GIS and analytic hierarchy process for land evaluation

Transcription

1 GIS ad aalytic hierarchy process for lad evaluatio Dr. Le Cah DINH Sub-Natioal Istitute of Agricultural Plaig ad Proectio Vieta Assoc. Prof. Dr. Tra Trog DUC Vieta Natioal Uiversity - Ho Chi Mih City (VNU-HCM) ttduc@hcut.edu.v Lad evaluatio plays a iportat role i lad use plaig. It provides critical iforatio to support lad use allocatio. Lad evaluatio for sustaiable lad use ust take ito accout several cosideratios icludig atural coditios, ecooic, social ad eviroetal coditios. Therefore, sustaiable lad use evaluatio is a ulti-criteria decisio aalysis (MCDA). MCDA iplies techiques used to aalyse a set of criteria providig decisio akers with the priorities, or weights, of these criteria (Zopouidis ad Pardalos, 200). May studies i this area have used the aalytic hierarchy process techique, referred to as the of Saaty (80), to deterie weights of criteria (Lu et al., 200). I the field of lad evaluatio, where decisios should be based o iputs fro a group of experts coig fro very differet backgrouds (such as agrooists, ecooists), quite a uber of studies applied allowig idividual decisio akig to deterie the weights of cosidered criteria. The results of such studies are therefore quite subective (Thapa ad Murayaa, 2008; Che, Yu ad Kha, 200). To facilitate the ivolveet of experts fro differet backgrouds i lad use evaluatio process ad reducig idividual s subectivity, the -group ethod (Lu et al., 200), i.e. i group decisio akig shall be utilised i this study. The origial techique iplicitly assues a crisp eviroet where experts could assig exact ubers ai = ai [,] [,] whe coparig the relative iportace of each pair of idicators (i, ). However, experts assessets have always ivolved certai abiguity ad ucertaity; cosequetly the evaluatio results could ot be accurate eough for decisio akig (Che et al., 20). To overcoe the liitatios of the origial crisp, this study proposes a cobiatio of two techiques the -group ad the fuzzy logic to create a Fuzzy -Group (FG). This cobied techique is used to assess the priority of differet criteria, eablig ore accurate capture of iforatio geerated durig a ulti-criteria decisio aalysis process. This study therefore proposes a itegratio of the GIS ad the FG to create a ew odel for hadlig spatial MCDA/MCDM probles, particularly, the lad use suitability aalysis. Theoretical backgrouds ad the costructio of the odel Fuzzy group (FG) - Selectig the fuzzy (F)ethod: Accordig to Kahraa (2008), a uber of basic F ethods exist ad these are the subect of iterest to various researchers. Aog the, the ethods of V. Laarhove, Pedrycz (83) ad Buckley (85) require very large coputatio eve for sall probles; the ethod of Cheg (6) is based o calculatio of both probability ad possibility, hece difficult to apply. O the other had, ethod of Chag (6) requires relatively less coputatio ad has siilar order of executio as i a 2

2 crisp eviroet. Therefore, i this study, F ethod of Chag (6) was selected to deterie the weights of criteria i the lad suitability aalysis. - Liguistic variable ad the liguistic values i pair-wise coparisos: Accordig to Srdevic ad Medeiros (2008) as well as Out, Efedigil ad Kara (200), the relatioship betwee variables describig the laguage of priority betwee two criteria i a crisp eviroet (Saaty, 80) with the fuzzy values of liguistic variables (the triagular fuzzy ubers) i pair-wise coparisos are described i table. Saaty s crisp Values Table : Origial ad fuzzified Saaty s values scale for pair-wise coparisos Liguistic scale describig the Fuzzified relative iportace betwee values two criteria (l,, u) Reciprocal fuzzy values (u,, l) Just equal (,, ) (,, ) Equal iportace (,, 2) (2,, ) 3 Weak iportace (2, 3, 4) (4, 3, 2) 5 Essetial or strog iportace (4, 5, 6) (6, 5, 4) Very strog iportace (6,, 8) (8,, 6) Extreely preferred (8,, ) (,, 8) (x-, x, x+); ((x+), x, (x-)); 2,4,6,8 Iterediate values x=2,4,6,8. x=2,4,6,8. Sources: Srdevic và Medeiros (2008); Out, Efedigil và Kara (200). - Fuzzy group (FG ): Let X ={x, x 2, x } be a obect set, whereas U ={ u, u 2,, u } is a goal set. M gi ( i =,2,..., ; =,2,..., ) are triagular fuzzy ubers represetig the perforace of the obect x i with regard to each goal u. ~ The fuzzy uber a ik = ( lik, ik, uik ) is the fuzzy pair-wise copariso atrix geerated by the k th expert about obect x i with regard to each goal u ; with l ik ik u ik ad l ik, ik, u ik [,] [,]. Judgets ade by the whole expert group ca be the aggregated usig the algorith of (Chag et al., 200) as follows: ~ A i = ( Li, M i, Ui ), i which: L i = i (l ik ), M i = ik, U i = ax(u ik ) k = ~ Oce the fuzzy udget atrix of the expert group ( Ai ) is obtaied, the algorith F of Chag (6) is used to calculate the weights of criteria followig the steps below: Step : The fuzzy sythetic extet value with respect to the i th obect is defied as: S i = = M gi i= = M gi (); i which: M gi = ( l,, u ) (2); M gi = li, i, = = = = i= = The copute the iverse of the equatio (3): ( u ) (3); i= i= i= i 2/2

3 i= = M gi =,, (4) ui i li i= i= i= Step 2: The degree of possibility of M 2 (l 2, 2,u 2 ) M (l,,u ) is defied as: V M M ) = sup[i( µ ( x), µ ( ))] (5) ( 2 M M 2 y y x, if 2 0, if l u2 V ( M 2 M) = hgt( M M 2) = µ M ( d) = else : (6) 2 l u2, ( 2 u2) ( l) Where d is the ordiate of the highest itersectio poit betwee µ M, µ M 2.To copare M ad M 2, both the values of V(M 2 M ) ad V(M 2 M ) are required. Step 3: The possibility degree of a covex fuzzy uber to be greater tha k covex fuzzy ubers M i (i =,2,...,k) ca be defied by V(M M, M 2,, M k ) = V[(M M ) ad (M M 2 ) ad ad (M M k )] = iv(m M i ); for i=,2,, k; Let d (A i )= iv(s i S k ), for i=,..., ; k=,2,, ; k i; The, the weight vector will be: [W ] = [d (A ), d (A 2 ),, d (A )] T Step 4: The oralized weight vectors ca be obtaied as follows: [W]= [d(a ), d(a 2 ),, d(a )] T ; Where [W] is a ofuzzy uber. Itegratio of GIS ad FG GIS ad FG are itegrated to costruct a odel for evaluatig sustaiable lad aageet (ESLM). The odel icludes the steps described i Figure : Step : Idetify the idicators that affect the sustaiability of lad use systes (LUS), the deterie the respective weights of these idicators by applyig the FG (Figure 2). Physical lad-use suitability aalysis (FAO, 6) Deterie the weights of idicators by usig FG Deterie sustaiable idicators Results of physical suitability Sustaiable evaluatio No Physical suitability ap Ed Yes Assessig social ipact of a LUS (Xi) Assessig ecooic ipact of a LUS (Xi) Assessig eviroetal ipact of a LUS (Xi) Overall weight (Wi) of each idicator for sustaiable lad-use suitability S= Xi*Wi Proposed ap of sustaiable laduse Ed Figure : Itegratio of GIS ad 3/2 FG for lad suitability aalysis

4 Costruct hierarchy of idicators Pair-wise copariso atrix of expert k th : [a ik ] CR k 0% Yes Fuzzified pair-wise copariso atrix [ a ~ ik ] The fuzzy aggregatio udget atrix: [ A ~ i ] Deterie weights of idicators (FG): [w] No (i). Idetify ad the costruct the hierarchy of sustaiability idicators. Next ask each k th expert to udge the relative iportace of the criteria, geeratig the pairwise copariso atrix [a ik ]. Check to esure the cosistecy ratio CR k <0%. (ii). Proceed to fuzzify the crisp pair-wise atrix [a ik ]of expert k th with the triagular fuzzy ubers (l,, u) ad the reciprocal fuzzy (u,, l) show i table, the result is a fuzzy udget atrix a = ( l,, u ). (iii). Aggregate fuzzy udget atrices of the group (Chag et al., 200) to obtai: A i = ( Li, M i, Ui ), i which: L i = i (l ik ), M i = ik, U i = ax(u ik ). k = ~ (iv). Based o ( A i ), use the F algorith of Chag(6) to calculate the weights of criteria. ~ ik ~ ik ik ik Figure 2: FG to deterie weights of idicators Step 2: Apply GIS to evaluate the physical lad use suitability usig theatic layers related to atural resources idicators such as rai fall, soil quality, groud slope, etc Oly lad use systes (LUSs) with high- to -argially physical suitability orders (S, S2, S3) are selected for total sustaiability evaluatio. Step 3: Apply GIS to evaluate total sustaiability icludig ecooic, social ad eviroetal ipacts i additio to physical suitability. Theatic iforatio layers are built i GIS, uited ad the calculate the suitability idex (Si) usig weighted average ethod; Si is classified to deterie the suitable regio with regards to all sustaiability aspects. i which, Si: suitability idex; w i : weight of criterio i; x i : score Si = ( wi xi ) c of criterio i, c : boolea value of liited criterio. i = = Applicatio of the odel to deterie sustaiable lad use for a studied area The studied area was La Dog provice i the cetral highlad of Vieta. Seve aor lad use types (LUT) were selected for evaluatig sustaiable lad use. They cosist of twoseasoal-paddy crops (LUT), oe-seasoal-paddy crop (LUT2), aual crops (LUT3), vegetable ad flower (LUT4), coffee (LUT5), tea (LUT6), ad cashew (LUT). Step - Idetificatio of sustaiability idicators ad their respective weights: FAO ethod (FAO 3b, 200) was applied to evaluate sustaiable lad i the case of La Dog provice. Accordig to our aalysis of atural, ecooic ad social coditio of this provice i cosultatio with experts of the related fields, the key idicators affect the sustaiability of LUS i the La Dog provice are idetified ad recorded i Table 2. The FG odel as described i Figure 2 is the applied for deteriig the weights of idicators. The process is as follows: 4/2

5 - For idicators of level These iclude ecooic idicators (EC), social idicators (SO), atural resources ad eviroet idicators (NRE). (i). experts were ivolved icludig three uiversity lecturers, three state aageet experts, three experts o lad resources. Their udget results are preseted i table 2: Table 2: Pair-wise udget atrix for idicators of level Idicators Judget of k th expert I J EC SO NRE NRE SO Cosistecy ratio (CR-% ) Note: EC: ecooic idicators; SO: social idicators; NRE: atural resources ad eviroet idicators (ii). The crisp pair-wise copariso atrix of each expert is the fuzzified. For exaple, fuzzified pair-wise copariso atrix of the st expert: Table 3: Crisp udget atrix of the st expert Table 4: Fuzzified udget atrix of the st expert EC SO NRE EC SO NRE EC 2 2 fuzzified 2/ 3/ 2/ 3/ SO NRE / 3/ I the sae way, the crisp udget atrices of all the reaiig experts are fuzzified. (iii). All the fuzzy udget atrices of the experts are aggregated usig the algorith of Chag et al.(200) ad Jaskowski et al.(200), results are preseted i Table 5. Table 5: Fuzzy aggregatio udget atrix of group for idicators of level Idicators EC SO NRE EC 3/8 / 3 8/ SO 2/ ¼ 2 NRE 8 2/ 5 4/ (iv). Fro the obtaied fuzzy aggregatio udget atrix, Chag's (2; 6) algorith is used to deterie the weights of idicators of level : + The fuzzy sythetic extet value with respect to the ith obect is defied as: S EC = (3/; 0/; 8/) (2; 6/; 2/3) = (0.; ; 2.52) S SO = (4/3; 5/3; 3/) (2; 6/; 2/3) = (0.0504; 0.05; ) S NRE = (/8; /5; 6/) (2; 6/; 2/3) = (0.08; ; 0.25) + The degree of possibility of M 2 (l 2, 2,u 2 ) M (l,,u ) is defied as: V(S EC S SO ) =.00; V(S EC S NRE ) =.00 V(S SO S EC ) = 0.3; V(S SO S NRE ) = 0. V(S NRE S EC ) = 0.65; V(S NRE S SO ) =.00 + The degree of possibility of a covex fuzzy uber to be greater tha k covex fuzzy ubers M i (i =,2,...,k) ca be defied by d (EC) = MiV(S EC S i ) =.00; S i = S SO, S NRE d (SO)= MiV(S SO S i ) = 0.3; S i = S EC, S NRE d (NRE)= MiV(S NRE S i ) = 0.65; S i = S EC, S SO [W ] = [d (EC); d (SO); d (NRE)] T = [.00; 0.3; 0.65] T 5/2

6 + The oralized weight vectors are defied as: [W] = [w EC ; w SO ; w NRE ] T = [0.4; 0.; 0.3] T ; Where [W] is a ofuzzy uber. The sae process are used to deterie the weights of idicators of level 2 ad level 3 as follows: - For idicators of level 2: + Ecooic idicators of level 2 icludes three idicators: gross value retur o far (GO), gross argi (GM), gross value productio/cost for cultivatio (B/C). Nie experts participated icludig three agricultural ecooists, three aagers of agricultural cultivatio, ad three experieced farers. Their respective udgets o relative iportace of each pair of idicators are show i table 6. Table 6: Pair-wise copariso atrix for ecooic idicators of level 2 Idicators Judget of expert k th I GO GM B/C GM B/C CR (%) Fuzzy aggregatio atrix of the group of these ie experts is preseted i table. Table : Fuzzy aggregatio udget atrix of group for ecooic idicators of level 2 Idicators GO GM B/C GO 6/ 5/ 2/ / GM 5 3/ 8/3 / B/C ¼ 3/8 The weight vector is obtaied as [w GO ; w GM ; w B/C ] T = [0.44; 0.36; 0.2] T + Social idicators of level 2 icludes five idicators: Labour (LB); farig habit (FH), goveret policy (GP), techical support (TS), fiacial resources (FS). Table 8 displays the pair-wise copariso atrix geerated by each of ie experts 3 agricultural policy researchers, three state aagers of agricultural policy, three experieced farers. Table 8: Pair-wise udget atrix for social idicators of level 2 Idicators Judget of expert k th i J Labour Farig habits Goveret policy ¼ Techical support Fiacial resources Farig habits Goveret policy Techical support 2 2 ½ 2 Fiacial resources ¼ ¼ Goveret policy Techical support Fiacial resources Techical support Fiacial resources CR (%) /2

7 The fuzzy aggregatio udget atrix of group for social idicator of level 2 show i table. Table : Fuzzy aggregatio udget atrix of group for social idicators of level 2 Idicators LB FH GP TS FR LB (Labour) 25/ / 2/ / 4/ 8 2 ½ 8 2 2/ FH (Farig 3/ habits) 2/ / / 3/ 8/ 4/ / 3/ / 2/ 52/ GP (Gov. policy) 3 6/ 2 TS (Tech. support) FR (Fia. resources) 4 ½ 4 ½ 2/ 2/ 3 6/ 2/ 5/ 4 3/ 5/ / 2 / 3/ 5/ The social weight vector [w LB ; w FH ; w GP ; w TS ; w FR ] T = [0.235; 0.00; 0.35; 0.3; 0.] T + Natural resources ad eviroet group has two idicators at level 2: atural resources (NR) ad eviroet (EN). Most experts rated the iportace of NR equal to EN. Thus each has a relative iportace value of For idicators of level 3: + Idicators at level 3 of atural resources (NR) icludes ie idicators: raifall (Ra), rai duratio (Ti), quality of soil (So), slope (Sl), depth (De ), larite (La), height (To), irrigatio coditio (Ir), flood (Fl). Accordig to experts workig for the Natioal Istitute of Agricultural Plaig ad Proectio (Miistry of Agriculture ad Rural Developet), these atural idicators are of the sae iportace, siilar to the view of FAO (6), so their respective weight value is or approxiately Idicators at level 3 of Eviroet (EN) cosist of three eleets: the aout of pesticides ad fertilizers ito the soil (FER), ehaced biological diversity (BIO), lad cover level (COV). Nie experts i the field of atural resource aageet ad eviroet participated, three of the are lecturers, three are state aagers, ad the last three are researchers. Their udgets are reported i Table 0. Table 0: Pair-wise udget atrix for eviroetal idicators of level 3 Idicators Judget of expert k th i FER BIO COV ½ 4 ¼ BIO COV 5 6 ¼ CR (%) The fuzzy aggregatio udget atrix of group for eviroetal idicators show i table. Table : Fuzzy aggregatio udget atrix of group for eviroetal idicators of level 3 Idicators FER BIO COV FER 5/2 5/ 2/ 3/2 BIO 5 2/ COV 2/3 /5 / 2/ 3/8 8/ The eviroetal weight vector [w FER ; w BIO ; w COV ] T = [0.355; 0.43; 0.502] T /2

8 All of the weight of each idicator at level, level 2, level 3 ad their overall weights are suarized i table 2. Table 2: Hierarchical structure ad weights of sustaiable idicators Level Level 2 Level 3 Code weights Overall weights Ecooic. Gross value retur o far GO W idicators 2. Gross argi GM W W= Gross value productio/cost for cultivatio B/C W Social. Labors LB W idicators 2. Farig habits FH W W2 3. Goveret policy GP W Techical support TS W Fiacial resources FR W Rai fall Ra W Rai duratio Ti W Natural Natural 3. Quality of soil So W resources Resources 4. Slope Sl W ad 5. Depth De W eviroet W3 6. Larite La W idicators 0.5. Height To W W3 8. Irrigatio coditio Ir W Flood Fl W Eviroet. Aout of pesticides ad FER W fertilizers ito the soil W32 2. Ehace biological diversity BIO W Lad cover level COV W The overall weights of 20 idicators are calculated as follows: - Ecooic group: w w (=, 2, 3); ex: w (B/C) = = Social group: w 2 w 2 (=,.., 5); ex: w LB = = Natural resources group: w 3 w 3 w 3 (=,..,); ex: w Ra = = Eviroetal group: w 3 w (=,2,3); ex: w COV = = Step 2 Apply GIS to evaluate the physical lad use suitability: Overlay ie theatic layers correspodig to ie atural resources cosideratios (fro raifall to flood) to costruct a lad appig uit (LMU) which fors the basis for assesset of physical/atural suitability. The LUSs which have high-to-argial suitability (S, S2, S3) are the selected to proceed to the ext step. Step 3 Apply GIS to evaluate total sustaiability for lad-use aageet: Each LUS will be rated o how it scores agaist each sustaiability idicator. The scorig scales ad values alog each idicator are defied i cosultacy with the experts ad experieced farers i La Dog. The scorig scales ad values are preseted i table 3 Table 3: Scorig scale ad values alog each idicator Idicator Idicators Scorig scale Scorig value Group (Xi) 8/2

9 Idicator Idicators Scorig scale Scorig value Group (Xi)... Gross value + Very high (> 60 illio VND) retur o far (GO) + High (30-60 illio VND) + Moderate (5-30 illio VND) 5 + Low ( < 5 illio VND).2. Gross argi + Very high (> 40 illio VND) (GM) + High (5-40 illio VND) + Moderate (5-5 illio VND) 5 + Low ( < 5 illio VND).3. B/C + Very high (> 2.0) + High (.5-2.0) + Moderate (.0 -.5) 5 + Low (<.0) Labour + Very itesive use of local labour + Itesive use of local labour force + Moderate use of local labour force Farig habits + Very highly suited to existig farers + Highly suited to existig farers + Not suited to existig farers habits Goveret policy + Ecouragig expasio of productio + Stabilized productio area 2.4. Techical support + Itesive techical support required + Little techical support required 2.5. Fiacial resources + Moderate productio cost eeded + High to very high productio cost 3. Natural Physical/Natural Suitability + S: Highly suitable + S2: Moderately suitable + S3: Margially suitable Iput of pesticides + Very high iput eeded ad fertilizers ito the soil + High iput eeded 5 + Moderate to low iput eeded 4.2. Biological Diversity Ehaceet + Poly-culture + Moo-culture 4.3. Lad cover level + cotiuous coverage of surface + seasoal coverage of surface Theatic layers for each idicator are built i GIS, uited the the suitability idex (Si) is calculated by usig the weighted average ethod. The results are a ap ad data o sustaiable lad suitability. Assesset of ecooic suitability aloe is also carried out. Figure 3 shows, for each of the seve studied LUTs, the aout of lad areas classified ito each level of suitability for Physical Suitability, Ecooic suitability, ad Sustaiable lad use suitability respectively. Figure 4 shows the ap of the sustaiable lad use of La Dog provice resulted fro the odel. /2

10 Areas (000ha) Phy Eco Sus Phy Eco Sus Phy Eco Sus Phy Eco Sus Phy Eco Sus Phy Eco Sus Phy Eco Sus LUT LUT2 LUT3 LUT4 LUT5 LUT6 LUT S: Highly suitable S2: Moderately suitable S3: Margially suitable N: Noe-suitable Figure 3: Copariso of physical (Phy), ecooic (Eco), ad sustaiable suitability (Sus) Figure 3 iplies that evaluatig for sustaiable lad aageet is essetial i lad use plaig. It supports the plaers to cosider reovig usustaiable LUS eve whe such a LUS has copoets rated highly i ters of physical suitability such as oe seasoal paddy crop (LUT 2). It also helps plaers to idetify a sustaiable LUS eve soe copoets of this LUS, whe cosiderig i ters ecooic suitability or physical suitability aloe, are classified as oly argialy or oderately suitable, for exaple the cashew (LUT ). I other words, if oly physical suitability evaluatio is carried out, the the oe seasoal paddy crop is obviously proposed for lad use i the future; or if oly the evaluatio of physical suitability ad ecooic suitability are coducted the cashew ay be excluded fro the lad use pla. + Evaluatig of curret lad suitability: Usig GIS to overlay the proposed ap (Figure 4) ad the curret lad-use ap ade i 200, the results support to ake the followig proposals i future lad use of La Dog Curretly about 36, ha of crops produced i areas that are classified as ot-suitable for cultivatio (due to steep slope, etc). I the future, these lad-use types should be coverted to forestry lad-use.,83 ha of curret Oe- seasoal paddy crops will be coverted to vegetable-flower type or two seasoal paddy crops. Figure 4: Map of proposals for sustaiable use of agricultural lad 0/2

11 + Evaluatig the odel: To assess the behavior of this proposed FG odel, the results it produced for La Dog provice, as show i Figure 5 ad referred to as FG s, are copared with results obtaied fro two other studies coducted for this sae regio. I 200, the SubNIAPP applied the Physical Suitability Evaluatio Method of FAO (6) ad the result of this study is preseted as FAO6 s i Figure 5. Results of aother evaluatio of sustaiable lad-use for La Dog usig ethod (Dih, 200) is also displayed i figure 5 ad referred to as s. The followig observatios are ade fro the copariso..000ha FAO6 FG FAO6 FG FAO6 FG FAO6 FG FAO6 FG FAO6 FG FAO6 FG LUT LUT2 LUT3 LUT4 LUT5 LUT6 LUT S: Highly sui table S2: Moderately suitable S3: Margially suitable N: Noe-suitable Figure 5: Copariso of results of three ethods: FAO6,, FG. - The axiu liited factor ethod (FAO, 6) follows that aog physical characteristic of a regio, the oe that has the worst quality decides its suitability level. Hece, the suitable areas classified by this ethod are quite safely liited. - With the ethod, the ot-suitable areas are less tha those obtaied fro the other two ethods (FAO6, FG). This observatio idicates that soe areas while cosidered ot-suitable i FAO ad FG odels are assessed as suitable by the odel. Thus the odel ay etail higher risk i its proposed lad use. - The areas classified as highly suitable (S) by the FG odel is larger tha the correspodig areas obtaied fro the odel. Thus the FG approach teds to adust a area fro oderately suitable (S3) to highly suitable (S), rather tha oves it fro ot-suitable category to suitable oes (S, S2, S3) as see i the approach. Thus, the FG odel sees to be ore reasoable for assessig lad use suitability i cases lad resources are scarce. Coclusio The itegrated odel of GIS ad FG creates a useful tool for evaluatig sustaiable lad aageet. I this odel GIS is used to aalyze spatial data while FG is used to deterie weights of lad-use sustaiability idicators. The odel eables: i) ivolveet of several experts coig fro very differet backgroud while reducig subectivity ecoutered i the origial approach, ii) ehacig accuracy of iforatio geerated for decisio akig by deteriig idicators weights i a fuzzy istead of a crisp eviroet iii) decisio akers (lad-use plaers, aagers) to ituitively solve spatial ulti-criteria decisio-akig i lad-use allocatio through ituitive ap i GIS. 2

12 This itegrated odel is applied to evaluate sustaiable lad aageet i La Dog provice. All key stakeholders of lad resources i La Dog have their represetatives oit i the process icludig farers, agrooists, ecooists, policy akers. Hece, the evaluatio results are cosidered appropriate for the local practice. The pla is to seek opportuities to expad the use of this odel to earby provices i the ear future. Refereces []. V.Y.C. Che, H.P. Lie, C.H. Liu, J.J.H. Liou, G.H Tzeg, L.S Yag (20), Fuzzy MCDM approach for selectig the best eviroet-watershed pla, Applied Soft Coputig (20) , ScieceDirect, Elsevier. [2]. Y. Che, J. Yu, S. Kha (200), Spatial sesitivity aalysis of ulti-criteria weights i GIS-based lad suitability evaluatio, Eviroetal Modellig & Software 25 (200), 582-5, Sciece Direct, Elsevier. [3]. C.W. Chag, R.W. Cheg, L.L. Hug (200), Applyig fuzzy hierarchy ultiple attributes to costruct a expert decisio akig process, Expert syste with applicatio 36 (200), Elsevier. [4]. P. Jaskowski, S. Biruk, R. Buco (200), Assessig cotractor selectio criteria weight with fuzzy ethod applicatio i group decisio akig, Autoatio i costructio (200), Elsevier. [5]. C. Kahraa (2008), Fuzzy Multi-Criteria Decisio Makig: Theory ad Applicatio with Recet Developets, Spriger, USA [6]. J. Lu, G. Zhag, D. Rua, F. Wu (200), Multi-Obective Group Decisio Makig: Method, software, ad applicatio with fuzzy techiques, World scietific Publishig, Sigapore. []. S. Out, T. Efedigil, S.S. Kara (200), A cobied fuzzy MCDM approach for selectig shoppig ceter site: A exaple fro Istabul, Turkey, Expert syste with applicatio 3 (200), 3-80, Sciece Direct, Elsevier. [8]. R.B.Thapa, Y.Murayaa (2008), Lad evaluatio for peri-urba agriculture usig aalytical hierarchical process ad geographic iforatio syste techiques: A case study of Haoi, Lad use policy, Vol.25, issue 2, pages , Elsevier. []. B. Srdevic, Y.D.P. Medeiros (2008), Fuzzy Assesset of Water Maageet Plas, Water Resources Maageet (2008) 22:8 84, Spriger Sciece. [0]. C. Zopouidis, P. M. Pardalos (200), Hadbook of MultiCriteria Aalysis, Applied optiizatio, Spriger, USA. 2/2