Table 1 - uploaded by Peter H Verburg
Content may be subject to copyright.
Source publication
Contexts in source publication
Context 1
... the model is used to determine the dynamics in crop distribution, livestock distribution of farm management, a wider range of parameters is needed. A list with examples of the data that can be used is found in Table 1. In general a large number of parameters can be collected from existing population maps and data of the agricultural census. ...
Context 2
... ArcMap this grid can be formatted for further presentation ( Figure 28). ArcMap example ncols 10 nrows 10 xllcorner 0 yllcorner 0 cellsize 1 NODATA_value -9999 ... ncols = number of columns nrows = number of rows xllcorner = x-coordinate of lower left corner yllcorner = y-coordinate of lower left corner cellsize = grid size ...
Citations
... This is calculated for each LUC class based on the past dynamics or by addressing different hypotheses of potential change in the future. The location characteristics define the "preference" for a specific LUC class at a moment in time (Verburg et al., 2005). These are empirically estimated from the relation between the LUC pattern and the determinant factors of change using the binomial logit model. ...
Land-use/cover (LUC) change is a global change forcing with important impacts on landslides, which highlights the need to evaluate the potential future evolution of these hazards under future LUC scenarios. Accordingly, the current paper provides a first national-level exploration on the possible effects of LUC pattern change on landslide
susceptibility (LS) in Romania, shedding light on the evolution of one of Europe’s landslide hotspot countries as well as one of the territories significantly affected by recent LUC change, especially after the fall of the communist regime. Two countrywide future LS maps were modelled for 2075, by applying a previously
developed national scale-adapted LS empirical procedure, integrating therein two existing long-term LUC pattern forecasts and assuming no change for the other landslide controlling factors. The estimated future LS maps were then compared to the current LS zonation using a pixel-based analysis to estimate the contribution of LUC class transitions to the LS variation in time. The quantitative examination of LS changes suggests an overall net slight decrease at the national level under both scenarios (on an average of 7.3% of the LUC change area), with more significant regional variations. The location and extent of the estimated potential LUC transitions reflect themselves into a clear influence on the future LS level manifesting itself, on average, over 7.1% of the country’s area. The analysis demonstrates that the expected increase in the forest-covered area brings the most important
contribution to the decrease of the LS level. An average of 44.2% of the total LS decrease area was mainly predicted by the transitions from pastures and natural grasslands/scrubs to forests, and 11.4% by that from heterogeneous agricultural areas to forests. Conversely, transitions to pastures and natural grasslands resulted in the most important contribution to the increase of the LS level (52.2%). Transitions among other agricultural lands resulted in slight susceptibility variations or in no change. Through the analysed LUC scenarios, differing in terms of the implementation of environmental management regulations inside protected areas, the study proves
that a more appropriate land management could have an important influence on decreasing LS, a finding of particular relevance for encouraging and extending a conscious application of protective land measures, especially on slopes which are degraded or prone to instability, in a country otherwise characterised by an overall
relative implementation of environmental policies. In addition, the analysis reveals that a significant proportion (24.2%) of the projected new built-up areas is expected to spread by 2075 over lands with moderate, high and very high LS, resulting in potentially new landslide exposure hotspots in the future in both rural and urban areas. The obtained results could be used in directing landslide risk reduction actions and decisions primarily towards the management of LUC and towards prioritizing areas for such measures, while, from a theoretical perspective, this approach could be extended to future investigations related to LUC change implications for landslide hazard and risk in Romania, but could also be followed in case of other national territories.
To access a view-only full-text version of the paper use the following link: https://authors.elsevier.com/a/1hNAr1Dk5AZFOu.
... The parameter was arranged in a matrix, which has value "1" for landuse type pairs allowed to process and value "0" for landuse type pairs not allowed to process or a constaint. This arrangement was direct the algorithm of an iterative process in Dyna-CLUE (Verburg et al. 2005). Figure 3 below summarizes the procedure of landuse change simulation. ...
Rice is the staple food and its cultivation requires a specific land condition. The population growth, urbanization, and plantation expansion together with socio-economic development are the driving factors of the riceland decline in Deli Serdang Regency of North Sumatera, Indonesia. As a consequence, likely availability and sustainability of rice production are threatened. Hence, it is important to understand how the future landuse and population change will affect the riceland area and production. In the lack of spatially simulated information for the future which could be useful in planning the riceland areas, the study objectives were to project the landuse change by 2040 under three scenarios, Business as Usual (BAU), Potential Riceland Protection (PRP) and Conservation Oriented (CO), and to investigate the impact of consumption demand on the sustainability of rice production. Landsat satellite data of 2009 and 2018, several spatial GIS data, and survey data were analyzed in ArcGIS, Dyna-CLUE, and SPSS software to generate the landuse classification and to simulate the future landuses; while the population projection by 2040 was derived from a Geometric Model. The results showed that forest and riceland areas will decrease with the continuous increase of plantation and urban areas under BAU scenario, but could be protected and increased under PRP scenario. The sustainability of rice production depends not only on the total riceland area, but also the productivity, the population growth, the consumption rate, and the policy. The simulated results of three scenarios serve as an important input to planning for protecting the riceland areas and thus sustained rice production in Deli Serdang Regency.
... Evaluation of the model's performance has been carried out in two ways: i) statistically, by evaluating the performance of the logistic models, and ii) quantitatively, by comparing the real data with the resulted probability maps. The statistical performance was tested using the ROC (Receiver Operating Characteristic), a measure for the goodness of fit of a logistic regression model similar to the R 2 statistics in Ordinary Least-Square regression (Verburg et al. 2005), widely applied to assess the performance of raster-based spatial models (Pontius and Schneider 2001). In the standard ROC approach, the predicted probability values (as the "test variable", here probability of built-up expansion) is compared with the true binary event (as the "state variable", here observed built-up expansion) to assess the spatial coincidence between the event and the probability values (Mas et al. 2013). ...
The paper investigates built-up areas expansion after the 1990 in one of the highly urbanized regions of Romania - Romanian Plain, in order to explore the urban sprawl phenomena and its temporal and regional disparities in relation to some of the main distance driving factors. The research uses Landsat 4/5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI) imagery to derive built-up areas and quantify their expansion over time in relation to fourteen distance explanatory factors: i.e., previous built-up areas, main road infrastructure, Bucharest city’s boundary, location of the urban centres classified according to demographic size and main economic function, forest land and water bodies. To estimate the influence of the predictors, the binary logistic regression was applied. Furthermore, to estimate the effectiveness of the predictor set in the variation of built-up areas expansion, the pseudo R² was calculated and discussed. Moreover, to understand the future potential trend of urban sprawl and its spatial pattern, the probability maps were generated by integrating the regression coefficients of the statistically significant predictors into the spatial modeling. For the results performance assessment, the statistic Receiver Operating Characteristic and the pixel-based comparison between the real and predicted data were used. To assess possible differences at spatial and temporal scale, the analysis was carried out at regional level, for two periods: 1990–2002 and 2002–2018. In general, our findings show inverse relationship between the distance driving factors and built-up areas expansion, but the estimated predictive power suggests important disparities within the study area over the analysed periods. Overall, the statistical analysis indicate that the distance to previous build-up areas, distance to road infrastructure, distance to Bucharest and other large urban centres, and distance to urban centres with dominant industrial and service functions were more influential to urban sprawl after 1990. Furthermore, the predicted spatial data shows the highest potential of urban sprawl in the future around Bucharest, in the proximity of existing built-up areas and road infrastructure. Because of its predictive character, the present study is to be a useful tool for land managers and policy makers.
... These factors encompass a significant share of the determinant factors of the forest-cover pattern included in others land-use/ cover models (e.g. [37,44,54,78,99]). ...
... To evaluate the predictive power of the determinant factors, the ROC (receiver operating characteristic) was used. This is a measure for the goodness of fit of a logistic regression model similar to the R 2 statistics in ordinary least-square regression [99], widely applied to assess the performance of raster-based spatial models [59,72]. The ROC characteristic is considered to be the appropriate methodology for the CLUE-S model, because a wide range of probabilities is used within the model calculations [99]. ...
... This is a measure for the goodness of fit of a logistic regression model similar to the R 2 statistics in ordinary least-square regression [99], widely applied to assess the performance of raster-based spatial models [59,72]. The ROC characteristic is considered to be the appropriate methodology for the CLUE-S model, because a wide range of probabilities is used within the model calculations [99]. The ROC statistic metric is the AUC (area under the curve), defined by comparing the observed landuse/cover (as the "state variable") with the predicted probability (as the "test variable"). ...
Forest-cover dynamics is of wide concern due to its role in climate change, biodiversity losses, water balance and land degradation, as well as social and economic development. Hence, exploring land-use/cover dynamic is important in order to improve our understanding of the causes of forest-cover change and to detect the future trend. Furthermore, projecting a future land-use/cover pattern can help identifying potential areas where forest-cover change will occur in the future and the potential consequences of these processes in order to improve land-use planning and policies. Similar to other East European countries, Romania is experiencing rapid land-use/cover changes after the breakdown of socialism; a clear trend was registered by deforestation, which reflects the consequences of a continuous forests dynamics and little environmental care. Consequently, this study, carried out in order to analyse the potential future cover-change, resulted in the land-use/cover scenario (2007–2050) simulated using CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) modelling framework, applied to development regions in Romania. Overall, the model results in different spatial patterns of land-use/cover change, projecting a slight increase in the forest-cover area of about 82,000 ha. Furthermore, the model simulated widespread deforestation, mainly in relation to agricultural land expansion. The area under the curve (AUC) for the relative operating characteristic (ROC) and the Kappa simulation (KSimulation) were used to assess the predictive power of the determinant factors included and to evaluate the spatial performance of the model. The obtained ROC/AUC values (0.83–0.88) indicate the great power of the determinant factors to explain the forest-cover pattern in the area. Furthermore, the KSimulation scores (0.69–0.79) highlight the potential of the CLUE-S model to simulate future forest-cover change in relation to the other land-use/cover categories. The results can provide useful inputs for effective forest resource management and environmental policies. Moreover, the spatial data obtained can contribute to exploring future potential environmental implications (e.g. assessing landslide and flood hazard scenarios, forest biomass dynamics and their impact on carbon allocation, or the impact of forest-cover change on ecosystem services).
... W kolejnych latach suma powierzchni, jakie -zgodnie z przewidywaniami -ma zająć każda forma użytkowania ziemi, musi być równa całkowitej powierzchni modelowanego obszaru. Trend czasowy zmian dla każdej klasy użytkowania ziemi w modelowanym przedziale czasu może być rosnący lub malejący, mogą także występować formy użytkowania, dla których nie przewiduje się żadnych zmian (Verburg et al. 2005, Verburg, Overmars 2009). Do powierzchni, które nie ulegają przekształceniom, najczęściej zalicza się trwałe elementy krajobrazu, takie jak np. ...
Models of land use change (LUCC models) play an important role in understan-ding the mechanisms of occurrence of changes in land use and are very useful in supporting the various policies directly or indirectly affecting the spatial distribu-tion of land use. In the last 20 years there has been intensive development of LUCC models in connection with the progress in obtaining detailed spatial information about land surface and processing techniques of spatial data. The LUCC models have develo ped into the models taking into account several forms of land use (so called integra ted models) and performing calculations in a regular grid of cells.
An example of a model, that was developed and modified several times over the past 20 years is CLUE framework. In this paper methodological assumptions and parameters of the model, as well as the history of its development and applications are presented.
... CLUE-S is a popular pattern based land use modeling system (e.g. Verburge et al., 2005;Castella et al., 2007;Wassenaar et al., 2007;Hurkmans et al., 2009;Neumann et al., 2011). CLUE-S evolves a gridded map of the study landscape forward, changing the land use of individual grid cells consistent with a probabilistic transition model (see the manual for a complete description, Verburg, 2010). ...
... The logistic regression coefficients represent the relationship between driving variables and land use choices (Verburge et al., 2005;Verburg, 2010). The dependent variable in such regressions is binary, either true or false. ...
Pattern based land use models are widely used to forecast land use change. These models predict land use change using driving variables observed on the studied landscape. Many of these models have a limited capacity to account for interactions between neighbouring land parcels. Some modellers have used common spatial statistical measures to incorporate neighbour effects. However, these approaches were developed for continuous variables, while land use classifications are categorical. Neighbour interactions are also endogenous, changing as the land use patterns change. In this study we describe a single variable measure that captures aspects of neighbour interactions as reflected in the land use pattern. We use a stepwise updating process to demonstrate how dynamic updating of our measure impacts on model forecasts. We illustrate these results using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to forecast land use change for the Deep Creek watershed in the northern Okanagan Valley of British Columbia, Canada. Results establish that our measure improves model calibration and that ignoring changing spatial influences biases land use change forecasts.
... where Pnbh i,t,lu is the impact of neighbouring cells, Comp t,lu are the location-specific preference maps, ELA Slu is the cost of conversion of the current land use type and Ploc i,t,lu explains the location suitability calculated through a logit model (Verburg et al., 2002) 'where P i is the probability of a grid cell for the occurrence of the considered land use type on location (i) and the (X's) are the location factors. The coefficients (β) are estimated through logistic regression using the actual land use pattern as dependent variable' (Verburg et al., 2005, p. 11). ...
Land use policies have a definite and lasting impact on the way that cities grow; however, it is difficult for policy- and decision-makers to observe and quantify the implications of their land use policies and strategies. There is thus a need for information and tools that can adequately support policy debates and influence decision-making through scientific evidence. Land use change models provide such a tool and have often been applied and tested in developed countries but lack the ability to simulate many of the multifaceted social problems observed in developing countries. Some more advanced models also require large amounts of data that are normally not available for South African cities. In this research, we adjust the existing Dyna-CLUE model to accommodate the unique multifaceted problems such as informal settlements, backyard shacks, rapid population growth and government interventions with regard to social housing projects and test the model for the city of Johannesburg. Two scenarios (AS-IS and Policy-Led) in combination with an urban development boundary (UDB) were tested and their effect was evaluated based on their influences on the cities spatial inequality, densification of the urban spatial pattern and increase in access to public transport. Results indicated that the Policy-Led scenario can improve the wealth and economic distributions between the north and south of the city. It can also provide more economic opportunities for the households living in the south of the city. Enforcing an UDB has a positive impact on urban sprawl and will result in increased densities across the city. The effect of the policies on the commuter distances indicated that both scenarios will lead to an overall increase in the number of households that have access to public transport, but the Policy-Led scenario will allow a greater number of low-income earners to have access to the public transport systems. We see great possibilities for using the existing model to simulate land use change in South African cities. The model requires less input data compared to some other modelling techniques and with small adjustments and adaptations can prove to be a useful tool for urban planners.
... In addition, the goodness-of-fit of a logistic regression model was evaluated here using the receiver operating characteristic (ROC). The ROC characteristic is a measure for the goodness of fit of a logistic regression model similar to the coefficient of determination (R2) statistic in an ordinary least square regression (Pontius and Schneider, 2001) The value of the ROC ranges between 0.0 (completely unfit) and 1.0 (perfectly fit), where the area under curve 0.5 is completely random (Verburg et al., 2005). ...
The study of land use and land cover simulation using the integration of geospatial models is very important in various aspects, especially sustainable use with minimum environmental impact. The main objectives of the study were: 1) to assess historical and recent LULC and its changes; 2) to simulate 3 different LULC scenarios using the CLUES model; 3) to assess soil erosion, water yield, and economic value and their changes; and 4) to allocate the optimum land use for 3 different scenarios. The 4 main components of the research methodology implemented here included: 1) data collection and preparation; 2) LULC simulation of 3 different scenarios; 3) soil erosion, water yield, and economic value assessment and their changes; and 4) the optimum land use allocation of 3 different scenarios. From the results of the LULC assessment between 2003 to 2013, urban and built-up land, cassava, sugarcane, water body, and miscellaneous land had increased while maize, perennial tree/orchard, and forest land had decreased. The most common important driving factor for location preference of the LULC types was population density. The simulation of 3 LULC scenarios in 2023 by the CLUES model revealed that urban and built-up land, cassava, sugarcane, water body and miscellaneous land would increase while maize, perennial tree/orchard, and forest land would decrease under Scenario I (Historical land use evolution). At the same time, the increase in cassava and sugarcane under Scenario II (Energy crop extension) came from maize, forest land, and miscellaneous land while most of the increasing forest land under Scenario III (Forest conservation and prevention) was converted from maize, sugarcane, and miscellaneous land. The optimum land use allocation of the 3 scenarios indicated that most of the agricultural land and forest land of Scenario I was allocated into the moderate and high suitability classes, respectively. In the meantime, most of the cassava and sugarcane as energy crops of Scenario II were located in the low and moderate suitability classes and moderate and high suitability classes, respectively, while the forest land with restriction rules was located in the high suitability class. Under Scenario III, the forest land was allocated in the moderate and high suitability classes and the agricultural land was distributed throughout all the suitability classes. On the basis of these results, it is suggested that the integration of the LULC change model (CLUES model), soil erosion model (USLE model), hydrologic model (SWAT model and SCS-CN method), and economic value measures (PV model) can be efficiently used as a tool for optimum land use allocation by considering LULC change and its impact from different scenarios.
... Within the raster system, all vector data is converted into grid data, allowing allocation of different attributes to each grid. Verburg et al., (2005) ...
... For CLUE -S to run, all input files must be communicated to the model in a consistent format (Verburg et al., 2005). The data was converted to ASCII raster format, such that all the files had the same grid size, extent, and projection. ...
Livestock production is an important contributor to rural development. In the past two decades, developing countries have experienced changes in market structures, climate and demographic characteristics. These changes have been accompanied by fast growth in demand for livestock products and the increasing dependence on livestock for sustainable livelihood systems. In response to these changes, there has been rapid land use and land cover changes, characterized by expansion of agricultural land, and land fragmentation. This has caused environmental degradation in several rural areas, including the River Njoro watershed. Policy makers and development agents are therefore, facing a dilemma on trade-offs between meeting the expanding demand for livestock products and sustainable utilization of the limited stock of natural resources. At the backdrop of this dilemma, this study sought to identify and characterize livestock production systems in Njoro River watershed using principal components and cluster analysis. A multinomial logistic regression model was then used to determine the factors that influence the spatial distribution of livestock production systems and Changes in Land Use Efficiency for Small extent (CLUE- S) model used to assess the effect of suggested policies on the spatial distribution of livestock production systems. Primary data used in the study was collected using a household survey. Data was managed and analyzed using Statistical Package for Social Sciences (SPSS) v15, STATA V9, and (CLUE-S) Modeling softwares. Results indicate that farmers in the watershed fall under three major livestock production systems: Intensive, Semi intensive, and Extensive. Land size, access to extension services, age of household head, altitude of the farm, distance of farm household to the river, number of extension visits, value of physical assets, access to credit, household size, household income, and involvement in off-farm activity are the factors found to significantly influence changes in livestock production systems. It was also observed that if the current trends in land use changes continue, the production of livestock products will continue to decline in the future. This study concludes that if the growth in food production has to surpass the population growth rate, relevant policy issues to enhance sustainable livestock production have to be addressed. Policy implications drawn from this study have focused on incentives for intensification, institutional reforms, improving livestock productivity, and innovations that enhance the synergies between livestock production and the environment.
This article was designed to develop the spatial model of land use suitability at Pattani basin and surrounding areas in Pattani province. Using the principle of integration and collect data from multiple sources such as the interpretation of Remote sensing (Landsat TM and Spot 5 satellite imagery and aerial photography) and monitoring data from field surveys, soil unit of land use data of The Land Development Department and study on the demographic characteristics from the document reports of the Office of Community Development, Pattani province, District Agricultural Office and related agencies. Assessing the suitability of the land use by using Geographic Information System (GIS) as cost effective techniques were used to develop and created spatial modeling. Analysis techniques to determine the weight-score, overlay and the appropriate interpretation of land use. The physical factors using by the guidelines of the Land Development Department and economic factors using by the cost of 4 major crops include : rice, rubber, fruit mixture and coconut, from the questionnaires were 60 samples household analyzed using mean, range and percent of income, variable costs, net return over variable costs and rate of net return. The results was found that most of the area or about 225,223.20 rai (67.80%) with the use of agricultural land in accordance with the physical condition of the land and return it to the subsistence level, inappropriate land use with the physical condition about 62,406.88 rais (18.79 %). The model can be used in policy formulation, planning, promotion and development of land suitable for agricultural potential of the land. The guidelines focus on promoting the conversion of agricultural land management to improve the quality of the soil, to transfer knowledge and technology to cultivate crops, the production and distribution system for agricultural products.
