Monitoring and assessment of land use status by GIS

The objectives of this study were to assess land suitability andto monitor the spatial and temporal changes in land use types (LUTs)by using GIS. The study was conducted in an irrigated area of 920ha, located in the East Mediterranean region of Turkey. Archiveddata of LUTs were utilized. Data analysis was conducted by usingArcInfo and ArcView GIS softwares. The land characteristics werecompared with land requirements of present LUTs by taking intoaccount the land evaluation results. Three major soil series andtwelve different LUTs were identified. Corn and citrus were themost planted LUTs in the assessment period. Generated mapsdemonstrated how LUTs changed spatially and temporally. Most LUTswere clustered in certain areas indicating that farmer'sselection of LUTs was affected by their habits and neighbor'sbehavior. Land suitability assessment results indicated thatCanakci and Mursel soil series were found to be highly suitable forall LUTs while Arikli soil series was determined to be moderately suitablecitrus, vegetables and watermelon. Citrus plantationmaps overlaid on soil series maps demonstrated that the citrushas not been planted completely on suitable areas. Landsuitability assessment showed that citrus plantation in Arikliseries would result in 40% yield loss and 58% of land is notused at its potential.     

Monte Carlo assessment of uncertainty in the simulated hydrological response to land use change

We quantified the uncertainty in hydrological response for a set of land use change scenarios by varying plant parameters within realistic uncertainty bounds in a Monte Carlo analysis. The results show that simulated hydrological fluxes significantly change after the introduction of outwintering suckler cow management, despite the presence of a significant amount of output uncertainty due to uncertainty in the plant parameterisation. The key to a proper uncertainty assessment was to consider the uncertainty in the difference between the scenarios instead of the absolute uncertainty of each single scenario. Additionally, a sensitivity analysis showed that changing soil properties in response to land use change does not result in significantly different results in the scenario analysis.     

Spatial based compromise programming for multiple criteria decision making in land use planning

Today, competing land use is continuing to occur in many developed regions. In the Agricultural Development Zone of Western Sydney Region, which is characterised by complex landscape patterns, land use competition is widespread. From a land use planning perspective, identification of suitable locations for a given type of land use is necessary for decision makers to formulate land use alternatives in different locations, based on existing land potential and constraints. For such a region, use of a simple method that implements a categorical system and considers only inherent land characteristics in the analysis is often inadequate to arrive at an optimal spatial decision. The primary aim of this paper is to develop spatial modelling procedures for agricultural land suitability analysis using compromise programming (CoPr) and fuzzy set approach within a geographical information systems (GIS) environment. Five main sets of spatial data for use as decision criteria were developed by using fuzzy set methodology: a land suitability index (LSI) for maximising the land productivity objective; an erosion tolerance index (ETI) for minimising the erosion risk objective; a runoff curve number (CN) for maximising the water discharge regulation objective; an accessibility (RP) measure for maximising the land accessibility objective; and the proximity to water body (WP) for minimising the water pollution objective. An L _p -metric was used in the analysis utilising different strategies with representative indices ranging from a situation where full tradeoff among criteria occurs to a noncompensatory condition. Different weighting combinations were also applied, and decision analysis was carried out by using values ranging from 0 to 1.0, where 1.0 is considered as an ideal point. The CoPr model demonstrated in this paper yielded a promising result, as several different techniques of sensitivity analysis show reasonably good results. Likewise, an overlay of that result with the present land use/land cover indicates a good corresponding spatial matching between existing land use (orchard and cultivated land), and the cells (land parcels) classified as the best in CoPr. The results are amenable to various map display techniques, either using continuous values or by defining different cut off points in the data space within a raster GIS environment.     

Quantifying Land-Use Alterations and Associated Hydrologic Impacts at a Wetland Area by Using Remote Sensing and Modeling Techniques

Land use changes and associated hydrologic disturbances, mainly caused by human activities, is a common reason for wetlands degradation worldwide. The particular scientific effort utilized remotely sensed data, GIS techniques and hydrologic modeling to estimate land use alterations during a 40-years period as well as associated changes in hydrologic parameters such as overland and underground flow, infiltration, evapotranspiration and water storages on ground surface. The results indicated significant variations in the hydrologic regime including a 6% increase in the annual evapotranspiration and a 10% increase in the soil water deficit that impose substantial impacts on the regional wetlands.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.