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Vegetation in Tanzania: assessing long term trends and effects of protection using satellite imagery
Biological Conservation 94 (2000) 297±309
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Vegetation in Tanzania: assessing long term trends and eects of protection using satellite imagery N.W. Pelkey a,*, C.J. Stoner b, T.M. Caro c a
Division of Environmental Science and Policy, University of California, Davis, CA 95616, USA b Section of Evolution and Ecology, University of California, Davis, CA 95616, USA c Department of Wildlife, Fish and Conservation Biology, University of California, Davis, CA 95616, USA Received 16 June 1999; received in revised form 10 September 1999; accepted 24 November 1999
Abstract Using normalized dierence vegetation index (NDVI) imagery, we examined changes in vegetative cover across Tanzania and found that overall greenness increased over 13 years from 1982 to 1994. We then assigned 8 km pixels to dierent habitat types using a vegetation map compiled from Landsat satellite imagery between 1978 and 1982. We found that woodland and forest pixels increased in greenness but that swamp pixels showed a marked decline in vegetative cover. National parks and game reserves, which have heavy restrictions on resource extraction and on-site patrols, both showed increases in vegetative cover, particularly for woodland pixels. Forest reserves, which are explicitly designed for forest protection but do not have on-site patrols, did no better than lands under no legal protection at all. Game controlled areas, which allow for settlement, cattle grazing, and hunting, suered worse habitat degradation than areas with no legal protection, with bushlands, grasslands, swamps and ``other lands'' pixels faring worse than baseline measures. These results show that complete protection and on-site policing are key elements in enhancing vegetation health in this region of tropical Africa, paralleling results for mammals in the same area. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Habitat change; NDVI; Protected areas; Tanzania; Vegetative greenness
1. Introduction There is enormous conservation interest in long term changes in vegetative cover arising from habitat conversion and fragmentation (e.g. Anderson et al., 1997; Ebinger, 1997; Taft, 1997) and from global climate change (e.g. Woodward, 1992; Cane et al., 1994; Rosenzweig and Parry, 1994). Fortunately, the advent of satellite imagery now gives us unparalleled databases to document such changes over time and there is a growing number of studies using these databases to examine a wide variety of vegetative phenomena (Gutman, 1989; Nicholson et al., 1990; Maselli et al., 1992; Defries and Townshend, 1994; Gutman and Ignatov, 1996; Nerry et al., 1998; Duchemin et al.,1999). To date, however, few studies have attempted to compare dierent types of vegetation across entire nations while at the same time accounting for the seasonal and yearly variability in vegetation condition. In an eort to increase our understanding of * Corresponding author. E-mail address: [email protected] (N.W. Pelkey).
recent changes in the vegetation of tropical Africa, we examined a 13-year data set from Tanzania, a country that contains a great diversity of vegetation types. The northern third of the country has been broadly classi®ed as savannah while the southern two thirds consist of miombo woodlands. Additionally, alpine forests and extensive thickets are quite widely represented (McClanahan and Young, 1996). Tanzania, therefore, provides a microcosm for monitoring vegetative changes in very dierent types of habitats. By using a conventional vegetation map to ground truth our normalized dierence vegetation index (NDVI) dataset, we have been able to separate habitat types as derived from satellite imagery and observe gains and losses in each of them over time. A second issue of conservation concern centres on the best way to conserve habitats (Mee and Carroll, 1997). On one hand there are those who argue that multipleuse areas that sanction human activities within their borders are the best form of conservation. The principle behind multiple-use areas is to allow plants or animals to be harvested on a sustainable basis and thus create an
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economic incentive to conserve wilderness areas (Kiss, 1990; Robinson and Redford, 1991; Western et al., 1994; Freese, 1997). Conversely, there are those who argue that classical protectionism is the best form of conservation since it has a proven track record and because people can eventually be expected to overexploit their resources in multiple-use areas (Kramer et al., 1997; Struhsaker, 1997). Thus far, there have been very few attempts to assess the ecacy of these dierent conservation methods from a biological standpoint, and the few that have, have focused on animal populations (Herremans, 1998; Getz et al., 1999). In order to broaden this debate and to focus attention towards habitats rather than single species, we compare changes in vegetation types in six dierent sorts of protected areas in Tanzania. These areas range from those that are fully protected in the sense of forbidding all forms of exploitation to multiple-use areas in which local people live and extract plant or animal resources. Subsequently, we examine how dierent forms of protection aect dierent vegetation types within their borders. Our goal is to determine which sorts of protected areas are most eective in increasing or maintaining various types of vegetative cover over the course of a 13 year period spanning the 1980s and 1990s. 2. Methods 2.1. Measures of vegetation conditions Changes in vegetation condition were calculated using a time series of Path®nder advanced very high resolution radiometer (AVHRR) land (PAL) data from 1982 to 1994. These images were processed by the National Aeronautic Space Administration (NASA)/ Goddard Space Flight Centre (GSFC) Path®nder group, and were derived from National Oceanic and Atmospheric Administration (NOAA), advanced very high resolution radiometer (AVHRR), global area coverage (GAC) data. The resulting 8 km resolution images were composed of a series of pixels, each with an NDVI value (see James and Kalluri, 1994 for an in depth report of processing procedures). NDVI values provide a measure of vegetation vigor with increasing NDVI values implying increasing green leaf biomass (Justice et al., 1985) which, in general, implies increasing vegetation health or condition. In Africa, NDVI values are highly subject to rainfall (e.g. Nicholson et al., 1990) and in some cases soil moisture (e.g. Cihlar et al., 1991). Given that they measure `greenness', they are also highly seasonal (e.g. Spanner et al., 1990). While original NDVI values range from ±1 to 1, the Path®nder processing group remapped these values to a positive integer scale (NDVI=(integer NDVI-128)0.008) ranging from 0 to 255 (see PAL documentation at NASA/ GSFC site http://daac.gsfc.nasa.-
gov/CAMPAIGN_DOCS/FTP_SITE/readmes/pal.html). Although these integer NDVI images are available in dekadal (10 day) maximum value composites via a Goddard Distributed Active Archive Centre (DAAC) internet site, we downloaded and analyzed monthly maximum value composites due to limitations in disk space and due to processing considerations. This produced 157 remaining integer NDVI images for analysis for each of the 14051 pixels. 2.2. Grouping vegetation types We assigned each 8 km pixel into a habitat category using a two step procedure. First, a vegetation map was digitized to provide starting vegetation categories. The map had been produced by the Government of the United Republic of Tanzania in 1984 using visual interpretation of Landsat satellite images taken between 1972 and 1978 (Fig. 1). This map gave the location of twelve dierent vegetation categories. Next, for each pixel, we compared the integer NDVI value for the ®rst year in our data set, the dry season (July, August, September, October) of 1982, with the original vegetation category for which it was located on the Government of the United Republic of Tanzania (1984) vegetation map. Subsequently, we extracted the integer NDVI values for all the pixels that corresponded with each vegetation type and calculated the mean integer NDVI value (and standard error) for each of these vegetation categories. Examining the means and standard errors (Fig. 2), we found that lowland forests were signi®cantly less green that either plantations or alpine forests, but that these three vegetation types were all substantially dierent from any of the other vegetation categories. This could potentially be due to lower atmospheric interference with the vegetation's re¯ectance at higher altitude. For the sake of convenience, we therefore combined the lowland and alpine forest types together, and refer to these and subsequent categories used in our analyses as habitat types. The plantation category was dropped due to its small area across the country and dierent management objective (pro®t). This left the three woodland types that were virtually indistinguishable from each other in terms of dry season NDVI. This is similar to the ®ndings of Hardy and Burgan (1999) for similar habitat types in the US. They were grouped together to simplify further analysis. The swamp and grassland categories had signi®cantly dierent NDVI pro®les and were therefore separated. Swamps were overlapped by the bushland category but these dierent categories were retained as separate entities. The two thicket categories were lumped together for convenience, although they were overlapped by grasslands. These three categories should have dierent responses to rainfall and other environmental conditions. Grasslands, for example,
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299
Fig. 1. Tanzania vegetation types (circa 1978). Modi®ed from Government of the United Republic of Tanzania (1984).
should become much greener with increased rainfall whereas swamps might become less green due to ¯ooding leading to submerged vegetation. Grasslands should likely recover more quickly than bushlands after a ®re, and swamps are, in general, less likely to burn. We also used a category termed ``other lands'' which represented ``disturbed various species'' as described in the Government of the United Republic of Tanzania (1984) vegetation map. Only pixels which started out with predominance of vegetation (i.e. average yearly calibrated NDVI values > 110 indicating some green vegetation during the year Ð all such pixels had real NDVI values >0 during at least part of the year) were used in the analyses. This was done to avoid analyzing changes that were primarily due to changes in soil moisture.
2.3. Protected areas in Tanzania There are six sorts of protected areas in Tanzania. These are National Parks (34,191 km2 in total) where no resource extraction is allowed, Game reserves (101,251 km2) which sanction limited tourist hunting, Forest reserves (53,690 km2) which permit selective logging, game controlled areas (100,088 km2) where resident hunting is allowed under license, and open areas (94,098 km2) in which most human activities, are allowed. The Ngorongoro conservation area (8.549 km2) is a unique protected area where Masai pastoralists are allowed to settle and graze their cattle but where hunting is forbidden (see Table 1 for details and Fig. 3 for locations of all these protected area categories). A small number of areas came under two forms of
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Fig. 2. Average and standard error bars for integer dry season NDVI by vegetation types represented in the Government of the United Republic of Tanzania vegetation map (1984). Table 1 Protection policies and activities in the six categories of protected area in Tanzania
Protection Policies Funding status Ranger patrolling Legal restrictions on resource use Activities Temporary settlements Permanent settlements Cattle grazing Tourist hunting (legal) Resident hunting (legal) Mining Bee keeping Hardwood extraction Firewood extraction
National park
Ngorongoro conservation area
Game reserve
Forest reserve
Game controlled area
Open area
Well funded Yes Heavy
Well funded Yes Moderately heavy
Moderate funding Yes Moderately heavy
No funding No Moderately heavy
No funding No Very light
No funding No Virtually none
No No No No No No No No No
Yes Yes Yes No No No No No Some
No No No Yes No No No No No
No No No No No Some No Yes Some
Yes No Yes Some Yes Some Yes Some Some
Yes Yes Yes Some Yes Some Yes Some Yes
protection, forest reserves and another form of protection. These areas were classi®ed as the alternative form of protection. We also had vegetative information for areas with no form of protection at all (502,907 km2). 2.4. Analyses 2.4.1. Calculating temporal changes in vegetation Changes in vegetation over time were calculated by ®tting a trend line though each 8 km pixel over the 157 monthly images (Fig. 4). This produced 14,051 slope values, one for each pixel's trend line. Positive slopes
indicate a general increase in vegetation greenness over time, while negative slopes indicate a general decline in vegetation greenness. By generating slope values for this long time series, we avoided the problem of comparing any two or a set of short run averages that may be subject to seasonal or short-term variations. For example, it is evident from Fig. 4 that any subset of years could be selected that would give heavy losses, heavy gains or no change; note the 1991±1994 subset in particular. Roughly 40% of the gains and 20% of the losses were signi®cantly dierent than 0. We made the conscious decision at the onset of the analyses to use all slope
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301
Fig. 3. Boundaries of dierent types of protected areas in Tanzania. Boundaries for national parks, game reserves, game controlled areas and open areas were obtained from Leader-Williams et al. (1996). Boundaries for forest reserves were derived from a wildlife conservation monitoring centre map which had been edited by Ken Campbell. Only forest reserves which do not overlap with other protected areas were used for analyses and are shown here. All map layers were resampled to within 0.5 km of known location on an 8 km resolution image (i.e. midpixel).
values since using only signi®cant slopes would substantially limit the spatial coverage. 2.4.2. The impact of control variables on vegetation Given the spatial nature of the data, local changes in greenness could be caused by strictly local phenomena. This spatial autocorrelation can be overcome by a variety
of methods including maximum likelihood spatial regression (Anselin, 1988), two stage least squares partial adjustment models (Land and Deane, 1992), and a Gibbs sampling based Bayesian approach to probit models (LeSage, 1997). The Anselin and LeSage approaches were not feasible for a data set of this size, given our computer resources. The Land and Deane
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Fig. 4. Integer NDVI values plotted against years month from 1981 through 1994, y=0.0156x+187.93, R2=0.0025. The x-axis is in months from July 1991. The 1991 data values were not used in the calculation of the slopes used in the analyses. The 1991 data was used to obtain the starting NDVI values. It is included in this ®gure for comparison. The dots indicate the monthly composite values. The connecting lines are included for visual continuity.
approach can suer from high multicolinearity among the predictor variables, thus individual signi®cance values are dicult to determine. We, therefore, used a modi®ed Land and Deane approach where we used canonical correlation analysis to obtain a set of local spatial condition proxy variables that were minimally correlated to the other explanatory variables, but explained variation in the spatial lags (Pelkey, 1997). We then used these spatial proxies as control variables in a logistic regression of the various vegetation categories, protection categories and the other control variables. These were distance from roads, elevation, latitude and longitude (Nicholson et al., 1990; Land & Deane, 1992; Begon et al. 1996). Values for these variables were obtained from base geographic layers provided by the World Resources Institute's Africa Data Sampler. The Digital elevation data were obtained from the United States Geological Service Terrain Base (ETOP30) database. Using Wald statistics, which provide an asympotic test of the probability that a given parameter equals zero (Cheema and Qadir, 1996), we found that the control variables were signi®cant with the exception of the average distance from any road (Table 2). Starting vegetation condition was positive and highly signi®cant, most likely as a result of two factors. First, the vegetation index used is bounded from above, thus pixels at the high end can only stay the same or decline; and second, those who extract resources are more likely to harvest more
Table 2 Logistic regression coecients for the covariatesa Variable
Coecient S.E.
Starting dry season NDVI 0.0449 Longitude ÿ0.0434 Latitude 0.1172 Average distance from ÿ0.0045 roads in pixel Elevation 0.0011 Spatial proxy 1 0.0832 Spatial proxy 2 ÿ0.1128 Spatial proxy 3 0.0190
0.0025 0.0115 0.0086 0.0028
Wald statistic
P-value
327.4732 14.1006 184.6044 2.6140
0.0000 0.0002 0.0000 0.1059
6.910ÿ5 248.8691 0.0000 0.0198 17.6197 0.0000 0.0193 34.1405 0.0000 0.0193 0.9653 0.3258
a The Wald statistic tests the likelihood that the parameter equals zero given the data. The spatial proxy variables are canonical variates produced by canonical correlation analyses. These are proxies for the local spatial variation in the vegetation that is not associated with the other covariates.
valuable resources ®rst. Vegetation losses were more likely in the north and west of the country (Table 2). Finally, higher altitudes were associated with greater losses of vegetation. This was unusual since higher elevations are usually associated with higher costs of harvest and human access. It may, however, be due to low human population density in the large expanse of low areas in the west and south of the country. Regarding distance from roads, the sign of the coecient was as
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expected, but was not signi®cant. This is probably due to the fact that the size of an 8 km pixel was too large to demonstrate a net loss of vegetation as a result of some of it being accessible to a nearby road. We nevertheless took the precaution of including distance from roads as a control variable because forest fragmentation and degradation are associated with roads in many parts of the tropics (e.g. Dale and Pearson, 1997) and hardwood extraction and ®rewood collection are facilitated by the presence of roads in many parts of Tanzania (TMC personal observation). Given that ®re may be very important in determining vegetative dynamics (Veblen et al., 1999), it would have been bene®cial to control for ®re as well. These data were not available over the same time scale, however. 2.4.3. Logistic regressions We analyzed the data in two ways. In the ®rst set of analyses, we recoded slope values according to the following scheme: pixels with negative slope values were coded as `1' (corresponding to `loss') and pixels with slopes that were positive or equal to zero were coded as `0' (corresponding to `no loss'). This was done because conservation biologists are often more concerned about losses in habitat protection than vegetative gains. We ran a logistic regression on all pixels coded for loss vs no loss against a variable of each possible type of habitat category, protected area category, and each habitat category in each type of protected area (e.g. grasslands in national parks, grasslands in game reserves, etc. depending on the analyses). Each of these regressions also included the location, elevation, distance from roads and the spatial variability proxies. We compared losses in vegetation type categories to the ``baseline'' other lands category. We compared losses in the protection categories to a baseline of ``no protection''. Finally, we compared vegetation by protection interactions to the baseline category ``unprotected other lands''. An odds ratio of 2 for given category means that that category was twice as likely to suer a vegetative decline as nonprotected degraded lands; an odds ratio of 0.33 would imply that that category is a third as likely to suer a vegetative decline as the baseline category. 2.4.4. General linear models analysis of variance (GLM ANOVA) In the second set of analyses, we generated adjusted slope values for each pixel. We produced adjusted slopes using a general linear model analysis of variance (GLM ANOVA) with habitat type and protected area categories as ``treatments''. We controlled for the location, elevation, distance from roads, and spatial variability by using these variables as ``covariates''. We then compared slopes for pixels found in dierent habitat types, in dierent protected areas, and ®nally in dierent habitat types in dierent protected areas.
303
3. Results 3.1. Changes in vegetation condition over time Tanzania became greener over the 13 year time period of our study as deduced by the increasing trend line in Fig. 4. While there is some variability in NDVI values, the general trend line is nevertheless increasing. This is consistent with Young and Anyamba (1999) analysis of NDVI values in China. They attribute some of the overall increase in NDVI from 1982 to 1992 to changes in the NOAA satellite sensors and data processing methods. The key results of our study, however, are comparisons of relative changes in NDVI. Given that sensor changes would aect the country as a whole and our correction for altitude and location, these sensor changes would not be likely to aect our results. In fact detrending this potential drift and rerunning the analyses strengthened our comparative results. When vegetation was broken down by habitat type in a logistic regression (Table 3A), forests, woodlands, and thickets were found to increase in greenness from 1982 to 1994 (i.e. showed negative loss coecients). Odds ratios showed that forests and woodlands were both signi®cantly less likely to suer a loss than areas in the ``other land'' category (Table 3A). Indeed forests were only about two thirds as likely to suer a loss as this category. In contrast, bushlands, grasslands, and, in particular, swamps declined in greenness from 1982 to 1994 (i.e. showed positive loss coecients). Odds ratios showed that swamps were signi®cantly more likely to suer a loss in greenness compared to areas in the ``other land'' category, and in fact were over twice as likely to suer a loss. When changes in greenness between habitat types were compared (Table 4A), it was found that all had signi®cantly greater slopes in greenness than areas in the ``other land'' category. In general, there were few other dierences in changes in greenness except that forest greenness increased signi®cantly more than woodlands, bushlands or grasslands; woodland greenness increased signi®cantly more than grasslands; and swamp signi®cantly more than grasslands. 3.2. Eects of protection When vegetation was broken down by protected area in a logistic regression (Table 3B) it was found that the four area categories (Table 1) with heavy or moderately heavy legal restrictions increased in greenness from 1982 to 1994 (i.e. showed negative loss coecients). Only national parks and game reserves were signi®cantly less likely to suer a loss than the unprotected area category. Game controlled areas and open areas showed a decline in greenness (positive loss coecients) and were more likely to suer vegetative declines than unprotected areas (odd ratio >1) but not signi®cantly.
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Table 3 Logit results for any vegetation loss from 1982±1994 by (A) habitat type and (B) protection categorya Variable
Coecient
Standard error
Odds ratio
Chi-square
P-value
A. Habitat types Forests Woodlands Thickets Bushlands Grasslands Swamps
ÿ0.425551 ÿ0.286893 ÿ0.170622 0.008572 0.08092 0.981524
0.175526 0.051823 0.136581 0.093912 0.072629 0.105532
0.65341 0.75059 0.84314 1.00861 1.08428 2.66852
5.88 30.65 1.56 0.01 1.24 86.5
0.0153 0.0000 0.2116 0.9273 0.2652 0.0000
B. Protection categories NP GR NCA FR GCA OA
ÿ0.87438 ÿ0.30548 ÿ0.28984 ÿ6.8110ÿ02 0.060289 0.112755
0.129243 7.9910ÿ2 0.224064 8.9510ÿ2 6.9110ÿ2 7.0610ÿ2
0.42 0.73 0.75 0.93 1.06 1.12
45.77 14.61 1.67 0.58 0.76 2.55
0 0.000132 0.195823 0.447007 0.382782 0.110227
a An odds ratio is the chance of a loss occurring in that habitat or protection category relative to the baseline category. Odds ratios less than one imply a lower chance of loosing habitat. A category with an odds ratio of 0.5 is only half as likely to lose greenness as the baseline category. Conversely, odds ratios greater than one imply a greater chance of losing habitat that the baseline category. A category with an odds ratio of three is three times as likely to lose habitat as the baseline category.
Table 4 Dierences in adjusted mean slope values for NDVI slopes for (A) habitat types and (B) protection categoriesa Number of pixels A. Habitat types Forests Woodlands Thickets Bushlands Grasslands Swamps Other Land
B. Protection categories NP GR NCA FR GCA OA No Protection
230 5972 317 882 1823 540 4275
Adjusted mean slope 0.026 0.020 0.021 0.018 0.018 0.022 0.014
Number of pixels
Adjusted mean slope
538 1600 140 836 1565 1482 7878
0.096 0.020 0.112 0.014 ÿ0.138 0.021 0.015
Forests
ÿ
Woodlands
Thickets
+
ÿ ÿ
ÿ
ÿ
ÿ
ÿ
NP
GR
+
ÿ + ÿ ÿ ÿ ÿ
+ ÿ ÿ ÿ
Bushlands
Grasslands
+
+ +
Swamps
ÿ
Other land
+ + + + + +
ÿ
+ ÿ
ÿ
NCA
FR
GCA
OA
No protection
ÿ ÿ
+ + +
+ + + ÿ
+
+ + +
ÿ ÿ ÿ
ÿ +
ÿ +
+ ÿ ÿ ÿ
ÿ +
a The ``+'' refers to the adjusted mean slope of the habitat type in the row at the left hand side being signi®cantly greater than the adjusted mean slope for the habitat type in the column. The ``ÿ'' refers to the adjusted mean slope of the habitat type in the row at the left hand side being signi®cantly smaller than the adjusted mean slope for the habitat type in the column. Blank cells indicate no signi®cant dierence. Alpha was set at P<0.05 corrected using the Bonferonni test.
Table 4B shows that national parks and the Ngorongoro conservation area showed signi®cantly greater increases in greenness than other protected area categories; the increase for the Ngorongoro conservation
area was even greater than national parks, however. Game reserves showed signi®cantly greater increases than game controlled areas and forest reserves. Surprisingly, open areas with almost no legal restrictions fared
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better than forest reserves, and, extraordinarily, the no protection category showed greater increases in greenness than game controlled areas. 3.3. Interaction eects Risk analyses showed that inside national parks, woodlands and swamps were signi®cantly less likely to suer a loss than areas categorized both as ``other lands'' and unprotected (Table 5). Inside game reserves, woodlands and bushlands were signi®cantly less likely to suer a loss than ``other lands'' in unprotected areas. Grasslands, on the other hand, showed a three-fold decline compared to these baseline areas. The Ngorongoro conservation area showed few tendencies for dierent categories to lose greenness. In forest reserves, woodlands were signi®cantly less likely to lose greenness than baseline areas. Inside game controlled areas, woodlands fared signi®cantly better than ``other lands'' with no protection. ``Other lands'', grasslands, bushlands and swamps were all signi®cantly more likely to lose greenness than the baseline, however. Open areas oered important protection for bushlands. ``Other lands'', woodlands and swamps in open areas fared signi®cantly worse than ``other lands'' with no protection, however. Examining the slopes of dierent vegetation types in each protected area (Fig. 5) shows that there was a clear pattern of reduction in the slope of greenness for grasslands as extent of complete protection declined. In contrast, most types of protected areas maintained healthy forests, woodlands and bushlands. Swamps declined in game controlled areas but fared well in forest reserves. 4. Discussion The use of large-scale multi-temporal remotely sensed image databases to assess changes in vegetation in Africa is not new. Much of that research has rightly
305
focused on the importance of climatic conditions. Nicholson et al. (1990) assessed the changes in East African vegetation based on rainfall and NOAA AVHRR images from 1982 to 1985 (see also Dregne and Tucker, 1988). They found a strong relationship between rainfall and vegetation condition. Unganai and Kogan (1998) used the AVHRR NDVI data set to track regional droughts in southern Africa. Fuller (1998) used multi-temporal NDVI images to assess changes in vegetation for parts of Senegal. That study found a strong signi®cant relationship between range productivity and agricultural productivity and NDVI. Prins and Kikula (1996) used multi-temporal Landsat multispectral scanner (MSS) data to address large-scale deforestation and regrowth in the Mbeya district of western Tanzania for seven data points between 1972 to 1988. They found a substantial increase in re-growth as tobacco farming areas were abandoned. The analysis here adds to the growing use of AVHRR NDVI composites in ecological assessment by applying a long-term temporal NDVI series to a broad scale assessment of protection eorts over time. It is also the ®rst study that we know of in Africa that uses the slope of the NDVI series as an estimate of change in condition where the initial vegetation category is known over a broad geographic region. 4.1. Habitat changes The ®rst set of results showed that vegetative cover which may be a proxy for vegetative productivity (Nicholson et al., 1990; Sannier et al., 1998) increased in Tanzania between 1982 and 1994 as judged from NDVI imagery. This is consistent with the ®ndings of Fuller and Prince (1996) which showed an overall increase in dry season NDVI that was above what would be expected from increases in rainfall alone. They attributed this to shifts in climate (but see Young and Anyamba, 1999). Nevertheless, when NDVI pixels were separated into dierent vegetation types based on the
Table 5 Odds ratios for habitat types in protected areasa
NP GR NCA FR GCA OA
Other land
Forests
Woodlands
0.6239 0.9385 1.0815 0.9057 1.5279* 1.2779*
0.6028 0.4511
0.5326* 0.5491* 0.7086 0.7802* 0.5152* 1.2138*
0.6407 0.7462 0.1381
Thickets 0.7033 1.1142 1.1341
Bushlands
Grasslands
Swamps
1.6116 0.2368* 0.749 1.3358 1.6457* 0.238*
2.3269 3.7373* 0.6658 0.7934 4.1789* 1.1323
0.2822* 0.6543 1.1784 1.589* 1.5076*
a An odds ratio is the relative chance of a loss occurring in that habitat or protection category. Odds ratios less than one imply a lower chance of loosing habitat. A category with an odds ratio of 0.5 is only half as likely to lose greenness as the baseline category. Conversely, odds ratios greater than one imply a greater chance of losing habitat that the baseline category. A category with an odds ratio of three is three three times as likely to lose habitat as the baseline category. Bold indicates lower odds of vegetative decline. A ``*'' indicates P<0.05. Empty cells indicate too few pixels to derive an odds ratio.
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Fig. 5. Mean adjusted slopes (and standard error) of the seven types of habitats in each area of protection. Larger values are associated with higher increases in greenness for that vegetation by protection category. Values are missing in areas where there were no (or very few) representative pixels.
1984 survey map of Tanzania, we found that forests increased signi®cantly but that swamps declined signi®cantly in greenness (Table 3A) when confounding variables of geographic location, elevation, and distance
from roads were taken into account. Forests also showed signi®cantly greater increases in greenness than woodlands, bushlands or grasslands. In contrast, there were no signi®cant changes for bushlands, grasslands or
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thickets. The results for grasslands was somewhat surprising given their resilience (McNaughton et al., 1997) but the poor overall performance of grasslands may have been due to losses in heavily grazed areas. Swamps, on average, suered great losses in vegetative health across the country. Although this could have been partially caused by the fact that increasing late season rainfall led to fuller swamps in the dry season which resulted in some pixels showing vegetation declines, the changes conform to a worldwide trend (Tolba et al., 1992). Swamps can be aected by increasing siltation or changes in the water table as suspected for the swamps in Katavi National Park for example, but our analyses do not allow us to attribute causal factors to the decline in swamp vegetation (for other diculties in assessing swamps with AVHRR data see Scepan, 1999). Nonetheless, we suspect such areas are being degraded by people based on information from other countries (Tolba et al., 1992). These results show that it is very important to avoid looking only at average vegetation conditions in Africa (see Fuller, 1998). Increasing vegetation production may well mask underlying problems in particular vegetation categories or in particular locations of interest. 4.2. Protected areas Comparing vegetation changes in dierent sorts of protected areas across the country, we found that national parks in particular, but also game reserves, showed signi®cantly less loss in vegetative health compared to baseline unprotected areas (Table 3B). Vegetation within these two types of protected areas seem to have undergone regeneration over the 13-year time span; in particular, woodlands have fared well (Table 5). Similarly, forest reserves and the Ngorongoro conservation area suered somewhat less degradation than unprotected areas although this was not signi®cant. These latter ®ndings were reinforced by the fact that dierent habitat types showed few signi®cant dierences compared to baseline measures in these two types of protected areas (Table 5). In contrast, we found that game controlled areas and open areas were more likely to suer degradation than unprotected areas (although again not signi®cantly, Table 3B). It is not surprising that protected areas that sanction human activity and that are protected on the ground by guard forces suered least loss in vegetative greenness. It is surprising, however, that the Ngorongoro conservation area and forest reserves did not dier signi®cantly from unprotected areas in extent of vegetation change. The dierence between these two areas and national parks and game reserves is that both are areas where people respectively graze cattle and selectively cut timber. This suggests that multiple-use areas are poor at encouraging vegetative health. Nevertheless, national parks, game reserves and the Ngorongoro conservation area all showed signi®cantly
307
greater increases in greenness than most other categories (Table 4B). The common feature of these sorts of protected areas is on-site patrols, although these may vary from regular to infrequent depending on location. Such patrols keep illegal hunters and woodcutters out of most areas (Caro et al., 1998; Caro et al., in press). These ®ndings, therefore, point to the importance of on-site enforcement in facilitating vegetative greenness. In contrast to other protected areas, game controlled and open areas showed a greater though not signi®cant degradation than unprotected areas over time (Table 3B). Indeed game controlled areas actually showed a signi®cantly lower increase in greenness than unprotected areas (Table 4B). Indeed the majority of habitat types in this protected area suered greater declines than baseline areas (Table 5). A number of dierent resources are taken from these three areas and they are almost never patrolled (Table 1). The fact that these areas lost as much vegetative greenness or even more than areas receiving no legal protection whatsoever is a cause of great concern. Findings from game controlled areas highlight the importance of both resource extraction and absence of policing as being detrimental to vegetative health and they stress the devastating impact of these factors working in concert. The only other study that has compared the fate of biological populations in areas of dierent protection across the nation of Tanzania examined mammal densities as derived from repeated aerial censuses (Caro et al., 1998). That study found that national parks and game reserves had higher densities of large ungulates than game controlled areas and open areas. In particular, the number of working vehicles and number of patrols per month were correlated with bualo and zebra densities, species favored by poachers (Caro et al., in press). Furthermore, across four studies in Africa, these two measures of antipoaching eort were common factors in reducing animal poaching (Caro et al., in press), and results from this study suggest they are also important in limiting vegetation losses in Tanzania. Taken together, the Caro et al. (1998) study and the ®ndings presented here reinforce the idea that complete protection backed up by on-site reinforcement in the form of patrols is the most eective form of conservation in the country. Although neither study can assess the relative importance of resource extraction or lack of policing in in¯uencing mammal populations and vegetative health, both studies show that these factors are detrimental to wildlife, especially when they act together. Acknowledgements Data in this study include data produced through the funding from the Earth Observing System Path®nder Program of NASA's Mission to Planet Earth in cooperation
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with National Oceanic and Atmospheric Administration. The data were provided by the Earth Observing System Data and Information System (EODIS), Distributed Active Archive Center at Goddard Space Flight Center which archives, manages, and distributes this data set. We thank Ken Campbell for information on location of forest reserves. We thank Uma Ramakrishnan for help in digitizing the original vegetation maps, and Pete Coppolillo and Matthew Zafonte for useful comments on methodology, and Mark Schwartz, John Lewis and an anonymous reviewer for helpful comments. Chantal Stoner thanks the Center for Population Biology and President's Undergraduate Fellowship Program at U.C. Davis for ®nancial support. Neil Pelkey thanks Paul Sabatier for the use of oce space and computer equipment. References Anderson, M.A., Thornhill, A., Koopowitz, H., 1997. Tropical forest disruption and stochastic biodiversity losses. In: Laurance, W.F., Bierregard Jr., R.O. (Eds.), Tropical Forest Remnants: Ecology, Management and Conservation of Fragmented Communities. The University of Chicago Press, Chicago, pp. 281±291. Anselin, L., 1988. Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. Begon, M., Harper, J.L., Townshend, C.R., 1996. Ecology: Individuals in Populations and Communities, 3rd ed. Blackwell Science, Oxford. Cane, M.A., Eshel, G., Buckland, R.W., 1994. Forecasting Zimbabwean maize using eastern equatorial Paci®c sea surface temperature. Nature 370, 204±205. Caro, T.M., Pelkey, N., Borner, M., Campbell, K.L.I., Woodworth, B.L., Farm, B.P., 1998. et. al., Consequences of dierent forms of conservation for large mammals in Tanzania: preliminary analyses. African Journal of Ecology 36, 303±320. Caro, T. M., Rejmanek, M. & Pelkey, N., in press. Which mammals bene®t from protection in East Africa? In: Entwistle, A., Dunstone, N. (Eds.), Has the Panda had its Day? Future Priorities for the Conservation of Mammal Biodiversity. Cambridge University Press, Cambridge. Cheema, S.M., Qadir, M.F., 1996. Three asymtotic tests and their empirical comparison. Sarhad Journal of Agriculture 12, 471±480. Cihlar, J., 1991. St. -Laurent, L. & Dyer, J. A., Relation between the normalized dierence vegetation index and ecological variables. Remote Sensing of Environment 35, 279±298. Dale, V.H., Pearson, S.M., 1997. Quantifying habitat fragmentation due to land use change in Amazonia. In: Laurance, W.F., Bierregaard Jr., R.O. (Eds.), Tropical Forest Remnants: Ecology, Managements, and Conservation of Fragmented Communties. The University of Chicago Press, Chicago, pp. 400±409. Defries, R.S., Townshend, J.R.G., 1994. NDVI-derived land cover classi®cations at a global scale. International Journal of Remote Sensing 15, 3567±3586. Dregne, H.E., Tucker, C.J., 1988. Green biomass and rainfall in semiarid sub-Saharan Africa. Journal of Arid Environments 15, 245±252. Duchemin, B., Goubier, J., Courrier, G., 1999. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sensing of Environment 67, 68±82. Ebinger, J.E., 1997. Forest communities in the midwestern United States. In: Schwartz, M. (Ed.), Conservation in Highly Fragmented Landscapes. Chapman and Hall, New York, pp. 3±23. Freese, C.H., 1997. Harvesting Wild Species: Implications for Biodiversity and Conservation. John Hopkins University, Baltimore.
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www.elsevier.com/locate/biocon
Vegetation in Tanzania: assessing long term trends and eects of protection using satellite imagery N.W. Pelkey a,*, C.J. Stoner b, T.M. Caro c a
Division of Environmental Science and Policy, University of California, Davis, CA 95616, USA b Section of Evolution and Ecology, University of California, Davis, CA 95616, USA c Department of Wildlife, Fish and Conservation Biology, University of California, Davis, CA 95616, USA Received 16 June 1999; received in revised form 10 September 1999; accepted 24 November 1999
Abstract Using normalized dierence vegetation index (NDVI) imagery, we examined changes in vegetative cover across Tanzania and found that overall greenness increased over 13 years from 1982 to 1994. We then assigned 8 km pixels to dierent habitat types using a vegetation map compiled from Landsat satellite imagery between 1978 and 1982. We found that woodland and forest pixels increased in greenness but that swamp pixels showed a marked decline in vegetative cover. National parks and game reserves, which have heavy restrictions on resource extraction and on-site patrols, both showed increases in vegetative cover, particularly for woodland pixels. Forest reserves, which are explicitly designed for forest protection but do not have on-site patrols, did no better than lands under no legal protection at all. Game controlled areas, which allow for settlement, cattle grazing, and hunting, suered worse habitat degradation than areas with no legal protection, with bushlands, grasslands, swamps and ``other lands'' pixels faring worse than baseline measures. These results show that complete protection and on-site policing are key elements in enhancing vegetation health in this region of tropical Africa, paralleling results for mammals in the same area. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Habitat change; NDVI; Protected areas; Tanzania; Vegetative greenness
1. Introduction There is enormous conservation interest in long term changes in vegetative cover arising from habitat conversion and fragmentation (e.g. Anderson et al., 1997; Ebinger, 1997; Taft, 1997) and from global climate change (e.g. Woodward, 1992; Cane et al., 1994; Rosenzweig and Parry, 1994). Fortunately, the advent of satellite imagery now gives us unparalleled databases to document such changes over time and there is a growing number of studies using these databases to examine a wide variety of vegetative phenomena (Gutman, 1989; Nicholson et al., 1990; Maselli et al., 1992; Defries and Townshend, 1994; Gutman and Ignatov, 1996; Nerry et al., 1998; Duchemin et al.,1999). To date, however, few studies have attempted to compare dierent types of vegetation across entire nations while at the same time accounting for the seasonal and yearly variability in vegetation condition. In an eort to increase our understanding of * Corresponding author. E-mail address: [email protected] (N.W. Pelkey).
recent changes in the vegetation of tropical Africa, we examined a 13-year data set from Tanzania, a country that contains a great diversity of vegetation types. The northern third of the country has been broadly classi®ed as savannah while the southern two thirds consist of miombo woodlands. Additionally, alpine forests and extensive thickets are quite widely represented (McClanahan and Young, 1996). Tanzania, therefore, provides a microcosm for monitoring vegetative changes in very dierent types of habitats. By using a conventional vegetation map to ground truth our normalized dierence vegetation index (NDVI) dataset, we have been able to separate habitat types as derived from satellite imagery and observe gains and losses in each of them over time. A second issue of conservation concern centres on the best way to conserve habitats (Mee and Carroll, 1997). On one hand there are those who argue that multipleuse areas that sanction human activities within their borders are the best form of conservation. The principle behind multiple-use areas is to allow plants or animals to be harvested on a sustainable basis and thus create an
0006-3207/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S0006-3207(99)00195-0
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economic incentive to conserve wilderness areas (Kiss, 1990; Robinson and Redford, 1991; Western et al., 1994; Freese, 1997). Conversely, there are those who argue that classical protectionism is the best form of conservation since it has a proven track record and because people can eventually be expected to overexploit their resources in multiple-use areas (Kramer et al., 1997; Struhsaker, 1997). Thus far, there have been very few attempts to assess the ecacy of these dierent conservation methods from a biological standpoint, and the few that have, have focused on animal populations (Herremans, 1998; Getz et al., 1999). In order to broaden this debate and to focus attention towards habitats rather than single species, we compare changes in vegetation types in six dierent sorts of protected areas in Tanzania. These areas range from those that are fully protected in the sense of forbidding all forms of exploitation to multiple-use areas in which local people live and extract plant or animal resources. Subsequently, we examine how dierent forms of protection aect dierent vegetation types within their borders. Our goal is to determine which sorts of protected areas are most eective in increasing or maintaining various types of vegetative cover over the course of a 13 year period spanning the 1980s and 1990s. 2. Methods 2.1. Measures of vegetation conditions Changes in vegetation condition were calculated using a time series of Path®nder advanced very high resolution radiometer (AVHRR) land (PAL) data from 1982 to 1994. These images were processed by the National Aeronautic Space Administration (NASA)/ Goddard Space Flight Centre (GSFC) Path®nder group, and were derived from National Oceanic and Atmospheric Administration (NOAA), advanced very high resolution radiometer (AVHRR), global area coverage (GAC) data. The resulting 8 km resolution images were composed of a series of pixels, each with an NDVI value (see James and Kalluri, 1994 for an in depth report of processing procedures). NDVI values provide a measure of vegetation vigor with increasing NDVI values implying increasing green leaf biomass (Justice et al., 1985) which, in general, implies increasing vegetation health or condition. In Africa, NDVI values are highly subject to rainfall (e.g. Nicholson et al., 1990) and in some cases soil moisture (e.g. Cihlar et al., 1991). Given that they measure `greenness', they are also highly seasonal (e.g. Spanner et al., 1990). While original NDVI values range from ±1 to 1, the Path®nder processing group remapped these values to a positive integer scale (NDVI=(integer NDVI-128)0.008) ranging from 0 to 255 (see PAL documentation at NASA/ GSFC site http://daac.gsfc.nasa.-
gov/CAMPAIGN_DOCS/FTP_SITE/readmes/pal.html). Although these integer NDVI images are available in dekadal (10 day) maximum value composites via a Goddard Distributed Active Archive Centre (DAAC) internet site, we downloaded and analyzed monthly maximum value composites due to limitations in disk space and due to processing considerations. This produced 157 remaining integer NDVI images for analysis for each of the 14051 pixels. 2.2. Grouping vegetation types We assigned each 8 km pixel into a habitat category using a two step procedure. First, a vegetation map was digitized to provide starting vegetation categories. The map had been produced by the Government of the United Republic of Tanzania in 1984 using visual interpretation of Landsat satellite images taken between 1972 and 1978 (Fig. 1). This map gave the location of twelve dierent vegetation categories. Next, for each pixel, we compared the integer NDVI value for the ®rst year in our data set, the dry season (July, August, September, October) of 1982, with the original vegetation category for which it was located on the Government of the United Republic of Tanzania (1984) vegetation map. Subsequently, we extracted the integer NDVI values for all the pixels that corresponded with each vegetation type and calculated the mean integer NDVI value (and standard error) for each of these vegetation categories. Examining the means and standard errors (Fig. 2), we found that lowland forests were signi®cantly less green that either plantations or alpine forests, but that these three vegetation types were all substantially dierent from any of the other vegetation categories. This could potentially be due to lower atmospheric interference with the vegetation's re¯ectance at higher altitude. For the sake of convenience, we therefore combined the lowland and alpine forest types together, and refer to these and subsequent categories used in our analyses as habitat types. The plantation category was dropped due to its small area across the country and dierent management objective (pro®t). This left the three woodland types that were virtually indistinguishable from each other in terms of dry season NDVI. This is similar to the ®ndings of Hardy and Burgan (1999) for similar habitat types in the US. They were grouped together to simplify further analysis. The swamp and grassland categories had signi®cantly dierent NDVI pro®les and were therefore separated. Swamps were overlapped by the bushland category but these dierent categories were retained as separate entities. The two thicket categories were lumped together for convenience, although they were overlapped by grasslands. These three categories should have dierent responses to rainfall and other environmental conditions. Grasslands, for example,
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299
Fig. 1. Tanzania vegetation types (circa 1978). Modi®ed from Government of the United Republic of Tanzania (1984).
should become much greener with increased rainfall whereas swamps might become less green due to ¯ooding leading to submerged vegetation. Grasslands should likely recover more quickly than bushlands after a ®re, and swamps are, in general, less likely to burn. We also used a category termed ``other lands'' which represented ``disturbed various species'' as described in the Government of the United Republic of Tanzania (1984) vegetation map. Only pixels which started out with predominance of vegetation (i.e. average yearly calibrated NDVI values > 110 indicating some green vegetation during the year Ð all such pixels had real NDVI values >0 during at least part of the year) were used in the analyses. This was done to avoid analyzing changes that were primarily due to changes in soil moisture.
2.3. Protected areas in Tanzania There are six sorts of protected areas in Tanzania. These are National Parks (34,191 km2 in total) where no resource extraction is allowed, Game reserves (101,251 km2) which sanction limited tourist hunting, Forest reserves (53,690 km2) which permit selective logging, game controlled areas (100,088 km2) where resident hunting is allowed under license, and open areas (94,098 km2) in which most human activities, are allowed. The Ngorongoro conservation area (8.549 km2) is a unique protected area where Masai pastoralists are allowed to settle and graze their cattle but where hunting is forbidden (see Table 1 for details and Fig. 3 for locations of all these protected area categories). A small number of areas came under two forms of
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Fig. 2. Average and standard error bars for integer dry season NDVI by vegetation types represented in the Government of the United Republic of Tanzania vegetation map (1984). Table 1 Protection policies and activities in the six categories of protected area in Tanzania
Protection Policies Funding status Ranger patrolling Legal restrictions on resource use Activities Temporary settlements Permanent settlements Cattle grazing Tourist hunting (legal) Resident hunting (legal) Mining Bee keeping Hardwood extraction Firewood extraction
National park
Ngorongoro conservation area
Game reserve
Forest reserve
Game controlled area
Open area
Well funded Yes Heavy
Well funded Yes Moderately heavy
Moderate funding Yes Moderately heavy
No funding No Moderately heavy
No funding No Very light
No funding No Virtually none
No No No No No No No No No
Yes Yes Yes No No No No No Some
No No No Yes No No No No No
No No No No No Some No Yes Some
Yes No Yes Some Yes Some Yes Some Some
Yes Yes Yes Some Yes Some Yes Some Yes
protection, forest reserves and another form of protection. These areas were classi®ed as the alternative form of protection. We also had vegetative information for areas with no form of protection at all (502,907 km2). 2.4. Analyses 2.4.1. Calculating temporal changes in vegetation Changes in vegetation over time were calculated by ®tting a trend line though each 8 km pixel over the 157 monthly images (Fig. 4). This produced 14,051 slope values, one for each pixel's trend line. Positive slopes
indicate a general increase in vegetation greenness over time, while negative slopes indicate a general decline in vegetation greenness. By generating slope values for this long time series, we avoided the problem of comparing any two or a set of short run averages that may be subject to seasonal or short-term variations. For example, it is evident from Fig. 4 that any subset of years could be selected that would give heavy losses, heavy gains or no change; note the 1991±1994 subset in particular. Roughly 40% of the gains and 20% of the losses were signi®cantly dierent than 0. We made the conscious decision at the onset of the analyses to use all slope
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301
Fig. 3. Boundaries of dierent types of protected areas in Tanzania. Boundaries for national parks, game reserves, game controlled areas and open areas were obtained from Leader-Williams et al. (1996). Boundaries for forest reserves were derived from a wildlife conservation monitoring centre map which had been edited by Ken Campbell. Only forest reserves which do not overlap with other protected areas were used for analyses and are shown here. All map layers were resampled to within 0.5 km of known location on an 8 km resolution image (i.e. midpixel).
values since using only signi®cant slopes would substantially limit the spatial coverage. 2.4.2. The impact of control variables on vegetation Given the spatial nature of the data, local changes in greenness could be caused by strictly local phenomena. This spatial autocorrelation can be overcome by a variety
of methods including maximum likelihood spatial regression (Anselin, 1988), two stage least squares partial adjustment models (Land and Deane, 1992), and a Gibbs sampling based Bayesian approach to probit models (LeSage, 1997). The Anselin and LeSage approaches were not feasible for a data set of this size, given our computer resources. The Land and Deane
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Fig. 4. Integer NDVI values plotted against years month from 1981 through 1994, y=0.0156x+187.93, R2=0.0025. The x-axis is in months from July 1991. The 1991 data values were not used in the calculation of the slopes used in the analyses. The 1991 data was used to obtain the starting NDVI values. It is included in this ®gure for comparison. The dots indicate the monthly composite values. The connecting lines are included for visual continuity.
approach can suer from high multicolinearity among the predictor variables, thus individual signi®cance values are dicult to determine. We, therefore, used a modi®ed Land and Deane approach where we used canonical correlation analysis to obtain a set of local spatial condition proxy variables that were minimally correlated to the other explanatory variables, but explained variation in the spatial lags (Pelkey, 1997). We then used these spatial proxies as control variables in a logistic regression of the various vegetation categories, protection categories and the other control variables. These were distance from roads, elevation, latitude and longitude (Nicholson et al., 1990; Land & Deane, 1992; Begon et al. 1996). Values for these variables were obtained from base geographic layers provided by the World Resources Institute's Africa Data Sampler. The Digital elevation data were obtained from the United States Geological Service Terrain Base (ETOP30) database. Using Wald statistics, which provide an asympotic test of the probability that a given parameter equals zero (Cheema and Qadir, 1996), we found that the control variables were signi®cant with the exception of the average distance from any road (Table 2). Starting vegetation condition was positive and highly signi®cant, most likely as a result of two factors. First, the vegetation index used is bounded from above, thus pixels at the high end can only stay the same or decline; and second, those who extract resources are more likely to harvest more
Table 2 Logistic regression coecients for the covariatesa Variable
Coecient S.E.
Starting dry season NDVI 0.0449 Longitude ÿ0.0434 Latitude 0.1172 Average distance from ÿ0.0045 roads in pixel Elevation 0.0011 Spatial proxy 1 0.0832 Spatial proxy 2 ÿ0.1128 Spatial proxy 3 0.0190
0.0025 0.0115 0.0086 0.0028
Wald statistic
P-value
327.4732 14.1006 184.6044 2.6140
0.0000 0.0002 0.0000 0.1059
6.910ÿ5 248.8691 0.0000 0.0198 17.6197 0.0000 0.0193 34.1405 0.0000 0.0193 0.9653 0.3258
a The Wald statistic tests the likelihood that the parameter equals zero given the data. The spatial proxy variables are canonical variates produced by canonical correlation analyses. These are proxies for the local spatial variation in the vegetation that is not associated with the other covariates.
valuable resources ®rst. Vegetation losses were more likely in the north and west of the country (Table 2). Finally, higher altitudes were associated with greater losses of vegetation. This was unusual since higher elevations are usually associated with higher costs of harvest and human access. It may, however, be due to low human population density in the large expanse of low areas in the west and south of the country. Regarding distance from roads, the sign of the coecient was as
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expected, but was not signi®cant. This is probably due to the fact that the size of an 8 km pixel was too large to demonstrate a net loss of vegetation as a result of some of it being accessible to a nearby road. We nevertheless took the precaution of including distance from roads as a control variable because forest fragmentation and degradation are associated with roads in many parts of the tropics (e.g. Dale and Pearson, 1997) and hardwood extraction and ®rewood collection are facilitated by the presence of roads in many parts of Tanzania (TMC personal observation). Given that ®re may be very important in determining vegetative dynamics (Veblen et al., 1999), it would have been bene®cial to control for ®re as well. These data were not available over the same time scale, however. 2.4.3. Logistic regressions We analyzed the data in two ways. In the ®rst set of analyses, we recoded slope values according to the following scheme: pixels with negative slope values were coded as `1' (corresponding to `loss') and pixels with slopes that were positive or equal to zero were coded as `0' (corresponding to `no loss'). This was done because conservation biologists are often more concerned about losses in habitat protection than vegetative gains. We ran a logistic regression on all pixels coded for loss vs no loss against a variable of each possible type of habitat category, protected area category, and each habitat category in each type of protected area (e.g. grasslands in national parks, grasslands in game reserves, etc. depending on the analyses). Each of these regressions also included the location, elevation, distance from roads and the spatial variability proxies. We compared losses in vegetation type categories to the ``baseline'' other lands category. We compared losses in the protection categories to a baseline of ``no protection''. Finally, we compared vegetation by protection interactions to the baseline category ``unprotected other lands''. An odds ratio of 2 for given category means that that category was twice as likely to suer a vegetative decline as nonprotected degraded lands; an odds ratio of 0.33 would imply that that category is a third as likely to suer a vegetative decline as the baseline category. 2.4.4. General linear models analysis of variance (GLM ANOVA) In the second set of analyses, we generated adjusted slope values for each pixel. We produced adjusted slopes using a general linear model analysis of variance (GLM ANOVA) with habitat type and protected area categories as ``treatments''. We controlled for the location, elevation, distance from roads, and spatial variability by using these variables as ``covariates''. We then compared slopes for pixels found in dierent habitat types, in dierent protected areas, and ®nally in dierent habitat types in dierent protected areas.
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3. Results 3.1. Changes in vegetation condition over time Tanzania became greener over the 13 year time period of our study as deduced by the increasing trend line in Fig. 4. While there is some variability in NDVI values, the general trend line is nevertheless increasing. This is consistent with Young and Anyamba (1999) analysis of NDVI values in China. They attribute some of the overall increase in NDVI from 1982 to 1992 to changes in the NOAA satellite sensors and data processing methods. The key results of our study, however, are comparisons of relative changes in NDVI. Given that sensor changes would aect the country as a whole and our correction for altitude and location, these sensor changes would not be likely to aect our results. In fact detrending this potential drift and rerunning the analyses strengthened our comparative results. When vegetation was broken down by habitat type in a logistic regression (Table 3A), forests, woodlands, and thickets were found to increase in greenness from 1982 to 1994 (i.e. showed negative loss coecients). Odds ratios showed that forests and woodlands were both signi®cantly less likely to suer a loss than areas in the ``other land'' category (Table 3A). Indeed forests were only about two thirds as likely to suer a loss as this category. In contrast, bushlands, grasslands, and, in particular, swamps declined in greenness from 1982 to 1994 (i.e. showed positive loss coecients). Odds ratios showed that swamps were signi®cantly more likely to suer a loss in greenness compared to areas in the ``other land'' category, and in fact were over twice as likely to suer a loss. When changes in greenness between habitat types were compared (Table 4A), it was found that all had signi®cantly greater slopes in greenness than areas in the ``other land'' category. In general, there were few other dierences in changes in greenness except that forest greenness increased signi®cantly more than woodlands, bushlands or grasslands; woodland greenness increased signi®cantly more than grasslands; and swamp signi®cantly more than grasslands. 3.2. Eects of protection When vegetation was broken down by protected area in a logistic regression (Table 3B) it was found that the four area categories (Table 1) with heavy or moderately heavy legal restrictions increased in greenness from 1982 to 1994 (i.e. showed negative loss coecients). Only national parks and game reserves were signi®cantly less likely to suer a loss than the unprotected area category. Game controlled areas and open areas showed a decline in greenness (positive loss coecients) and were more likely to suer vegetative declines than unprotected areas (odd ratio >1) but not signi®cantly.
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Table 3 Logit results for any vegetation loss from 1982±1994 by (A) habitat type and (B) protection categorya Variable
Coecient
Standard error
Odds ratio
Chi-square
P-value
A. Habitat types Forests Woodlands Thickets Bushlands Grasslands Swamps
ÿ0.425551 ÿ0.286893 ÿ0.170622 0.008572 0.08092 0.981524
0.175526 0.051823 0.136581 0.093912 0.072629 0.105532
0.65341 0.75059 0.84314 1.00861 1.08428 2.66852
5.88 30.65 1.56 0.01 1.24 86.5
0.0153 0.0000 0.2116 0.9273 0.2652 0.0000
B. Protection categories NP GR NCA FR GCA OA
ÿ0.87438 ÿ0.30548 ÿ0.28984 ÿ6.8110ÿ02 0.060289 0.112755
0.129243 7.9910ÿ2 0.224064 8.9510ÿ2 6.9110ÿ2 7.0610ÿ2
0.42 0.73 0.75 0.93 1.06 1.12
45.77 14.61 1.67 0.58 0.76 2.55
0 0.000132 0.195823 0.447007 0.382782 0.110227
a An odds ratio is the chance of a loss occurring in that habitat or protection category relative to the baseline category. Odds ratios less than one imply a lower chance of loosing habitat. A category with an odds ratio of 0.5 is only half as likely to lose greenness as the baseline category. Conversely, odds ratios greater than one imply a greater chance of losing habitat that the baseline category. A category with an odds ratio of three is three times as likely to lose habitat as the baseline category.
Table 4 Dierences in adjusted mean slope values for NDVI slopes for (A) habitat types and (B) protection categoriesa Number of pixels A. Habitat types Forests Woodlands Thickets Bushlands Grasslands Swamps Other Land
B. Protection categories NP GR NCA FR GCA OA No Protection
230 5972 317 882 1823 540 4275
Adjusted mean slope 0.026 0.020 0.021 0.018 0.018 0.022 0.014
Number of pixels
Adjusted mean slope
538 1600 140 836 1565 1482 7878
0.096 0.020 0.112 0.014 ÿ0.138 0.021 0.015
Forests
ÿ
Woodlands
Thickets
+
ÿ ÿ
ÿ
ÿ
ÿ
ÿ
NP
GR
+
ÿ + ÿ ÿ ÿ ÿ
+ ÿ ÿ ÿ
Bushlands
Grasslands
+
+ +
Swamps
ÿ
Other land
+ + + + + +
ÿ
+ ÿ
ÿ
NCA
FR
GCA
OA
No protection
ÿ ÿ
+ + +
+ + + ÿ
+
+ + +
ÿ ÿ ÿ
ÿ +
ÿ +
+ ÿ ÿ ÿ
ÿ +
a The ``+'' refers to the adjusted mean slope of the habitat type in the row at the left hand side being signi®cantly greater than the adjusted mean slope for the habitat type in the column. The ``ÿ'' refers to the adjusted mean slope of the habitat type in the row at the left hand side being signi®cantly smaller than the adjusted mean slope for the habitat type in the column. Blank cells indicate no signi®cant dierence. Alpha was set at P<0.05 corrected using the Bonferonni test.
Table 4B shows that national parks and the Ngorongoro conservation area showed signi®cantly greater increases in greenness than other protected area categories; the increase for the Ngorongoro conservation
area was even greater than national parks, however. Game reserves showed signi®cantly greater increases than game controlled areas and forest reserves. Surprisingly, open areas with almost no legal restrictions fared
N.W. Pelkey et al. / Biological Conservation 94 (2000) 297±309
better than forest reserves, and, extraordinarily, the no protection category showed greater increases in greenness than game controlled areas. 3.3. Interaction eects Risk analyses showed that inside national parks, woodlands and swamps were signi®cantly less likely to suer a loss than areas categorized both as ``other lands'' and unprotected (Table 5). Inside game reserves, woodlands and bushlands were signi®cantly less likely to suer a loss than ``other lands'' in unprotected areas. Grasslands, on the other hand, showed a three-fold decline compared to these baseline areas. The Ngorongoro conservation area showed few tendencies for dierent categories to lose greenness. In forest reserves, woodlands were signi®cantly less likely to lose greenness than baseline areas. Inside game controlled areas, woodlands fared signi®cantly better than ``other lands'' with no protection. ``Other lands'', grasslands, bushlands and swamps were all signi®cantly more likely to lose greenness than the baseline, however. Open areas oered important protection for bushlands. ``Other lands'', woodlands and swamps in open areas fared signi®cantly worse than ``other lands'' with no protection, however. Examining the slopes of dierent vegetation types in each protected area (Fig. 5) shows that there was a clear pattern of reduction in the slope of greenness for grasslands as extent of complete protection declined. In contrast, most types of protected areas maintained healthy forests, woodlands and bushlands. Swamps declined in game controlled areas but fared well in forest reserves. 4. Discussion The use of large-scale multi-temporal remotely sensed image databases to assess changes in vegetation in Africa is not new. Much of that research has rightly
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focused on the importance of climatic conditions. Nicholson et al. (1990) assessed the changes in East African vegetation based on rainfall and NOAA AVHRR images from 1982 to 1985 (see also Dregne and Tucker, 1988). They found a strong relationship between rainfall and vegetation condition. Unganai and Kogan (1998) used the AVHRR NDVI data set to track regional droughts in southern Africa. Fuller (1998) used multi-temporal NDVI images to assess changes in vegetation for parts of Senegal. That study found a strong signi®cant relationship between range productivity and agricultural productivity and NDVI. Prins and Kikula (1996) used multi-temporal Landsat multispectral scanner (MSS) data to address large-scale deforestation and regrowth in the Mbeya district of western Tanzania for seven data points between 1972 to 1988. They found a substantial increase in re-growth as tobacco farming areas were abandoned. The analysis here adds to the growing use of AVHRR NDVI composites in ecological assessment by applying a long-term temporal NDVI series to a broad scale assessment of protection eorts over time. It is also the ®rst study that we know of in Africa that uses the slope of the NDVI series as an estimate of change in condition where the initial vegetation category is known over a broad geographic region. 4.1. Habitat changes The ®rst set of results showed that vegetative cover which may be a proxy for vegetative productivity (Nicholson et al., 1990; Sannier et al., 1998) increased in Tanzania between 1982 and 1994 as judged from NDVI imagery. This is consistent with the ®ndings of Fuller and Prince (1996) which showed an overall increase in dry season NDVI that was above what would be expected from increases in rainfall alone. They attributed this to shifts in climate (but see Young and Anyamba, 1999). Nevertheless, when NDVI pixels were separated into dierent vegetation types based on the
Table 5 Odds ratios for habitat types in protected areasa
NP GR NCA FR GCA OA
Other land
Forests
Woodlands
0.6239 0.9385 1.0815 0.9057 1.5279* 1.2779*
0.6028 0.4511
0.5326* 0.5491* 0.7086 0.7802* 0.5152* 1.2138*
0.6407 0.7462 0.1381
Thickets 0.7033 1.1142 1.1341
Bushlands
Grasslands
Swamps
1.6116 0.2368* 0.749 1.3358 1.6457* 0.238*
2.3269 3.7373* 0.6658 0.7934 4.1789* 1.1323
0.2822* 0.6543 1.1784 1.589* 1.5076*
a An odds ratio is the relative chance of a loss occurring in that habitat or protection category. Odds ratios less than one imply a lower chance of loosing habitat. A category with an odds ratio of 0.5 is only half as likely to lose greenness as the baseline category. Conversely, odds ratios greater than one imply a greater chance of losing habitat that the baseline category. A category with an odds ratio of three is three three times as likely to lose habitat as the baseline category. Bold indicates lower odds of vegetative decline. A ``*'' indicates P<0.05. Empty cells indicate too few pixels to derive an odds ratio.
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Fig. 5. Mean adjusted slopes (and standard error) of the seven types of habitats in each area of protection. Larger values are associated with higher increases in greenness for that vegetation by protection category. Values are missing in areas where there were no (or very few) representative pixels.
1984 survey map of Tanzania, we found that forests increased signi®cantly but that swamps declined signi®cantly in greenness (Table 3A) when confounding variables of geographic location, elevation, and distance
from roads were taken into account. Forests also showed signi®cantly greater increases in greenness than woodlands, bushlands or grasslands. In contrast, there were no signi®cant changes for bushlands, grasslands or
N.W. Pelkey et al. / Biological Conservation 94 (2000) 297±309
thickets. The results for grasslands was somewhat surprising given their resilience (McNaughton et al., 1997) but the poor overall performance of grasslands may have been due to losses in heavily grazed areas. Swamps, on average, suered great losses in vegetative health across the country. Although this could have been partially caused by the fact that increasing late season rainfall led to fuller swamps in the dry season which resulted in some pixels showing vegetation declines, the changes conform to a worldwide trend (Tolba et al., 1992). Swamps can be aected by increasing siltation or changes in the water table as suspected for the swamps in Katavi National Park for example, but our analyses do not allow us to attribute causal factors to the decline in swamp vegetation (for other diculties in assessing swamps with AVHRR data see Scepan, 1999). Nonetheless, we suspect such areas are being degraded by people based on information from other countries (Tolba et al., 1992). These results show that it is very important to avoid looking only at average vegetation conditions in Africa (see Fuller, 1998). Increasing vegetation production may well mask underlying problems in particular vegetation categories or in particular locations of interest. 4.2. Protected areas Comparing vegetation changes in dierent sorts of protected areas across the country, we found that national parks in particular, but also game reserves, showed signi®cantly less loss in vegetative health compared to baseline unprotected areas (Table 3B). Vegetation within these two types of protected areas seem to have undergone regeneration over the 13-year time span; in particular, woodlands have fared well (Table 5). Similarly, forest reserves and the Ngorongoro conservation area suered somewhat less degradation than unprotected areas although this was not signi®cant. These latter ®ndings were reinforced by the fact that dierent habitat types showed few signi®cant dierences compared to baseline measures in these two types of protected areas (Table 5). In contrast, we found that game controlled areas and open areas were more likely to suer degradation than unprotected areas (although again not signi®cantly, Table 3B). It is not surprising that protected areas that sanction human activity and that are protected on the ground by guard forces suered least loss in vegetative greenness. It is surprising, however, that the Ngorongoro conservation area and forest reserves did not dier signi®cantly from unprotected areas in extent of vegetation change. The dierence between these two areas and national parks and game reserves is that both are areas where people respectively graze cattle and selectively cut timber. This suggests that multiple-use areas are poor at encouraging vegetative health. Nevertheless, national parks, game reserves and the Ngorongoro conservation area all showed signi®cantly
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greater increases in greenness than most other categories (Table 4B). The common feature of these sorts of protected areas is on-site patrols, although these may vary from regular to infrequent depending on location. Such patrols keep illegal hunters and woodcutters out of most areas (Caro et al., 1998; Caro et al., in press). These ®ndings, therefore, point to the importance of on-site enforcement in facilitating vegetative greenness. In contrast to other protected areas, game controlled and open areas showed a greater though not signi®cant degradation than unprotected areas over time (Table 3B). Indeed game controlled areas actually showed a signi®cantly lower increase in greenness than unprotected areas (Table 4B). Indeed the majority of habitat types in this protected area suered greater declines than baseline areas (Table 5). A number of dierent resources are taken from these three areas and they are almost never patrolled (Table 1). The fact that these areas lost as much vegetative greenness or even more than areas receiving no legal protection whatsoever is a cause of great concern. Findings from game controlled areas highlight the importance of both resource extraction and absence of policing as being detrimental to vegetative health and they stress the devastating impact of these factors working in concert. The only other study that has compared the fate of biological populations in areas of dierent protection across the nation of Tanzania examined mammal densities as derived from repeated aerial censuses (Caro et al., 1998). That study found that national parks and game reserves had higher densities of large ungulates than game controlled areas and open areas. In particular, the number of working vehicles and number of patrols per month were correlated with bualo and zebra densities, species favored by poachers (Caro et al., in press). Furthermore, across four studies in Africa, these two measures of antipoaching eort were common factors in reducing animal poaching (Caro et al., in press), and results from this study suggest they are also important in limiting vegetation losses in Tanzania. Taken together, the Caro et al. (1998) study and the ®ndings presented here reinforce the idea that complete protection backed up by on-site reinforcement in the form of patrols is the most eective form of conservation in the country. Although neither study can assess the relative importance of resource extraction or lack of policing in in¯uencing mammal populations and vegetative health, both studies show that these factors are detrimental to wildlife, especially when they act together. Acknowledgements Data in this study include data produced through the funding from the Earth Observing System Path®nder Program of NASA's Mission to Planet Earth in cooperation
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