NDVI–rainfall relationship in the Semliki watershed of the equatorial Nile

Abstract The use of remotely sensed data to describe, study, monitor and provide greater understanding on watershed’s landscape dynamics have proven to be useful worldwide, especially where timely and reliable ground information are neither available nor accessible like in most of the developing world. This paper, thus presents the first documented relationship between NDVI and rainfall in humid central Africa, and serves as a precursor in the understanding of the interaction between catchment characteristics and water resources of the Semliki region. This study investigates spatial and temporal relationship between NDVI and satellite-derived rainfall. Normalized Difference Vegetation Index (NDVI) time series derived from the NOAA–AVHRR (National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer) satellite data and FEWS satellite-derived rainfall are analyzed. The Semliki watershed (23,621 km2) of the equatorial Nile basin is the study area. Monthly NDVI time series of 21 Semliki’s subcatchments (S3–S23) over 7 years (2001–2007) were processed and extracted with Windisp 5.1 from 10-day NDVI maximum value composite images. At the monthly time step, only 12 subcatchments had weak positive correlation (0.23–0.42) at 5% significance level and the rest were not significantly correlated. Incorporating a 1-month lag effect increased the correlation coefficients between the two variables (0.28–0.68) with 17 subcatchments being significantly correlated at the designated level. The topography within the catchment was found to play a defining role for NDVI values. S15, S16, S18 and S20, the subcatchments covering the Mount Ruwenzori characterized by elevations up to 4862 m above sea level, consistently recorded the lowest values of NDVI over time and no significant correlation could be established between rainfall and NDVI.

[1]  Kazuhito Ichii,et al.  Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation , 2001 .

[2]  J. Paruelo,et al.  Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina , 2000 .

[3]  Vladimir U. Smakhtin,et al.  Daily flow time series patching or extension: a spatial interpolation approach based on flow duration curves , 1996 .

[4]  Wim G.M. Bastiaanssen,et al.  Satellite surveillance of evaporative depletion across the Indus Basin , 2002 .

[5]  H. Fritz,et al.  Spatial patterns of the NDVI–rainfall relationship at the seasonal and interannual time scales in an African savanna , 2006 .

[6]  Piermaria Corona,et al.  Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy , 2006 .

[7]  M. Ma,et al.  Interannual variability of vegetation cover in the Chinese Heihe River Basin and its relation to meterological parameters , 2006 .

[8]  Graham J. Borradaile,et al.  Statistics of Earth Science Data: Their Distribution in Time, Space and Orientation , 2003 .

[9]  José A. Sobrino,et al.  Improved land surface emissivities over agricultural areas using ASTER NDVI , 2006 .

[10]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[11]  William M. Baugh,et al.  Correcting satellite data to detect vegetation signal for eco-hydrologic analyses , 2007 .

[12]  Kuolin Hsu,et al.  Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation , 1999 .

[13]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[14]  Stephen Warwick Looney,et al.  How to Use Tests for Univariate Normality to Assess Multivariate Normality , 1995 .

[15]  Y. Richard,et al.  A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa , 1998 .

[16]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[17]  Colin Camerer.  Judgment and decision making, J. Frank Yates. Englewood Cliffs, New Jersey, Prentice-Hall inc. 1990 , 1991 .

[18]  David M. Stoms,et al.  Potential NDVI as a baseline for monitoring ecosystem functioning , 2000 .

[19]  S. Garrigues,et al.  Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products , 2008 .

[20]  Stephen D. Prince,et al.  Biophysical stratification of the Amazon basin , 1999 .

[21]  L. Ji,et al.  Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices , 2003 .

[22]  E. Boegh,et al.  Remote sensing based estimation of evapotranspiration rates , 2004 .

[23]  J. Tenhunen,et al.  On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .

[24]  Raghavan Srinivasan,et al.  Evaluating different NDVI composite techniques using NOAA-14 AVHRR data , 2003 .

[25]  J. Comiso,et al.  Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI , 2008 .

[26]  C. Samimi,et al.  Assessing spatio‐temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia , 2006 .

[27]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[28]  S. Islam,et al.  Estimation of surface evaporation map over Southern Great Plains using remote sensing data , 2001 .

[29]  M. Todd,et al.  A Combined Satellite Infrared and Passive Microwave Technique for Estimation of Small-Scale Rainfall , 1999 .

[30]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

[31]  S. Running,et al.  Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data , 1989 .

[32]  A. Plumptre,et al.  The biodiversity of the Albertine Rift , 2007 .

[33]  George J. Huffman,et al.  P2.2 VERIFICATION OF RAINFALL ESTIMATES OVER AFRICA USING RFE, NASA MPA-RT, AND CMORPH , 2003 .

[34]  Karsten H. Jensen,et al.  Use of remotely sensed precipitation and leaf area index in a distributed hydrological model , 2002 .

[35]  Nadège Martiny,et al.  Compared regimes of NDVI and rainfall in semi‐arid regions of Africa , 2006 .

[36]  Pieter van der Zaag,et al.  The use of remote sensing for monitoring environmental indicators: The case of the Incomati estuary, Mozambique , 2006 .

[37]  D. Mazvimavi,et al.  Estimation of flow characteristics of ungauged catchments : case study in Zimbabwe , 2003 .