Although all variables were found to be significant predictors of landcover change in at least one of the 4 models, the most important predictors across all pairs of image dates (assessed by the number of times the trees have splits based on these variables, and the level of hierarchy at which these splits occur) were:
1. the distance from the nearest road, 2. the management zone in 2000, and 3. the distance to the nearest water body.
After these three variables, the next most important predictors were: 4. the municipality in which the satellite pixels lies, and
5. the slope.
In each case (among all classification trees) where splits were made based on distance from the nearest road, pixels nearer to roads were consistently more likely to convert to built- up and/or fields. This result was expected since field crops are easier to bring to market when they are harvested near roads (discussed further below). The management zones in 2000 demarcate areas with very different landuse laws within the Park, and therefore have a strong effect on landcover change. It is interesting that the 1993 management zones did not show a strong effect. There are two likely reasons for this: (1) many similarities exist between the zone boundaries of both zoning dates, and the effect of the 2000 management zones masks the effect of the 1993 management zones; and (2) some of the 1993
management zones were quite similar to one another in landuse management regime (the Extensive Mass Recreation Area, the Intensive Mass Recreation Area, and the Protected Mass Recreation and Scenic Area), reducing the effect of the different zones on LU/LCC. The effect of the distance to the nearest water body is likely due to GNP laws, written in 1990 and 1991, that severely restrict landowners from developing land near water bodies (the distance is dependent upon the type of water body) (Gauja National Park 1999). The effects of the management zones and the distance to the nearest water body show that Park laws significantly affect landuse change. The directions of the effects are also consistent with Park Administration’s intentions, as discussed below. The effects of the municipality on landcover change shows that the conversion of land (and by inference, of development) has occurred unevenly throughout the Park, as discussed below. Finally, also presented below, the influence of slope primarily affects where new wetland and water landcover classes are found, as is to be expected since wetlands and water only occur in flat areas.
Specific Effects of Predictor Variables
The distance from roads had a consistent effect, as can be seen in Figures 4.1 – 4.4, across all classification trees, and in some cases at multiple levels with one classification tree: the nearer to roads, the more likely each pixel was to convert to built-up and/or fields. This result mirrors the findings of Rudel & Horowitz (1993) and Pichon (1997, 1999) that roads are associated with land clearing in the Ecuadorian Amazon; of Pan et al. (2004) that found that farm proximity to roads is significantly associated with farm-level landscape patterns in the Ecuadorian Amazon; of Chomitz & Gray (1996) that roads facilitate nearby development in Belize; and of Pedlowski et al. (2005) that found that deforestation in conservation areas (called conservation units) in Rondônia, Brazil occurred more frequently near the road network.
The influence of the 2000 management zones on landcover change shows that the Park’s management zones had their intended effects. In each tree model, except for the 1985 – 1994 model (which did not include the 2000 management zones variable), tree splits were made showing that the core zones (Nature Reserve and Nature Conservancy) had consistently higher probabilities of conversion to forest and wetlands, whereas the non-core zones
(Cultural Historic, Landscape Protection, and Neutral) had consistently higher probabilities of conversion to built-up and fields. This reflects the Park Administration’s commitment to preserving the natural landscape in the core areas of the Park, which has, in recent years, superseded the Park Administration’s commitment to preserving the existing landscape in the non-core zones, as discussed in Chapter 2. As stated in the November 1999 Management Plan for Gauja National Park (Petersen 1999), “[t]he traditional rural landscape with its harmony and cosiness is of secondary significance after the primary – the ancient valley of
the Gauja River and its tributary valleys.” The “ancient valley of the Gauja River and its tributary valleys” fall within the core zones of the Park, and it is the non-core zones that house the majority of the “traditional rural landscape” within the Park. These results show that the non-core zones have undergone considerable development and transition to
agriculture. When land changed in the core zones, however, it more often changed to elements of the natural landscape (forest and wetlands), which make up the landscape that the core zones were designated to protect.
The distance to the nearest large body of water was found to be an important predictor of landcover change, with one major split in each regression tree. This variable showed both expected and unexpected results. The expected result was that, in each classification tree, pixels nearer to large water bodies were more likely to convert to water than pixels farther from large water bodies (splits were made among the four classification trees at different distances from large water bodies – between 51 and 87 meters). This is likely the result of expanding or fluctuating boundaries of lakes and rivers. In addition, pixels near large water bodies were more likely to convert to forest and less likely to convert to fields than pixels farther from large water bodies. This finding may be due to the
aforementioned GNP law (Gauja National Park 1999) that severely restricts landowners from altering land near water bodies (the specified distance depends on the type of water body). This shows that the GNP policies have been successful in limiting landcover changes near water bodies. There was one exception to this: in the 1994 – 1999 model, for areas within core zones, change pixels that were less than 64 meters from large water bodies were less likely to convert to forest and more likely to convert to built-up than pixels greater than 64
meters from large water bodies. This could be due to small sample size problems, as the number of affected pixels was limited.
Splits were made based on the municipality of each pixel in three of the four classification trees (not in the 1985 – 1994 model). There were certain municipalities in which pixels were more likely to change to forest and wetlands in all three models. The effects, however, of municipality on the probability of pixels changing to shrubs, built-up, and fields varied from model to model. This finding suggests that the rates of different types of landcover change differ from region to region within the Park. This reflects the fact that economic development has occurred at uneven rates in different regions of the Park
throughout this time period. This finding is consistent with the claim of Vilnis Burcevs, Mayor of the Kocenis municipality (30 percent of which lies inside GNP), who reported in an interview in 2002 that economic development was occurring unevenly throughout the Park, and that most development was occurring in and near the Park’s tourist centers (Burcevs, personal communication 2002). Burcevs lobbies for municipalities like Kocenis, where little development was occurring, so that they could be subsidized by the Park’s tourism revenues to compensate residents for landuse restrictions in the Park.
Changes over Time in Importance of Predictor Variables
A time-series of satellite images is particularly useful for tracking when
landuse/landcover changes occurred. This analysis was able to determine when certain variables were most important in predicting landcover change.
It is interesting that the municipality variable was not one of the most important predictors in the 1985 – 1994 model, yet it was important in the later models. In the 1985 – 1994 model, the municipality variable does not present in the pruned tree (Figure 4.1),
though in the un-pruned model (not shown because the un-pruned tree is too large to display graphically on one page) it appears in the 3rd and 4th levels of the tree hierarchy, with
relatively small node deviance values. The municipality variable does present in the pruned trees for the 1994 – 1999 model and the 1999 – 2002 models. This shows that the
municipality variable was more important in the models for later periods than in the model for the early period (1994 – 1999). For the majority of the time period in the 1985 – 1994 model, Latvia was still part of the Soviet Union (1985 – 1991), and the economy was centrally planned. The effects of local government were weak during this period, and many landuse planning decisions were made at higher political levels. As land restitution took place in Latvia (including within GNP) throughout the mid- and late- 1990’s, and as local governments became more influential with time, local-level political and economic factors increased in importance regarding landcover change in the Park, and therefore may have led to differentiation in levels of development between regions of the Park.
It is also interesting that, although distance from the nearest road was important in each model, it was the most important predictor of landcover change only in the 1999 – 2002 model. A possible explanation for this is that Latvia’s economy began to grow very quickly by this period, and land conversion for economic activities became more common. It is possible that proximity to roads tends to have a stronger effect on landuse change during economically prosperous periods.