Abstract
Vegetation dynamics have been visibly influenced by climate variability. The Normalized Difference Vegetation Index (NDVI) has been the most commonly used index in vegetation dynamics. The study was conducted to examine the effects of climatic variability (rainfall) on NDVI for the periods 1982–2015 in the Gojeb River Catchment (GRC), Omo-Gibe Basin, Ethiopia. The spatiotemporal trend in NDVI and rainfall time series was assessed using a Theil–Sen (Sen) slope and Mann–Kendall (MK) statistical significance test at a 95% confidence interval. Moreover, the residual trend analysis (RESTREND) method was used to investigate the effect of rainfall and human induction on vegetation degradation. The Sen’s slope trend analysis and MK significant test indicated that the magnitude of annual NDVI and rainfall showed significant decrement and/or increment in various portions of the GRC. The concurrent decrement and/or increment of annual NDVI and rainfall distributions both spatially and temporarily could be attributed to the significant positive correlation of the monthly (RNDVI-RF = 0.189, P≤0.001) and annual (RNDVI-RF = 0.637, P≤0.001) NDVI with rainfall in almost all portions of the catchment. In the GRC, a strongly negative decrement and strong positive increment of NDVI could be derived by human-induced and rainfall variability, respectively. Accordingly, the significant NDVI decrement in the downstream portion and significant increment in the northern portion of the catchment could be attributed to human-induced vegetation degradation and the variability of rainfall, respectively. The dominance of a decreasing trend in the residuals at the pixel level for the NDVI from 1982, 1984, 2000, 2008 to 2012 indicates vegetation degradation. The strong upward trend in the residuals evident from 1983, 1991, 1998 to 2007 was indicative of vegetation improvements. In the GRC, the residuals may be derived from climatic variations (mainly rainfall) and human activities. The time lag between NDVI and climate factors (rainfall) varied mainly from two to three months. In the study catchment, since vegetation degradations are mainly caused by human induction and rainfall variability, integrated and sustainable landscape management and climate-smart agricultural practices could have paramount importance in reversing the degradation processes.
Highlights
Vegetation dynamics have been visibly influenced by climate variability due to biophysical responses of plant respiration and photosynthesis [1, 2]. e remotely sensed Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very-High-Resolution Radiometer (AVHRR) of National Oceanic and Atmospheric Administration (NOAA) satellite [3, 4] has been the most commonly used index in vegetation dynamics [5, 6]. e NOAA satellites contain AVHRR sensor [7], which collects spectral information in the visible and near-infrared regions, facilitating calculation of the NDVI [8]. e main reasons for using AVHRR-NDVI images in many environmental studies are Advances in Meteorology its high temporal and adequate spatial resolution, good calibration, and low price [3]
The aim of this study is to examine the spatiotemporal vegetation dynamics (Normalized Difference Vegetation Index (NDVI)) and its response to climate variability using GIMMS3g from 1982 to 2015 in Gojeb River Catchment, Omo-Gibe Basin, Ethiopia. e objectives of this study are to (1) investigate the monthly, seasonal, and annual NDVI trend in relation to climate variability in Gojeb River Catchment during the past 34 years; (2) analyze the correlation between NDVI and climatic factors in order to determine the impact of climatic variability in vegetation dynamics at temporal and spatial scales; and (3) distinguish the relative importance of the impacts of climate variability and human activities on vegetation dynamics using wavelet analysis in the Gojeb River Catchment of Omo-Gibe Basin, Ethiopia
Description of the Study Area. e study was conducted at the Gojeb River Catchment (GRC), a part of the OmoGibe basin in Ethiopia (Figure 1). e Omo-Gibe basin, third largest perennial river in Ethiopia next to the Baro Akobo and Blue Nile rivers, lies between 5°31′ to 10°54′ N and 33°0′ to 36°17′ E and covers about 79,000 km2 of land area in South and Southwest Ethiopia [25]. e GRC is located between 7°00′–7°50′ N latitude and 35°30′–37°20′ E and covers a total area of 6932.345 km2 with altitudinal ranges from 817 to 2500 m
Summary
Vegetation dynamics have been visibly influenced by climate variability due to biophysical responses of plant respiration and photosynthesis [1, 2]. e remotely sensed Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very-High-Resolution Radiometer (AVHRR) of National Oceanic and Atmospheric Administration (NOAA) satellite [3, 4] has been the most commonly used index in vegetation dynamics [5, 6]. e NOAA satellites contain AVHRR sensor [7], which collects spectral information in the visible and near-infrared regions, facilitating calculation of the NDVI [8]. e main reasons for using AVHRR-NDVI images in many environmental studies are Advances in Meteorology its high temporal and adequate spatial resolution, good calibration, and low price [3]. E remotely sensed Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very-High-Resolution Radiometer (AVHRR) of National Oceanic and Atmospheric Administration (NOAA) satellite [3, 4] has been the most commonly used index in vegetation dynamics [5, 6]. NDVI is calculated as the difference between near-infrared and visible reflectance values normalized over the sum of the two and ranges from -1 to +1 for a given pixel [8]. Because of high reflectance in the NIR portion of the Electromagnetic Spectrum (EMS), healthy vegetation is represented by NDVI values between 0.1 and 1. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in both visible and NIR portions of the EMS [9]. An NDVI value of zero means no vegetation and a value close to +1 (0.8–0.9) indicates the highest possible density of green leaves [8]
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