A conceptual framework of agricultural land use planning with BMP for integrated watershed management

Land use planning is an important element of the integrated watershed management approach. It not only influences the environmental processes such as soil and stream bed erosion, sediment and nutrient concentrations in streams, quality of surface and ground waters in a watershed, but also affects social and economic development in that region. Although its importance in achieving sustainable development has long been recognized, a land use planning methodology based on a systems approach involving realistic computational modeling and meta-heuristic optimization is still lacking in the current practice of integrated watershed management. The present study proposes a new approach which attempts to combine computational modeling of upland watershed processes, fluvial processes and modern heuristic optimization techniques to address the water-land use interrelationship in its full complexity. The best land use allocation is decided by a multi-objective function that minimizes sediment yields and nutrient concentrations as well as the total operation/implementation cost, while the water quality and the production benefits from agricultural exploitation are maximized. The proposed optimization strategy considers also the preferences of land owners. The runoff model AnnAGNPS (developed by USDA), and the channel network model CCHE1D (developed by NCCHE), are linked together to simulate sediment/pollutant transport process at watershed scale based on any assigned land use combination. The greedy randomized adaptive Tabu search heuristic is used to flip the land use options for finding an optimum combination of land use allocations. The approach is demonstrated by applying it to a demonstrative case study involving USDA Goodwin Creek experimental watershed located in northern Mississippi. The results show the improvement of the tradeoff between benefits and costs for the watershed, after implementing the proposed optimal land use planning.     

An Exploration of Scenarios to Support Sustainable Land Management Using Integrated Environmental Socio-economic Models

Scenario analysis constitutes a valuable deployment method for scientific models to inform environmental decision-making, particularly for evaluating land degradation mitigation options, which are rarely based on formal analysis. In this paper we demonstrate such an assessment using the PESERA–DESMICE modeling framework with various scenarios for 13 global land degradation hotspots. Starting with an initial assessment representing land degradation and productivity under current conditions, options to combat instances of land degradation are explored by determining: (1) Which technologies are most biophysically appropriate and most financially viable in which locations; we term these the “technology scenarios”; (2) how policy instruments such as subsidies influence upfront investment requirements and financial viability and how they lead to reduced levels of land degradation; we term these the “policy scenarios”; and (3) how technology adoption affects development issues such as food production and livelihoods; we term these the “global scenarios”. Technology scenarios help choose the best technology for a given area in biophysical and financial terms, thereby outlining where policy support may be needed to promote adoption; policy scenarios assess whether a policy alternative leads to a greater extent of technology adoption; while global scenarios demonstrate how implementing technologies may serve wider sustainable development goals. Scenarios are applied to assess spatial variation within study sites as well as to compare across different sites. Our results show significant scope to combat land degradation and raise agricultural productivity at moderate cost. We conclude that scenario assessment can provide informative input to multi-level land management decision-making processes.     

Web-based GIS for collaborative planning and public participation: an application to the strategic planning of wind farm sites

Spatial planning typically involves multiple stakeholders. To any specific planning problem, stakeholders often bring different levels of knowledge about the components of the problem and make assumptions, reflecting their individual experiences, that yield conflicting views about desirable planning outcomes. Consequently, stakeholders need to learn about the likely outcomes that result from their stated preferences; this learning can be supported through enhanced access to information, increased public participation in spatial decision-making and support for distributed collaboration amongst planners, stakeholders and the public. This paper presents a conceptual system framework for web-based GIS that supports public participation in collaborative planning. The framework combines an information area, a Multi-Criteria Spatial Decision Support System (MC-SDSS) and an argumentation map to support distributed and asynchronous collaboration in spatial planning. After analysing the novel aspects of this framework, the paper describes its implementation, as a proof of concept, in a system for Web-based Participatory Wind Energy Planning (WePWEP). Details are provided on the specific implementation of each of WePWEP's four tiers, including technical and structural aspects. Throughout the paper, particular emphasis is placed on the need to support user learning throughout the planning process.     

Ohio's Balanced Growth Program: a case study of collaboration for planning and policy design

This paper describes the collaborative planning process for a new landscape planning programme in Ohio that seeks to influence land urbanisation patterns through joint local land use decision making on a watershed basis. The programme was developed through a collaborative process by a state agency-appointed task force that included agency staff and a wide range of stakeholders. The paper describes the process in terms of the collaborative mechanisms, the participants, the programmatic outputs, and the social and organisational outcomes that set the foundation for enhanced watershed quality through better land use decision-making practices. Key collaborations formed during the process were inter-agency collaborations, a non-profit organisation that partnered with the agencies, and that of state agencies with local governments to develop watershed-based land use plans. A most critical outcome was creation of a learning community, through an exploratory research process that used multiple methods of data gathering and consensus-building deliberation. The paper is based on a review of published documents and plans, meeting minutes, participant observation of committee and workgroup meetings and interactive research.     

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.     

A framework for assessing the impact of land use policy on community exposure to air toxics

Our research focuses on the linkage between land use planning policy and the spatial pattern of exposure to air toxics emissions. Our objective is to develop a modeling framework for assessment of the community health risk implications of land use policy. The modeling framework is not intended to be a regulatory tool for small-scale land use decisions, but a long-range planning tool to assess the community health risk implications of alternative land use scenarios at a regional or subregional scale. This paper describes the development and application of an air toxic source model for generating aggregate emission factors for industrial and commercial zoning districts as a function of permitted uses. To address the uncertainty of estimating air toxics emission rates for planned general land use or zoning districts, the source model uses an emissions probability mass function that weights each incremental permitted land use activity by the likelihood of occurrence. We thus reduce the uncertainty involved in planning for development with no prior knowledge of the specific industries that may locate within the land use district. These air toxics emission factors can then be used to estimate pollutant atmospheric mass flux from land use zoning districts, which can then be input to air dispersion and human health risk assessment models to simulate the spatial pattern of air toxics exposure risk. The model database was constructed using the California Air Toxics Inventory, 1997 US Economic Census, and land assessment records from several California counties. The database contains information on more than 200 air toxics at the 2-digit Standard Industrial Classification (SIC) level. We present a case study to illustrate application of the model. LUAIRTOX, the interactive spreadsheet model that applies our methodology to the California data, is available at http://www2.bren.ucsb.edu/~mwillis/LUAIRTOX.htm.     

许昌市土地资源利用结构优化配置研究

土地资源优化配置是城市化进程中面临的主要问题,土地利用结构优化配置是土地利用总体规划的主要内容和基本方法,也是区域经济和社会发展规划的重要依据.在对许昌市土地利用变化分析的基础上,结合当地社会经济发展的需求和有关规划控制指标,借助线性规划模型求解未来许昌市土地利用结构的优化方案,力求提高土地利用的综合效益,实现土地资源永续利用和国民经济持续发展.     

Multi-scale analysis of a household level agent-based model of landcover change

Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models. The ABM is composed of a series of heterogeneous agents who make landuse decisions on a portfolio of cells in a raster-based programming environment. The model is calibrated using measures of fit derived from both spatial composition and spatial pattern metrics from multi-temporal landcover data interpreted from historical aerial photography. A model calibration process is used to find a best-fit set of parameter weights assigned to agents' preferences for different landuses (agriculture, pasture, timber production, and non-harvested forest). Previous research using this model has shown how a heterogeneous set of agents with differing preferences for a portfolio of landuses produces the best fit to landcover changes observed in the study area. The scale dependence of the model is explored by varying the resolution of the input data used to calibrate the model (observed landcover), ancillary datasets that affect land suitability (topography), and the resolution of the model landscape on which agents make decisions. To explore the impact of these scale relationships the model is run with input datasets constructed at the following spatial resolutions: 60, 90, 120, 150, 240, 300 and 480 m. The results show that the distribution of landuse-preference weights differs as a function of scale. In addition, with the gradient descent model fitting method used in this analysis the model was not able to converge to an acceptable fit at the 300 and 480 m spatial resolutions. This is a product of the ratio of the input cell resolution to the average parcel size in the landscape. This paper uses these findings to identify scale considerations in the design, development, validation and application of ABMs of landcover change.     

Emerging conflict in collaborative mapping: Towards a deeper understanding?

In spatial planning and environmental management, maps are found very helpful in many situations, but in other situations they may also deepen conflicts or be ignored. This paper addresses the question what explains such phenomena, with particular emphasis on, and reference to, collaborative policymaking settings in the Netherlands. We have elaborated the concept of 'frames' and 'framing' to structure and analyze the function of maps in the context of deliberative policymaking. With help of discourse analysis, a method adopted from the policy sciences, we have observed ongoing multi-actor processes and focused on discussions around maps in several case studies. From the observations of both the maps themselves and the discussions and actions with the maps, we have interpreted the meaning and frames represented 'in' the map images, as well as the various perspectives of actors 'on' the collaborative use of the map. We have conceptualized three fundamentally different perspectives or frames on the function of maps in the decision-making process. The cross-frame debate over maps is illustrated with the recollection of one particular map-making example. We conclude that the conflicts observed in a number of case studies can be adequately explained with help of the three identified frames, and provide some recommendations for the profession of mapping.     

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems     

Scenario-based stakeholder engagement: incorporating stakeholders preferences into coastal planning for climate change

Climate change poses many challenges for ecosystem and resource management. In particular, coastal planners are struggling to find ways to prepare for the potential impacts of future climate change while dealing with immediate pressures. Decisions on how to respond to future risks are complicated by the long time horizons and the uncertainty associated with the distribution of impacts. Existing coastal zone management approaches in the UK either do not adequately incorporate changing stakeholder preferences, or effectively ensure that stakeholders are aware of the trade-offs inherent in any coastal management decision. Using a novel method, scenario-based stakeholder engagement, which brings together stakeholder analysis, climate change management scenarios and deliberative techniques, the necessary trade-offs associated with long term coastal planning are explored. The method is applied to two case studies of coastal planning in Christchurch Bay on the south coast of England and the Orkney Islands off the north coast of Scotland. A range of conflicting preferences exist on the ideal governance structure to manage the coast under different climate change scenarios. In addition, the results show that public understanding of the trade-offs that have to be made is critical in gaining some degree of public support for long term coastal decision-making. We conclude that scenario-based stakeholder engagement is a useful tool to facilitate coastal management planning that takes into account the complexities and challenges of climate change, and could be used in conjunction with existing approaches such as the Shoreline Management Planning process.     

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China’s Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10⁹ $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.     

Land Management in the American Southwest: A State-and-Transition Approach to Ecosystem Complexity

State-and-transition models are increasingly being used to guide rangeland management. These models provide a relatively simple, management-oriented way to classify land condition (state) and to describe the factors that might cause a shift to another state (a transition). There are many formulations of state-and-transition models in the literature. The version we endorse does not adhere to any particular generalities about ecosystem dynamics, but it includes consideration of several kinds of dynamics and management response to them. In contrast to previous uses of state-and-transition models, we propose that models can, at present, be most effectively used to specify and qualitatively compare the relative benefits and potential risks of different management actions (e.g., fire and grazing) and other factors (e.g., invasive species and climate change) on specified areas of land. High spatial and temporal variability and complex interactions preclude the meaningful use of general quantitative models. Forecasts can be made on a case-by-case basis by interpreting qualitative and quantitative indicators, historical data, and spatially structured monitoring data based on conceptual models. We illustrate how science- based conceptual models are created using several rangeland examples that vary in complexity. In doing so, we illustrate the implications of designating plant communities and states in models, accounting for varying scales of pattern in vegetation and soils, interpreting the presence of plant communities on different soils and dealing with our uncertainty about how those communities were assembled and how they will change in the future. We conclude with observations about how models have helped to improve management decision-making.     

From land cover change to land function dynamics: a major challenge to improve land characterization

Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.     

生态用地研究进展综述

尽管生态用地的提出时间较晚,但与生态用地相关的研究较多,主要涉及其内涵、外延、分类体系、效益和质量评价、数量和空间布局规划及保护等。对生态用地研究中急需重点解决的问题,如生态用地内涵、外延的统一界定和最优生态用地数量及空间格局的确定等进行了探讨。     

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.     

建设用地省际空间再配置——改进建设用地空间配置现状的路径选择

从我国建设用地需求不断增长且供需之间存在着严重结构性不均衡的实际背景出发,在考察我国相关土地政策的建设用地空间配置现状的基础上,从正反两个角度论证了实施建设用地省际空间再配置的可能性,并提出了在建设用地省际空间再配置完成从理论可能到事实可行时应警惕的若干问题,指出如果机制设计合理,实施建设用地省际空间再配置将真正成为一种改进建设用地空间配置现状的多赢路径,也将成为推动我国经济增长和区域协调发展的新动力。     

Downscaling climate change scenarios in an urban land use change model

The objective of this paper is to describe the process through which climate change scenarios were downscaled in an urban land use model and the results of this experimentation. The land use models (Urban Growth Model [UGM] and the Land Cover Deltatron Model [LCDM]) utilized in the project are part of the SLEUTH program which uses a probabilistic cellular automata protocol. The land use change scenario experiments were developed for the 31-county New York Metropolitan Region (NYMR) of the US Mid-Atlantic Region. The Intergovernmental Panel on Climate Change (IPCC), regional greenhouse gas (GHG) emissions scenarios (Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios) were used to define the narrative scenario conditions of future land use change. The specific research objectives of the land use modeling work involving the SLEUTH program were threefold: (1) Define the projected conversion probabilities and the amount of rural-to-urban land use change for the NYMR as derived by the UGM and LCDM for the years 2020 and 2050, as defined by the pattern of growth for the years 1960-1990; (2) Down-scale the IPCC SRES A2 and B2 scenarios as a narrative that could be translated into alternative growth projections; and, (3) Create two alternative future growth scenarios: A2 scenario which will be associated with more rapid land conversion than found in initial projections, and a B2 scenario which will be associated with a slower level of land conversion. The results of the modeling experiments successfully illustrate the spectrum of possible land use/land cover change scenarios for the years 2020 and 2050. The application of these results into the broader scale climate and health impact study is discussed, as is the general role of land use/land cover change models in climate change studies and associated environmental management strategies.     

A conjoint analysis of landholder preferences for reward-based land-management contracts in Kapingazi watershed, Eastern Mount Kenya

Unsustainable land-use decisions and agricultural practices have become the key drivers of deteriorating watershed services in developing countries. However, landholders may have little or no incentives to take these impacts into account in their decision-making process. In recent years, reward-based provision of environmental services has emerged as an important market-based incentive for motivating landholders to adopt environmentally friendly land-use changes and agricultural practices. In this regard, for instance, the Pro-Poor Rewards for Environmental Services in Africa (PRESA) project has emerged as a large network to support and facilitate reward mechanisms in Africa. However, in many African rural settings, little is known about landholder attitudes and preferences related to the alternative land-management schemes. Using locally identified sets of six key land-management attributes, this paper applies conjoint methods to evaluate landholder preferences towards alternative land-management schemes aimed at enhancing the provision of watershed services in the River Kapingazi catchment in central Kenya. Data were collected from primary sources through focus groups and a questionnaire based conjoint survey. Three conjoint models were used; a traditional conjoint ratings model, a binary logit model, and an ordered logit model. Results from the focus groups indicated that shortage of water for both domestic use and irrigation was perceived as the most acute environmental problem in the area. Deforestation, poor river bank management and agricultural practices were identified as the major causes of the problem. Results from conjoint models show that the three principal attributes influencing landholder's ratings and probability of adopting the proposed land management options were 'size of land area to be committed', 'length of contract period', and 'granting or prohibiting rights to harvest environmental products from the committed land'. Thus, these attributes should be the focal points in designing land-management contract for watershed services in the study area.     

Simulating Spatial Pattern and Dynamics of Military Training Impacts for Allocation of Land Repair Using Images

The land management of US Army installations requires information on land conditions and their history for planning future military training activities and allocation of land repair. There is thus a strong need for methodology development to estimate the land conditions and cumulative military training impacts for the purpose of repair and restoration. In this study, we simulated at Fort Riley, USA, spatial patterns and temporal dynamics of military training impacts on land conditions quantified as percent ground cover using an image-aided spatial conditional co-simulation algorithm. Moreover, we estimated the historical percent ground cover as a measure of the cumulative impacts, and then calculated the allocation of land repair and restoration based on both current and historical land conditions. In addition, we developed a loss function method for allocation of land repair and restoration. The results showed: (1) this co-simulation algorithm reproduced spatial and temporal variability of percent ground cover and provided estimates of uncertainties with the correlation coefficients and root mean square errors between the simulated and observed values varying from 0.63 to 0.88 and from 23% to 78%, respectively; (2) with and without the cumulative impacts, the obtained spatial patterns of the land repair categories were similar, but their land areas differed by 5% to 40% in some years; (3) the combination of the loss function with the co-simulation made it possible to estimate and computationally propagate the uncertainties of land conditions into the uncertainties of expected cost loss for misallocation of land repair and restoration; and (4) the loss function, physical threshold, and probability threshold methods led to similar spatial patterns and temporal dynamics of the land repair categories, however, the loss function increased the land area by 5% to 30% for intense and moderate repairs and decreased the area by 5% to 30% for no repairs and light repairs for most of the years. This approach provided the potential to improve and automate the existing land rehabilitation and maintenance (LRAM) system used for the land management of the U.S. Army installations, and it can be applied to the management of other civil lands and environments. In conclusion, this study overcame the important gaps that exist in the methodological development and application for simulating land conditions and cumulative impacts due to human activities, and also in the methods for the allocation of land for repair and restoration.