Vegetation and Rainfall Analysis

The “pixel-level” is the addressed unit for the use of remote sensing data (Fisher, 1997).

Depending on the spatial resolution of the image, also named raster data, derived from a sensor, information within the area a pixel is covering can be derived. Remote sensing became a leading method to get information about current situations and changing patterns on the land surface (Shoshany, Goldshleger, & Chudnovsky, 2013). Vegetation, i.e. vegetation indices, are mainly used as a proxy for the status of land and key indicator for desertification (Helldén & Tottrup, 2008; Nkonya et al., 2011; de Jong et al., 2012). Vegetation indices are derived from remote sensing imagery extracting the spectral information of two or more bands that thereby provide information on “terrestrial photosynthetic activity and canopy structural variations” (Huete et al., 2002: 195). Well known in LD assessment is the observation of the Normalized Difference Vegetation Index (NDVI) which gives information about density, amount and health of vegetation by using near infrared (NIR) and red light (RED)¹⁰ to estimate green biomass. It is calculated by the difference of bands of NIR and RED over their sum (Tucker, 1979; Prince, 1991; Huete et al., 2002). Equation 1 shows the calculation of the NDVI according to (Huete et al., 2002; Jiang et al., 2008).

𝑁𝐷𝑉𝐼 = ^{𝑁𝐼𝑅−𝑅𝐸𝐷}_{𝑁𝐼𝑅+𝑅𝐸𝐷} [Equation 1]

The NDVI reaches values between -1 and +1, where healthier and more dense vegetation expresses higher NDVI values compared to dry and sparse vegetation with much lower NDVI values. Therefore the NDVI is often mentioned in line with measurement of green vegetation, greenness (Bannari et al,. 1995; Ma, Morrison, & Dwyer, 1996) or greening and browning trends with regard to decreasing vegetation trends (de Jong et al., 2011a; de Jong et al., 2012). The greener and healthier vegetation the more flat its cell walls are. Thereby the more incident sunlight can be reflected and the more RED is absorbed which leads to a higher NDVI value.

Vice versa the more sparse and “brown” vegetation cover appears, the less RED is absorbed and the less NIR is reflected. This calculation leads to different NDVI values around the globe where

10 Also known as “visible light” (Tucker et al. 2005)

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e.g. tropical regions with evergreen rainforest show higher NDVI values (0.6-0.8) than shrubland cover in Savanna regions in Sub-Saharan Africa (0.2-0.3) (Weier & Herring, 2000). Negative NDVI values refer to water, desert, snow-cover or even clouds and thereby non-vegetated areas.

Discussions about the reliability of the NDVI are still ongoing as the index does not distinguish between vegetation types and could create false alarms with regard to LD if a decreasing trend is e.g. only the result of land conversion. In some areas such as often in Namibia or South Africa an increasing NDVI could be based on bush encroachment which is actually a form of LD and not a sign for increasing productivity (Klerk, 2004; Helldén & Tottrup, 2008). Criticism of the NDVI is also linked to missing soil information which should be included as especially in arid areas vegetation is sparse and “greenness” is not measured in the same amount as in more humid regions. Dark soils might result in a higher NDVI than actually measured if solely vegetation would be taken into account (Huete, 1988). The soil-adjusted vegetation index (SAVI) therefore reduces soil and canopy background by correcting the NDVI with a soil brightness correction factor (Huete, 1988). Moreover, the enhanced vegetation index (EVI) is nowadays preferred when the study area is located in areas with high biomass (Huete et al., 2002; Geerken & Ilaiwi, 2004). As the NDVI saturates when vegetation cover reaches high levels the EVI is performing better in areas with high productivity (Huete et al., 2002; Pettorelli, 2013).

Equation 2 shows the calculation of the EVI with 𝜌 as atmospherically corrected or partially atmospherically corrected surface reflectance. G is included as gain factor limiting the EVI value to a fixed range (Vacchiano et al., 2011). The canopy background adjustment is represented by L while 𝐶₁ and 𝐶₂ depict coefficients of aerosol resistance used by the blue band (𝐵𝐿𝑈𝐸 ) (Huete et al., 2002).

𝐸𝑉𝐼 = 𝐺 _𝜌 ^{𝜌𝑁𝐼𝑅− 𝜌𝑅𝐸𝐷}

𝑁𝐼𝑅+𝐶₁ × 𝜌_𝑅𝐸𝐷−𝐶₂ × 𝜌_{𝐵𝐿𝑈𝐸}+ 𝐿 [Equation 2]

In addition to the red and the near infrared band the blue band is added to remove residual atmosphere contamination such as smoke or thin clouds (Huete et al., 2002). Especially in tropical regions we have high cloud cover due to evaporation and evapotranspiration. Moreover the soil-adjusted vegetation index (SAVI), where the impact of soil type is included by an additional variable, and the EVI do highly correlate (Huete et al., 2002). Comparing NDVI and EVI values showed that NDVI values were always slightly higher than the EVI-values, especially in areas with high biomass production.

Nevertheless the NDVI is still the index most often used when identifying LD processes which is justified by its good spectral and temporal availability (Maselli, Gilabert, & Conese, 1998).

According to Bai et al. (2008: 223) where LD is also defined as a “long-term decline in ecosystem function and productivity” the NDVI can be used to derive “deviation from the norm” and can therefore “serve as a proxy assessment of LD and improvement – if other factors” such as rainfall or slopes “that may be responsible are taken into account”.

31 NDVI and EVI: Data Source

For the national study (chapter IV) two different NDVI time series analysis were taken into account based on two different datasets. For the long-term analysis from 1982 to 2006 data by the Global Inventory Modeling and Mapping Studies (GIMMS) derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) NDVI data with a spatial resolution of 8km was used. Imageries are biweekly maximum value composites for five continents¹¹. Effects that could influence the observation of vegetation such as volcanic stratospheric aerosol caused by volcanic eruptions, calibration or view geometry are corrected in the GIMMS data (Tucker et al., 2005).

The second and main time series analysis covering the years 2001 to 2011 was based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra NDVI (Product: MOD13A1) with 500m resolution. MODIS was launched in 2000 and provides NDVI images with spatial resolution of 1km, 500m and 250m. The sensor has two different orbits – MODIS Terra and MODIS Aqua – which cross the equator at different points in time. MODIS Terra crosses it at approximately 10.30 am while MODIS Aqua crosses it at 1.30pm. As Kenya is located at the equator and facing high cloud cover starting from noon onwards in most regions MODIS Terra data were chosen for this analysis. Nevertheless, Terra and Aqua values are reported to be strongly correlated (R²=0.97) (Gallo et al., 2005). The data are collected every 16 days, summing up to 22 datasets per year.

The local level, focusing on the high-productive regions in western Kenya takes advantage of the Enhanced Vegetation Index (EVI) and includes data by MODIS Terra (MOD13A1) with 500m resolution and a bimonthly temporal resolution.

Even if a higher resolution secured a more detailed analysis MODIS data with 500m resolution was selected matching with the spatial resolution of the MODIS land cover data. In doing so no resampling method was needed which could have modified the data and falsify results.

Rainfall Data

Vegetation growth is highly depending on precipitation which is validated by many studies showing high correlations and interplays (Malo & Nicholson, 1990; Davenport & Nicholson, 1993; Nicholson & Farrar, 1994; Herrmann, Anyamba, & Tucker, 2005). Therefore it is aimed at including rainfall data to especially highlight those areas where trend in vegetation could be referred to rainfall amounts and trends.

Rainfall Estimates 2.0 (RFE) (Xie & Arkin, 1997) data are available via the Famine Early Warning System Network (FEWSNET) portal¹² and provided on a daily and monthly temporal resolution.

11 Africa (AF), Australia and New Zealand (AZ), Eurasia (EA), North America (NA) as well as South America and Central America (SA).

12 Data download via:

http://earlywarning.usgs.gov/fews/downloads/index.php?regionID=af&productID=3&periodID=6 (last accessed:

08.02.2015).

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Here, 10day decades are chosen which result in three datasets each month. The previous dataset RFE 1.0 based data collection on an interpolation method combining Meteosat and WMO Telecommunication System (GTS) data while additional including warm cloud information for daily precipitation estimates (Xie & Arkin, 1997). The RFE 2.0 data improved estimation of precipitation by also including stational rainfall data. Satellite infrared data by Meteosat 7, providing data in in 30-minute intervals, is included in the calculation of RFE 2.0. Moreover areas where clouds are depicted and which cross temperatures of less than 235K are included in the RFE 2.0 dataset (Xie & Arkin, 1997).

Stationary rainfall data was not possible to obtain. While common used rainfall data are available from the Climate Research Unit (CRU) with 0.5° resolution (50km) or the Tropical Rainfall Measuring Mission (TRMM) data with 0.25° resolution (28km), here RFE data was selected due to a spatial resolution of 8km by 8km per pixel which is higher than any other rainfall dataset based on remote sensing imagery.

Trend Analysis

Long time-series analysis is obviously preferred in research but especially when remote sensing data is taken into account. The use of long time series is limited by the respective sensor and the starting time of a campaign. AVHRR GIMMS NDVI data (Tucker, Pinzon, & Brown, 2004) was used for first insights of general vegetation development and behavior over a 25-year period from 1982 to 2006¹³ for the national study on Kenya (chapter III) before looking into vegetation development between 2001 and 2011 which is the time period with main focus for the ongoing study – the national and the local study.

Trends were detected among mean annual values of NDVI using the slope of the linear regression (see Equation 4) according to Xie, Sha, & Yu (2008).

𝑦 = 𝑚𝑥 + 𝑏 [Equation 3]

The parameter 𝑦 is the predicted NDVI value with the slope 𝑚 – the here used trend – within the observed time period. Information on the y-intercept is given with 𝑏.

The slope of the linear regression is used for all trend analysis in this thesis including vegetation trends, rainfall trends and later on trends of socio-economic data in the local study over a ten year penal survey in four waves between 2000 and 2010.

Significant trends are calculated with p<0.05 and used in both studies to correct vegetation trends for rainfall. This gives the opportunity to focus on human-induced changes on land by extracting the main limiting factor for vegetation growth.

13 At the time the study was conducted NOAA AVHRR NDVI data by GIMMS was only available until 2006

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