. (2022). NDBSI: A normalized difference bare soil index for remote sensing to improve bare soil mapping accuracy in urban and rural areas. Catena, 214, 106265. https://doi.org/10.1016/j.catena.2022.106265
  44. Liu, Z., Wang, L., & Li, B. (2022). Quality assessment of ecological environment based on Google Earth Engine: A case study of the Zhoushan Islands. Frontiers in Ecology and Evolution, 10, 918756. https://doi.org/10.3389/fevo.2022.918756
  45. Lu, J., Guan, H., Yang, Z., & Deng, L. (2021). Dynamic monitoring of spatial-temporal changes in eco-environment quality in Beijing based on remote sensing ecological index with Google Earth Engine. Sensors and Materials, 33(12), 4595–4606. https://doi.org/10.18494/SAM.2021.3572
  46. Mushkani, R. A., & Ono, H. (2022). Urban planning, political system, and public participation in a century of urbanization: Kabul, Afghanistan. Cogent Social Sciences, 8(1), 2045452. https://doi.org/10.1080/23311886.2022.2045452
  47. Nichol, J. (2005). Remote sensing of urban heat islands by day and night. Photogrammetric Engineering & Remote Sensing, 71(5), 613–621. https://doi.org/10.14358/PERS.71.5.613
  48. Nichol, J. (2009). An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis. Photogrammetric Engineering & Remote Sensing, 75(5), 547–556. https://doi.org/10.14358/PERS.75.5.547
  49. Nourani, V., Foroumandi, E., Sharghi, E., & Dąbrowska, D. (2021). Ecological-environmental quality estimation using remote sensing and combined artificial intelligence techniques. Journal of Hydroinformatics, 23(1), 47–65. https://doi.org/10.2166/hydro.2020.048
  50. Ochoa-Gaona, S., Kampichler, C., de Jong, B. H. J., Hernández, S., Geissen, V., & Huerta, E. (2010). A multi-criterion index for the evaluation of local tropical forest conditions in Mexico. Forest Ecology and Management, 260(5), 618–627. https://doi.org/10.1016/j.foreco.2010.05.018
  51. Ouyang, Z., Wang, Q., Zheng, H., Zhang, F., & Hou, P. (2014). National ecosystem survey and assessment of China (2000–2010). Bulletin of Chinese Academy of Sciences, 29(4), 462–466. https://doi.org/10.3969/j.issn.1000-3045.2014.04.009
  52. Pan, J., Li, H., & Li, Y. (2022). Spatiotemporal change analysis of environmental quality in mining areas based on long-term Landsat images. Geocarto International, 37(26), 11052–11067. https://doi.org/10.1080/10106049.2022.2046862
  53. Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sensing, 12(15), 2411. https://doi.org/10.3390/rs12152411
  54. Priya, M. V., Kalpana, R., Pazhanivelan, S., Kumaraperumal, R., Ragunath, K. P., Vanitha, G., Nihar, A., Prajesh, P. J., & Vasumathi, V. (2023). Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu. Journal of Applied and Natural Science, 15(3), 1170–1177. https://doi.org/10.31018/jans.v15i3.4803
  55. Rao, N. S. (2014). Spatial control of groundwater contamination, using principal component analysis. Journal of Earth System Science, 123(4), 715–728. https://doi.org/10.1007/s12040-014-0430-3
  56. Rasouli, H. (2022). Climate change impacts on water resource and air pollution in Kabul sub-basins, Afghanistan. Advances in Geological and Geotechnical Engineering Research, 4(1), 11–27. https://doi.org/10.30564/agger.v4i1.4312
  57. Reza, M. I. H., & Abdullah, S. A. (2011). Regional Index of Ecological Integrity: A need for sustainable management of natural resources. Ecological Indicators, 11(2), 220–229. https://doi.org/10.1016/j.ecolind.2010.08.010
  58. Rodriguez, P. S., Schwantes, A. M., Gonzalez, A., & Fortin, M.-J. (2024). Monitoring changes in the Enhanced Vegetation Index to inform the management of forests. Remote Sensing, 16(16), 2919. https://doi.org/10.3390/rs16162919
  59. Samarawickrama, U., Piyaratne, D., & Ranagalage, M. (2017). Relationship between NDVI with Tasseled cap indices: A remote sensing based analysis. International Journal of Innovative Research in Technology, 3(12), 13–19.
  60. Schwarz, N., Schlink, U., Franck, U., & Großmann, K. (2012). Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—An application for the city of Leipzig (Germany). Ecological Indicators, 18, 693–704. https://doi.org/10.1016/j.ecolind.2012.01.001
  61. Sedighkia, M., & Datta, B. (2023). Monitoring ecological status of wetlands using linked fuzzy inference system-remote sensing analysis. Ecological Informatics, 74, 101971. https://doi.org/10.1016/j.ecoinf.2022.101971
  62. Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3–4), 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004
  63. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
  64. Shao, Z., Ding, L., Li, D., Altan, O., Huq, M. E., & Li, C. (2020). Exploring the relationship between urbanization and ecological environment using remote sensing images and statistical data: A case study in the Yangtze River Delta, China. Sustainability, 12(14), 5620. https://doi.org/10.3390/su12145620
  65. Sun, C., Li, J., Liu, Y., Cao, L., Zheng, J., Yang, Z., Ye, J., & Li, Y. (2022). Ecological quality assessment and monitoring using a time-series remote sensing-based ecological index (ts-RSEI). GIScience & Remote Sensing, 59(1), 1793–1816. https://doi.org/10.1080/15481603.2022.2138010
  66. Tilt, J. H., Unfried, T. M., & Roca, B. (2007). Using objective and subjective measures of neighborhood greenness and accessible destinations for understanding walking trips and BMI in Seattle, Washington. American Journal of Health Promotion, 21(4 Suppl.), 371–379. https://doi.org/10.4278/0890-1171-21.4s.371
  67. Tong, S., Zhang, J., Bao, Y., Lai, Q., Lian, X., Li, N., & Bao, Y. (2018). Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982–2013. Journal of Geographical Sciences, 28(5), 595–610. https://doi.org/10.1007/s11442-018-1493-x
  68. Uddin, M. N., Saiful Islam, A. K. M., Bala, S. K., Islam, G. M. T., Adhikary, S., Saha, D., Haque, S., Fahad, M. G. R., & Akter, R. (2019). Mapping of climate vulnerability of the coastal region of Bangladesh using principal component analysis. Applied Geography, 102, 47–57. https://doi.org/10.1016/j.apgeog.2018.12.011
  69. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008
  70. Wafa, W., Hairan, M. H., & Waizy, H. (2020). The impacts of urbanization on Kabul City’s groundwater quality. International Journal of Advanced Science and Technology, 29(4), 10796–10809. https://www.researchgate.net/publication/346108892
  71. Wang, H., Liu, C., Zang, F., Liu, Y., Chang, Y., Huang, G., Fu, G., Zhao, C., & Liu, X. (2023). Remote sensing-based approach for the assessing of ecological environmental quality variations using Google Earth Engine: A case study in the Qilian Mountains, Northwest China. Remote Sensing, 15(4), 960. https://doi.org/10.3390/rs15040960
  72. Wang, J., Ding, J., Ge, X., Qin, S., & Zhang, Z. (2022). Assessment of ecological quality in Northwest China (2000–2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality. Journal of Arid Land, 14(11), 1196–1211. https://doi.org/10.1007/s40333-022-0085-x
  73. Westman, W. E. (1987). Monitoring the environment by remote sensing. Trends in Ecology & Evolution, 2(11), 333–337. https://doi.org/10.1016/0169-5347(87)90109-1
  74. White, D. C., Lewis, M. M., Green, G., & Gotch, T. B. (2016). A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin. Ecological Indicators, 60, 1309–1320. https://doi.org/10.1016/j.ecolind.2015.01.032
  75. Williams, M., Longstaff, B., Buchanan, C., Llansó, R., & Dennison, W. (2009). Development and evaluation of a spatially-explicit index of Chesapeake Bay health. Marine Pollution Bulletin, 59(1–3), 14–25. https://doi.org/10.1016/j.marpolbul.2008.11.018
  76. Willis, K. S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233–242. https://doi.org/10.1016/j.biocon.2014.12.006
  77. World Bank. (2014). World Development Indicators [Data set]. http://databank.worldbank.org/data/views/reports/tableview.aspx
  78. Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., … Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195
  79. Xiong, Y., Xu, W., Lu, N., Huang, S., Wu, C., Wang, L., Dai, F., & Kou, W. (2021). Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecological Indicators, 125, 107518. https://doi.org/10.1016/j.ecolind.2021.107518
  80. Xu, H. (2008). A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276. https://doi.org/10.1080/01431160802039957
  81. Xu, H. (2013). A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24), 7853–7862. https://doi.org/10.5846/stxb201208301223
  82. Xu, H., Wang, M., Shi, T., Guan, H., Fang, C., & Lin, Z. (2018). Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93, 730–740. https://doi.org/10.1016/j.ecolind.2018.05.055
  83. Xu, J., Zhao, H., Yin, P., Wu, L., & Li, G. (2019). Landscape ecological quality assessment and its dynamic change in coal mining area: A case study of Peixian. Environmental Earth Sciences, 78(24), 708. https://doi.org/10.1007/s12665-019-8747-5
  84. Yan, Y., Zhuang, Q., Zan, C., Ren, J., Yang, L., Wen, Y., Zeng, S., Zhang, Q., & Kong, L. (2021). Using the Google Earth Engine to rapidly monitor impacts of geohazards on ecological quality in highly susceptible areas. Ecological Indicators, 132, 108258. https://doi.org/10.1016/j.ecolind.2021.108258
  85. Yang, S., & Su, H. (2023). Evaluation of urban ecological environment quality based on Google Earth Engine: A case study in Xi’an, China. Polish Journal of Environmental Studies, 32(1), 927–942. https://doi.org/10.15244/pjoes/152448
  86. Yuan, B., Fu, L., Zou, Y., Zhang, S., Chen, X., Li, F., Deng, Z., & Xie, Y. (2021). Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. Journal of Cleaner Production, 302, 126995. https://doi.org/10.1016/j.jclepro.2021.126995
  87. Zhang, A., Tariq, A., Quddoos, A., Naz, I., Aslam, R. W., Barboza, E., Ullah, S., & Abdullah-Al-Wadud, M. (2025). Spatio-temporal analysis of urban expansion and land use dynamics using Google Earth Engine and predictive models. Scientific Reports, 15(1), 6993. https://doi.org/10.1038/s41598-025-92034-4
  88. Zhang, X., Long, T., He, G., & Guo, Y. (2019). Gobal forest cover mapping using Landsat and Google Earth Engine cloud computing. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1–5). IEEE. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820469
  89. Ziter, C. (2016). The biodiversity–ecosystem service relationship in urban areas: A quantitative review. Oikos, 125(6), 761–768. https://doi.org/10.1111/oik.02883