Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Earth Sci.    2021, Vol. 15 Issue (3) : 595-605    https://doi.org/10.1007/s11707-021-0916-7
RESEARCH ARTICLE
Vegetation dynamics in response to human and climatic factors in the Tanzanian Coast
Herrieth MACHIWA1,2, Bo TIAN1, Dhritiraj SENGUPTA1, Qian CHEN1, Michael MEADOWS3,4,5, Yunxuan ZHOU1()
1. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
2. University of Dar es Salaam, College of Information and Communication Technologies, Department of Computer Science and Engineering, Dar es Salaam 33335, Tanzania
3. Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographical Sciences, East China Normal University, Shanghai 200241, China
4. Department of Environmental & Geographical Sciences, University of Cape Town, Rondebosch 7701, South Africa
5. College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
 Download: PDF(1202 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes. The Tanzanian coast, rich in biodiversity, is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited. This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), meteorological, and Globeland30 Landsat data sets. Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000–2018 using the Mann-Kendall test and Pearson correlation respectively. The results reveal a significant positive trend in temperature (β>0, Z = 2.87) and a non-significant trend in precipitation (|Z|<1.96). A positive relationship between NDVI and precipitation is observed. Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large. Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land. The analysis of land use and land cover in the region reveals an increase in cultivated land, shrubland, grassland, built-up land and bare land, while forests, wetland and water all decreased between 2000 and 2020. The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products (firewood, timber, charcoal). The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources.
Keywords remote sensing      NDVI      climate variations      spatio-temporal changes      LULCC      coastal Tanzania     
Corresponding Author(s): Yunxuan ZHOU   
Online First Date: 25 November 2021    Issue Date: 17 January 2022
 Cite this article:   
Herrieth MACHIWA,Bo TIAN,Dhritiraj SENGUPTA, et al. Vegetation dynamics in response to human and climatic factors in the Tanzanian Coast[J]. Front. Earth Sci., 2021, 15(3): 595-605.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0916-7
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I3/595
Fig.1  Map of Tanzania showing location of the study area.
Land use class Description
Cultivated land Land use for agriculture including paddy fields, irrigated and dry farmland, vegetation and fruit gardens, etc.
Grassland Land covered by grasses mainly used for grazing
Forest Natural and secondary forest covered with trees, including woodlands, dense and open forests
Shrubland Land that is dominated by shrubs and bushes
Wetland Land consisting of standing water bodies and wetland plants such as mangroves and salt marshes
Water Land covered by water bodies such as rivers, lakes and ponds
Built-up land Land that is modified by human activities including residential, industrial, transportation and other infrastructures
Bare land Land which is typically not covered by any vegetation, i.e., bare soil, sandy beaches
Tab.1  Land use land cover classes used in the classification
Fig.2  Inter-annual climatic variations for (a) Temperature; (b) Precipitation in Tanzania coast from 2000 to 2018.
Fig.3  Inter-annual variations and relationship between NDVI and climatic variables (a) Tanzania coastal regions; (b) Tanga; (c) Mtwara; (d) Dar es Salaam for 2000–2018.
Fig.4  Land use and land cover change maps in the Tanzania coast for 2000, 2010, and 2020.
Land Use Year 2000 Year 2010 Year 2020 Change 2000–2010 Change 2010–2020
Land/ha Percent/% Land/ha Percent/% Land/ha Percent/% Land/ha Percent/% Land/ha Percent/%
Cultivated land 923375.88 6.35 1356133.50 9.32 1744766.19 12.06 432757.62 2.98 388632.69 2.74
Forest 9063002.97 62.29 7917180.39 54.42 7596891.27 52.53 −1145822.58 −7.87 −320289.12 −1.89
Shrubland 50422.41 0.35 125711.10 0.86 121369.95 0.84 75288.69 0.52 −4341.15 −0.02
Grassland 4240074.78 29.14 4870028.79 33.48 4698764.10 32.49 629954.01 4.33 −171264.69 −0.99
Built-up land 89119.35 0.61 92005.20 0.63 121395.69 0.84 2885.85 0.02 29390.49 0.21
Wetland 126976.86 0.87 124706.70 0.86 127366.56 0.88 −2270.16 −0.02 −252073.26 0.02
Water 54883.17 0.38 52965.09 0.36 40192.20 0.28 −1918.08 −0.01 −12772.89 −0.09
Bareland 1399.59 0.01 8951.22 0.06 11739.15 0.08 7551.63 0.05 2787.93 0.02
Total 14549255.01 100 14547681.99 100 14462485.11 100 – – – –
Tab.2  Table showing land use land cover change between 2000 and 2020
Land Use/cover Year 2010
Bareland/ha Built-up/ha Cultivated/ha Forest/ha Grassland/ha Shrubland/ha Water/ha Wetland/ha
Year 2000 Bareland 517.79 0.06 2.69 7.86 131.46 25.31 302.17 3.87
Built-up 24407.70 4504.51 1043.26 580.03 1.91 0.27 91.27
Cultivated 1.28 1021.13 162886.29 56354.72 35782.97 501.43 59.66 107.61
Forest 28.55 789.60 259756.60 638772.65 144135.89 5382.81 187.72 313.64
Grassland 196.13 565.35 104977.66 74894.20 188192.49 18088.20 2512.36 1567.12
Shrubland 35.83 0.10 22.11 548.75 787.76 70.44 0.28
Water 157.65 1.81 200.33 175.74 4628.46 66.06 6909.19 254.62
Wetland 20.66 0.65 70.09 741.43 527.28 30.72 622.84 10393.70
Total 957.89 26786.31 532398.26 772011.97 374527.34 24884.20 10664.645 12732.10
Tab.3  Land use and cover change detection matrix for years 2000–2010
Land Use/cover Year 2010
Bareland/ha Built-up/ha Cultivated/ha Forest/ha Grassland/ha Shrubland/ha Water/ha Wetland/ha
Year 2010 Bareland 780.89 4.08 2.36 5.95 140.11 34.47 1.77 24.58
Built-up 3.45 28809.37 5001.11 37.45 56.79 7.37 0.88 2.04
Cultivated 5.00 1080.70 384978.71 60201.11 36100.10 700.66 37.02 336.83
Forest 40.74 905.00 207259.33 574708.65 3576.06 6007.34 133.54 70.25
Grassland 207.00 801.00 105012.11 85056.21 201011.40 422.37 73.52 1625.70
Shrubland 47.00 120.63 206.30 927.10 418.62 30803.71 34.35 23.82
Water 168.00 5.15 176.77 96.00 59.83 40.03 5700.78 42.65
Wetland 32.00 89.06 64.00 658.92 38.62 14.22 55.69 9716.61
Total 1284.08 31814.98 702700.69 721691.39 241401.52 38030.17 6037.55 11842.49
Tab.4  Land use and cover change detection matrix for years 2010–2020
Administrative region Rural/urban coastal regions population/%
1988 2002 2012
Tanga 82.4/17.6 81.6/18.4 78.4/21.6
Pwani 85.2/14.8 78.9/21.1 67.2/32.8
Dares Salaam 10.4/89.6 6.1/93.9 0/100
Lindi 85.0/15.0 84.0/16.0 81.3/18.7
Mtwara 85.6/14.4 79.7/20.3 77.1/22.9
Tab.5  Tanzania rural and urban coastal regions population census of 1988, 2002, and 2012
1 C Abonyo, M Isabirye, D Mfitumukiza, M Magunda, J Poesen, J Deckers, A C Kasedde (2007). Land use change and local people’s perception of the effects of change in Ssese islands, Uganda. National Agricultural Research Organisation, Uganda, 1–25
2 D W Aheto, S Kankam, I Okyere, E Mensah, A Osman, F E Jonah, J C Mensah (2016). Community-based mangrove forest management: implications for local livelihoods and coastal resource conservation along the Volta estuary catchment area of Ghana. Ocean Coast Manage, 127: 43–54
https://doi.org/10.1016/j.ocecoaman.2016.04.006
3 R Cao, W Jiang, L Yuan, W Wang, Z Lv, Z Chen (2014). Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. J Geogr Sci, 24(6): 963–979
https://doi.org/10.1007/s11442-014-1131-1
4 L B Chang’a, P Z Yanda, J Ngana (2010). Spatial and temporal analysis of recent climatological data in Tanzania. J Geogr Reg Plan, 3(3): 44–65
5 J Chen, X Cao, S Peng, H Ren (2017). Analysis and applications of GlobeLand30: a review. ISPRS Int J Geoinf, 6(8): 230
https://doi.org/10.3390/ijgi6080230
6 J Choumert-Nkolo (2018). Developing a socially inclusive and sustainable natural gas sector in Tanzania. Energ Policy, 118: 356–371
https://doi.org/10.1016/j.enpol.2018.03.070
7 L Cockx, L Colen, J De Weerdt, Y Gomez, S Paloma (2019). Urbanization as a driver of changing food demand in Africa: evidence from rural-urban migration in Tanzania. JRC 107918. Luxembourg: Publications Office of the European Union
8 L Cui, L Wang, R P Singh, Z Lai, L Jiang, R Yao (2018). Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China). Environ Sci Pollut Res Int, 25(22): 21867–21878
https://doi.org/10.1007/s11356-018-2340-4 pmid: 29796889
9 A Daham, D Han, M Rico-Ramirez, A Marsh (2018). Analysis of NDVI variability in response to precipitation and air temperature in different regions of Iraq, using MODIS vegetation indices. Environ Earth Sci, 77(389): 1–24
10 I B Danladi, B M Kore, M Gül (2017). Vulnerability of the Nigerian coast: an insight into sea level rise owing to climate change and anthropogenic activities. J Afr Earth Sci, 134: 493–503
https://doi.org/10.1016/j.jafrearsci.2017.07.019
11 F Detsch, I Otte, T Appelhans, A Hemp, T Nauss (2016). Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania. Remote Sens Environ, 178: 70–83
https://doi.org/10.1016/j.rse.2016.03.007
12 R Dubayah, J B Blair, S Goetz, L Fatoyinbo, M Hansen, S Healey, M Hofton, G Hurtt, J Kellner, S Luthcke, J Armston, H Tang, L Duncanson, S Hancock, P Jantz, S Marselis, P L Patterson, W Qi, C Silva (2020). The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci Remote Sens, 1: 100002
https://doi.org/10.1016/j.srs.2020.100002
13 O Eludoyin, C Wokocha, G Ayolagha (2011). GIS assessment of land use and land cover changes in OBIO/AKPOR LGA, Rivers State, Nigeria. Res J Environ Earth Sci, 3(4): 307–313
14 R Fensholt, S R Proud (2012). Evaluation of earth observation based global long term vegetation trends—comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ, 119: 131–147
https://doi.org/10.1016/j.rse.2011.12.015
15 C Funk, G J Husak, J Michaelsen, S Shukla, A Hoell, B Lyon, M P Hoerling, B Liebmann, T Zhang, J Verdin G Galu, G Eilerts, Rowland (2013). Attribution of 2012 and 2003–12 rainfall deficits in eastern Kenya and southern Somalia. In: Explaining Extreme Events of 2012 from a Climate Perspective. Peterson T C, Hoerling M P, Stott P A, Herring S C, eds., Bull Am Meteorol Soc, 94(9): S45–S48
16 N Gorelick, M Hancher, M Dixon, S Ilyushchenko, D Thau, R Moore (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
17 I H Hassan, M V Mdemu, R S Shemdoe, F Stordal (2014). Drought pattern along the coastal forest zone of Tanzania. Atmos Clim Sci, 4(03): 369–384
https://doi.org/10.4236/acs.2014.43037
18 N Hosonuma, M Herold, V De Sy, R S De Fries, M Brockhaus, L Verchot, A Angelsen, E Romijn (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environ Res Lett, 7(4): 044009
https://doi.org/10.1088/1748-9326/7/4/044009
19 F Huang, X Mo, Z Lin, S Hu (2016). Dynamics and responses of vegetation to climatic variations in Ziya-Daqing basins, China. Chin Geogr Sci, 26(4): 478–494
https://doi.org/10.1007/s11769-016-0807-0
20 C Idukunda, C B M Haule, L Nahayo (2020). Vulnerability of coastal vegetation to human activities in Tanzania. Am J Geophys Geochem Geosyst, 6(3): 74–81
21 T Igbawua, J Zhang, Q Chang, F Yao (2016). Vegetation dynamics in relation with climate over Nigeria from 1982 to 2011. Environ Earth Sci, 75(6): 518
https://doi.org/10.1007/s12665-015-5106-z
22 IPCC (2 014 a). Climate Change 2014: Synthesis Report. In: Core Writing Team, Pachauri R K, Meyer L A, eds. Contribution of Working Groups I, II and III to the Fifth Assessment Report of Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland
23 IPCC (2014b). Summary for Policymakers. In: Edenhofer O R, Pichs-Madruga Y, Sokona E, Farahani S, Kadner K, Seyboth A, Adler I, Baum S, Brunner P, Eickemeier B, Kriemann J, Savolainen S, Schlömer C, von Stechow T, Zwickel J, Minx C, eds. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press
24 P Jones (2017). Formalizing the informal: Understanding the position of informal settlements and slums in sustainable urbanization policies and strategies in Bandung, Indonesia. Sustainability, 9(8): 1436
https://doi.org/10.3390/su9081436
25 J Kashaigili, P Levira, E Liwenga, M Mdemu (2014). Analysis of climate variability, perceptions and coping strategies of Tanzanian coastal forest dependent communities. Am J Clim Chan, 3, 212–222
https://doi.org/10.4236/ajcc.2014.32020
26 A S Kebede, R J Nicholls (2012). Exposure and vulnerability to climate extremes: population and asset exposure to coastal flooding in Dar es Salaam, Tanzania. Reg Environ Change, 12(1): 81–94
https://doi.org/10.1007/s10113-011-0239-4
27 A L Kijazi, C Reason (2005). Relationships between intraseasonal rainfall variability of coastal Tanzania and ENSO. Theor Appl Climatol, 82(3–4): 153–176
https://doi.org/10.1007/s00704-005-0129-0
28 A L Kijazi, C Reason (2009). Analysis of the 1998 to 2005 drought over the northeastern highlands of Tanzania. Clim Res, 38(3): 209–223
https://doi.org/10.3354/cr00784
29 J Kimaro, L Lulandala (2013). Human influences on tree diversity and composition of a coastal forest ecosystem: the case of Ngumburuni Forest Reserve, Rufiji, Tanzania. Int J For Res, 2013: 305874, 1–7
https://doi.org/10.1155/2013/305874
30 K Kirui, J Kairo, J Bosire, K Viergever, S Rudra, M Huxham, R Briers (2013). Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean Coast Manage, 83: 19–24
https://doi.org/10.1016/j.ocecoaman.2011.12.004
31 E F Lambin, H J Geist (2006). Land-Use and Land-Cover Change: Local Processes and Global Impacts. Berlin Heidelberg: Springer-Verlag
32 Q Liu, Z Yang, F Han, Z Wang, C Wang (2016). NDVI-based vegetation dynamics and their response to recent climate change: a case study in the Tianshan Mountains, China. Environ Earth Sci, 75(16): 1189
https://doi.org/10.1007/s12665-016-5987-5
33 D López-Carr, N G Pricope, J E Aukema, M M Jankowska, C Funk, G Husak, J Michaelsen (2014). A spatial analysis of population dynamics and climate change in Africa: potential vulnerability hot spots emerge where precipitation declines and demographic pressures coincide. Popul Environ, 35(3): 323–339
https://doi.org/10.1007/s11111-014-0209-0
34 J G Lyimo, J O Ngana, E Liwenga, F Maganga (2013). Climate change, impacts and adaptations in the coastal communities in Bagamoyo District, Tanzania. Environ Econ, 4(1): 63–71
35 D C Masalu (2000). Coastal and marine resource use conflicts and sustainable development in Tanzania. Ocean Coast Manage, 43(6): 475–494
https://doi.org/10.1016/S0964-5691(00)00039-9
36 M Matsa, K Muringaniza (2011). An assessment of the land use and land cover changes in Shurugwi District, Midlands Province, Zimbabwe. Ethiop J Environ Stud Manag, 4(2): 88–100
https://doi.org/10.4314/ejesm.v4i2.10
37 S Mberego, K Sanga-Ngoie, S Kobayashi (2013). Vegetation dynamics of Zimbabwe investigated using NOAA-AVHRR NDVI from 1982 to 2006: a principal component analysis. Int J Remote Sens, 34(19): 6764–6779
https://doi.org/10.1080/01431161.2013.806833
38 C Mligo (2011). Anthropogenic disturbance on the vegetation in Makurunge woodland, Bagamoyo district, Tanzania. Tanzan J Sci, 37: 94–108
39 H Mongi, A E Majule, J G Lyimo (2010). Vulnerability and adaptation of rain fed agriculture to climate change and variability in semi-arid Tanzania. Afr J Environ Sci Technol, 4(6): 371–381
https://doi.org/10.5897/AJEST09.207
40 NBS and OCGS (2013). 2012 Population and Housing Census: Population Distribution by Administrative Areas. National Bureau of Statistics (NBS), Ministry of Finance, Dar es Salaam and Office of Chief Government Statistician (OCGS), President’s Office, Finance, Economy and Development Planning, Zanzibar, The United Republic of Tanzania
41 Nordic Development Fund (2014). Coastal Profile for Tanzania 2014. In: Investment Prioritization for Resilient Livelihoods and Ecosystems in Coastal Zones of Tanzania. Volume IV—Mitigation of Threats. Helsinki: Nordic Development Fund
42 D Nwaga, J Jansa, M A Angue, E Frossard (2010). The potential of soil beneficial micro-organisms for slash-and-burn agriculture in the Humid Forest Zone of Sub-Saharan Africa. In: Dion P, ed. Soil Biology and Agriculture in the Tropics. Soil Biology 21, Berlin Heidelberg: Springer, 81–107
43 F A L Pacheco, L F Sanches Fernandes, R F Valle Junior, C A Valera, T C T Pissarra (2018). Land degradation: multiple environmental consequences and routes to neutrality. Curr Opin Environ Sci Health, 5: 79–86
https://doi.org/10.1016/j.coesh.2018.07.002
44 S Park, D Kang, C Yoo, J Im, M I Lee (2020). Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data. ISPRS J Photogramm Remote Sens, 162: 17–26
https://doi.org/10.1016/j.isprsjprs.2020.02.003
45 N N Patel, E Angiuli, P Gamba, A Gaughan, G Lisini, F R Stevens, A J Tatem, G Trianni (2015). Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int J Appl Earth Obs Geoinf, 35: 199–208
https://doi.org/10.1016/j.jag.2014.09.005
46 A Rautiainen, T Virtanen, P E Kauppi (2016). Land cover change on the Isthmus of Karelia 1939–2005: agricultural abandonment and natural succession. Environ Sci Policy, 55: 127–134
https://doi.org/10.1016/j.envsci.2015.09.011
47 D S Reddy, P R C Prasad (2018). Prediction of vegetation dynamics using NDVI time series data and LSTM. Model Earth Syst Environ, 4(1): 409–419
https://doi.org/10.1007/s40808-018-0431-3