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Understanding the spatial temporal vegetation dynamics in Rwanda

(2016) REMOTE SENSING. 8(2).
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Abstract
Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country's vegetation has improved throughout the study period, while 14.1% of the country's vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The mosaic grassland/forest or shrubland was severely degraded, followed by sparse vegetation, grassland or woody vegetation regularly flooded on water logged soil, artificial surfaces and broadleaved forest regularly flooded. The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration.
Keywords
LEAF-AREA INDEX, EAST-AFRICA, TREND ANALYSIS, NDVI DATA, TIME-SERIES, DEGRADATION ASSESSMENT, SATELLITE DATA, RIVER-BASIN, RAINFALL, RESPONSES, AVHRR, Hurst exponent, MODIS, NDVI, rainfall, Rwanda, vegetation, dynamics

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Citation

Please use this url to cite or link to this publication:

MLA
Ndayisaba, Felix, Hao Guo, Anming Bao, et al. “Understanding the Spatial Temporal Vegetation Dynamics in Rwanda.” REMOTE SENSING 8.2 (2016): n. pag. Print.
APA
Ndayisaba, F., Guo, H., Bao, A., Guo, H., Karamage, F., & Kayiranga, A. (2016). Understanding the spatial temporal vegetation dynamics in Rwanda. REMOTE SENSING, 8(2).
Chicago author-date
Ndayisaba, Felix, Hao Guo, Anming Bao, Hui Guo, Fidele Karamage, and Alphonse Kayiranga. 2016. “Understanding the Spatial Temporal Vegetation Dynamics in Rwanda.” Remote Sensing 8 (2).
Chicago author-date (all authors)
Ndayisaba, Felix, Hao Guo, Anming Bao, Hui Guo, Fidele Karamage, and Alphonse Kayiranga. 2016. “Understanding the Spatial Temporal Vegetation Dynamics in Rwanda.” Remote Sensing 8 (2).
Vancouver
1.
Ndayisaba F, Guo H, Bao A, Guo H, Karamage F, Kayiranga A. Understanding the spatial temporal vegetation dynamics in Rwanda. REMOTE SENSING. 2016;8(2).
IEEE
[1]
F. Ndayisaba, H. Guo, A. Bao, H. Guo, F. Karamage, and A. Kayiranga, “Understanding the spatial temporal vegetation dynamics in Rwanda,” REMOTE SENSING, vol. 8, no. 2, 2016.
@article{8576818,
  abstract     = {Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country's vegetation has improved throughout the study period, while 14.1% of the country's vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The mosaic grassland/forest or shrubland was severely degraded, followed by sparse vegetation, grassland or woody vegetation regularly flooded on water logged soil, artificial surfaces and broadleaved forest regularly flooded. The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration.},
  articleno    = {129},
  author       = {Ndayisaba, Felix and Guo, Hao and Bao, Anming and Guo, Hui and Karamage, Fidele and Kayiranga, Alphonse},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keywords     = {LEAF-AREA INDEX,EAST-AFRICA,TREND ANALYSIS,NDVI DATA,TIME-SERIES,DEGRADATION ASSESSMENT,SATELLITE DATA,RIVER-BASIN,RAINFALL,RESPONSES,AVHRR,Hurst exponent,MODIS,NDVI,rainfall,Rwanda,vegetation,dynamics},
  language     = {eng},
  number       = {2},
  pages        = {17},
  title        = {Understanding the spatial temporal vegetation dynamics in Rwanda},
  url          = {http://dx.doi.org/10.3390/rs8020129},
  volume       = {8},
  year         = {2016},
}

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