Response of Seasonal Vegetation Dynamics to Climatic Constraints in Northeastern Burundi

Batungwanayo, Pacifique; Vanclooster, Marnik; Koropitan, Alan F.

doi:10.4236/gep.2020.89010

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…Eludoyin et al [46] and Uzoh [47] described wet and dry seasons of Nigeria in such that the rainy (wet) season occur from April to October with moderate rainfall of 1500 to 2000 mm and average temperature of 28 0 C. Results from this study showed that plant thrived well during rainy season across the land uses compared to the dry season that is characterized by high temperature (average of 30 0 C) with little or no rainfall. Dry season last for 4-5 months in the rainforest belt of Nigeria.…”

Section: Effect Of Land Use and Season On Vegetation Indicesmentioning

confidence: 61%

GIS-based Characterization of Land Use, Land Cover Patterns and Microclimate of Agricultural and Agroforestry Landscapes in a Rainforest Zone of Nigeria

Abel,

Samuel

2024

IJECC

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There is increasing need for development of sustainable land use and landscape management practices to avert accelerating trends of land, water and ecosystem degradation and for climate mitigation. This study characterized land use and land cover patterns and microclimate of permanent (forest, agroforestry, fallow, cocoa, oil palm, citrus and ornamental plant field) and annual crop land use systems in a rainforest zone of Nigeria using space-based remote sensing technology. The goal is to evaluate land use and land cover patterns and microclimate along agricultural and agroforestry landscapes in a rainforest zone of Nigeria. Land use types were: permanent (forest, agroforestry, fallow, cocoa, oil palm, citrus and ornamental plant field) and annual cropland. Vegetation indices (Normalized Difference Vegetation Index: NDVI, Normalized Difference Water Index: NDWI and Soil Adjusted Vegetation Index: SAVI) were deployed for characterizing land use vegetation cover patterns in relation to vigour and health in addition to responses to weather variables (temperature and rainfall). The NDVI intensities of vegetation cover from the land use types showed differences in vigour and health of vegetation during the rainy and dry seasons of 2017 to 2019. The NDWI of vegetation cover intensity indicates differences in moisture conditions of vegetation cover, the vegetation of the land use systems had more water content (received more rainfall) in 2017 compared to 2018 and 2019 during the rainy season while during the dry season of 2019, NDWI intensity was highest compare to 2018 and 2017. NDVI and NDWI also showed that vegetation cover of permanent land uses had better vigour and health compared to annual (maize) field. SAVI was applied to correct NDVI of vegetation cover patterns of land use types with reference to canopy gaps (soil brightness within canopy especially in spots where vegetation cover is sparse). High SAVI intensities were obtained during rainy compared to the low values during dry season (sparse vegetation cover). Decreasing order of SAVI intensities were agroforestry, oil palm, ornamental plant field, citrus, cocoa, fallow land and maize crop field. Result from the correlations among vegetation indices (NDVI, NDWI and DSAVI) were strong association (R2 = 1) among the years and seasons. The strong R2 values imply that less than 10% of changes in NDWI (the explanatory variables) can be explained by changes in NDVI and SAVI (the dependent variable). Temperature and rainfall differed within months and years of study. Temperatures were highest for March, April and May while rainfall was highest for September of 2017 and 2018 and in October, 2019. Significantly lower rainfall amounts were received for January, February, November and December. The vegetation indices (NDVI, NDWI and SAVI) indicated vigour and water contents of the land use types within seasons and years as well as responses to weather variables (rainfall and temperature in particular). The biophysical findings from this study may advance capacities to cope with climate change challenges and ecosystem conservation. Information generated will find use as strategies for ecologically sound and sustainable land use systems and policy for mainstreaming climate mitigation in in the study area.

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Section: Effect Of Land Use and Season On Vegetation Indicesmentioning

confidence: 61%

GIS-based Characterization of Land Use, Land Cover Patterns and Microclimate of Agricultural and Agroforestry Landscapes in a Rainforest Zone of Nigeria

Abel,

Samuel

2024

IJECC

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“…As a result of years of monitoring, showed that the decrease in vegetation area was also related to the decrease in water content in the area. This, in turn, means that changes in vegetation dynamics are related to changes in the environment [10,[17][18][19][20]. Results of the use of NDVI indices in determining the area covered by vegetation found that the use of this index in quantitative indicators was effective [13].…”

Section: Resultsmentioning

confidence: 99%

“…The NDVI, which is produced from satellite data, is frequently used to examine the conditions under which live, green vegetation grows and to show how vegetation dynamics respond to climate change [10]. By measuring the relative growth and health of the crop, remote sensing vegetation indices can be used to measure growth in agricultural variability [11].…”

Section: Introductionmentioning

confidence: 99%

Vegetation monitoring in the South Aral Sea region by remote sensing and GIS

Jaksibaev

Gabbarov²,

Abdurahmanov

et al. 2023

E3S Web Conf.

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Vegetation plays an important role in the study of the environment at the local level. Because plants help to understand the negative changes taking place in the region in a timely manner. One of the most effective ways to get reliable and high-quality information about the condition of plants in a short time is remote sensing. The research selected one of the southern Aral Sea regions of Uzbekistan, which is closest to the dried-up part of the Aral Sea. The research examined changes in the condition of water bodies and sparse and dense vegetation over the past 9 years. The research was conducted using ArcGIS software from the family of modern GIS technologies, using data from Landsat 8. Based on the data obtained from these methods, it was found that the water sources and sparse and dense vegetation areas change over months and years. At the same time, depending on the level of vegetation cover, the periods of agricultural pasture use and fodder harvesting were determined. Using these methods, we are able to make the necessary predictions for the use of pastures.

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“…The Inter-Tropical Convergence Zone (ITCZ) crosses Burundi twice a year and is a major factor in determining precipitation seasonality [30]. Similar to the entire country of Burundi, the northern part of Burundi is normally characterized by four distinct seasons classified into A, B, C, and D seasons [31]. Season A runs for four months: September, October, November, and December (SOND), widely known as the short rain season.…”

Section: The Study Area's Description and Datasetsmentioning

confidence: 99%

Assessment of Two Drought Indices to Quantify and Characterize Drought Incidents: A Case Study of the Northern Part of Burundi

Ndayiragije

Nkunzimana

2022

Atmosphere

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Droughts are natural catastrophes that cost the health and wealth of humans due to their harmful effects on the natural environment, ecology, hydrology, and agriculture in particular. Droughts are recurring incidents that last for prolonged periods of time in the northern part of Burundi. Despite the region being prone to drought and often suffering from dry conditions, drought has not been widely investigated. For the quantification and characterization of dryness conditions, this research utilized two drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), at 2-, 6-, 24-, and 48-month timescales, where 2-, 6-, 24-, and 48-months correspond to agricultural and hydrological droughts, respectively. The two drought indices were compared, and the difference between SPEI and SPI was illustrated by quantifying and characterizing drought incidents. The findings revealed that different types of droughts threatened the northern part of Burundi during the periods of 1993–2000 and 2002–2009. Both indices illustrated that 2005, 2006, and 2007 were extremely dry years. The drought incidents detected by the SPEI index were classified into moderate and severe categories, characterized by long duration and greater magnitude. In contrast, the drought incidents detected by SPI were classified into the “extremely dry” category, characterized by limited duration and lower magnitude but with higher intensities. This research highlighted that SPEI differs from SPI in quantifying and characterizing droughts and highly suggests the use of both SPEI and SPI when assessing droughts. The outcome of this study will be useful in drought prevention and mitigation strategies across Burundi, specifically for agricultural purposes.

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Response of Seasonal Vegetation Dynamics to Climatic Constraints in Northeastern Burundi

Cited by 5 publications

References 48 publications

GIS-based Characterization of Land Use, Land Cover Patterns and Microclimate of Agricultural and Agroforestry Landscapes in a Rainforest Zone of Nigeria

GIS-based Characterization of Land Use, Land Cover Patterns and Microclimate of Agricultural and Agroforestry Landscapes in a Rainforest Zone of Nigeria

Vegetation monitoring in the South Aral Sea region by remote sensing and GIS

Assessment of Two Drought Indices to Quantify and Characterize Drought Incidents: A Case Study of the Northern Part of Burundi

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