IV. RESULT AND DISCUSSION

Transcription

1 IV. RESULT AND DISCUSSION The result of forest cover change simulation during 4 years was described. This process is done by using the information of land cover condition obtained from satellite imagery in 2002 and Land Cover Identified Short-interval trend of land cover changes ( ) have been visually classified. Seven land cover classes were spectrally separable, i.e., forest, built up area, plantation, agricultural land, shrub/bush, barren land, and water body. In general, identification of the land cover types is based on their own characteristics. Forest class identified in this study covers primary forest and logged-over forest either on dry land or swamp forest. Built-up area is the entire appearance of the built-up land including industry. Plantation consisted of oil palm and rubber, whether planted or still an empty land. Agricultural land consisted of crop land/ pasture, confined feedings operation, paddy field, and other agricultural land either dry land or swampy area. Shrub/bush class consisted of shrub, bush, and low vegetation either dry land or swampy area. Barren land consisted of open land with no vegetation either dry land or swampy area. Finally, water body includes streams, canals, lakes, and reservoirs. Explanation of each of the above mentioned land criteria represented reality in the study area and then adjusted to satellite image observations. The use of satellite imagery of the landsat image has low resolutions (30mx30m). Hence, the number of land cover types is reduced to 6 main classes because two of the seven classes noticeable have almost the same characters. The two classes i.e., shrub/bush, and barren land are grouped into one class of shrub/barren land before entered in the M-CA. However, there are types of land cover changes grouped according to seasonal changes. Land cover types are considered seasonal changes such as paddy field, swamp, and water body. Seasonal changes are treated separately because the types of land cover are flexible changes either during the dry season or rainy season. 29

2 Figure 4.1 Changed and unchanged of land cover map ( ) in Rokan Hulu. (a) (b) (c) (d) Figure 4.2 Land cover condition of agricultural land such as: (a) un-irrigated agricultural field, (b) mixed agriculture, (c) agriculture conversion from forest, and (d) paddy field. 30

3 (a) (b) (c) (d) Figure 4.3 Land cover condition of built up area such as: (a) settlement, (b) commercial complex, (c) industrial, and (d) residential hotels. (a) (b) (c) Figure 4.4 Land cover condition of forest such as: (a) limited production forest, (b) natural forest, and (c) production forest. Cover can be considered as the presence of a particular area of the ground known as the land cover. Satellite imagery especially Landsat 7 ETM+ provide 31

4 information of land cover that represents the actual conditions in the ground. Land cover types that have been classified in this study were to describe the reality of land cover in study area. The realities were illustrated in the Figure 4.2, 4.3, 4.4, 4.5, 4.6, and 4.7. (a) (b) (c) Figure 4.5 Land cover condition of plantation area such as: (a) oil palm plantation converted from forest, (b) oil palm plantation, and (c) rubber plantation. (a) (b) (c) Figure 4.6 Land cover condition of shrub/barren land such as: (a) swamp shrub, (b) dry shrub, and (c) barren land. 32

5 Figure 4.7 Land cover condition of water body. Qualitative land cover classification was done to determine the level of similarity of information in the geographic data with the reality. In addition, change detection techniques using threshold values to determine which pixels have been changed or not in respect of land cover. As noted by Ernani (2006) the change detection statistics routine is used to compile a detailed tabulation of changes between two classification images. Furthermore, the possibility of error in the visual classification processes could occur and affect the changes when cross-tabulation between land cover of 2002 and 2005 was conducted. Sometimes, source of error is likely to be derived from uncorrected classification as well as registration. As Wijanarto (2006) states accurate result of change detection of images is determined by several factors; such as comparable images in term of good registration of the images, interpretable images, and method for getting a meaningful difference image from change detection. As can be seen in Figure 4.1, change in agricultural land into forest is one example of these errors. Other than source of error derived from uncorrected as well as registration, changes from agricultural land, forest, and shrub/barren land into water body occurred because water body is belong as seasonal change. 33

6 Therefore, this change is expected be reduced and approached the possible change to occur as smooth as possible by visual interpretation. Visual interpretation of landsat image remote sensing data had been carried out by taking into consideration short-term trend of images ( ) by on-screen digitizing. The cross classification results are illustrated in Table 4.1. Table 4.1 Change detection statistics report for period (pixels) Cells in : Land Cover 2005 Total of 2002 Cl. 1 Cl. 2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 Cl.1. Water body Cl.2. Shrub/barren land Cl.3. Forest Cl.4. Agricultural land Cl.5. Built up area Cl.6. Plantation Total of Class changes Image Difference Percents 0.3% 21% 33% 18% 0.2% 22% Land Cover 2002 Based on statistics of Table 4.1, changes during the period 2002 to 2005 were found. Changes increases in water body about 0.3%, shrub/barren land about 21%, built up area about 0.2%, and plantation about 22%. Whereas decreases occurred in the two other land cover classes such as forest about 33%, and agricultural land about 18% Image Classification Trends ( ) In terms of image classification, generalization signifies an increasing separation in time and/or space between training and testing data. In this study, the classification process of landsat 7 ETM+ has been done in 2002, 2005, and This process was done by visual classification method to obtain land cover for each time series. Visual classification is semi-automatic method using the technique on screen digitizing supported by the ground check data and reference map. The classification results for the 2002, 2005, and 2009 Landsat 7 ETM+ can be seen in Figure 4.8, 4.9, and

7 Figure 4.8 Land cover map of Rokan Hulu result from classification in

8 Figure 4.9 Land cover map of Rokan Hulu result from classification in

9 Figure 4.10 Land cover map of Rokan Hulu result from classification in

10 A synthesis of evolution of the different land cover classes identified from remotely sensed data is shown in Table 4.2. Table 4.2 Changes in land cover classes between 2002 and 2009 (hectares) Years Water Body Shrub/Barren land Forest Agricultural land Built up Plantation , , , , , , , , , , , , , , , , , ,040.2 Figure 4.11 compares the trend of changes in land cover areas that occurred during It could be observed that the changes in land cover areas, especially forest cover was decreasing each year (233,818 ha in 2002, 176,177.4 ha in 2005, and 122,296.6 ha in 2009). Total area of the forest land was 233,818 ha in There was a decrease in forest cover area of 57,640.6 ha in 2005 and 111,521.4 ha in 2009 compared to Areal (Ha) Graphic of Land Cover area Calculation 2002, 2005 and Water Body Forest Built up Years Shrub/Barren land Agricultural land Plantation Figure 4.11 Land cover changes condition (classification results). 38

11 Figure 4.12 illustrates actual land cover conditions adjusted to the appearance in the 2009 data. The illustration represents the dominant land cover types in the study area. Shrub/Barren land Water Body Agricultural land Built up area Forest Plantation Figure 4.12 Actual condition adjusted to landsat images of 2009 in Rokan Hulu. 39

12 Decrease in forest cover area is followed by an increase in plantation areas. The use of land for plantations increased dramatically and very dominant since 2005 till 2009 (194,065.4 ha in 2002, 249,579.9 ha in 2005, and 440,040.2 ha in 2009). This phenomenon indicates that the change of land that occurred in Rokan Hulu became dominant for plantations. Another phenomenon is the decrease in agricultural land areas of the three time series with a total area of 186,464.2 ha for agricultural land in 2002, decreased to 157,686 ha in 2005, and 56,128.3 ha in While changes in water body, shrub/barren land, and built up area classes are not significant from the time series compared with others Markov Change Detection The calculation of transition probability and transition area matrix during has been done to determine the characteristics of the past and current land cover. This process is performed to predict the next four years (2009). This calculation was made after the change analysis of land cover in the study area ( ). Types of land cover were computed by this method consisting of six classes: water body, shrub/barren land, forest, agricultural land, built up area, and plantation. The transitional area matrix and transitional probability of land cover types in two different periods have been obtained using the method of combination on the land cover type maps in corresponding periods, as shown in Table 4.3 and Table 4.4, respectively. Table 4.3 Transition area matrix of cells/pixels expected to changes for 2009 Cells in : Land Cover 2005 Cl. 1 Cl. 2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 Total of 2002 Land Cover 2002 Cl.1. Water body Cl.2. Shrub/barren land Cl.3. Forest Cl.4. Agricultural land Cl.5. Built up area Cl.6. Plantation Total of

13 Table 4.4 Transition probabilities matrix used for forest cover changes projection for 2009 considering the time series trend ( ) Cells in : Land Cover 2005 Cl. 1 Cl. 2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 Land Cover 2002 Cl.1. Water body Cl.2. Shrub/barren land Cl.3. Forest Cl.4. Agricultural land Cl.5. Built up area Cl.6. Plantation Projection of forest cover change is carried out for 2009 using land cover short-term trend ( ). The trends are considered to evaluate the influence of the trend duration in forest cover change projection. In this study, the 2009 transition probabilities tables are constructed from land cover images of 2002 and The transition area matrix has the number of cell values in columns from left to right produced by the multiplication of each column by the number of cells. The values are expected to change from each land cover class in 2009 considering the short-term trend of While in the transition probability, the values are representing the transition situation between the land cover types during the period of 2002 and 2005, respectively. Hence, there are several impossible changes of classes in the transition matrix. For example, the impossible change of built up area into forest as much as 1142 pixels or to other classes as shown in Table 4.3. The total area is expected to change to forest over the next time period. The matrix was produced by multiplication of each column in the transition probability matrix by the number of cells of corresponding land cover in the next time period. Moreover, it is the result of cross tabulation of the two images adjusted by the proportional error. This condition has been performed to predict the changes during 3 years period ( ) to the next 4 years period (2009). 41

14 4.4. The Markov Cellular Automata Forest cover change model uses spatial knowledge (GIS and remote sensed data), temporal data (a transition area matrix and transition probability) and considers spatial interaction through the definition of the transition rules. Probability map is a factor that sets the direction of change in surrounding cells as a function of cell conditions itself and adjacent cells (cellular automata concept) Transition Rules: Probability Maps Probability maps for forest cover change are one of the parameters aside from temporal data (a transition area matrix and transition probability) needed in this simulation. The transitions rule results from biophysical factors are contributing to forest cover changes. (a) (b) (c) Boolean standardized values: unprobable change forest (0), probable change forest (1) (d) (e) Figure 4.13 Probability maps of (a) elevation, (b) river, (c) road, (d) settlement and (e) slope considering landscape features. 42

15 Probability map was created based on evaluation of probability criteria for forest cover change in the study area using the Multi Criteria Evaluation. The criteria used are the proximity to roads, rivers, population centers, and criteria of elevation, while slopes are processed based on the level of probability to changes (Figure 4.13). Factors of river and population centers are based on actual condition in 2005 which is the basis of land cover image for the next four years (2009). The criteria mentioned above have been developed and standardized as simple Boolean aggregation values (0 and 1) to find the most probable area. In this technique, instead that all factors have equal importance in the final probability map of forest cover change, a location is considered probable only if meeting all criteria. The result is the best location possible for forest cover change as interrelation between factors. Land probability maps highlight reasonable changes of future forest cover (Figure 4.14). Figure 4.14 Probability map for forest cover change result from Boolean approach of multi criteria evaluation. 43

16 Thus, probability map is used for scenario projection and modeling of forest cover condition in the future. As performed by Houet et al (2006) the set of all probability maps of each land cover was used to project and model future scenarios. Driving factors of change have been developed into a probability map for future forest change through multiplication of criteria based on probability criteria of each factor Markov Cellular Automata Simulation Simulation of forest cover changes using M-CA has been done by making the three components as inputs. These components are basis land cover image, markov transition area file and, transition probability image collection. This process was done by determining the number of the 4 time steps ( ) as the total number of iterations. While, filtering type 5x5 cell contiguity filter is used for forest cover from 2005 to The simulation of forest cover changes conditions in 2009 was predicted from 2005 as basis land cover image (Figure 4.18). Table 4.5 Matrix of Kappa Index Agreement (KIA) to validation of prediction result compared with the actual data (pixels) Matrix Actual Non Forest Forest Total projection Non Forest Projection Forest Total Actual Overall Kappa = 68.5% The past and current conditions of forest cover affect the future condition. Therefore, measuring forest cover changes in 2002 and 2005 have been made to predict forest cover change in The simulation result of forest cover change is then compared with the actual forest cover in 2009 (classification). The comparison between the simulation changes with the actual changes uses the KIA to see how far the accuracy of the model runs. Calculation results was obtained 68.5% overall accuracy (Table 4.5). The value is good enough because the 44

17 achievement is higher than the standard value of 60%. It indicates the degree of agreement between simulation and actual maps. There is no guarantee that a totally different model could not have produced the proper result. But this model still produces good enough prediction. As suggested by Montserud et al, (1992) the value of kappa of 75% or greater shows a very good to excellent performance, while a value of less than 40% is poor. The validation of the markov cellular automaton is understood as the assessment of how closely it resembles the simulations to reality. Resemblance simulation results with reality are the success of the model. In general, M-CA model tends to bias of the outcome, it is plus with its static models, black box, and every land types including water. In this study, the case like these also occur that indicated by the number of small biases spread everywhere in the map of the simulation. These things have an influence to validation results of simulation and actual. However, differences also occur because of the rapid rate of land conversion in the last 4 years period by the local people participate to reduction of forest cover changes actual. Table 4.6 Forest cover changes in Rokan Hulu result from classification of 2002, 2005, 2009 and simulation of 2009 (hectares) Forest/ (Simulation) Years Ha % Ha % Ha % Ha % Non Forest 503, , , , Forest 233, , , , Total Area 737, , , , Trend of forest cover change is a major concern which continues to be degradation from year to year respectively of 2002, 2005, and 2009 as illustrated in Table 4.6. Total area of non forest is 503, ha and the forest area is 503, ha or 31.72% from total area in The forest area is 176, ha with 23.90% from total area or decrease to 57,640.6 ha in At the different time, the same thing happened in the forest area of 2009 (classification result) is 122, ha or 16.59% from total area. The forest area decreases to 57,640.6 ha in 2005 and 111, ha in 2009 (classification result). Thus, from observations through the three time series show the changes in forest cover decreased. 45

18 Figure 4.15 Forest cover map of Rokan Hulu result from classification in

19 Figure 4.16 Forest cover map of Rokan Hulu result from classification in

20 Figure 4.17 Forest cover map of Rokan Hulu result from classification in

21 Figure 4.18 Forest cover map of Rokan Hulu result from prediction in

22 On the other hand, result from the simulation look a little different with the actual conditions. This difference is shown by the 113, ha forest areas or 15.43% from total area in 2009 (simulation) compared with 122, ha or 16.59% from total area in 2009 (actual). The ratio of forest cover areas in simulation is 8, ha less than the actual area in Percentage Graphic of Forest and non Forest Area Calculation 2002, 2005, 2009 (Actual), and 2009 (Simulation) (Simulation) Years % Non Forest % Forest Figure 4.19 Percentage of forest and non forest condition in Rokan Hulu result from classification of 2002, 2005, 2009 and simulation of Visually apparent that the simulation of forest covers does not closely resemble the actual data of the northeast and northwest of Rokan Hulu. On the other site, the simulation of forest covers does closely resemble the actual data of the southeast, south, and southwest. This is probably due to the conversion rate of forest area into palm oil plantation which is relatively past in this area supported by low flat elevation in the north compared with hilly high elevation in the south. Plantation Service of Rokan Hulu (2006) has programmed the development of plantation area to increase production through building people s oil palm plantation. 50

23 The rate of forest cover change can be use as monitoring and supervision of forest management and the trends of change in the future. Thus, forest cover changes in the future can be anticipated. Therefore, in this study has been conducted of forest change detection in 2002, 2005, and They are expected to be a reference for monitoring and supervision of the rate of forest change on the study area (Figure 4.15, 4.16, and 4.17). Forest changes condition overtime is illustrated in Figure This condition indicates that the forest control occurred in the study area is not optimal. Furthermore, the percentage rate of decline in forest cover area over time period is not separated from socio-economic condition of local people. As concluded by Forestry Service of Rokan Hulu (2006) forest control is not optimal because of lack of human resources, forest conversion for oil palm plantations, over-cutting, illegal logging, local people claims against the area, and forest boundaries are not clear. In additional, protected forest area has a function in supporting the sustainability of human life. Protected forest cover changes marked the occurrence of environmental function change. These changes can be monitored and controlled through the forest cover change detections. Hence, further analysis related to this study is change of forest area in protected forest area. As mentioned in the previous chapter, Rokan Hulu has the three protected areas such as Bukit Suligi, Mahato, and Sei Rokan. Changes of forest cover affect the existence of protected forest. This is proved by decreasing protected forest areas on the three time series that include simulation result. Base on the protected forest maps obtained from the Rokan Hulu local Forestry official showed in three locations. The protected forest cover maps derived from image classification in 2002, 2005, and 2009 were illustrated in Figure 4.20, 4.21, and While, the protected forest cover map result from prediction in 2009 was illustrated in Figure The total protected forest area is 50, ha (100%), and Bukit Suligi protected forest area is 17, ha (34.01%). Likewise, Mahato protected forest area is 17, ha (35.34%) and Sei Rokan protected forest area is 15, ha (30.65%). Mahato protected forest has a larger area than Bukit Suligi and Sei Rokan, respectively in

24 Figure 4.20 Protected forest map of Rokan Hulu result from classification in

25 Figure 4.21 Protected forest map of Rokan Hulu result from classification in

26 Figure 4.22 Protected forest map of Rokan Hulu result from classification in

27 Figure 4.23 Protected forest map of Rokan Hulu result from prediction in

28 In 2005, the total protected forest area decreased to 32, ha, Bukit Suligi protected forest is 13, ha (40.32%). Mahato protected forest area decreased to 4, ha (13.38%) and Sei Rokan protected area to 15, ha (46.30%). Table 4.7 Protected forest changes in Rokan Hulu result from classification of 2002, 2005, 2009 and simulation of 2009 (hectares) Protected Forest/ (simulation) Years Ha % Ha % Ha % Ha % Bukit Suligi 17, , , , Mahato 17, , , Sei Rokan 15, , , , Total 50, , , , Graphic of Protected Forest Area Calculation 2002, 2005, 2009 and 2009 (Simulation) Areal (Ha) Bukit Suligi Mahato Sei Rokan (simulation) Years Figure 4.24 Protected forest condition in Rokan Hulu result from classification of 2002, 2005, 2009, and simulation of

29 A decrease in the protected forest areas also occurred in The total protected forest area was reduced to 24, ha as a result from the decreasing protected forest areas of Bukit Suligi, Mahato, dan Sei Rokan with areas of, 9, ha (38.41%), ha (3.04%), and 14, ha (58.55%), respectively in 2009 (actual). While, the total protected forest areas is 22, ha accumulated from degradation of protected forest area of Bukit Suligi (7, ha or 34.02%), Mahato (3, ha or 13.26%), and Sei Rokan (22, ha or 52.72%) in 2009 (simulation) (Table 4.7). There is a real difference between the actual and simulation as seen by the changes in the Mahato protected forest area with a ratio of 2, ha area in simulation which is greater than the actual. The decrease in protected forest area is supported by land conversion activities of Mahato protected forest area to oil palm plantation by the local people. The dynamics of the protected forest area change in Rokan Hulu is illustrated in Figure This degradation was caused by land conversion activity from forest to other land use that occurred in the study area regardless of protected forest as part of watershed protection. Bukit Suligi protected forest has a main role in the hydrological system as a water catchment area of major rivers in Riau. It has been degraded due to land conversion into palm oil and rubber plantation in this area. Mahato protected forest area except as water catchment area also serves as Sumatran rhinoceros conservation those haves also had been degraded due to land conversion into oil palm plantation. The same condition occurs in Sei Rokan protected forest. Thus, decrease in protected forest areas affects the function of forest in the study area. The functions are expected to be optimized again through replanting and others. As formulated by Forestry Service of Rokan Hulu (2006) optimizing forest functions as a water catchment, ecosystem stability, and as a refuge of flora and fauna. 57

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