Integration of RS and GIS to Assess Human Impact on Ecosystem Change in Llanos Area (Venezuela)
The landscape indices represent the quantify aspects of spatial pattern that can be correlated with the spatial process. One of the indices, the fractal dimension is shown to be correlated with the degree of human manipulation of the landscape. The fractal dimension is an index of the complexity of shapes on the landscape. If the landscape is composed of simple geometric shapes like squares and rectangles, the fractal dimension will be small, approaching 1.0. If the landscape contains many patches with complex and convoluted shapes, the fractal dimension will be large. The fractal dimensions of 20 coverage were calculated in various scales for the modulated area and non-modulated area (Table 1).
Table 1 The fractal dimension on multi-scales in the modulated area and non-modulated area
| Scale | Modulated area
Fractal dimension | Non-modulated area
Fractal dimension |
| 1960 | 1988 | 1960 | 1988 |
| Patch area >0. 5 ha | 1.200 | 1.406 | - | 1.402 |
| Patch area > 5 ha | 1.196 | 1.390 | 1.086 | 1.386 |
| Patch area > 10 ha | 1.214 | 1.372 | 1.086 | 1.346 |
| Patch area > 25 ha | 1.216 | 1.244 | 1.126 | 1.368 |
| Patch area > 50 ha | 1.156 | 1.016 | 1.100 | 1.306 |
In the non-modulated area, the fractal dimensions increase from 1960 to 1988 in multi-scale. These differences of the landscape indices between the two periods illustrate the natural fragmentation processes. The patches were fragmented to small pieces and the shapes of the patch became more complex.
In the modulated area, the fractal dimensions increase from 1960 to 1988 on the patch scales of: patch area >0. 5 ha, patch area > 5 ha, patch area >10 ha and patch area >25 ha. The differences indicate the natural fragmentation processes on the four patch scales as well. In the contrast, the fractal dimension decreases from 1960 to 1988 on the patch scale large than 50 ha. The change shows an opposite direction of the natural fragmentation process. The shape patch became simple like square and rectangle. It is anthropogenic process that can be associated with the construction of dikes.
The landscape properties is highly scale dependent (Keitt,T.H. 1997). Ecological studies more often have a spatial component and include landscape scale parameters (Baker W. And Cai Y., 1992). The results show that on the scale of patch area > 50 ha. Landscape indices captures major features of pattern
¾ the effects of the construction of dikes. The landscape indices are sensitive to the calculation scale (O'Neill, R.V.; Hunsaker, C.T.; et al. 1996).
An ecosystem change assessing model is developed by using GIS and logistic regression analysis. The logistic regression model has been studied the Mt.Graham red squirrel habitat (Pereira, J.M.C., and Itami, R.M., 1991), crane habitat (Herr,A.M., and Queen, L.P., 1993) and bobwhite habitat (Roseberry,J.L.,et.al.1994). The logistic regression analysis can contain numeric as well as categorical data. The change of ecosystem is the dependent variable. The independent variables included the environmental variables (ecosystem units) and anthropogenic variables. (eg. Distance to dike) Using GIS function the shortest distances will be measured between the ecosystem change and anthropogenic features. The land use anthropogenic features (dikes etc.) are extracted from TM.
The logistic model and the probability of ecosystem change developed in this study is represented in the following:
Where Y is the exponent of the logistic equation;
P is the probability ecosystem change at a particular location.
The output probability values range from 0 to 1 indicating 0 to 100 percent probability of ecosystem change. The result can be illustrated by GIS in a digital map.
The descriptive statistics of the anthropogenic independent variables are the Distance to dike, Distance to residential area, Distance to main road, Distance to dirt road, Distance to path and Distance to airport. The ecosystem change map as the dependent variable is only having two codes, 0 is non-changed and 1 is changed. The dependent and the independent variables are exactly identical in location and pixel size.
Applying the logistic model, which the pixel size is 150m by 150m and the sample area is 30pixel by 30pixel, assessed the ecosystem change probability. A raster format was used for it is more effective at presenting spatially continuous phenomena than is a vector format. The result is a probability of the ecosystem changing effected by the anthropogenic factors (Figure 2).
5. Summary
In general agreement with the ground truths, the models suggested that the construction of the dikes have the unique directional influence on the ecosystem change. Close to the dikes, the probabilities of the ecosystem change increase. This may suggest the construction of the dikes is the human disturbances that are widely spread in the Llanos modular area, and roads, airport and the residential sites provided the important impacts for the ecosystem change as well. This study should contribute to understand the human impact on the flooding savanna in Llanos area.
Figure 2 The probability of the ecosystem changing effected by the anthropogenic factors
The structure and function of a landscape can be perceived differently at different scales, and it is important for the observer to decide upon appropriate scales for a study. To develop the GIS model, not only the sample scale effects the result of model; the cell size also influences the model establishing. This is the particular issue of modelling in the GIS environment.
References
- Baker W. And Cai Y., 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology, vol.7(4), p291-302.
- Gisele, C. L., 1998. GIS support for SWOT analysis applied to land evaluation, Msc. thesis of Wageningen Agriculture University, the Netherlands.
- Herr,A.M., and Queen, L. P., 1993. Crane nabitat evaluation using GIS and remote sensing, Photogrammetric Engineering & Remote Sensing, 59(10): 1531-1538.
- Keitt, T. H. et. al., 1997. Detecting critical scales in fragmented landscapes, http://life.csu.edu.au/consecol/vol1/iss1/art4/
- Pereira, J.M.C., and Itami, R.M., 1991. GIS-based habitat modeling using logistic multiple regression: a study of the Mt. Graham red squirrel, Photogrammetric Engineering & Remote Sensing, 57(11) 1475-1486.
- Quattrochi, D.A., and Pelletier, R. E., 1990. Remote sensing for analysis of landscapes: an introduction, Quantitative Methods in Landscape Ecology, ed. Turner, M. G., and Gardner, R.H. 53-76.
- Roseberry, J. L., et.al.1994. Assessing the potential impact of conservation reserve program lands on bobwhite habitat using remote sensing, GIS, and habitat modeling, Photogrammetric Engineering & Remote Sensing, 60(9): 1139-1143.
