Abstract
It is necessary to understand vegetation evolution and its sensitivity to the global climate, particularly with regard to ecosystems and environmental balance. 25 watersheds were selected in Algeria for this study. Here, the vegetation index (NDVI) and climatic variables (precipitation and temperature) were used to verify the temporal-spatial patterns and impact of the time difference from 1981 to 2021 by applying the correlation coefficient and time delay analysis. The NDVI showed a significant decline, especially in recent years, and spatial differences in NDVI in all areas of study were narrow (slope values from 0.0005 to 0.04), decrease in surface water area from year to year was observed in all regions. The vegetation index was negatively associated with low rainfall and high temperatures. The vegetation’s reaction to temperature has been greater than that too rainfall. In general, a time lag in the vegetation response was found over a time period of at least 1 month. This study provided new insights into variations in vegetation change and the importance of vegetation recovery.
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References
Bai Y, Guo C, Degen AA, Ahmad AA, Wang W, Zhang T, Li W, Ma L, Huang M, Zeng H, Qi L, Long R, Shang Z (2020) Climate warming benefits alpine vegetation growth in Three-River Headwater Region, China. Sci Total Environ 742:140574. https://doi.org/10.1016/j.scitotenv.2020.140574
Bhuyan U, Zang C, Vicente-Serrano S, Menzel A (2017) Exploring relationships among tree-ring growth, climate variability, and seasonal leaf activity on varying timescales and spatial resolutions. Remote Sens 9:526. https://doi.org/10.3390/rs9060526
Chen T, De Jeu R, Liu Y, Van Der Werf G, Dolman A (2014) Using satellite-based soil moisture to quantify the water driven variability in NDVI: a case study over mainland Australia. Remote Sens Environ 140:330–338. https://doi.org/10.1016/.j.rse.2013.08.022
Chu H, Venevsky S, Wu C, Wang M (2019) NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Sci Total Environ 650:2051–2062. https://doi.org/10.1016/j.scitotenv.2018.09.115
Claverie M, Vermote EF, Franch B, Masek JG (2015) Evaluation of the landsat-5 TM and landsat-7 ETM+ surface reflectance products. Remote Sens Environ 169:390–403. https://doi.org/10.1016/j.rse.2015.08.030
Ding Y, Li Z, Peng S (2020) Global analysis of time-lag and accumulation effects of climate on vegetation growth. Int J Appl Earth Obs Geoinf 92:102179. https://doi.org/10.1016/j.jag.2020.102179
Eisavi V, Homayouni S, Yazdi AM, Alimohammadi A (2015) Land cover mapping based on random forest classification of multitemporal spectral and thermal images. Environ Monit Assess 187:291. https://doi.org/10.1007/s10661-015-4489-3
Filippa G, Cremonese E, Galvagno M, Isabellon M, Bayle A, Choler P, Carlson BZ, Gabellani S, di Cella UM, Migliavacca M (2019) Climatic drivers of greening trends in the Alps. Remote Sens 11:2527. https://doi.org/10.3390/rs11212527
Gao W, Zheng C, Liu X, Lu Y, Chen Y, Wei Y, Ma Y (2022) NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: a case study in the Mu Us Sandy Land, China. Ecol Ind 137:108745. https://doi.org/10.1016/j.ecolind.2022.108745
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Hu Y, Dao R, Hu Y (2019) Vegetation change and driving factors: contribution analysis in the Loess Plateau of China during 2000–2015. Sustainability 11:1320. https://doi.org/10.3390/su11051320
Irons JR, Dwyer JL, Barsi JA (2012) The next Landsat satellite: the LANDSAT data continuity mission. Remote Sens Environ 122:11–21. https://doi.org/10.1016/j.rse.2011.08.026
Islam A, Islam HMT, Shahid S, Khatun MK, Ali MM, Rahman MS, Ibrahim SM, Almoajel AM (2021) Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. J Environ Manag 289:112505. https://doi.org/10.1016/j.jenvman.2021.112505
Jong R, Schaepman ME, Furrer R, Bruin S, Verburg PH (2013) Spatial relationship between climatologies and changes in global vegetation activity. Glob Change Biol 19:1953–1964. https://doi.org/10.1111/gcb.12193
Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series, 1: LandTrendr—temporal segmentation algorithms. Remote Sens Environ 114:2897–2910. https://doi.org/10.1016/j.rse.2010.07.008
Kennedy RE, Yang Z, Gorelick N, Braaten J, Cavalcante L, Cohen W, Healey S (2018) Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens 10:691. https://doi.org/10.3390/rs10050691
Li C, Leal Filho W, Yin J, Hu R, Wang J, Yang C, Yin S, Bao Y, Ayal DY (2018) Assessing vegetation response to multi-time-scale drought across inner Mongolia plateau. J Clean Prod 179:210–216
Liu C, Liu J, Zhang Q, Ci H, Gu X, Gulakhmadov A (2022) Attribution of NDVI dynamics over the globe from 1982 to 2015. Remote Sens 14:2706. https://doi.org/10.3390/rs14112706
Liu S, Cheng F, Dong S, Zhao H, Hou X, Wu X (2017) Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI. Sci Rep 7(1):1–10. https://doi.org/10.1038/s41598-017-04038-4
Liu YL, Lei HM (2015) Responses of natural vegetation dynamics to climate drivers in China from 1982 to 2011. Remote Sens 7:10243–10268. https://doi.org/10.3390/rs70810243
Liu Q, Zhao DS, Wu SH, Dai EF, Gao JB (2019) Using the NDVI to analyze trends and stability of grassland vegetation cover in Inner Mongolia. Theor Appl Climatol 135(3–4):1629–1640. https://doi.org/10.1007/s00704-018-2614-2
Luo X, Ao X, Zhang Z, Wan Q, Liu X (2020) Spatiotemporal variations of cultivated land use efficiency in the Yangtze River economic belt based on carbon emission constraints. J Geogr Sci 30:535–552. https://doi.org/10.1007/s11442-020-1741-8
Luo Z, Wu W, Yu X, Song Q, Yang J, Wu J, Zhang H (2018) Variation of net primary production and its correlation with climate change and anthropogenic activities over the Tibetan Plateau. Remote Sens 10:1352. https://doi.org/10.3390/rs10091352
Ma Q, Chai L, Hou F, Chang S, Ma Y, Tsunekawa A, Cheng Y (2019) Quantifying grazing intensity using remote sensing in alpine meadows on Qinghai-Tibetan Plateau. Sustainability 11:417. https://doi.org/10.3390/su11020417
Martiny N, Camberlin P, Richard Y, Philippon N (2006) Compared regimes of NDVI and rainfall in semi-arid regions of Africa. Int J Remote Sens 27(23):5201–5223. https://doi.org/10.1080/01431160600567787
Myneni RB, Hoffman S, Knyazikhin Y et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231. https://doi.org/10.1016/S0034-4257(02)00074-3
Pan N, Feng X, Fu B, Wang S, Ji F, Pan S (2018) Increasing global vegetation browning hidden in overall vegetation greening: insights from time-varying trends. Remote Sens Environ 214:59–72. https://doi.org/10.1016/j.rse.2018.05.018
Parteli EJR (2022) Predicted expansion of sand deserts. Nat Clim Chang 12:967–968. https://doi.org/10.1038/s41558-022-01506-2
Peng J, Wu C, Zhang X, Wang X, Gonsamo A (2019) Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Glob Change Biol 25:2174–2188. https://doi.org/10.1111/gcb.14627
Piao S, Mohammat A, Fang J, Cai Q, Feng J (2006) NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Glob Environ Chang 16:340–348. https://doi.org/10.1016/j.gloenvcha.2006.02.002
Qin J, Ma M, Shi J, Ma S, Wu B, Su X (2023) The time-lag effect of climate factors on the forest enhanced vegetation index for subtropical humid areas in China. Int J Environ Res Public Health 20:799. https://doi.org/10.3390/ijerph20010799
Roerink GJ, Menenti M, Soepboer W, Su Z (2003) Assessment of climate impact on vegetation dynamics by using remote sensing. Phys Chem Earth 28:103–109. https://doi.org/10.1016/S1474-7065(03)00011-1
Seddon AWR, Macias-Fauria M, Long PR, Benz D, Willis KJ (2016) Sensitivity of global terrestrial ecosystems to climate variability. Nature 531(7593):229–243. https://doi.org/10.1038/nature16986
Vicente-Serrano SM, Gouveia C, Camarero JJ, Beguería S, Trigo R, Lópezmoreno JI, Azorínmolina C, Pasho E, Lorenzolacruz J, Revuelto J (2013) Response of vegetation to drought time-scales across global land biomes. Proc Natl Acad Sci USA 110:52–57. https://doi.org/10.1073/pnas.1207068110
Wang M, Fu J, Wu Z, Pang Z (2020a) Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the Yellow River China. ISPRS Int J Geo-Inf 282:1. https://doi.org/10.3390/ijgi9040282
Wang H, Kang C, Tian Z, Zhang A, Cao Y (2020b) Vegetation periodic changes and relationships with climate in Inner Mongolia Based on the VMD method. Ecol Indic 34:107964. https://doi.org/10.1016/j.gecco.2022.e02034
Wang H, Liu D, Lin H, Montenegro A, Zhu X (2015) NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. Int J Climatol 35:687–698. https://doi.org/10.1002/joc.4013
Wu D, Zhao X, Liang S, Zhou T, Huang K, Tang B, Zhao W (2015) Time-lag effects of global vegetation responses to climate change. Glob Chang Biol 21(9):3520–3531. https://doi.org/10.1111/gcb.12945
Xu H, Wang X, Zhang X (2016) Alpine grasslands response to climatic factors and anthropogenic activities on the Tibetan Plateau from 2000 to 2012. Ecol Eng 92:251–259. https://doi.org/10.1016/j.ecoleng.2016.04.005
Xu G, Zhang H, Chen B, Zhang H, Innes JL, Wang G, Yan J, Zheng Y, Zhu Z, Myneni RB (2014) Changes in vegetation growth dynamics and relations with climate over China landmass from 1982 to 2011. Remote Sens 6:3263–3283. https://doi.org/10.3390/rs6043263
Zhan C, Liang C, Zhao L, Jiang S, Niu K, Zhang Y, Cheng L (2022) Vegetation dynamics and its response to climate change in the Yellow River Basin, China. Front Environ Sci 450:1
Zhang Q, Buyantuev A, Fang X, Han P, Li A, Li FY, Liang C, Liu Q, Ma Q, Niu J et al (2020) Ecology and sustainability of the Inner Mongolian Grassland: looking back and moving forward. Landsc Ecol 35:2413–2432. https://doi.org/10.1007/s10980-020-01083-9
Zhang YL, Gao JG, Liu LS, Wang ZF, Ding MJ, Yang XC (2013) NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: a case study in the Koshi river basin in the middle Himalayas. Global Planet Change 108:139–148. https://doi.org/10.1016/J.GLOPLACHA.2013.06.012
Zhao J, Huang S, Huang Q, Wang H, Leng G, Fang W (2020) Time-lagged response of vegetation dynamics to climatic and teleconnection factors. CATENA 189:104474. https://doi.org/10.1016/j.catena.2020.104474
Zhe M, Zhang X (2021) Time-lag effects of NDVI responses to climate change in the Yamzhog Yumco Basin, South Tibet. Ecol Indic 124:107431. https://doi.org/10.1016/j.ecolind.2021.107431
Zhou L, Tian Y, Myneni RB et al (2014) Widespread decline of Congo rainforest greenness in the past decade. Nature 509:86–90
Zhou X, Yamaguchi Y, Arjasakusuma S (2018) Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: a cross-border study on the Mongolian Plateau. Sci Total Environ 616–617:730–743. https://doi.org/10.1016/j.scitotenv.2017.10.253
Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsat 4–7, 8, and Sentinel 2 images. Remote Sens Environ 159:269–277. https://doi.org/10.1016/j.rse.2014.12.014
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HK: designed the research, methodology, and software, implemented the data computation, formal analysis, and cartography. EB: review and editing and supervision. AZ: validation. All authors participated significantly in the content development and authorized this manuscript’s publishing.
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Keria, H., Bensaci, E. & Zoubiri, A. Assessment of the long-term effects of climate on vegetation in 25 watersheds in dry and semi-dry areas, Algeria. Nat Hazards 120, 7575–7596 (2024). https://doi.org/10.1007/s11069-024-06532-1
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DOI: https://doi.org/10.1007/s11069-024-06532-1