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A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa

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A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa Ecosystems (2007) DOI: 10.1007/s10021-007-9026-y A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa S. Serneels,1 M. Linderman,2 and E. F. Lambin1,* 1Department of Geography, University of Louvain, 3, place Louis Pasteur, 1348 Louvain-la-Neuve, Belgium; 2Department of Geography, University of Iowa, Iowa City, Iowa 52242, USA ABSTRACT The aim of this study was to characterize the short- react in a different way to interannual climate term land-cover change processes that were de- variability, leading to different values of the change tected in Eastern Africa, based on a set of change indices depending on the land use type. The impact metrics that allow for the quantification of inter- of land use is more reflected in interannual vari- annual changes in vegetation productivity, changes ability of vegetation productivity and overall in vegetation phenology and a combination of change in the vegetation, whereas changes in both. We tested to what extent land use, fire phenology are mainly driven by climate variability activity and livestock grazing modified the vegeta- and affect most vegetation types in similar ways. tion response to short-term rainfall variability in Our multilevel approach led to improved models East Africa and how this is reflected in change and clearly demonstrated that climate influence metrics derived from MODerate Imaging Spec- plays at a different scale than land use, fire and trometer (MODIS) time series of remote sensing herbivore grazing. It helped us to understand data. We used a hierarchical approach to disen- dynamics within and between biomes in the study tangle the contribution of human activities and area and investigate the relative importance of climate variability to the patterns of short-term different factors influencing short-term variability vegetation change in East Africa at different levels in change indices at different scales. of organization. Our results clearly show that land use significantly influences the vegetation response Key words: MODIS; land use; multilevel model; to rainfall variability as measured by time series of East Africa; human impact; climate variability. MODIS data. Areas with different types of land use INTRODUCTION phic and anthropogenic processes interact in a complex and dynamic way, and lead to a wide Time series of satellite data have long been used to range of ecosystem responses at different scales study land-cover changes at global to regional (Nicholson 2001). Both a quantification of the scales. One of the major challenges is to distinguish magnitude of change as well as a characterization between changes linked to interannual climate of the change processes are required to unravel the variability and land-cover changes induced by driving forces of change and their effects on land- anthropogenic processes. These climatic, geomor- cover dynamics (Vanacker and others 2005). Many studies have linked interannual variability in veg- etation activity to climate variability such as rainfall Received 21 April 2006; accepted 10 October 2006 *Corresponding author; e-mail: [email protected] anomalies and ENSO events (for example, Daven- S. Serneels and others port and Nicholson 1993; Anyamba and Eastman BACKGROUND 1996; Eklundh 1998; Plisnier and others 2000;Vanacker and others 2005). However, little is It has proven to be very difficult to disentangle the known about the interactions between human land effects of climate variability and human land use in use and the short-term variability of vegetation remote sensing time series. With the availability of activity at regional scales. Until now, the hierar- increasing time series of geometrically accurate, chical organization of ecosystems, human activi- atmospherically corrected MODIS data, possibilities ties, and their interactions at different levels, from to study the interactions between land use and the landscape to the region, has largely been ig- climate factors at the landscape scale have opened nored in remote sensing studies. Processes at these up. The MODIS sensors aboard the Terra and Aqua different levels are interdependent. Factors oper- platforms now provide daily global coverage at 250 ating at one level of the hierarchy might influence m to 1 km resolution. The improved radiometric processes at a higher or lower level, and should and geolocation accuracy of these sensors provides thus be analyzed simultaneously. data particularly suited for detailed regional studies The aim of this study was to identify the hu- of land-cover change (Huete and others 2002). man influence on short-term land-cover changes Most studies investigating the link between in- that were detected in Eastern Africa based on a terannual AVHRR-NDVI variability and driving set of change metrics that allow quantification of forces of this variability focused on one particular interannual changes in vegetation productivity, potential correlate. Several studies demonstrated changes in vegetation phenology and a combina- positive correlations between Normalized Differ- tion of both (Linderman and others 2005). ence Vegetation Index (NDVI) anomalies and Vanacker and others (2005) showed that several ENSO events in East Africa (for example, Anyamba ecosystems in sub-Saharan Africa are highly sen- and Eastman 1996; Myneni and others 1996; sitive to short-term rainfall variability. We tested Nicholson and Kim 1997; Anyamba and others to what extent land use, fire activity and livestock 2002). Davenport and Nicholson (1993) analyzed grazing modify the vegetation response to short- annual integrated NDVI-rainfall associations for ten term rainfall variability in East Africa and how vegetation formations in East Africa and found a this is reflected in the change metrics developed strong similarity between temporal and spatial by Linderman and others (2005). We used land- patterns of NDVI when annual rainfall is below cover change metrics derived from 3 years of 1 about 1,000 mm and monthly rainfall does not km resolution, bidirectionally corrected reflec- exceed approximately 200 mm. In this range, NDVI tance values of MODerate Imaging Spectrometer was found to be a sensitive indicator of the inter- (MODIS) data (2000–2003) (Schaaf and others annual variability of rainfall. However, Eklundh 2002) in combination with data on rainfall, land (1998) cautions that the correlations between AV- use, fire activity, livestock density, human popu- HRR-NDVI and rainfall become much weaker lation density and conservation status of the land. when one looks at monthly or 10 day time scales. We used a hierarchical approach to disentangle On average, only 10% of the variation in 10-day the contribution of human activities and climate NDVI values could be explained by concurrent and variability to the patterns of short-term vegetation preceding rainfall, and up to 36% for monthly data. change in East Africa at different levels of eco- Vanacker and others (2005) linked rainfall vari- system organization. The data were aggregated ability to short-term land-cover change derived into units of similar pedomorphological charac- from MODIS time series and found that indices of teristics (referred to as landforms), which served rainfall variability (total annual rainfall and change as the first level of analysis in the multilevel in rainfall seasonality) were positively related to regression. The biomes as defined by White the magnitude of land-cover change. Physiognomic (1983) were used as the second level of analysis. vegetation types were found to react in specific and Mixed models were used to estimate the propor- distinct ways to short-term fluctuations in rainfall, tion of different aspects of change in land cover with grasslands and shrublands being particularly that could be explained by anthropogenic and sensitive to short-term rainfall variability, and for- climatic factors at the landform and biome levels. est and woodlands being more resilient. This hierarchical approach allows the comparison Another body of work investigated the impact of of the different scales at which climate, topogra- agricultural land use, fire and conservation on eco- phy, land-use/land-cover and soil characteristics system functioning. Annual profiles, seasonality and influence interannual variability in vegetation interannual variability in integrated AVHRR-NDVI activity and dynamics. have been studied for areas under agriculture (small A Multilevel Analysis of the Impact of Land use grain crops, row crops and irrigation) and were Multilevel models were developed in the 1980s compared to profiles for rangelands in North and and found their first applications in the social sci- South America. Agricultural practices reduce the ences, which have focused on the effects of the interannual variability of the NDVI signal and social context on individual behavior (for example, change the seasonality of the signal, even if the an- Aitkin and others 1981; Raudenbush and Bryk nual integrated NDVI (iNDVI) for crops and range- 1986). More recently, multilevel models have lands remains comparable (Guershman and Paruelo found applications in ecology (for example, Pear- 2005; Paruelo and others 2001). Eva and Lambin son and others 2004; Wu and David 2002; Noda (2000) linked fire activity to land-cover changes in 2004) and land-use science (Polsky and Easterling different ecosystems in Africa and South America. 2001; Hoshino 2001; Pan and Bilsborrow 2005). They found that fires play different roles within the We have defined spatial entities at two levels of different
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