要学习化妆Modeling the Spatial Dynamics of Regional Land U:The CLUE-S Model现在进行时练习题及答案
PETER H.VERBURG*
Department of Environmental Sciences Wageningen University
P.O.Box37
6700AA Wageningen,The Netherlands
sunny英文怎么读and
Faculty of Geographical Sciences
Utrecht University
P.O.Box80115
3508TC Utrecht,The Netherlands
were怎么读
WELMOED SOEPBOER
英文地址怎么写A.VELDKAMP
Department of Environmental Sciences Wageningen University
P.O.Box37
6700AA Wageningen,The Netherlands
RAMIL LIMPIADA
VICTORIA ESPALDON
School of Environmental Science and Management University of the Philippines Los Ban˜os
College,Laguna
cabletester
4031,Philippines SHARIFAH S.A.MASTURA
Department of Geography
Universiti Kebangsaan Malaysia
43600Bangi
Selangor,Malaysia
ABSTRACT/Land-u change models are important tools for integrated environmental management.Through scenario analysis they can help to identify near-future critical locations in the face of environmental change.A dynamic,spatially ex-plicit,land-u change model is prented for the regional scale:CLUE-S.The model is specifically developed for the analysis of land u in small ,a watershed or province)at afine spatial resolution.The model structure is bad on systems theory to allow the integrated analysis of land-u change in relation to socio-economic and biophysi-cal driving factors.The model explicitly address the hierar-chical organization of land u systems,spatial connectivity between locations and stability.Stability is incorporated by a t of variables that define the relative elasticity of the actual land-u type to conversion.The ur can specify the t-tings bad on expert knowledge or survey data.Two appli-cations of the model in the Philippines and Malaysia are ud to illustrate the functioning of the model and its validation.
Land-u change is central to environmental man-agement through its influence on biodiversity,water and radiation budgets,trace gas emissions,carbon cy-cling,and livelihoods(Lambin
and others2000a, Turner1994).Land-u planning attempts to influence the land-u change dynamics so that land-u config-urations are achieved that balance environmental and stakeholder needs.
Environmental management and land-u planning therefore need information about the dynamics of land u.Models can help to understand the dynamics and project near future land-u trajectories in order to target management decisions(Schoonenboom1995).Environmental management,and land-u planning specifically,take place at different spatial and organisa-tional levels,often corresponding with either eco-re-gional or administrative units,such as the national or provincial level.The information needed and the man-agement decisions made are different for the different levels of analysis.At the national level it is often suffi-cient to identify regions that qualify as“hot-spots”of land-u ,areas that are likely to be faced with rapid land u conversions.Once the hot-spots are identified a more detailed land u change analysis is often needed at the regional level.
At the regional level,the effects of land-u change on natural resources can be determined by a combina-tion of land u change analysis and specific models to asss the impact on natural resources.Examples of this type of model are water balance models(Schulze 2000),nutrient balance
models(Priess and Koning 2001,Smaling and Fresco1993)and erosion/dimen-tation models(Schoorl and Veldkamp2000).Most of-
KEY WORDS:Land-u change;Modeling;Systems approach;Sce-
nario analysis;Natural resources management
*Author to whom correspondence should be addresd;email:
加油的英文怎么写pverburg@gissrv.iend.wau.nl
DOI:10.1007/s00267-002-2630-x Environmental Management Vol.30,No.3,pp.391–405©2002Springer-Verlag New York Inc.
ten the models need high-resolution data for land u to appropriately simulate the process involved.
Land-U Change Models
战色The rising awareness of the need for spatially-ex-plicit land-u models within the Land-U and Lan
d-Cover Change rearch community(LUCC;Lambin and others2000a,Turner and others1995)has led to the development of a wide range of land-u change models.Whereas most models were originally devel-oped for deforestation(reviews by Kaimowitz and An-geln1998,Lambin1997)more recent efforts also address other land u conversions such as urbaniza-tion and agricultural intensification(Brown and others 2000,Engelen and others1995,Hilferink and Rietveld 1999,Lambin and others2000b).Spatially explicit ap-proaches are often bad on cellular automata that simulate land u change as a function of land u in the neighborhood and a t of ur-specified relations with driving factors(Balzter and others1998,Candau 2000,Engelen and others1995,Wu1998).The speci-fication of the neighborhood functions and transition rules is done either bad on the ur’s expert knowl-edge,which can be a problematic process due to a lack of quantitative understanding,or on empirical rela-tions between land u and driving ,Pi-janowski and others2000,Pontius and others2000).A probability surface,bad on either logistic regression or neural network analysis of historic conversions,is made for future conversions.Projections of change are bad on applying a cut-off value to this probability sur-face.Although appropriate for short-term projections,if the trend in land-u change continues,this methodology is incapable of projecting changes when the demands for different land-u types change,leading to a discontinua-tion of the trends.Moreover,the models are usually capable of simulating the conversion of one land-u type deforestation)becau they do not address competition between land-u types explicitly.
The CLUE Modeling Framework
The Conversion of Land U and its Effects(CLUE) modeling framework(Veldkamp and Fresco1996,Ver-burg and others1999a)was developed to simulate land-u change using empirically quantified relations be-tween land u and its driving factors in combination with dynamic modeling.In contrast to most empirical models,it is possible to simulate multiple land-u types simultaneously through the dynamic simulation of competition between land-u types.
This model was developed for the national and con-tinental level,applications are available for Central America(Kok and Winograd2001),Ecuador(de Kon-ing and others1999),China(Verburg and others 2000),and Java,Indonesia(Verburg and others 1999b).For study areas with such a large extent the spatial resolution of analysis was coar(pixel size vary-ing between7ϫ7and32ϫ32km).This is a con-quence of the impossibility to acquire data for land u and all driving factors atfiner spatial resolutions.A coar spatial resolution requires a different data rep-rentation than the common reprentation for data with afine spatial resolution.Infine resolution grid-bad approaches land u is defined by the most dom-inant land-u type within the pixel.However,such a data reprentation would lead to large bias in the land-u distribution as some class proportions will di-minish and other will increa with scale depending on the spatial and
probability distributions of the cover types(Moody and Woodcock1994).In the applications of the CLUE model at the national or continental level we have,therefore,reprented land u by designating the relative cover of each land-u type in each pixel, e.g.a pixel can contain30%cultivated land,40%grass-land,and30%forest.This data reprentation is di-rectly related to the information contained in the cen-sus data that underlie the applications.For each administrative unit,census data denote the number of hectares devoted to different land-u types.
When studying areas with a relatively small spatial ex-tent,we often ba our land-u data on land-u maps or remote nsing images that denote land-u types respec-tively by homogeneous polygons or classified pixels. When converted to a raster format this results in only one, dominant,land-u type occupying one unit of analysis. The validity of this data reprentation depends on the patchiness of the landscape and the pixel size chon. Most sub-national land u studies u this reprentation of land u with pixel sizes varying between a few meters up to about1ϫ1km.The two different data repren-tations are shown in Figure1.
Becau of the differences in data reprentation and other features that are typical for regional appli-cations,the CLUE model can not directly be applied at the regional scale.This paper describes the mod-ified modeling approach for regional applications of the model,now called CLUE-S(the Conv
ersion of Land U and its Effects at Small regional extent). The next ction describes the theories underlying the development of the model after which it is de-scribed how the concepts are incorporated in the simulation model.The functioning of the model is illustrated for two ca-studies and is followed by a general discussion.
392P.H.Verburg and others
Characteristics of Land-U Systems
This ction lists the main concepts and theories that are prevalent for describing the dynamics of land-u change being relevant for the development of land-u change models.
Land-u systems are complex and operate at the interface of multiple social and ecological systems.The similarities between land u,social,and ecological systems allow us to u concepts that have proven to be uful for studying and simulating ecological systems in our analysis of land-u change (Loucks 1977,Adger 1999,Holling and Sanderson 1996).Among tho con-cepts,connectivity is important.The concept of con-nectivity acknowledges that locations that are at a cer-tain distance are related to each other (Green 1994).Connectivity can be a direct result of biophysical ,dimentation in the lowlands is a direct result of erosion in the uplands,
but more often it is due to the movement of species or humans through the landscape.Land degradation at a certain location will trigger farmers to clear land at a new location.Thus,changes in land u at this new location are related to the land-u conditions in the other location.In other instances more complex relations exist that are rooted in the social and economic organization of the system.The hierarchical structure of social organization caus some lower level process to be constrained by higher level ,the establishments of a new fruit-tree plantation in an area near to the market might in fluence prices in such a way that it is no longer pro fitable for farmers to produce fruits in more distant areas.For studying this situation an-other concept from ecology,hierarchy theory,is u-ful (Allen and Starr 1982,O ’Neill and others 1986).This theory states that higher level process con-strain lower level process whereas the higher level process might emerge from lower level dynamics.This makes the analysis of the land-u system at different levels of analysis necessary.
Connectivity implies that we cannot understand land u at a certain location by solely studying the site characteristics of that location.The situation at
neigh-
Figure 1.Data reprentation and land-u model ud for respectively ca-studies with a national/continental extent and local/regional extent.
Modeling Regional Land-U Change
393
boring or even more distant locations can be as impor-tant as the conditions at the location itlf.
Land-u and land-cover change are the result of many interacting process.Each of the process operates over a range of scales in space and time.The process are driven by one or more of the variables that influence the actions of the agents of land-u and cover change involved.The variables are often re-ferred to as underlying driving forces which underpin the proximate caus of land-u change,such as wood extraction or agricultural expansion(Geist and Lambin 2001).The driving factors include demographic ,population pressure),economic , economic growth),technological factors,policy and institutional factors,cultural factors,and biophysical factors(Turner and others1995,Kaimowitz and An-geln1998).The factors influence land-u change in different ways.Some of the factors directly influ-ence the rate and quantity of land-u he amount of forest cleared by new i
ncoming migrants. Other factors determine the location of land-u he suitability of the soils for agricultural land u.Especially the biophysical factors do po constraints to land-u change at certain locations, leading to spatially differentiated pathways of change.It is not possible to classify all factors in groups that either influence the rate or location of land-u change.In some cas the same driving factor has both an influ-ence on the quantity of land-u change as well as on the location of land-u change.Population pressure is often an important driving factor of land-u conver-sions(Rudel and Roper1997).At the same time it is the relative population pressure that determines which land-u changes are taking place at a certain location. Intensively cultivated arable lands are commonly situ-ated at a limited distance from the villages while more extensively managed grasslands are often found at a larger distance from population concentrations,a rela-tion that can be explained by labor intensity,transport costs,and the quality of the products(Von Thu¨nen 1966).The determination of the driving factors of land u changes is often problematic and an issue of dis-cussion(Lambin and others2001).There is no unify-ing theory that includes all process relevant to land-u change.Reviews of ca studies show that it is not possible to simply relate land-u change to population growth,poverty,and infrastructure.Rather,the inter-play of veral proximate as well as underlying factors drive land-u change in a synergetic way with large variations caud by location specific conditions (Lambin and others2001,Geist and Lambin2001).In
regional modeling we often need to rely on poor data describing this complexity.Instead of using the under-lying driving factors it is needed to u proximate vari-ables that can reprent the underlying driving factors. Especially for factors that are important in determining the location of change it is esntial that the factor can be mapped quantitatively,reprenting its spatial vari-ation.The causality between the underlying driving factors and the(proximate)factors ud in modeling (in this paper,also referred to as“driving factors”) should be certified.
Other system properties that are relevant for land-u systems are stability and resilience,concepts often ud to describe ecological systems and,to some extent, social systems(Adger2000,Holling1973,Levin and others1998).Resilience refers to the buffer capacity or the ability of the ecosystem or society to absorb pertur-bations,or the magnitude of disturbance that can be absorbed before a system changes its structure by changing the variables and process that control be-havior(Holling1992).Stability and resilience are con-cepts that can also be ud to describe the dynamics of land-u systems,that inherit the characteristics from both ecological and social systems.Due to stability and resilience of the system disturbances and external in-fluences will,mostly,not directly change the landscape structure(Conway1985).After a natural disaster lands might be abandoned and the population might tempo-rally migrate.However,people wil
l in most cas return after some time and continue land-u management practices as before,recovering the land-u structure (Kok and others2002).Stability in the land-u struc-ture is also a result of the social,economic,and insti-tutional structure.Instead of a direct change in the land-u structure upon a fall in prices of a certain product,farmers will wait a few years,depending on the investments made,before they change their cropping system.
The characteristics of land-u systems provide a number requirements for the modelling of land-u change that have been ud in the development of the CLUE-S model,including:
●Models should not analyze land u at a single scale,
but rather include multiple,interconnected spatial scales becau of the hierarchical organization of land-u systems.
hotsauce●Special attention should be given to the driving
factors of land-u change,distinguishing drivers that determine the quantity of change from drivers of the location of change.
●Sudden changes in driving factors should not di-
rectly change the structure of the land-u system as
a conquence of the resilience and stability of the
平假名翻译land-u system.
394P.H.Verburg and others
●
The model structure should allow spatial interac-tions between locations and feedbacks from higher levels of organization.
Model Description
Model Structure
The model is sub-divided into two distinct modules,namely a non-spatial demand module and a spatially explicit allocation procedure (Figure 2).The non-spa-tial module calculates the area change for all land-u types at the aggregate level.Within the cond part of the model the demands are t
ranslated into land-u changes at different locations within the study region using a raster-bad system.
For the land-u demand module,different alterna-tive model speci fications are possible,ranging from simple trend extrapolations to complex economic mod-els.The choice for a speci fic model is very much de-pendent on the nature of the most important land-u conversions taking place within the study area and the scenarios that need to be considered.Therefore,the demand calculations will differ between applications and scenarios and need to be decided by the ur for the speci fic situation.The results from the demand
module need to specify,on a yearly basis,the area covered by the different land-u types,which is a direct input for the allocation module.The rest of this paper focus on the procedure to allocate the demands to land-u conversions at speci fic locations within the study area.
The allocation is bad upon a combination of em-pirical,spatial analysis,and dynamic modelling.Figure 3gives an overview of the procedure.The empirical analysis unravels the relations between the spatial dis-tribution of land u and a ries of factors that are drivers and constraints of land u.The results of this empirical analysis are ud within the model when sim-ulating the compe
tition between land-u types for a speci fic location.In addition,a t of decision rules is speci fied by the ur to restrict the conversions that can take place bad on the actual land-u pattern.The different components of the procedure are now dis-cusd in more detail.Spatial Analysis
The pattern of land u,as it can be obrved from an airplane window or through remotely nd im-ages,reveals the spatial organization of land u in relation to the underlying biophysical and
socio-eco-
Figure 2.Overview of the modeling
procedure.
Figure 3.Schematic repren-tation of the procedure to allo-cate changes in land u to a raster bad map.
Modeling Regional Land-U Change
395
