{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:05:18Z","timestamp":1774497918105,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National key R&amp;D projects","award":["2018YFE0105900"],"award-info":[{"award-number":["2018YFE0105900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The Mara River Basin of Africa has a world-famous ecosystem with vast vegetation, which is home to many wild animals. However, the basin is experiencing vegetation degradation and bad climate change, which has caused conflicts between people and wild animals, especially in dry seasons. This paper studied the vegetation greenness (VG), vegetation greenness trends (VGT), and their responses to climate change in dry seasons in the Mara River Basin, Africa. Firstly, based on Google Earth Engine (GEE) platform and Sentinel-2 images, the vegetation distribution map of the Mara River Basin was drawn. Then dry seasons MODIS NDVI data (January to February and June to September) were used to analyze the VGT. Finally, a random forest regression algorithm was used to evaluate the response of VG and VGT to temperature and precipitation derived from ERA5 from 2000 to 2019 at a resolution of 250 m. The results showed that the VGT was fluctuating in dry seasons, and the spatial differentiation was obvious. The greenness increasing trends both upstream and downstream were significantly larger than that of in the midstream. The responses of VG to precipitation were almost twice larger than temperature, and the responses of VGT to temperature were about 1.5 times larger than precipitation. The climate change trend of rising temperature and falling precipitation will lead to the degradation of vegetation and the reduction of crop production. There will be a vegetation degradation crisis in dry seasons in the Mara River Basin in the future. Identifying the spatiotemporal changes of VGT in dry seasons will be helpful to understand the response of VG and VGT to climate change and could also provide technical support to cope with climate-change-related issues for the basin.<\/jats:p>","DOI":"10.3390\/ijgi11080426","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T20:49:28Z","timestamp":1659041368000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Vegetation Greenness Trend in Dry Seasons and Its Responses to Temperature and Precipitation in Mara River Basin, Africa"],"prefix":"10.3390","volume":"11","author":[{"given":"Wanyi","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenke","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinya","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-3976","authenticated-orcid":false,"given":"Priyanko","family":"Das","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouming","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8376-1694","authenticated-orcid":false,"given":"Binglin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of African Studies, Nanjing University, Nanjing 210023, China"},{"name":"School of Geography and Planning, Nanning Normal University, Nanning 530001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","first-page":"e01299","article-title":"Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982\u20132014","volume":"24","author":"Lamchin","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF00138369","article-title":"A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR","volume":"17","author":"Nicholson","year":"1990","journal-title":"Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102030","DOI":"10.1016\/j.gloenvcha.2019.102030","article-title":"Accelerating savanna degradation threatens the Maasai Mara socio-ecological system","volume":"60","author":"Li","year":"2020","journal-title":"Glob. Environ. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1038\/nature04070","article-title":"Determinants of woody cover in African savannas","volume":"438","author":"Sankaran","year":"2005","journal-title":"Nature"},{"key":"ref_5","first-page":"249","article-title":"Assessment of NDVI variations in responses to climate change in the Horn of Africa","volume":"23","author":"Ghebrezgabher","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_6","first-page":"1","article-title":"Spatio-temporal vegetation dynamics and relationship with climate over East Africa","volume":"502","author":"Musau","year":"2016","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s11069-015-1635-8","article-title":"Recent trends in vegetation dynamics in the South America and their relationship to rainfall","volume":"77","author":"Barbosa","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dhillon, M.S., Dahms, T., K\u00fcbert-Flock, C., Steffan-Dewenter, I., Zhang, J., and Ullmann, T. (2022). Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria. Remote Sens., 14.","DOI":"10.3390\/rs14030677"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Hern\u00e1ndez-L\u00f3pez, D., Ballesteros, R., and Moreno, M.A. (2021). Improving the accuracy of multiple algorithms for crop classification by integrating sentinel-1 observations with sentinel-2 data. Remote Sens., 13.","DOI":"10.3390\/rs13020243"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"012064","DOI":"10.1088\/1755-1315\/481\/1\/012064","article-title":"Using NDVI algorithm in Sentinel-2A imagery for rice productivity estimation (Case study: Compreng sub-district, Subang Regency, West Java)","volume":"481","author":"Khoirunnisa","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ma, C., Johansen, K., and McCabe, M.F. (2022). Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14051205"},{"key":"ref_12","first-page":"528","article-title":"Integrating AVHRR and MODIS data to monitor NDVI changes and their: Relationships with climatic parameters in Northeast China","volume":"18","author":"Mao","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1080\/01431160512331326783","article-title":"Monitoring of forage conditions with MODIS imagery in the Xilingol steppe, Inner Mongolia","volume":"26","author":"Kawamura","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ecolind.2013.04.009","article-title":"Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI","volume":"34","author":"Petus","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"015504","DOI":"10.1088\/1748-9326\/7\/1\/015504","article-title":"Environment, vegetation and greenness (NDVI) along the North America and Eurasia Arctic transects","volume":"7","author":"Walker","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.01.001","article-title":"The vegetation greenness trend in Canada and US Alaska from 1984\u20132012 Landsat data","volume":"176","author":"Ju","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Potter, C. (2018). Recovery rates of Wetland Vegetation Greenness in severely burned ecosystems of Alaska derived from satellite image analysis. Remote Sens., 10.","DOI":"10.3390\/rs10091456"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.rse.2017.11.017","article-title":"Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada","volume":"206","author":"Fang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, X., Wang, H., Zheng, K., Li, H., Wang, G., and An, Z. (2021). Monitoring vegetation greenness in response to climate variation along the elevation gradient in the three-river source region of China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030193"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ecolind.2018.01.031","article-title":"Monitoring changes of NDVI in protected areas of southern California","volume":"88","author":"Gillespie","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.actao.2021.103804","article-title":"Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia","volume":"114","author":"Touhami","year":"2022","journal-title":"Acta Oecologica"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1023\/A:1009723906370","article-title":"Woody vegetation spatial patterns in a semi-arid savanna of Burkina Faso, West Africa","volume":"132","author":"Couteron","year":"1997","journal-title":"Plant Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1111\/aje.12378","article-title":"Using GIS and remote sensing to explore the influence of physical environmental factors and historical land use on bushland structure","volume":"55","author":"Mutiti","year":"2017","journal-title":"Afr. J. Ecol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1111\/j.1365-2028.2007.00821.x","article-title":"El Ni\u00f1o-Southern Oscillation, rainfall, temperature and Normalized Difference Vegetation Index fluctuations in the Mara-Serengeti ecosystem","volume":"46","author":"Ogutu","year":"2008","journal-title":"Afr. J. Ecol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111953","DOI":"10.1016\/j.rse.2020.111953","article-title":"Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem","volume":"247","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1046\/j.1461-0248.2003.00447.x","article-title":"ENSO, rainfall and temperature influences on extreme population declines among African savanna ungulates","volume":"6","author":"Ogutu","year":"2003","journal-title":"Ecol. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1080\/02626667.2013.853121","article-title":"Comparaison du r\u00e9gime d\u2019\u00e9coulement, de l\u2019hydraulique en rivi\u00e8re et des communaut\u00e9s biologiques en vue de d\u00e9duire les relations d\u00e9bit-\u00e9cologie de la rivi\u00e8re Mara au Kenya et en Tanzanie","volume":"59","author":"McClain","year":"2014","journal-title":"Hydrol. Sci. J."},{"key":"ref_29","first-page":"9","article-title":"Water Demand Simulation Using WEAP 21: A Case Study of the Mara River Basin, Kenya","volume":"3","year":"2018","journal-title":"Int. J. Nat. Resour. Ecol. Manag."},{"key":"ref_30","unstructured":"WREM International Inc. (2008). Mara River Basin Monograph: Final Report, WREM International Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1017\/S0030605317001338","article-title":"The Serengeti will die if Kenya dams the Mara River","volume":"51","author":"Mnaya","year":"2017","journal-title":"Oryx"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.catena.2013.11.017","article-title":"Assessment of water resources availability and demand in the Mara River Basin","volume":"115","author":"Dessu","year":"2014","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.5194\/hess-15-2245-2011","article-title":"Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: Results of a modeling study to support better resource management","volume":"15","author":"Mango","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_34","first-page":"102","article-title":"Vulnerability and Adaptation Assessment in the Mara river basin","volume":"100","author":"Zermoglio","year":"2019","journal-title":"Who"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10661-015-4489-3","article-title":"Land cover mapping based on random forest classification of multitemporal spectral and thermal images","volume":"187","author":"Eisavi","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.proenv.2015.03.028","article-title":"Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries","volume":"24","author":"Jhonnerie","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"14585","DOI":"10.1111\/gcb.14585","article-title":"Elephants limit aboveground carbon gains in African savannas","volume":"25","author":"Davies","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"19","article-title":"Application of harmonic analysis of NDVI time series (HANTS)","volume":"108","author":"Verhoef","year":"1996","journal-title":"Fourier Anal. Temporal NDVI S. Afr. Am. Continents."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric Tests Against Trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.2174\/1874839201307010011","article-title":"Changing wildlife populations in nairobi national park and adjoining athi-kaputiei plains: Collapse of the migratory wildebeest","volume":"7","author":"Ogutu","year":"2013","journal-title":"Open Conserv. Biol. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.2166\/wcc.2022.299","article-title":"Evaluation of four bias correction methods and random forest model for climate change projection in the Mara River Basin, East Africa","volume":"13","author":"Das","year":"2022","journal-title":"J. Water Clim. Chang."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1111\/j.1365-2699.2008.02017.x","article-title":"The spatial distribution of vegetation types in the Serengeti ecosystem: The influence of rainfall and topographic relief on vegetation patch characteristics","volume":"36","author":"Reed","year":"2009","journal-title":"J. Biogeogr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dutton, C.L., Subalusky, A.L., Anisfeld, S.C., Njoroge, L., Rosi, E.J., and Post, D.M. (2018). The influence of a semi-Arid sub-catchment on suspended sediments in the Mara River, Kenya. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192828"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.scitotenv.2019.07.189","article-title":"Humans reshape wetlands: Unveiling the last 100 years of morphological changes of the Mara Wetland, Tanzania","volume":"691","author":"Bregoli","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_49","first-page":"65","article-title":"Reliability of the Environmental Feasibility Studies to the Mining and Construction Projects: A Case of Mara River Basin in Tanzania","volume":"7","author":"Mwemezi","year":"2017","journal-title":"Am. J. Environ. Eng."},{"key":"ref_50","unstructured":"Pruijssen, M.J. (2015). FLEX-Topo Modelling of Water Use and Demand in the Mara River Basin, Kenya, Delft University of Technology."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bartzke, G.S., Ogutu, J.O., Mukhopadhyay, S., Mtui, D., Dublin, H.T., and Piepho, H.P. (2018). Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0202814"},{"key":"ref_52","first-page":"2339","article-title":"Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 simulations","volume":"9","author":"Wainwright","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1111\/j.1365-2028.2005.00587.x","article-title":"Oscillations in large mammal populations: Are they related to predation or rainfall?","volume":"43","author":"Ogutu","year":"2005","journal-title":"Proc. Afr. J. Ecol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/20964129.2018.1530054","article-title":"Impact of climate change on biodiversity and associated key ecosystem services in Africa: A systematic review","volume":"4","author":"Sintayehu","year":"2018","journal-title":"Ecosyst. Health Sustain."},{"key":"ref_55","unstructured":"Brown, J.D. (2013). Biogeography, Sinauer Associates, TTESOL International Association."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1111\/j.1365-2486.2006.01115.x","article-title":"Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions","volume":"12","author":"Thuiller","year":"2006","journal-title":"Glob. Chang. Biol."},{"key":"ref_57","unstructured":"Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., and Genova, R.C. (2014). Climate Change 2014 Impacts, Adaptation, And Vulnerability Part B: Regional Aspects: Working Group II Contribution to The Fifth Assessment Report of The Intergovernmental Panel On Climate Change, Cambridge University Press."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/416389a","article-title":"Ecological responses to recent climate change","volume":"416","author":"Walther","year":"2002","journal-title":"Nature"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature01333","article-title":"Fingerprints of global warming on wild animals and plants","volume":"421","author":"Root","year":"2003","journal-title":"Nature"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:02Z","timestamp":1760140682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":59,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["ijgi11080426"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11080426","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}