Monitoring the the Impacts of Climate Change and Variability on the Phenology of Natural Vegetation Using 250m MODIS-NDVI Satellite Data: Cace Study of the Dryland Ecosystem of Sokoto, North-Westrn Nigeria.

https://doi.org/10.22146/ijg.61697

Abubakar Magaji Jibrillah(1*), Nathanial Bayode Eniolorunda(2), Garba Abdulmumin Budah(3), Dalhatu Ahmad(4)

(1) Usmanu Danfodiyo University, Sokoto, Nigeria
(2) Usmanu Danfodiyo University, Sokoto, Nigeria
(3) Usmanu Danfodiyo University, Sokoto, Nigeria
(4) National Space Research and Development Agency (NSRDA) Abuja, Nigeria
(*) Corresponding Author

Abstract


Recent climate change and variability together with other anthropogenic drivers have exerted tremendous pressure on the fragile dryland ecosystem of Sokoto, North-western Nigeria. Vegetation phenology is one of the active indicators of the impacts of climate change on the ecosystem. This study aimed to monitor how the ecosystem of the area responds to the challenges associated with climate change in order to provide baseline information for policies and programmes geared towards addressing these challenges. It explored the applications of remote sensing data (MODIS-NDVI), GIS and statistical analyses in achieving this aim. Image processing operations such as data extraction, raster calculations, geometric transformations and creation of the region of interest were conducted using ArcGIS 10.5 model builder while TIMESAT software was used determined the vegetation phenological events such as the start, end and length of the growing seasons. The results indicated a persistent decline in the length of the growing seasons of the major vegetation classes in the area due to late onset and early cessation of the growing season which is positively correlated with rainfall distribution. From the year 2001 to 2016, 36% and 33% declined in the length of the growing season were recorded for shrubs and grasses respectively. These are positively correlated with the annual rainfall distributions in the area, with the correlation coefficient of r = 0.40 and r = 0.36 for the shrubs and grasses respectively. Implications of these on the ecosystem and livelihoods of the people in the area were discussed and ways forward suggested.

Keywords


Phenology; VPhenology; Vegetation; Climate Change; Dryland; Ecosystem.egetation; Climate Change; Dryland; Ecosystem.

Full Text:

PDF


References

Adole, T., Dash, J., & Atkinson, P. M. (2016). A systematic review of vegetation phenology in Africa. Ecological Informatics, 34, 117–128. https://doi.org/10.1016/j.ecoinf.2016.05.004

Adole, T., Dash, J., & Atkinson, P. M. (2018). Characterising the land surface phenology of Africa using 500 m MODIS EVI. Applied Geography, 90, 187–199. https://doi.org/10.1016/j.apgeog.2017.12.006

Barker, T. (2007). Climate Change 2007 : An Assessment of the Intergovernmental Panel on Climate Change. Change, 446, 12–17. https://doi.org/10.1256/004316502320517344

Bohovic, R., Dobrovolny, P., & Klein, D. (2016). The spatial and temporal dynamics of remotely-sensed vegetation phenology in central Asia in the 1982-2011 period. European Journal of Remote Sensing, 49(1), 279–299. https://doi.org/10.5721/EuJRS20164916

Broich, M., Huete, A., Tulbure, M. G., Ma, X., Xin, Q., Paget, M., … Held, A. (2014). Land surface phenological response to decadal climate variability across Australia using satellite remote sensing. Biogeosciences, 11(18), 5181–5198. https://doi.org/10.5194/bg-11-5181-2014

Cao, X., Chen, J., Matsushita, B., & Imura, H. (2010). Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area. International Journal of Remote Sensing, 31(6), 1589–1604. https://doi.org/10.1080/01431160903475274

Davis, G. (1982). Rainfall and Temperatue. In P. S. Abdu (Ed.), Sokoto State in Maps: An Atlas of physical and Human Resources. Ibadan, Nigeria: University Press. 8 - 15.

Eklundh, L., & Jönsson, P. (2015). TIMESAT 3.2 with parallel processing Software Manual. Lund University. Retrieved from http://www.nateko.lu.se/TIMESAT/

FAO. (2010). Global Forest Resources Assessment 2010. FAO Forestry Paper (Vol. 163). Rome. https://doi.org/ISBN 978-92-5-106654-6

FAO. (2013). Climate change guidelines for forest managers. FAO Forestry Paper No. 172. Rome: Food and Agricultural Organisation of the United Ntions.

Filipponi, F., Smiraglia, D., Mandrone, S., & Tornato, A. (2021). Cropland Mapping Using Earth Observation Derived Phenological ametrics. In IECAG (pp. 4–10). Italia: MDPI.

Hmimina, G., Dufrêne, E., Pontailler, J., Delpierre, N., Aubinet, M., Caquet, B., … Soudani, K. (2013). Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132, 145–158. https://doi.org/http://dx.doi.org/10.1016/j.rse.2013.01.010

Ibrahim, S. A., Kaduk, J., Tansey, K., Balzter, H., Mohammed, U., & Tansey, K. (2021). Detecting phenological changes in plant functional types over West African savannah dominated landscape. International Journal of Remote Sensing, 42(2), 567–594. https://doi.org/10.1080/01431161.2020.1811914

Igboabuchi, N. A., Echereme, C. B., & Ekwealor, K. U. (2018). Phenology in Plants : Concepts and Uses. International Journal of Science and Research Methodology, 11(1), 8–24.

Iliya, M. A. (1999). Income Diversification in the Semi-arid Zone of Nigeria: A Study of Gigane, Sokoto, North-west Nigeria. Kano, Nigeria: . Centre for Research and Documentation (CDC).

IPCC. (2001). Climate change 2001 : Impacts, Adaptation, and Vulnerability. Cambridge University Press. Retrieved from https://www.ipcc.ch/ipccreports/tar/wg2/pdf/wg2TARchap1.pdf

IPCC. (2014). Summary for Policymakers. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/CBO9781107415324

Jiao, N. Z., Chen, D. K., Luo, Y. M., Huang, X. P., Zhang, R., Zhang, H. B., … Zhang, F. (2015). Climate change and anthropogenic impacts on marine ecosystems and countermeasures in China. Advances in Climate Change Research, 6(2), 118–125. https://doi.org/10.1016/j.accre.2015.09.010

MA. (2005). Millennium Ecosystem Assessment Findings. Retrieved from http://limnology.wisc.edu/courses/zoo725/2007lectures/0423_MA.pdf

Ma, X., Huete, A., Yu, Q., Coupe, N. R., Davies, K., Broich, M., … Eamus, D. (2013). Spatial Patterns and Temporal Dynamics in Savanna Vegetation Phenology across the North Australian Tropical Transect. Remote Sensing of Environment, 139, 97–115. https://doi.org/10.1016/j.rse.2013.07.030

Niang, I., Ruppel, O. C., Abdrabo, M. A., Essel, A., Lennard, C., Padgham, J., & Urquhart, P. (2014). Africa: Climate Change 2014: Impacts, Adaptation and Vulnerability - Contributions of the Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,. Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/CBO9781107415386.002

NIMET. (2010). Nigeria Climate Review Bulletin 2007. Nigerian Meteorological Agency. Abuja, Nigeria. Retrieved from https://www.fidelityworldwideinvestment.com/static/pdf/legal-documents/UT-nonUCITS/trust-deeds/moneybuilder-cash-isa/Doc_c.pdf

Osunmadewa, B. A., Gebrehiwot, W. Z., & Csaplovics, E. (2018). Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets. Open Geosci, 10, 1–11.

Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918), 37–42. https://doi.org/10.1038/nature01286

Primack, R. B., & Miller-Rushing, A. J. (2011). Broadening the study of phenology and climate change. New Phytologist, 191(2), 307–309. https://doi.org/10.1111/j.1469-8137.2011.03773.x

Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M., Gray, J. M., … Frolking, S. (2018). Data Descriptor : Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data, 28, 1–24.

Rodriguez-Galiano, V. F., Dash, J., & Atkinson, P. M. (2015). Intercomparison of satellite sensor land surface phenology and ground phenology in Europe. Geophysical Research Letters, 42(7), 2253–2260. https://doi.org/10.1002/2015GL063586

Stefanov, W. L., & Netzband, M. (2005). Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment, 99(1–2), 31–43. https://doi.org/10.1016/j.rse.2005.04.024

Tan, B., Gao, F., Tan, B., Gao, F., Wolfe, R. E., Pedelty, J. A., … Nightingale, J. (2011). An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 361–371. https://doi.org/10.1109/JSTARS.2010.2075916

Tang, H., Li, Z., Zhu, Z., Chen, B., Zhang, B., & Xin, X. (2015). Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China. Remote Sensing, 7(9), 11914–11932. https://doi.org/10.3390/rs70911914

Thayn, J. B. (2011). Assessing Remote Sensing Techniques for Measuring Vegetation Phenology. Institute for Geospatial Analysis and Mapping. 2011 ASPRS Annual Conference Milwaukee, Wisconsin Retrieved from http://www.asprs.org/a/publications/proceedings/Milwaukee2011/files/Thayn.pdf

Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Eklundh, L., & Ard, J. (2021). Remote Sensing of Environment Calibrating vegetation phenology from Sentinel-2 using eddy covariance , PhenoCam , and PEP725 networks across Europe. Remote Sensing of Environment, 260, 1–15. https://doi.org/10.1016/j.rse.2021.112456

Ugoyibo, O. V., Joy, A., & Chinwe, O. (2021). Assessment of Climate Variability on Vegetation Phenology Cycle in Wetland Region of Nigeria. Journal of Meteorology and Climate Science, 19(1), 60–65.

Vintrou, E., Begue, A., Baron, C., Saad, A., Seen, D. Lo, & Traor??, S. B. (2014). A comparative study on satellite- and model-based crop phenology in West Africa. Remote Sensing, 6, 1367–1389. https://doi.org/10.3390/rs6021367

Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A. K., Wang, T., Zurita-milla, R., … Paganini, M. (2019). Remote Sensing of Environment Vegetation phenology from Sentinel-2 and fi eld cameras for a Dutch barrier island. Remote Sensing of Environment, 215, 517–529. https://doi.org/10.1016/j.rse.2018.03.014

Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. C., … Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416(6879), 389–395. https://doi.org/10.1038/416389a

Wu, A., Xiong, X., & Cao, C. (2008). Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites. International Journal of Remote Sensing 29(7), 1997-2010, DOI: 10.1080/01431160701355272

Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., … Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471–475. https://doi.org/10.1016/S0034-4257(02)00135-9



DOI: https://doi.org/10.22146/ijg.61697

Article Metrics

Abstract views : 1300 | views : 1005

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2023 Abubakar Magaji Jibrillah, Nathanial Bayode Eniolorunda1, Garba Abdulmumin Budah, Dalhatu Ahmad

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC