Tropical deforestation is a complicated process associated with multifarious problems ranging from technical to policy related issues (Murdiyarso and Lebel, 1996). The proximate causes of deforestation, for example, vary with forest types, the physical environments, the socio-economic activities and the cultural contexts. Major technical problems associated with the deforestation of Loei province of northern Thailand includes shifting cultivation, illegal logging, forest encroachment and forest fire. Land tenure and titling systems are problems related to policy issues. The symptoms of deforestation observed from both satellite data and field observation are in the form of loss of forest cover, forest fragmentation, loss of biodiversity, land degradation and declining income from forest products. Thus the effect of deforestation is diverse ranging from economic to ecological to social.
Resource planners and decision-makers need information on the rate and impacts of tropical deforestation. Moreover, providing information on the potential deforestation risk areas will be highly useful in resource allocation and priority action to these highly vulnerable areas. Such predictive information is essential to support the implementation of appropriate policy responses to deforestation. The forest areas with the highest probability of deforestation in a near future should receive priority attention and preventive actions.
Models - an analogous to the real system - can be used to help solve complex environmental problems including deforestation. Numerous deforestation models exist in the literature. Spatial, statistical models are born from the combination of remote sensing, Geographic Information Systems (GIS) and multivariate mathematical models . In the present study mapping of the degree of deforestation risk in the Loei province of northern Thailand has been attempted using GIS technology. This involved the construction of a GIS model designed to classify the forest resource into categories reflecting the deforestation risk.
The main objective of the present exercise is to predict or forecast
spatial projections of future trends of deforestation by identifying deforestation
risk prone areas. This will help the decision makers in formulating forest
management strategies.
Forest classification map of 1985 and 1991 were obtained from Royal Forest Department and UNEP/GRID-Bangkok database. Similarly, road map, town map, and drainage map were extracted from the UNEP/GRID database.
Population data were collected and linked with the GIS database. These
datasets are available in ARC/Info vector format at the scale of 1:250,000.
Forest classification map of 1985 and 1991, road map, drainage map, population
density map and town maps have been presented in the following figures
(Fig. 4.7 to 4.11).
Index overlay is a type of arithmetic overlay that creates a new coverage by performing arithmetic operations. In other words, index overlaying is a simple modeling technique that involves combination of number of map layers according to their importance expressed with weightings, depending on the perceived importance of each variable. It is one of the most commonly used methods in simple decision analysis. Weightings of different layers are based on the field information collected during ground truthing.
Fig. 4.7 Forest Classification Map of Loei Province - 1991
Fig. 4.8 Forest Classification Map of Loei Province - 1985
Fig. 4.9 Population Density Map of Loei Province, Thailand
Fig. 4.10 Town Map of Loei Province, Thailand
Fig. 4.11. Deforestation Map of Loei Province, Thailand (1985-1991)
The most important factors considered in preparing deforestation risk map are proximity to roads, proximity to population centers, population density and forest protection level. All of these factors are not equally important. Forest protection level was found to be of higher importance than road network and population pressure. Proximity to town received least important.
To derive the deforestation risk map with respect to different criteria,
information content of each map layers were "scored" according to the potential
risk. The score of each layer was 'integrated' to calculate the total sum,
which were further sub-divided into different risk zones. Index overlay
file for the deforestation model is presented in the table below.
Table 1.0 Index overlay for deforestation risk model
| Coverage | Weightings (%) | Category | Score |
| Coverage 1: Roads
(1 km, 3km 5 kms Buffer)
Coverage 2: Towns ( 2 kilometers buffer zone)
Coverage 3: Population Density
Coverage 4: Forest Cover
Coverage 5: Forest protection level
|
25
10
25
5
35 |
Class
1 km buffer 2.5 km buffer Outside buffer zone
Class 2 km buffer zone 5 km buffer zone Outside buffer zone Density Very High High Medium Low Very Low Class Forest Non-forest Class High Low |
9
5 0
9 5 0
10 8 4 2 1 0 -1
1 8 |
The method for calculation for index modelling is:
å [ ( Weight of Map i) x (Score for Class) / ( Total Weight, 100)
A negative value excludes a class from index overlay. The negative value in fact represents non-forest areas. Deforestation risk model map of Loei province has been presented in Fig.10.12.
Fig. 4.12 Deforestation Risk Model Map, Loei Province, Northern Thailand
4.6 Summary and Conclusions
From the multi-temporal and multi-seasonal analysis of 1985/86 and 1992/93 AVHRR 1 km satellite data, Loei province of northern Thailand was identified as one of the 'hot spot' areas for detailed investigation. Preliminary investigation of coarse spatial resolution satellite data indicated that land use/land cover changes are occurring in the province across time and space. These changes are both temporal and seasonal. Knowing this, an area of 60 km x 60 km was investigated in detail using high spatial resolution SPOT multi-spectral satellite data acquired on March 21, 1996. Landsat MSS dated December 19, 1985 was acquired and analyzed for change analysis.
The province is characterized by one of the most forest based resources consists of 20 reserve forest areas, 5 national parks and one wildlife sanctuary. From the satellite data analysis, it was found that, the dominant land use/land cover type in the province is forest that includes closed forest, degraded forest, hill forest and plantations. Shifting cultivation is a common phenomenon being practiced on the old alluvium terraces and fans for cultivating upland rice, maize, groundnut, beans and cassava. Transplanted rice is the predominant crop in the river basin and in the valleys. Some part of the cultivated areas are devoted towards cultivation of cash crops such as, soybean, sugarcane, tobacco, maize, peanuts, vegetables and fruit trees. Among the plantations, rubber plantations are very common occurring in regular shape.
The change assessment shows that change of forest land to other land use is the main type of land use/land cover change observed in the province. The underlying causes for the forest cover change are multifarious. Shifting cultivation, illegal logging, forest fire, expansion of agricultural land due to intense population pressure. Agricultural activities are changing in terms of change in management regimes and change in cropping pattern (mainly from rice to cash crops such as sugarcane, cassava, rubber, etc.). Migration from other provinces to this relatively less populated province has created additional stress on the fragile natural resources of the province.
The deforestation risk model map provides areas likely to experience
severe pressure due to shifting cultivation, illegal felling and forest
encroachment. The model, however, does not include soil and rainfall data,
two important parameters for vegetation distribution. "Scoring" and "Weighing"
process was performed using filed information which demands further refinement.
Because of the above-mentioned reasons, the model may or may not reflect
the reality. The reader should keep in mind that the purpose of the present
study was to develop a simple model useful for classroom teaching. The
focus is therefore on the methodology. It is recommended to perform 'remodeling'
based on detailed field information
References
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Murdiyarso, D. & L. Lebel. 1996. Forest Management Problems in Southeast Asia. Ecosystem Modelling Tools for the Analysis of the Impacts of Global Change on Sustainable Management of Tropical Forests (Workshop Summary Report). BIOTROP-GCTE Southeast Asian Impacts Centre, Indonesia.
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