Spatiotemporal Distribution of the Impact of Climate Change and Human Activities on NDVI in China

1. LIU Y., TIAN J., LIU R., DING L., 2021, Influences of climate change and human activities on NDVI changes in China, Remote Sensing 13: 4326, https://doi.org/10.3390/rs13214326.
DOI: https://doi.org/10.3390/rs13214326   Google Scholar

2. CHANG J., LIU Q., WANG S., HUANG C., 2022, Vegetation dynamics and their influencing factors in China from 1998 to 2019, Remote Sensing 14: 3390, https://doi.org/10.3390/rs14143390.
DOI: https://doi.org/10.3390/rs14143390   Google Scholar

3. PU M., ZHAO Y., NI Z., HUANG Z., PENG W., ZHOU Y., LIU J., GONG Y., 2022, Spatial-temporal evolu-tion and driving forces of NDVI in China’s Giant Panda National Park, International Journal of Environmental Research and Public Health 19: 6722, https://doi.org/10.3390/ijerph19116722.
DOI: https://doi.org/10.3390/ijerph19116722   Google Scholar

4. YIN T., ZHAI Y., ZHANG Y., YANG W., DONG J., LIU X., FAN P., YOU C., YU L., GAO Q., WANG H., ZHENG P., WANG R., 2023, Impacts of climate change and human activities on vegetation coverage variation in mountainous and hilly areas in Central South of Shandong Province based on tree-ring, Frontiers in Plant Science 14: 1158221, https://doi.org/10.3389/fpls.2023.1158221.
DOI: https://doi.org/10.3389/fpls.2023.1158221   Google Scholar

5. LI X., WANG Y., ZHAO Y., ZHAI J., LIU Y., HAN S., LIU K., 2024, Research on the impact of climate change and human activities on the NDVI of arid areas – A case study of the Shiyang River Basin, Land 13: 533, https://doi.org/10.3390/land13040533.
DOI: https://doi.org/10.3390/land13040533   Google Scholar

6. CHEN X., HE Q., YE T., LIANG Y., LI Y., 2024, Decoding spatiotemporal dynamics in atmospheric CO2 in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis, Science of The Total Environment 908: 167917, https://doi.org/10.1016/j.scitotenv.2023.167917.
DOI: https://doi.org/10.1016/j.scitotenv.2023.167917   Google Scholar

7. SIFRIYANI S., RASJID M., ROSADI D., ANWAR S., WAHYUNI R.D., JALALUDDIN S., 2022, Spa-tial-temporal epidemiology of COVID-19 using a geographically and temporally weighted regression model, Symmetry 14: 742, https://doi.org/10.3390/sym14040742.
DOI: https://doi.org/10.3390/sym14040742   Google Scholar

8. ZHAO M., WANG H., SUN J., TANG R., CAI B., SONG X., HUANG X., HUANG J., FAN Z., 2023, Spatio-temporal characteristics of soil Cd pollution and its influencing factors: A geographically and temporally weighted regression (GTWR) method, Journal of Hazardous Materials 446: 130613, https://doi.org/10.1016/j.jhazmat.2022.130613.
DOI: https://doi.org/10.1016/j.jhazmat.2022.130613   Google Scholar

9. XU Y., YANG Y., CHEN X., LIU Y., 2022, Bibliometric analysis of global NDVI research trends from 1985 to 2021, Remote Sensing 14: 3967, https://doi.org/10.3390/rs14163967.
DOI: https://doi.org/10.3390/rs14163967   Google Scholar

10. ZHANG H., GUO J., LI X., LIU Y., WANG T., 2023, Spatiotemporal variation in and responses of the NDVI to climate in Western Ordos and Eastern Alxa, Sustainability 15: 4375, https://doi.org/10.3390/su15054375.
DOI: https://doi.org/10.3390/su15054375   Google Scholar

11. PETTORELLI N., 2015, The normalized difference vegetation index, Oxford University Press, Oxford, https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001.
DOI: https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001   Google Scholar

12. JESPERSEN R.G., ANDERSON-SMITH M., SULLIVAN P.F., DIAL R.J., WELKER J.M., 2023, NDVI changes in the Arctic: Functional significance in the moist acidic tundra of Northern Alaska, PLoS ONE 18(4): e0285030, https://doi.org/10.1371/journal.pone.0285030.
DOI: https://doi.org/10.1371/journal.pone.0285030   Google Scholar

13. LIU Y., LI Z., CHEN Y., LI Y., LI H., XIA Q., KAYUMBA P.M., 2022, Evaluation of consistency among three NDVI products applied to High Mountain Asia in 2000–2015, Remote Sensing of Environment 269: 112821, https://doi.org/10.1016/j.rse.2021.112821.
DOI: https://doi.org/10.1016/j.rse.2021.112821   Google Scholar

14. XIE F., FAN H., 2021, Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and land sur-face temperature (LST): Is data reconstruction necessary?, International Journal of Applied Earth Observation and Geoinformation 101: 102352, https://doi.org/10.1016/j.jag.2021.102352.
DOI: https://doi.org/10.1016/j.jag.2021.102352   Google Scholar

15. LIU Q., YANG Z., HAN F., 2016, NDVI-based vegetation dynamics and their response to recent climate change: A case study in the Tianshan Mountains, China, Environmental Earth Sciences 75: 1189, https://doi.org/10.1007/s12665-016-5987-5.
DOI: https://doi.org/10.1007/s12665-016-5987-5   Google Scholar

16. ZHANG Y., ZHANG L., WANG J., DONG G., WEI Y., 2023, Quantitative analysis of NDVI driving factors based on the geographical detector model in the Chengdu-Chongqing region, China, Ecological Indicators 155: 110978, https://doi.org/10.1016/j.ecolind.2023.110978.
DOI: https://doi.org/10.1016/j.ecolind.2023.110978   Google Scholar

17. ZHANG H., LI L., ZHAO X., CHEN F., WEI J., FENG Z., HOU T., CHEN Y., YUE W., SHANG H., 2024, Changes in vegetation NDVI and its response to climate change and human activities in the Ferghana Basin from 1982 to 2015, Remote Sensing 16: 1296, https://doi.org/10.3390/rs16071296.
DOI: https://doi.org/10.3390/rs16071296   Google Scholar

18. GUO L., LI J., ZHANG C., XU Y., XING J., HU J., 2024, Evaluating the impact of human activities on vege-tation restoration in mining areas based on the GTWR, ISPRS International Journal of Geo-Information 13: 132, https://doi.org/10.3390/ijgi13040132.
DOI: https://doi.org/10.3390/ijgi13040132   Google Scholar

19. REN Y., ZHANG F., ZHAO C., CHENG Z., 2023, Attribution of climate change and human activities to vege-tation NDVI in Jilin Province, China during 1998–2020, Ecological Indicators 153: 110415, https://doi.org/10.1016/j.ecolind.2023.110415.
DOI: https://doi.org/10.1016/j.ecolind.2023.110415   Google Scholar

20. YUAN X., LI L., CHEN X., SHI H., 2015, Effects of precipitation intensity and temperature on NDVI-based grass change over Northern China during the period from 1982 to 2011, Remote Sensing 7: 10164-10183, https://doi.org/10.3390/rs70810164.
DOI: https://doi.org/10.3390/rs70810164   Google Scholar

21. HUSSAIN S., RAZA A., ABDO H.G., 2023, Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan, Geoscience Letters 10: 33, https://doi.org/10.1186/s40562-023-00287-6.
DOI: https://doi.org/10.1186/s40562-023-00287-6   Google Scholar

22. WU Y., YANG J., LI S., GUO C., YANG X., XU Y., YUE F., PENG H., CHEN Y., GU L., 2023, NDVI-based vegetation dynamics and their responses to climate change and human activities from 2000 to 2020 in Miaoling Karst Mountain Area, SW China, Land 12: 1267, https://doi.org/10.3390/land12071267.
DOI: https://doi.org/10.3390/land12071267   Google Scholar

23. LU Q., ZHAO D., WU S., 2019, Using the NDVI to analyze trends and stability of grassland vegetation cover in Inner Mongolia, Theoretical and Applied Climatology 135: 1629–1640, https://doi.org/10.1007/s00704-018-2614-2.
DOI: https://doi.org/10.1007/s00704-018-2614-2   Google Scholar

24. HUANG B., WU B., BARRY M., 2010, Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices, International Journal of Geographical Information Science 24: 383–401.
DOI: https://doi.org/10.1080/13658810802672469   Google Scholar

25. BAI Y., WU L., QIN K., ZHANG Y., SHEN Y., ZHOU Y., 2016, A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD, Remote Sensing 8: 262, https://doi.org/10.3390/rs8030262.
DOI: https://doi.org/10.3390/rs8030262   Google Scholar