policy imprints in sudanian forests: trajectories of …jfisher/other/fisher_draft_agee.pdfpolicy...

Policy imprints in Sudanian forests

Fisher et al., 2005 Agriculture, Ecosystems, and Environment

1

Policy imprints in Sudanian forests: Trajectories of vegetation change under uniform land management practices in West Africa Jeremy I. Fisher, MS1*, John F. Mustard, PhD1, and Patrice Sanou, MS2

5 1Department of Geological Sciences, Brown University, Providence, Rhode Island, USA 2Centre SIG et Télédétection – Adjaratou, Ouagadougou, Burkina Faso *Corresponding Author:

Department of Geological Sciences, Box 1846, Brown University, Providence, RI 02912 USA 10

Tel: 401.863.9845

Email: [email protected]

Submission to Agriculture, Ecosystems, and Environment

15

Re-Draft 4a

Key Words: Sudanian West Africa, Burkina Faso, remote sensing, time-series, aforestation,

riparian area, Nouhao Valley, MVVN, woody encroachment, land use land cover change,

AVHRR, Landsat20



2

Abstract

Land-cover dynamics in Sudanian West African savannas are complex functions of

climate variability, human migration, and increasingly intensive land-uses. The Nouhao Valley

Project (Projet de Mise en Valeur de la Vallée de la Nouhao - MVVN) in southeast Burkina Faso

became a large-scale (2000 km2) natural experiment in land-use land cover change after a 5

resettlement plan was activated in 1984. In the MVVN, policy dictates the boundaries of

contiguous and separated zones of pastoral and agricultural land-uses. The unregulated region

surrounding the MVVN serves as an experimental control. Regional trends of primary

productivity and landscape-scale changes in vegetation density were observed using two

independent satellite sensors (AVHRR time series and Landsat, respectively). Primary 10

productivity trends indicate that while greater southeast Burkina Faso recovered gradually

following droughts in the early 1980s (at a rate of 10 g C m-2 y-2), the pastoral region of the

Nouhao Valley experienced relatively enhanced growth (19 g C m-2 y-2). Landscape-scale

analysis of green vegetation trends from five transition-season Landsat TM scenes indicates

marginal increases (+5-15% green leaf cover) in all open savannas from 1984-2002, but very 15

large increases in the pastoral river valleys (+20-25%) and relative losses in agricultural and

unregulated river valleys (+7-10%) compared to the mean sense of change. The multi-scale

quantitative satellite analyses suggest a strong correlation between vegetation density changes

and the zonal boundaries in the study area. These land-use boundaries are dictated by policy. The

local patterns of change are overprinted on broader climate-driven variability. A remote sensing 20

approach provides an alternative angle to which we might examine drivers of change in savanna

riparian areas. Differentiating the degree of forest loss, riparian growth, or wood encroachment is

critical to understanding multi-use trajectories of change..



3

1. Introduction

Land cover dynamics in Sudanian West African savannas are governed by interacting

aspects of climate variability, human migration, and increasingly intensive land-uses. In

sculpting policies towards ecologically and economically sound land resource use in marginal

environments, it is critical to decouple the related impacts of climate shifts and anthropogenic 5

change (Batterbury and Warren, 2001; Bilsborrow, 2002). In the densely populated sub-humid

Sudanian belt of West Africa, increasingly intensive sustenance agriculture and imported

pastoral practices appear to physically transform ecosystem structure (Baker, 2000; Smith, 1992;

Warren, 2002; Gray, 1999). Researchers and managers have noted signs of landscape

degradation in reduced soil fertility (Gray, 1999) and structure (Bilsborrow, 2002), gallery forest 10

loss from extraction and fires (Bilsborrow, 2002; Lykke, 2000; Jeltsch et al., 1997), and

increased regional and local drought severity (Taylor et al., 2002). To assess ecologically

sustainable management strategies, we require a firmer understanding of how ecosystems, and

the services they provide, are impacted by types of land-uses, climatological changes, and

demographics. This research suggests a strategy to decouple the effects of climate and 15

anthropogenic impact on landscape-scale land-cover change in the ecotone of Sudanian West

Africa.

The Sudanian zone of West Africa is climatologically defined by uni-modal annual

precipitation between 800 and 1200 mm (McMillan et al., 1993), and falls between

approximately 9-13ºN. The Sudanian zone is a climatological ecotone, and generally forms a 20

transition region from northern migratory pastoral groups to southern agricultural populations

(Ouédraogo, 2002). While pastoral and agricultural populations have historically occupied

adjacent lands, recent climatological, political, and demographic changes have increased the



4

overlap (Smith, 1992). The two overlapping land-uses generally complicate our ability to assess

human-climate interactions. However, where land-uses are controlled, we are able to examine

the influence of pastoral and agricultural groups.

The West African droughts of 1968-1973 and 1983-1984 were triggers of large-scale

population movement, forcing traditional pastoralists into intensively agricultural areas 5

(McMillan et al., 1993; Smith, 1992; Baker, 2000). The increased intensity of land-use on the

poorer soils of this region precipitated conflict between agricultural and pastoral groups (Moore

et al., 1999). Internationally sponsored agricultural development programs multiplied in West

African countries in response to the droughts and perceived subsequent degradation (Baker,

2000; FAOSTAT, 2002). In several instances, local governments created and enforced zones of 10

strictly regulated land-uses. Such natural experiments are provided in the land management areas

(LMAs) created under the United Nations’ Onchocerciasis Control Program (OCP). The limited

size and site-specific nature of many of these projects provide opportunities to observe the long-

term effects of these natural experiments. Our strategy to decouple climatic change and human

use is to examine a case in which land-use has been held constant. We show in this study that the 15

imprints of policy-dictated land-use boundaries are clearly observable despite underlying

regional climatic shifts.

The study is performed at the Nouhao Valley LMA in south-east Burkina Faso, an LMA

project which both effectively segregated pastoralist and agricultural practices. Our study

integrates temporally rich regional satellite vegetation data and validated landscape-scale spectral 20

analysis to derive the trajectory of vegetation change under different land-use regimes. In

particular, we explore changes as influenced by pastoralists, agriculturalists, and in mixed use

areas.



5

This paper explores landscape-scale trajectories of change under controlled regimes of

land-use and suggests potential drivers based on these observations. Both anthropogenic and

climate variability occur at local, landscape, and regional scales of observation. In a temporally

and spatially variable landscape (Wardell, Reenberg et al., 2003; Laris, 2002), we quantify trends

at multiple scales to decouple underlying climate influences and overprinted land-use changes. 5

To determine that a change is anthropogenically induced, we apply a joint criteria: rapid

ecosystem change which follows political boundaries cannot be due completely to natural

disturbance, but rather the interaction between management and climate variance (Otterman,

1972). We propose a regionally applicable landscape-scale method for observing vegetation

change, and contribute a different perspective to our understanding of human induced land-cover 10

change in a highly dynamic and intensively utilized landscape.

2. Background

2.1 Basis for study

The Nouhao River basin in southeast Burkina Faso, re-settled under the West Africa 15

OCP, was divided by the Burkinabé government for settlement priorities. Two significant

communities in the Sudanian savanna, rain fed agriculturalists and semi-nomadic pastoralists,

were allocated separate lands. We hypothesize that the trajectories of land cover are significantly

different under the tenure of pastoralists, agriculturalists, and ‘unregulated’ mixed use outside of

the experimental area. Large-scale regional trends of land-cover change which do not coincide 20

with policy boundaries may be due to climate shifts, but we infer that localized land-cover

changes which conform to political boundaries are due to land-use.



6

The study of land-use policies or regional trends of land-cover change requires an

understanding of the different land-use roles of pastoralists and agriculturalists. The southern

Sudanian climate zone is a transition area between northern migratory Sahelian pastoral cultures

and southern agriculturalists (Ouédraogo, 2002; Moore, Kabore et al., 1999; Smith 1992).

Traditionally, mutually-beneficial land sharing agreements governed land tenure and resource 5

use (Baker, 2000; Howorth and O’Keefe, 1999). In a typical arrangement, pastoralists graze

cattle on new wet-season growth in less arable lands, while agriculturalists grow crops on more

fertile soils. In the dry season, pastoralists move herds onto agricultural lands to feed on crop

residue (McMillan et al., 1993; Smith, 1992). The transhumant, or regular seasonal migration,

provides pastoralists with a seasonally dependable food supply (Kinwa et al., 1996, McMillan et 10

al., 1993), while agriculturalists benefit from dairy-grain trades (Moore et al., 1999), cleared

fields, cattle tenure, and fertilizer left by the herds (Howorth and O’Keefe 1999). Ethnographic

research suggests that increasing population density and subsequent expanding agricultural land-

use (Madulu, 2003; Boserup, 1975), coupled with large-scale immigration from the north during

the droughts (Sanon, 2001) strained the symbiosis, leading to conflict (Moore et al., 1999; 15

Batterbury and Warren, 2001) and overly intensive land-tenure techniques (Ouédraogo 2002).

Policies and research in West Africa have been shaped by resource-use conflicts and the

resulting perceived land degradation. In Burkina Faso, policies to prevent the trois luttes, or

“three struggles” (bush fires, shifting cultivation, and transhumance), began in 1985 (Wardell et

al., 2003); all land was nationalized under the Agrarian Reform Laws (RAF) of 1991 (Kinwa et 20

al., 1996). Under increasing population pressure, agricultural extensification (increasing the area

under cultivation) impinges on common resources (Boserup 1975; Batterbury and Warren,

2001), which are often pastoral lands (Madulu, 2003). Larger herd sizes on smaller tracts of land



7

are blamed for overgrazing and reducing vegetative cover (Kinwa et al., 1996; Batterbury and

Warren, 2001). Woodland loss is often attributed to the agricultural practices of firing savanna

(Lykke, 1996 and 1997), burning crop residue, and expanding into riparian floodplains (Moore et

al., 1999; McMillan et al., 1993). The intersection between these two sets of practices produces a

highly variable and complex landscape. This research provides an opportunity to observe land-5

cover responses to agricultural and pastoral practices, one step in quantifying drivers of Sudanian

land-cover change.

2.2 The OCP and the basin resettlement program

In 1974 the United Nations created the OCP to combat the black fly vector of the blood-10

borne parasitic disease Onchocerciasis (River Blindness) in southern Sudanian river basins

(Samba, 1994). The parasite ravaged communities across seven countries’ river basins, forcing

emigration and eventual abandonment. The populations of the river basins, already low in the

pre-colonial period (Smith, 1992), were strained by the slave trade, border conflicts, and

ineffectual colonial management (Asiwaju, 1976), and completely ravaged by Onchocerciasis in 15

the mid-century. In 1982, the World Heath Organization (WHO) found a curative medication for

the parasite and by the early 1980s Onchocerciasis was controlled in some central river basins

(McMillan, 1995). Spontaneous immigration began as fertile basin lands became available for

agriculture (Batterbury and Warren, 2001). 1,700 families had moved to the Volta basins by

1979, and another 63,000 arrived at planned and unplanned settlements over the next 15 years 20

(McMillan et al., 1993). Under the OCP, the government of Burkina Faso created the Volta

Valley Authority (AVV) with the mandate of studying the basins and effectively settling new

migrants (McMillan, 1995; McMillan et al., 1993).



8

2.3 Study Location, the Nouhao Valley Project (MVVN)

In 1988, the AVV created the Nouhao Valley LMA (MVVN) in south east Burkina Faso.

The Nouhao Valley is a tributary of the Nakambe River (formerly the White Volta) in Boulgou

department on the Ghana boarder (Figure 1). The valley study area is 66 km long by 43 km wide 5

with a center at 0.16°W, 11.45°N. The valley is divided into five wedge-shaped administrative

regions. Each department has a pastoral region towards the valley interior and an agricultural

region in the highlands. The total effect is a ring of 105,000 ha of agricultural land surrounding

93,000 ha of pastoral lands (SECAM 2002).

The MVVN was designed to conform to the agricultural development goals of the 1980s. 10

In the first stage, the zones were demarcated for the separation of pastoralists (traditionally the

Fulbé or Peul people) and agriculturalists (here the Mossi and Bissa peoples). This separation

was intended to promote the sedentarization of the pastoralists, alleviate growing conflicts over

land tenure, and restore vegetation cover (Ferretti, 2002; MVVN, 1994; McMillan et al., 1993,

Sanon, 2001). Secondarily, a 172 km long and 30 m wide buffer, cleared and maintained locally, 15

was established around the entire pastoral region to prevent the spread of early season fires into

the pastoral region (SECAM, 2002). Finally, infrastructure was put in place for cattle

vaccination, agricultural financial extension, cattle and vegetable marketing, and education.

Agriculturalists in the pastoral zone were relocated (McMillan et al., 1993) and the separation is

still self-enforced. All MVVN participants agree to certain guidelines governing their 20

agricultural and pastoral practices (MVVN, 1994). Presently, agriculturalists cultivate millet and

red and white sorghum, with lesser amounts of rice and groundnuts (SECAM, 2002).



9

2.4 Remote sensing in West Africa

Remote sensing studies have advanced the understanding of regional trends and

contributed to the debate on resource use in critical environments. Broad regional remote sensing

studies in West Africa have revealed important discoveries about increasing regional aridity and

vegetation loss (Tucker et al., 1991; Gray, 1999) and anthropogenically induced changes in 5

climate-vegetation relationships (e.g. Prince et al., 1998; Rasmussen et al., 2001; Prince et al.,

1998; Wardell et al., 2003; Li et al., 2004; Budde et al., 2004). Landscape-scale studies point to a

wide variety of land-use influences on long-term land-cover changes, including forest loss

(Wardell et al., 2003) and aforestation (Fairhead and Leach, 1996), as well as local patterns of

use related to ethnographic differences (McMillan et al., 1993; Gray, 1999). 10

Satellite-based studies, like other ecological monitoring tools, are prone to unique

difficulties in the rapidly shifting semi-arid landscape of West Africa, such as atmospheric

uncertainty and few stable ground-control points (which provide spectral continuity). Fires,

rotating crops, changing land uses, and interannual variability of vegetation phenology due to

precipitation fluctuations add uncertainty when observing temporal trends in West African 15

landscape structure. Despite these challenges, satellite data allows replicable and consistent

observations at large spatial scales, is well suited to observe differential impacts of climate and

anthropogenic change, and highlights processes which may be considered insignificant at the

field scale.

There are a range of scales at which vegetation dynamics may be explored; coarse 20

resolution satellites are able to obtain data with a rapid repeat coverage, allowing analysis of

temporal dynamics; finer scale sensors with less frequent coverage are able to analyze changing

spatial structure. These two scales, used in tandem, often reveal important characteristics of



10

vegetation dynamics in time and space (DeFries et al., 1998). In this study, we compare temporal

and spatial trends from two significantly different satellite datasets at regional and local scales to

deconstruct the influence of climatological and anthropogenic changes (Budde et al., 2004). We

utilize both the coarse resolution AVHRR instrument to examine temporal trends, and a series of

higher resolution Landsat data to deconstruct some of the potential causes and drivers of these 5

trends.

2.4.1 Regional-scale vegetation trends: AVHRR

The Advanced Very High Resolution Radiometer (AVHRR) instrument on NOAA

satellites 7, 9, 11, and 14 is a wide-angle, spatially-coarse resolution sensor, capable of capturing 10

global datasets (GES-DAAC, 2003). Since July of 1981, the AVHRR sensor has obtained 4-km

global data (commonly binned to 8-km cells to filter clouds) in red, infrared (NIR), and two

thermal channels (Figure 2). Healthy vegetation is strongly absorbent in red wavelengths and

very reflective in the NIR (Tucker et al., 1991); thus the difference in NIR and red reflectance,

when normalized by the total brightness yields the Normalized Difference Vegetation Index 15

(NDVI), a well established proxy for vegetation abundance and health (Tucker et al., 1991;

Prince et al., 1998; Holm et al., 2003). NDVI is strongly related to the fraction of

photosynthetically absorbed radiation (FPAR). NDVI is defined as:

NDVI =R - RR + R

NIR red

NIR red

where Rred,NIR is the derived surface reflectance in red and NIR wavelengths. 20

Vegetation obtains a maximum NDVI when leaves are full and robust. As vegetation

senesces, chlorophyll ceases absorbing visible light and Rred increases, lowering NDVI (Tucker

et al., 1991; GES-DAAC, 2003). Seasonal NDVI reflects leaf phenologies over time (Justice et



11

al., 1985), and follows patterns indicative of vegetation type and plant robustness (Dall’Olmo

and Karnieli, 2002; Defries et al., 2000). In semi-arid systems, the area under a the NDVI curve

phenology curve is a proxy for total annual phytomass (Holm et al., 2003) and is strongly

correlated with net primary productivity (NPP) (Prince et al., 1998; Tucker et al., 1991). Annual

shifts in total phytomass are dependent on both rainfall variability (Holm et al., 2003; Tucker et 5

al., 1991) and changing vegetation types (Prince et al., 1998; Elmore et al., 2000; Bradley and

Mustard 2005). Although precipitation data is sparse for the study region, existing data

(SECAM, 2002) indicates that there is low spatial variability in annual rainfall across the MVVN

(805 ± 61 mm in 2000, 750 ± 99 mm in 2001). We infer that areas immediately adjacent to the

MVVN have had similar precipitation and should therefore follow the same precipitation-based 10

phytomass trends. Furthermore, coherent spatial patterns of NPP variability in the greater

MVVN region are likely due to land-cover change and not rainfall spatial variability (Li et al.,

2004).

2.4.2 Landscape-scale vegetation dynamics: Landsat TM and ETM+ 15

The Landsat satellite series have been in operation since 1972, although continual global

mapping capabilities were not fully implemented until the 1990s. Recent series Landsat satellites

carry the Thematic Mapper (TM and the newer ETM+) 30-m resolution sensors with seven

bands of spectral information in the visible and near infrared (NIR) wavelength regions (Figure

2). The medium resolution and long history of the Landsat satellite are useful for examining 20

landscape-scale trends, but suffer from infrequent observations. Although Landsat has repeat

coverage every 16 days, few scenes outside of North America were captured or archived prior to



12

the late 1990s, and cloud coverage often obscures scenes which were captured; nevertheless, a

good suite of scenes exist for temporal change analysis.

The TM sensor placement of visible and NIR bands allows spectral discrimination

between basic land-cover components, a property which has been utilized often for multi-

spectral classification and analysis of spectral properties in arid environments (e.g. Elmore, 5

2000; Adams, 1993; Asner, 2002). In particular, the spectral differences between green

vegetation, non-photosynthetic vegetation, and soil (Figure 2) allow a quantitative assessment of

fractional abundance of each component from spectral mixture analysis (SMA). SMA is an

inverse equation system which derives the fractional cover of a small number of pure

components on the ground from the spectral properties of each pixel (Smith et al., 1990a; Elmore 10

et al., 2000; Asner and Heidebrecht 2002; Adams et al., 1995; Mustard and Sunshine, 1999).

Fractional abundance is a physical quantity which can be directly related to ground areal

abundance of land cover components. When properly derived and validated, SMA fractional

abundance is more robust and accurate than the commonly used NDVI, and has a linear

relationship with ground vegetation abundance (Asner and Heidebrecht, 2002). Elmore et al., 15

(2000) found that SMA estimates in semi-arid systems were accurate within ±4.0% of measured

live cover and predicted the correct trajectory of vegetation change with 87% accuracy. The

SMA continuous field fractional cover representation is often a more revealing analytical

technique than classifications, which cannot capture gradients common to arid environments.

The use of TM-derived change analysis has been extensively explored in tropical and 20

arid-lands classification-based studies (Wardell et al., 2003; Rasmussen et al., 2001; Fairhead

and Leach 1996; Geoghegan et al., 2003) often to determine rates of deforestation. In contrast,

multi-temporal SMA studies effectively track vegetative change in arid environments due to



13

environmental change (Smith et al., 1990a; Elmore et al., 2000) and forest structure (Asner and

Heidebrecht, 2002; Adams et al., 1995). By examining trends in derived vegetation abundance

through time our study tracks absolute trajectories and gradational vegetation response to

environmental change (e.g. Elmore et al., 2000; Bradley and Mustard, 2005).

5

3. Methods

3.1 Regional trend analysis: AVHRR

We reconstructed broad scale, long term changes in biomass by examining trends of NPP

derived from the AVHRR satellite record. Daily, 8-km Pathfinder AVHRR NDVI data was

collected (GES-DAAC, 2003) for continental Africa for the years 1982-2000 and cropped to 10

northern Ghana and south-east Burkina Faso. To derive NPP, cloud-free time-series are required

for each pixel. Cloud cover yields lower than expected NDVI (and thus false image of vegetation

robustness), and thus we must filter the NDVI time-series to extract the maximum NDVI

envelope. This envelope represents real biophysical variability, rather than cloud artifact or

sensor noise. We followed a technique similar to the Best Index Slope Extraction (BISE) of 15

Viovy et al. (1992) to retain the largest number of ‘clean’ data points in the AVHRR time series.

The BISE algorithm assumes that rapid deviations from more slowly changing phenological

patterns are due to noise, and retains only the maximum NDVI envelope. Unlike the more

commonly used Maximum Value Composite (MVC) of Holden (1986), BISE retains as much

data as possible to construct an accurate phenological time-series. 20

Following Prince et al (1998) and others (Li et al., 2004; Budde et al., 2004), a proxy for

NPP was calculated from the average NDVI from January to December of each year. The

phenological cycle in Sudanian West Africa transitions into the dry season in October, and



14

reaches a minimum at peak dry season, between December and April (Devineau, 1999, Figure

3b). NPP follows annual precipitation patterns and appears in this region to generally increase

over time. A linear best-fit line was constructed at each pixel through the NPP data points, the

slope yielding an estimate of phytomass change per year (1994 is excluded due to sensor errors).

Change trajectories were analyzed regionally, around the study location, and in the agricultural 5

and pastoral zones. While short-period fluctuations of NPP may reflect the elasticity of different

vegetation types to rainfall (Bradley and Mustard, 2005), we infer that decadal-period variability

is due largely to compositional change (Holm et al., 2003).

3.2 Landsape trend analysis: Landsat TM 10

Landscape scale trajectories of vegetation were constructed from 30-m Landsat TM and

ETM+ data, selected to highlight changes in woody vegetation cover. At the conclusion of the

wet season in October and early November, while most grasses have begun to senesce, woody

vegetation still retains canopy leaves (Devineau 1999), and brush fires do not yet dominate the

landscape (Laris, 2002; Mbow, 2003). Therefore, we obtained Landsat TM and ETM+ scenes 15

(WRS P194 / R52, centroid at 0º19'W 11º34'N) acquired during the transition from the Sudanian

wet to dry seasons in 1984, 1989, 1999, 2001, and 2002. Scenes were geo-rectified to the 2002

scene with 30-m accuracy. Data was spectrally corrected for Raleigh atmospheric scatter by

histogram-based dark-pixel subtraction (Table 1), adjusted to reflectance by standard equations

(NASA, 2003), and spectrally rectified through linear band adjustment (Elmore et al., 2000). 20

A multi-temporal comparison of spectral properties requires that the spectra be corrected

for atmospheric variability such that they measure in the same units. With no a-priori knowledge

of atmospheric conditions, spectra are corrected to a baseline scene by comparing uniform



15

ground-control locations. Ideally, stable non-vegetated surfaces with a wide range of brightness

(sands and rock outcrops, deep water, and large built surfaces) are used in this task. However,

very few stable points were available due to scene heterogeneity and a limited number of

spectrally pure pixels. Therefore, spectral adjustments were based on the principle that a large

number of relatively constant land-covers (gallery forest, irrigated agriculture, senesced 5

grasslands, and bare soil) should appear, on average, spectrally constant (Tokola et al., 1999).

Spectral corrections were obtained from 30 carefully selected regions of interest (ROIs) per

scene to correct the visible and NIR bands relative to the 2002 scene. Spectra were checked after

correction and found to conform to expected spectral properties of known land covers.

The equations governing SMA solve for the fractional areal abundance of pure 10

endmember spectra (see Elmore et al., 2000 for a discussion on the methods and errors of SMA

in semi-arid environments). Four general land cover endmembers were chosen for the SMA in an

iterative process (Mustard and Sunshine, 1999; Elmore et al., 2000). These endmembers included

green vegetation (GV - obtained from riparian areas), soil (SOIL - from an open airfield and other

known locations), non-photosynthetic vegetation (NPV - defined from laboratory reflectance 15

spectra), and a pure shade component (DARK - to account for topography and shadows) (see

Figure 2). SMAs were calculated for each TM and ETM+ scene with the final SOIL, GV, NPV,

and DARK endmembers. The result of the SMA are separate data planes of endmember

fractional areal abundance (0-1) (SOILF, GVF, NPVF, and DARKF).

GVF was normalized (GVN) to reflect the abundance of green vegetation compared to 20

other real components by removing the highly variable DARKF. DARKF is dependant on solar

angle, slope, aspect, and vegetation structure.

GV GVGV SOIL NPVN

F

F F F= + +



16

We examined the trajectory of vegetation change in a linear fit of the leafy green

vegetation areal abundance (GVN) through time. Increasing GVN may be due to healthier

vegetation or additional vegetation within a pixel. Therefore, changes in GVN may reflect

changing ground cover composition, or a differential response to climate conditions. Our

selection of transition season scenes is calculated to reduce the impact of green grasses on the 5

detected GVN, and therefore we assume that the value of GVN at this time-period is related to the

abundance of shrubs and trees; we confirmed that GVN in 2002 was strongly related to shrub

and tree abundance calculated from a 2003 field campaign (see section 4, Validation).

A best fit line of GVN (through time at each pixel) represents a two-decade trajectory of

change in shrub and tree areal abundance, regardless of sudden or gradual change. An iterative 10

regression algorithm was developed to ignore data which had burned immediately prior to image

acquisition. Minor brush fires significantly change the value of GVN in the year of the fire.

However, savanna vegetation regenerates quickly after seasonal fires; we would thus expect that

longer trends of vegetation change are not impacted, barring a severe burn. In our analysis, the

total trend is of more relevance than individual burns. To filter out active fires and recent char, 15

an algorithm iteratively eliminated points with high absolute residuals when the standard

deviation between all GVN and the predicted line exceeded 0.05. After eliminating potential

burns, the slope, offset, and standard deviation were recorded at each pixel.

3.3 Land-cover transects 20

Forty-eight (48) land-cover transects were run in October of 2003 in the Nouhao Valley

MVVN in a variety of environments (Figure 4, Table 2). These transects were designed to

quantify the ground observed areal abundance of green leafy cover, grasses, soil, small



17

herbaceous growth (including vegetable and peanut crops), and row crops (millet, sorghum, and

maize). Transect locations were chosen in large (>=1 km2), relatively homogenous areas as well

as regions which incorporated rarer cover types, such as gallery forest. Transects began at least

sixty meters from the nearest road or path and proceeded approximately orthogonally for 180 to

250 meters. Cover type was recorded in 5 meter increments, one type per location. Shrubs taller 5

than 0.5 meter were recorded, as were trees overhanging the transect line. Total transect

abundance was calculated as fractional areal cover (GVT) of each type to match the metric

derived from the Landsat data. A global positioning system (GPS) was used to record the start

and end coordinate to 10 m accuracy. Two overview photographs were obtained each at the

beginning and end of the transects. Transect information was transferred to a geographical 10

information system (GIS).

3.4 GIS and metadata

Statistics were obtained under a geographical information system (GIS) provided by the

MVVN (Sanou, 2002). The GIS demarcated administrative regions and the zone types (Figure 15

1). A rectangular region outside the MVVN was added to explore landscape response outside the

study area. The region surrounds the regional capital, Tenkodogo, the agricultural community of

Ouargaye, and the main regional road linking eastern Burkina Faso with Ghana and Togo. The

additional polygon was labeled as the control area, as these communities have changed

demographically and agriculturally in accordance to regional politics and drivers, rather than 20

under the rules of the MVVN.

4. Validation



18

A 30 meter buffer was created around each transect line in the GIS system and overlaid

on the satellite derived GVN. Values of GVT were regressed against GVN for each buffered

transect (Figure 5). The nearly 1-to-1 slope (0.98) indicates that GVN in 2002 accounts for 37%

of the variance in GVT in 2003. In other words, despite a full year of change as well as potential

registration errors, the spectrally-calculated green vegetation fraction (GVN) is shown to 5

represent shrub and tree cover as observed on the ground one year later. Twenty percent more

green cover is detected in GVN than recorded in GVT, an offset which describes the presence of

other non shrub and tree green vegetation, such as herbaceous cover, non-senesced grasses, and

late-season crops. In SMA, the greatest separation of the GV endmembers is derived from the

steep red-NIR slope of healthy vegetation (see Figure 2). Although the scenes were chosen 10

temporally to provide the best phenological separation between grasses and tree cover, some

grass retains photosynthetic capacity past the transition season. However, approximately the

same degree of spatial variability in grass senescence should be expected on an annual basis, and

even other statistically significant variations do not create large, spatially contiguous errors.

Grasses and other non-senesced vegetation account for the offset and some variability around the 15

best fit line. Other expected errors arise from early defoliating deciduous species (Acacia

macrostachya and Annona senegalensis - Devineau, 1999), trees with sparse cover (Acacia spp.),

and differences in land uses between image acquisition in 2002 and field work in 2003. In at

least one location, a significant number of trees had been felled in September of 2002 to prepare

a pastoral area (illegally) for cultivation. Although these caveats limit accuracy, differences are 20

minimized in this analysis. The validation indicates that SMA extracts meaningful patterns and

gradients of tree and shrub cover in these scenes.



19

5. Results

The southern Sudanian zone in Burkina Faso and northern Ghana show a positive

vegetation change from 1984 to 2002. At respectively increasingly fine resolution, there are clear

and consistent spatial patterns distinguishing the rate of vegetation growth in the region, the

MVVN, the pastoral zone of the MVVN, and in particular, the highly vegetated riparian areas of 5

the pastoral zone. The broad pattern of vegetation increase across the region appears in the

AVHRR phenological component of this study. We found this region displays a rate of growth

exceeding 9.85 ± 4.25 g C m-2 y-2 (average in area in Figure 6a). The dominant positive growth

rate does not reflect political boundaries; rather it appears to be a function of increased

precipitation through the period 1984 to 2002 (Tucker et al., 1991, Prince et al., 1998). Spatial 10

patterns in the slope of NPP suggest that the heavily populated and nutrient poor Mossi plateau

to the northwest (Kinwa et al., 1996, McMillan et al., 1993) has not benefited from the increased

rainfall; the Nakanbe River (White Volta) basin, and the MVVN in particular have responded

strongly. The increases of 9.9 ± .9, 13.8 ± 0.7, 18.0 ± 0.7 g C m-2 y-2 within the MVVN control

zone, agricultural zone, and pastoral zone (respectively) indicate that patterns of land use are 15

imprinted on the regional greening. The region surrounding the MVVN, particularly the river

basins to the south and east, have similar physical characteristics to the MVVN area, and should

therefore be expected to react similarly to the MVVN under changing climatic conditions. An

examination of a higher resolution analysis reveals that land use patterns strongly influence the

impact of the climate signal. 20

At the landscape scale, we see a difference in the growth rate of riparian and open

savanna. We define a riparian area as any pixel which exceeded 0.8 GVN in any year of the

analysis (1984 to 2002). In these areas, the contrast between the rate of growth in the pastoral



20

and agricultural zones is approximately 9% in riparian areas, and 1-5% in non-riparian areas

(Figure 7a). Fifty-seven percent (57.1%) of gallery forest and riparian areas in the agricultural

zone (of 2,300 Ha) had gains of over 5% green cover from 1984 to 2002, but the losses were

spatially coherent and confined to river valleys (Figure 6c). Eighty-eight percent (87.5%) of

riparian areas in the pastoral zone (of 7,400 Ha) increased over 5% in the same period; while 5

only 61.2% (of 1,900 Ha) showed increases in the control area. A plot of green vegetation

fraction (GVN) trajectory in each zone, arranged by the maximum GVN from 1984 to 2002

(Figure 7b) indicates that the change in vegetation fraction is a function of both the land-cover

type and land-use categorization. In the pastoral area, there is a nearly linear relationship

between denser vegetation and a higher rate of growth, with losses occurring in sparse vegetation 10

areas. In both the agricultural and control zones, growth rates peak at areas with 60-80%

maximum GVN, or closed savannas. The pattern suggests that the highest contrast between the

pastoral and agricultural regions occurs in dense forest cover.

At the MVVN, the rate of growth of green vegetation is correlated with administrative

boundaries, particularly in highly vegetated riparian areas. The policy-defined boundary between 15

the pastoral and agricultural zones appears as a sharp difference in the rate of vegetation change

(Figure 6b). Green vegetation abundance (GVN) increased from 1984 to 2002 by 8.71, 6.22, and

11.49% at the control, agricultural, and pastoral zones, respectively. There is a marked contrast

at the border between the agricultural and pastoral zones, a pattern which also appears in other

global and regional analyses. The contemporary half kilometer MODIS (Moderate Resolution 20

Imaging Spectrometer) continuous-field tree-cover product produced by Hansen et al. (2002)

indicates similar magnitude differences in 2001 leafy green cover between the zones of the

MVVN. However, we show in the landscape-scale (Landsat TM) analysis that the delineation



21

between tree and shrub abundance was not pronounced in the early 1980’s at the project

inception. Although regional green cover is increasing, it has increased more rapidly inside the

MVVN pastoral zone than in the control or agriculture zones.

6. Discussion 5

Evidence is presented from two independent lines of multi-temporal satellite data

analyses that boundaries of land-use policies are highly correlated with significant changes in

vegetation structure in the northern Sudanian climate zone of West Africa. Both a coarse-spatial,

high-temporal resolution AVHRR analysis and a high-spatial, multi-temporal resolution Landsat

TM study reveal that specific land-use policies impart an overprint of land-cover change over 10

regional climatic precipitation shifts. Thus, although broadly the gains in vegetated cover may

result from precipitation, the sharp differences in trajectories at the pastoral boundaries are a

function of land use. The uniform gains in control region woody areas (Figure 7b) indicate that

increased precipitation has indeed increased apparent vegetation cover by approximately 15%.

The rise in vegetation cover in the pastoral zone, strongly correlated with woody vegetation 15

abundance, suggest that woody areas have become more densely vegetated. Conversely, the

lower gains in the agricultural region suggest that woody areas have not benefited from increased

precipitation in the same fashion as neighboring regions. Trajectories of the gallery forest in

Figure 7b are based on very few pixels and may not accurately represent regional processes.

Prior studies suggests that changes in relatively stable wooded environments of forest 20

galleries and riparian areas are the suitable indicators of degradation (Lykke, 1996; Wardell et

al., 2002), unsustainable resource use (Fairhead and Leach, 1996; Vescovi et al. 2002), or land-

cover recovery. That we see the most drastic changes in riparian areas implies that these trends



22

are significant for the long-term ecological outcome in this region. Although the regulated land

uses within the MVVN allow this study to control for land uses, the exact drivers of land-cover

change are too complex to be derived from this line of research. A number of potential drivers

have been suggested in the literature, some are supported by the conclusions of this research. We

address assumptions intrinsic to remote sensing research, followed by observed overprints of 5

long-term forest cover gains and losses.

6.1 Remote sensing assumptions

This study is a satellite remote sensing analysis of land cover change. As such, it is

subject to a unique set of difficulties and advantages in dynamic remote locations. In particular 10

there is always a potential for systematic error due to manual calibration errors or faulty

analytical techniques. This study is conducted in a region in which surface spectral contrast is

generally low (all surfaces are bright), and thus there is a higher potential than normal for these

errors. The methods presented attempt to mitigate these impacts by using a robust (Small, 2005),

well-documented (Adams et al; Elmore et al., 2000), and non-biased analytical approach to 15

survey green vegetation cover (SMA). However, it is still subject to a basic set of assumptions,

primarily that a misguided atmospheric correction could feasibly change the results: rather than

the pastoral riparian areas increasing in vegetated cover while other areas remained stagnant, the

pastoral riparian areas may have remained stagnant while other areas decreased significantly and

uniformly in vegetated cover. We place confidence in our results as this scenario is possible but 20

unlikely, as SMA is robust to atmospheric error. It is critical to note that even if the absolute

sense of change is incorrect, the relative differences seen in the riparian areas are significant. The



23

most dramatic changes were observed through the riparian areas, and so this research

concentrates on the potential changes occurring in this theoretically stable environment.

A second caveat includes a basic set of assumptions about ground cover composition; a

possible explanation of our results is that species in the pastoral riparian area are fundamentally

different than those in the agricultural riparian areas. If grasses in the riparian pastoral zone are 5

more responsive to precipitation or maintain photosynthetic capacity longer under increased

precipitation than agricultural riparian species, we might see similar results. If our observations

track increasing rainfall, then in this scenario we would observe increasing vegetation cover as a

function of rainfall only. However, this scenario poorly explains the stability of all other land

covers and the loss of green vegetation exclusively in the agricultural riparian zone. In addition, 10

that this phenomenon does not appear to have been recorded previously suggests that, if true, it

would be a significant finding.

As in all remote sensing studies, field validation is critical, and the field results presented

here act only as a set of baseline observations. Further ground analysis will reveal if these trends

are significant or permanent. However, we find these patterns striking, and the correlation with 15

land use is clear. We present several alternative scenarios to explain the gains and losses of green

vegetation cover from 1984-2002.

6.2 Vegetation gains and losses in the MVVN

The greatest grains in green vegetation were in the pastoral zone, particularly in riparian 20

valleys. The pastoral zone was designated for the exclusive use of a limited number of herders,

and is protected by a continuous and effective fire break around the perimeter. A reduced

frequency of fires, natural or anthropogenic, gives competitive advantage to perennial shrubs and



24

trees (Lykke, 1996; Baker, 2000), but would not necessarily be exclusive to riparian areas.

Kumar et al. (2002) suggests that soil types may alter phenological parameters, but we would not

expect a sharp boundary at the pastoral / agricultural boundary due only to soil differences.

Anecdotal evidence from field workers at the MVVN suggests that the last two decades have

seen drastic increases in woody vegetation throughout the pastoral region. From a ground 5

perspective, the pastoral zone appears to have a higher percentage of woody cover in upland and

lowland savannas than the open savannas of the agricultural zone.

The locally higher density of cattle around riparian areas (for watering purposes)

increases soil fertility when the cattle density is below a harmful threshold (Lykke, 2000; Jeltsch

et al., 1997), and assists woody growth by the selective consumption of grasses and reduction in 10

fire intensity (Sawadogo et al., 2005). The tall grasses Angropogon gayanas and A. ascinodis

thrive from cattle-induced soil fertility (Gray, 1999) and may be locally denser in the pastoral

zone, but would not be detected by remote examination of phenological differences from other

grasses. A longer growing season due to increased precipitation would theoretically impact

riparian and non-riparian grasses, thus increasing greenness across the entire region, not just the 15

pastoral region selectively. A comparison between cattle density (as collected in a 1996 census;

SECAM, 2002) and the overall trajectory of green vegetation in each administrative and land-use

region suggests that higher cattle numbers may influence the vegetation recovery rate (Figure

8a) in the pastoral zone (r2 = 0.67), particularly in riparian areas (r2 = 0.74).

Losses in green cover throughout the entire region appear most pronounced in sparsely 20

vegetated and eroded areas, a pattern which conforms to one definition of degradation. The

losses in sparse areas imply loss of soil fertility, erosion of vegetation-supporting organic top-

soils, or reduced retention of soil moisture on open soils. The relatively lower gains in vegetation



25

cover in heavily vegetated regions (Figure 7b) and spatial coherency of losses in agricultural

zone riparian areas (Figure 6c) suggest potential deforestation (Kinwa et al., 1996; Moore et al.,

1999; Fairhead and Leach, 1996; Lykke, 1996), increased floodplain agriculture (McMillan et

al., 1993; MVVN, 1994), river-bank erosion, or the loss of savanna-gallery margin forest by

repeated fires in the agricultural zone (Lykke, 1996). Despite these direct anthropogenic impacts, 5

this study and Vescovi et al. (2002) suggest that there is not a first-order relationship between

population density and long term changes in vegetated land-cover (Figure 8b).

The different trajectories from the agricultural and control zones are largely imprints of

legacy and land-use. We can understand the changes in the unregulated control area to represent

the impact of climatic shifts and regional population dynamics on the underlying trajectory of 10

greenness.

7. Conclusions

Land-cover dynamics in Sudanian West Africa bear the imprint of both climatic

variability and land use; this research has shown that these two drivers of change are separable 15

spatially and temporally. We observe that changes in woody vegetation abundance are spatially

correlated with policy-driven land-use boundaries at the Nouhao Valley project, and that this

contrast has evolved from the inception of the project, leading to the conclusion that the project

has altered trajectories of vegetation growth, particularly in riparian areas. Although we cannot

quantify the type of change which has occurred, our evidence suggests that we observe increases 20

in woody vegetation abundance in riparian areas in pastoral areas, and decreases under

agricultural tenure. The sharp contrast between riparian land-cover dynamics of the pastoral and

agricultural land-uses suggests that the balance between these two land-uses impacts long-term



26

trajectories of land cover change. This remote sensing approach provides a complimentary

broad-scale view to field intensive studies. The conclusions drawn from this controlled natural

experiment and pilot analysis suggest options for effectively monitoring the structural impact of

mixed pastoral and agricultural practices through the broader West African Sudanian ecotone.

5



27

References

Adams, J. B., D. E. Sabol, et al. (1995). Classification of Multispectral Images Based on

Fractions of Endmembers - Application to Land-Cover Change in the Brazilian Amazon.

Remote Sensing of Environment 52(2): 137-154. 5

Asiwaju, A. I. (1976). Migrations as revolt: the example of the Ivory Coast and the Upper Volta

before 1945. The Journal of African History 17(4): 577-594.

Asner, G. P. and K. B. Heidebrecht (2002). Spectral unmixing of vegetation, soil and dry carbon 10

cover in arid regions: comparing multispectral and hyperspectral observations.

International Journal of Remote Sensing 23(19): 3939-3958.

Baker, K. M. (2000). Indigenous Land Management in West Africa: An Environmental

Balancing Act. Oxford, UK, Oxford University Press. 15

Batterbury, S. and A. Warren (2001). The African Sahel 25 years after the great drought:

assessing progress and moving towards new agendas and approaches. Global

Environmental Change-Human and Policy Dimensions 11(1): 1-8.

20

Bilsborrow, R. E. (2002). Migration, Population Change, and the Rural Environment. ESCP

Report - Issue 8: 69-94.



28

Boserup, E. (1975). The impact of population growth on agricultural output. The Quarterly

Journal of Economics 89 (2): 257-270.

Bradley, B. A. and J. F. Mustard (2005). Identifying Land Cover Variability Distinct from Land

Cover Change: Cheatgrass in the Great Basin. Remote Sensing of Environment 94 (2): 5

204-213

Budde, M. E., G Tappan, J. Rowland, J. Lewis, L.L. Tieszen. (2004) Assessing land cover

performance in Senegal, West Africa using 1-lm integrated NDVI and local variance

analysis. Journal of Arid Environments. 59: 481-498 10

Clark, R. N., G. A. Swayze, et al. (2003). Library splib05a, USGS Open File Report 03-395,

USGS.

Dall'Olmo, G. and A. Karnieli (2002). Monitoring phenological cycles of desert ecosystems 15

using NDVI and LST data derived from NOAA-AVHRR imagery. International Journal

of Remote Sensing 23(19): 4055-4071.

Defries, R. S., M. C. Hansen, et al. (2000). Global continuous fields of vegetation characteristics:

a linear mixture model applied to multi-year 8 km AVHRR data. International Journal of 20

Remote Sensing 21(6-7): 1389-1414.

Devineau, J. L. (1999). Seasonal rhythms and phenological plasticity of savanna woody species



29

in a fallow farming system (south-west Burkina Faso). Journal of Tropical Ecology 15:

497-513.

Elmore, A. J., J. F. Mustard, et al. (2000). Quantifying vegetation change in semiarid

environments: Precision and accuracy of spectral mixture analysis and the Normalized 5

Difference Vegetation Index. Remote Sensing of Environment 73(1): 87-102.

Fairhead, J. and M. Leach (1996). Misreading the African Landscape: Society and ecology in a

forest-savanna mosaic. Cambridge, UK, Cambridge University Press.

10

FAOSTAT (2002). FAO Statistical Databases, Food and Agriculture Organization of The United

Nations.

Ferretti, S. (2002). Projet de Mise en Valeur de la Vallée de la Nouhao. Milan, Italy, Association

Internationale des Volontaires Laïcs (LVIA). 15

Geoghegan, J., L. Schneider, et al. (2003). Spatially Explicit, Statistical Land-Change Models in

Data-Sparse Conditions. Integrated land-change science and tropical deforestation in the

southern Yucatan: final frontiers. B. L. Turner, J. Geoghegan and D. R. Foster. New

York, Oxford University Press: 247-270. 20

GES-DAAC (2003). PAL Daily Tile Timeseries Data, GES DISC DAAC.



30

Gray, L. C. (1999). Is land being degraded? A multi-scale investigation of landscape change in

southwestern Burkina Faso. Land Degradation & Development 10(4): 329-343.

Hanan, N. P., S. D. Prince, et al. (1995). Estimation of Absorbed Photosynthetically Active

Radiation and Vegetation Net Production Efficiency Using Satellite Data. Agricultural 5

and Forest Meteorology 76(3-4): 259-276.

Hansen, M., R. DeFries, et al. (2003). 500m MODIS Vegetation Continuous Fields. College

Park, Maryland, The Global Land Cover Facility.

10

Hansen, M. C., R. S. DeFries, et al. (2002). Towards an operational MODIS continuous field of

percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing

of Environment 83(1-2): 303-319.

Holben, B. N. (1986). Charecteristics of maximum-value composite images from temporal 15

AVHRR data. International Journal of Remote Sensing. 7, 1417-1434.

Holm, A. M., S. W. Cridland, et al. (2003). The use of time-integrated NOAA NDVI data and

rainfall to assess landscape degradation in the arid shrubland of Western Australia.

Remote Sensing of Environment 85(2): 145-158. 20

Howorth, C. and P. O'Keefe (1999). Farmers do it better: Local management of change in

southern Burkina Faso. Land Degradation & Development 10(2): 93-109.



31

Jeltsch, F., S. J. Milton, et al. (1997). Analysing shrub encroachment in the southern Kalahari: a

grid-based modelling approach. Journal of Applied Ecology 34(6): 1497-1508.

Justice, C. O., J. R. G. Townshend, B. N. Holben and C. J. Tucker (1985). Analysis of the 5

phenology of global vegetation using meteorological satellite data. International Journal

of Remote Sensing. 6, 1271-1318.

Kinwa, H. K. N., A. Andrew, et al. (1996). A study-tour report on community-based land

management in West Africa: Burkina Faso and Niger. Johannesburg, Land and 10

Agriculture Policy Centre.

Kumar, L., M. Reitkerk, et al. (2002). Relationship between vegetation growth rates at the onset

of the wet season and soil type in the Sahel of Burkina Faso: implications for resource

utilization at large scales. Ecological Modelling 149(1-2): 143-152. 15

Laris, P. (2002). Burning the seasonal mosaic: Preventative burning strategies in the wooded

savanna of southern Mali. Human Ecology 30(2): 155-186.

Li, J., J. Lewis, J. Rowland, G. Tappan, L.L. Tieszen. (2004) Evaluation of land performance in 20

Senegal using multi-temporal NDVI and rainfall series. Journal of Arid Environments.

59: 463-480



32

Lykke, A. M. (1996). How gallery forest turns into savanna: an example from Senegal. The

Biodiversity of African Plants. L. J. G. v. d. M. e. al. Netherlands, Kluwer: 323-328.

Lykke, A. M. (1997). Reflections on vegetation management of sub-humid savannas. 9th Danish

Sahel Workshop, SEREIN Occasional Paper 5: 47-69. 5

Lykke, A. M. (2000). Refining the Ecological Aspects of Disequilibrium Theories for Africa's

Pastoral Drylands. Nairobi, Kenya, United Nations Environment Programme.

Madulu, N. F. (2003). Impact of Population Pressure and Policy Changes on Common Property 10

Resource Availability in Rural Tanzania. Promoting Common Property in Africa:

Networks for Influencing Policy and Governance of Natural Resources, Cape Town.

Mbow, C., B. Sambou, et al. (2003). Vegetation and fire readiness in main morphological units

of Niokolo Koba National Park (Southeast Senegal). Geografisk Tidsskeift 103: 37-44. 15

McMillan, D. E. (1995). Sahel visions : planned settlement and river blindness control in

Burkina Faso. Tucson, University of Arizona Press.

McMillan, D. E., J.-B. Nana, et al. (1993). Settlement and development in the river blindness 20

control zone : case study, Burkina Faso. Washington, D.C., World Bank.

Moore, K. M., D. Kabore, et al. (1999). Conflict and Natural Resource Management in



33

Agricultural and Pastoral Systems of Arid and Semi-Arid Regions of West Africa,

SANREM CRSP (West Africa).

Mustard, J. F. and J. M. Sunshine (1999). Spectral Analysis for Earth Science: Investigations

Using Remote Sensing Data. Remote Sensing for the Earth Sciences, Manual of Remote 5

Sensing. A. Rencz. New York, J. Wiley and Sons Inc. 3: 251-307.

MVVN (Projet de Mise en Valeur de la Vallée de la Nouhao), (1994). Document de Projet.

Ouagadougou, Burkina Faso, Ministère des Affaires Etrangères: Direction Générale de la

Coopération au Développement. 10

MVVN (Projet de Mise en Valeur de la Vallée de la Nouhao), (2002). Enquête sur les effectifs

du cheptel dans la Vallée de la Nouhao: rapport final. Tenkodogo, Burkina Faso,

Ministère des Ressources Animales, Secretariat Général.

15

NASA, Landsat Project Science Office (2003). Landsat 7 Science Data Users Handbook, NASA

at (http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html).

Otterman, J. (1974). Baring high-albedo soils by overgrazing: a hypothesized desertification

mechanism. Science. 186(4163): 531-533 20

Ouédraogo, M. (2002). Land tenure and rural development in Burkina Faso: issues and

strategies. London, International Institute for Environment and Development.



34

Prince, S. D. (2002). Spatial and temporal scales for detection of desertification. Global

desertification : do humans cause deserts? J. F. Reynolds and D. M. Stafford Smith.

Berlin, Dahlem University Press: 23-40.

5

Prince, S. D., E. B. De Colstoun, et al. (1998). Evidence from rain-use efficiencies does not

indicate extensive Sahelian desertification. Global Change Biology 4(4): 359-374.

Rasmussen, K., B. Fog, et al. (2001). Desertification in reverse? Observations from northern

Burkina Faso. Global Environmental Change-Human and Policy Dimensions 11(4): 271-10

282.

Samba, E. M. (1994). The Onchocerciasis Control Programme in West Africa: An example of

effective public health management. Geneva, World Health Organization.

15

Sanon, T. (2001). Des fulbé dans la Vallée de la Nouhao. Tenkodogo, Burkina Faso, Projet de

Mise en Valeur de la Vallée de la Nouhao.

Sanou, P. (2002). GIS dataset for Nouhao Valley MVVN, Burkina Faso. Ouagadougou, Burkina

Faso, Centre SIG et Télédétection Adjaratou. 20

Sawadogo, L., D. Tiveau, R. Nygård. (2005). Influence of selective tree cutting, livestock and

prescribed fire of herbaceous biomass in the savannah woodlands of Burkina Faso, West



35

Africa. Agriculture, Ecosystems and Environment. 105: 335-345

Schmidt, H. and A. Karnieli (2002). Analysis of the temporal and spatial vegetation patterns in a

semi-arid environment observed by NOAA AVHRR imagery and spectral ground

measurements. International Journal of Remote Sensing 23(19): 3971-3990. 5

SECAM (Société d'Etudes de Conseils et d'Assistance Multisectorielle), (2002). Etude

Agrostologique dans la zone pastorale amenagee de la Vallée de la Nouhao. Tenkodogo,

Burkina Faso, Projet de Mise en Valeur de la Vallée de la Nauhao.

10

Smith, A. B. (1992). Pastoralism in Africa: Origins and Development Ecology. London, Hurst &

Co.

Smith, M. O., S. L. Ustin, et al. (1990). Vegetation in Deserts .1. A Regional Measure of

Abundance from Multispectral Images. Remote Sensing of Environment 31(1): 1-26. 15

Smith, M. O., S. L. Ustin, et al. (1990). Vegetation in Deserts .2. Environmental-Influences on

Regional Abundance. Remote Sensing of Environment 31(1): 27-52.

Taylor, C. M., E. F. Lambin, et al. (2002). The influence of land use change on climate in the 20

Sahel. Journal of Climate 15(24): 3615-3629.

Tokola, T., S. Lofman, et al. (1999). Relative calibration of multitemporal Landsat data for forest



36

cover change detection. Remote Sensing of Environment 68(1): 1-11.

Tucker, C. J., H. E. Dregne, et al. (1991). Expansion and Contraction of the Sahara Desert from

1980 to 1990. Science 253(5017): 299-301.

5

Vescovi, F. D., S. J. Park, et al. (2002). Detection of human-induced land cover changes in a

savannah landscape in Ghana: I. Change detection and quantification. EARSeL

workshop: Remote sensing for developing countries, Bonn, Germany.

Viovy, N., O. Arino, et al. (1992). The Best Index Slope Extraction (BISE) - a Method for 10

Reducing Noise in NDVI Time-Series. International Journal of Remote Sensing 13(8):

1585-1590.

Wardell, D. A., A. Reenberg, et al. (2003). Historical footprints in contemporary land use

systems: forest cover changes in savannah woodlands in the Sudano-Sahelian zone. 15

Global Environmental Change-Human and Policy Dimensions 13(4): 235-254.

Warren, A. (2002). Land degradation is contextual. Land Degradation & Development 13(6):

449-459.



37

Figure Captions:

Figure 1

Vegetation abundance map of Nouhao Valley Land Management Area (MVVN) in

south-east Burkina Faso, West Africa, 2002. The experimental area is divided into five 5

administrative regions and two land uses, cattle and sheep pastoralists in the central region and

millet / sorghum agriculturalists in the in the surrounding area. The control area has similar

physical features, population, and technology, but is not regulated.

Figure 2 10

Spectral endmembers used in the spectral mixture analysis. The red edge in green

vegetation is detected by both the Landsat and AVHRR satellites.

Figure 3

a) A schematic of the AVHRR filter which removes clouds from raw daily data. The 15

moving window searches 50 days for an acceptable value of NDVI to construct a phenology. In

(b), the average phenology calculated by the algorithm in the MVVN is compared to average

rainfall in Nimey, Niger, the only consistent precipitation station. (c) The area under the

phenology can be used to calculate annual NPP (June to May), which closely tracks annual

rainfall. 20

Figure 4



38

Location of representative transects from the field campaign (not all numbers shown).

Riparian areas (exceeding 0.6 GVN, or approximately 80% shrub and tree cover) are indicated in

gray.

Figure 5 5

Shrub and tree fraction determined from the transects (GVT) is compared to the fractional

abundance of green vegetation determined spectrally (GVN). The variance and 20% offset are

primarily functions of non-senesced grasses which contribute to GVT, and potential image mis-

registration.

10

Figure 6

The AVHRR NPP slope analysis (a) reveals a primarily positive region-wide signal

which is strongly enhanced in the Nouhao Valley area. In the entire MVVN (b), a slope analysis

(enhanced by vegetation abundance) indicates that forest galleries are gained in the pastoral zone

and lost in the agricultural zone. Detail in (c) reveals losses and gains concentrated in riparian 15

areas and a sharp contrast at the agricultural and pastoral zone boundary.

Figure 7

All administrative districts in the MVVN gained in the abundance of gallery forest from

1984 to 2002 (a) in the riparian areas. The contrast between changes in the pastoral and 20

agricultural areas is much more pronounced in the riparian areas than in non-riparian zones.

Chart (b) indicates that this contrast increases with higher abundances of vegetation.



39

Figure 8

(a) There is a correlation between cattle density and gain in vegetation abundance from

1984-2002. (b) There is no relationship between human density and gain in vegetation

abundance from 1984-2002 in the agricultural zone.

5

Date 11/5/1984 11/11/1989 11/7/1999 10/27/2001 10/30/2002Sensor L5 TM L5 TM L7 ETM+ L7 ETM+ L7 ETM+

Wavelength (µm)0.4787 78 76 54 79 640.561 30 22 35 58 46

0.6614 33 23 29 55 380.8346 14 5 13 20 17

1.65 25 3 2 18 122.208 6 3 3 19 11

Dark pixel subtraction

Owner

Text Box

Table 1

Transect ID

Zone * Lat (N) Lon (W) Grass ** Herb / Crop **

Soil ** Shrub / Tree **

GVN Transect ID

Zone * Lat (N) Lon (W) Grass ** Herb / Crop **

Soil ** Shrub / Tree **

GVN

1 M 11.770 -0.335 0.00 0.53 0.42 0.05 0.29 24 A 11.341 -0.302 0.26 0.03 0.10 0.61 0.552 M 11.774 -0.337 0.32 0.42 0.00 0.26 0.56 25 A 11.372 -0.292 0.72 0.00 0.03 0.25 0.593 M 11.763 -0.321 0.00 0.68 0.32 0.00 0.29 26 M 11.384 -0.357 0.23 0.35 0.39 0.03 0.364 M 11.755 -0.287 0.76 0.00 0.12 0.12 0.47 27 M 11.460 -0.357 0.19 0.13 0.39 0.29 0.455 M 11.743 -0.247 0.16 0.48 0.04 0.32 0.41 28 M 11.679 -0.331 0.61 0.00 0.13 0.26 0.376 M 11.729 -0.208 0.00 0.52 0.24 0.24 0.35 29 M 11.672 -0.321 0.00 0.95 0.05 0.00 0.407 A 11.707 -0.204 0.51 0.27 0.05 0.16 0.49 30 M 11.661 -0.302 0.00 0.55 0.26 0.19 0.429 P 11.676 -0.209 0.59 0.00 0.11 0.30 0.44 31 P 11.630 -0.250 0.32 0.06 0.10 0.52 0.5310 P 11.664 -0.208 0.06 0.16 0.03 0.74 0.56 32 P 11.599 -0.200 0.24 0.08 0.04 0.63 0.6211 P 11.680 -0.204 0.13 0.35 0.10 0.42 0.70 36 M 11.595 -0.080 0.84 0.00 0.00 0.16 0.5612 P 11.656 -0.222 0.51 0.08 0.06 0.35 0.49 37 M 11.584 -0.090 0.42 0.00 0.29 0.29 0.4813 A 11.647 -0.256 0.29 0.09 0.03 0.59 0.78 38 M 11.562 -0.116 0.74 0.00 0.00 0.26 0.3714 A 11.561 -0.327 0.48 0.00 0.24 0.28 0.39 39 A 11.549 -0.118 0.59 0.00 0.00 0.41 0.4315 M 11.644 -0.089 0.40 0.24 0.04 0.32 0.66 40 A 11.541 -0.107 0.35 0.00 0.00 0.65 0.6716 P 11.599 -0.112 0.68 0.00 0.13 0.19 0.53 41 P 11.530 -0.089 0.48 0.00 0.00 0.52 0.5517 P 11.594 -0.126 0.65 0.03 0.10 0.23 0.44 42 P 11.525 -0.081 0.59 0.00 0.14 0.27 0.3918 P 11.577 -0.107 0.71 0.00 0.06 0.23 0.23 43 P 11.526 -0.080 0.04 0.72 0.12 0.12 0.4819 P 11.545 -0.126 0.45 0.13 0.10 0.32 0.63 44 P 11.621 -0.336 0.03 0.68 0.19 0.10 0.2920 M 11.276 -0.311 0.29 0.52 0.19 0.00 0.51 45 P 11.623 -0.336 0.29 0.58 0.00 0.13 0.3621 M 11.214 -0.255 0.35 0.05 0.12 0.49 0.47 46 P 11.566 -0.307 0.22 0.52 0.11 0.15 0.5622 A 11.295 -0.312 0.65 0.16 0.13 0.06 0.41 47 M 11.542 -0.296 0.10 0.45 0.42 0.03 0.3023 A 11.324 -0.306 0.42 0.23 0.29 0.06 0.43 48 M 11.499 -0.282 0.61 0.16 0.16 0.06 0.45

* M = Mixed use area outside of MVVN, P = Pastoral Zone, A = Agricultural Zone** Estimated percent area cover, derived from transects

Owner

Text Box

Table 2

Owner

Text Box

Figure 1

Owner

Text Box

Figure 2

Rainfall (Nimey) and NPP (Nouhao Valley)

Rainfall and NDVI average

Aver

age

Nim

ey R

ainf

all (

mm

)19

82-2

000

Aver

age

ND

VI

1982

-200

0

Rainfall NDVI

Cum

mul

ativ

e R

ainf

all (

mm

) in

Nim

ey, N

iger

NP

P (g

C m

-2 y

-1)

at N

ouha

o Va

lley

Date

3a

3b

Owner

Text Box

Figure 3

Owner

Text Box

Figure 4

Owner

Text Box

Figure 5

Owner

Text Box

Figure 6

Owner

Text Box

Figure 7

Owner

Text Box

Figure 8

policy imprints in sudanian forests: trajectories of …jfisher/other/fisher_draft_agee.pdfpolicy...

Documents