Static Land Use Research Articles

Urban Tourism Traffic Analysis Zone Division Based on Floating Car Data

Tourism traffic has a considerable influence on the state of urban traffic in tourist cities. To consider tourism traffic demand in the division of conventional traffic analysis zones (TAZ), a spatial analysis method combining dynamic traffic state features with static land use and road network characteristics is proposed to define tourism traffic analysis zones (TTAZs). Taking Xiamen Island as an example, first, point of interest (POI) data for the tourism elements on Xiamen Island and kernel density estimation (KDE) are applied to determine the core zones impacted by tourism traffic. Second, within the impacted zones, this paper studies the dynamic distribution of the tourism traffic for peak hours during holidays and non-tourism period by employing spatial autocorrelation method based on floating car data (FCD) and determines the area of slow traffic agglomeration of tourism traffic. In view of the distribution of tourism infrastructure, land use, tourism traffic state distribution and road network, this study identified the characteristics of slow traffic agglomeration in the area near Siming Road and divided four TAZs into six TTAZs. By further dividing the urban TTAZs, this paper hopes to provide a reference for urban traffic planning and management, tourism planning and land use planning.

Assessing the effectiveness of protected areas for panda conservation under future climate and land use change scenarios

While the relatively stable land use and land cover (LULC) patterns is an important feature of protected areas (PAs), the influence of this feature on future species distribution and the effectiveness of the PAs has rarely been explored. Here, we assessed the role of land use patterns within PAs on the projected range of the giant panda (Ailuropoda melanoleuca) by comparing projections inside and outside of PAs for four model configurations: (1) only climate covariates, (2) climate and dynamic land use covariates, (3) climate and static land use covariates and (4) climate and hybrid dynamic-static land use covariates. Our objectives were twofold: to understand the role of protected status on projected panda habitat suitability and evaluate the relative efficacy of different climate modeling approaches. The climate and land use change scenarios used in the models include two shared socio-economic pathways (SSPs) scenarios: SSP126 [an optimistic scenario] and SSP585 [a pessimistic scenario]. We found that models including land-use covariates performed significantly better than climate-only models and that these projected more suitable habitat than climate-only models. Static land-use models projected more suitable habitat than both the dynamic and hybrid models under SSP126, while these models did not differ under SSP585. China's panda reserve system was projected to effectively maintain suitable habitat inside PAs. Panda dispersal ability also significantly impacted outcomes, with most models assuming unlimited dispersal forecasting range expansion and models assuming zero dispersal consistently forecasting range contraction. Our findings highlight that policies targeting improved land-use practices should be an effective means for offsetting some of the negative effects of climate change on pandas. As the effectiveness of PAs is projected to be maintained, we recommend the judicious management and expansion of the PA system to ensure the resilience of panda populations into the future.

An application of land use, transport, and economy interaction model

Governments need to assess the benefits of transport projects to prioritize investments. It is imperative for governments to have tools that can closely estimate the actual benefits and impacts of investments in significant transport projects.Traditionally, the benefits of a transport project have been estimated by using a transport model, assuming fixed land use for the base and project case in the future. However, this approach cannot measure the impact of land-use changes as residents and businesses relocate to take advantage of lower travel and/or freight costs resulting from the project. Consequently, the benefits of the project may be under or overestimated, depending on its position within the transport network and how it reshapes land use patterns in the future. To overcome this drawback in existing models, this paper firstly presents a literature review of advances in the land use transport interaction model (LUTI), then discusses the development of a LUTI model for Victoria, Australia (VLUTI) by integrating the Victorian Integrated Transport Model with a Spatial Computable General Equilibrium model (SCGE). The land use component (SCGE) was developed to represent a highly resolved zone system, detailed classifications of occupations and industries, and spatial interactions via commuting, and trade in goods and services, and thus enables VLUTI to simulate a detailed and nuanced picture of land use and transport interactions in a complete economic environment.The literature review also indicates limited research in applying a LUTI to estimate land use change benefits. This paper presents an application of VLUTI by looking at differences in the conventional transport benefits under both static and dynamic land use scenarios. In the static method, the land use in the project case is unchanged from the base case. In the dynamic case, the land use in the project case, at a future point in time, is endogenously adjusted within VLUTI. It presents a method to correct, in the dynamic case, the benefits as estimated by the rule of a half, which usually assumes static land use. The paper concludes with suggestions for further developments and improvements of VLUTI such as incorporating an activity-based transport model and an environmental model to progress toward an integrated land-use, transport, economy, and environment model capable of assessing the full impact of a transport project in a holistic way.

Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1

Abstract. Roughness features (e.g., rocks, vegetation, furrows) that shelter or attenuate wind flow over the soil surface can considerably affect the magnitude and spatial distribution of sediment transport in active aeolian environments. Existing dust and sediment transport models often rely on vegetation attributes derived from static land use datasets or remotely sensed greenness indicators to incorporate sheltering effects on simulated particle mobilization. However, these overly simplistic approaches do not represent the three-dimensional nature or spatiotemporal changes of roughness element sheltering. They also ignore the sheltering contribution of non-vegetation roughness features and photosynthetically inactive (i.e., brown) vegetation common to dryland environments. Here, we explore the use of a novel albedo-based sheltering parameterization in a dust transport modeling application of the Weather Research and Forecasting model with Chemistry (WRF-Chem). The albedo method estimates sheltering effects on surface wind friction speeds and dust entrainment from the shadows cast by subgrid-scale roughness elements. For this study, we applied the albedo-derived drag partition to the Air Force Weather Agency (AFWA) dust emission module and conducted a sensitivity study on simulated PM10 concentrations using the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model as implemented in WRF-Chem v4.1. Our analysis focused on a convective dust event case study from 3–4 July 2014 for the southwestern United States desert region discussed by other published works. Previous studies have found that WRF-Chem simulations grossly overestimated the dust transport associated with this event. Our results show that removing the default erodibility map and adding the drag parameterization to the AFWA dust module markedly improved the overall magnitude and spatial pattern of simulated dust conditions for this event. Simulated PM10 values near the leading edge of the storm substantially decreased in magnitude (e.g., maximum PM10 values were reduced from 17 151 to 8539 µg m−3), bringing the simulated results into alignment with the observed PM10 measurements. Furthermore, the addition of the drag partition restricted the erroneous widespread dust emission of the original model configuration. We also show that similar model improvements can be achieved by replacing the wind friction speed parameter in the original dust emission module with globally scaled surface wind speeds, suggesting that a well-tuned constant could be used as a substitute for the albedo-based product for short-duration simulations in which surface roughness is not expected to change and for landscapes wherein roughness is constant over years to months. Though this alternative scaling method requires less processing, knowing how to best tune the model winds a priori could be a considerable challenge. Overall, our results demonstrate how dust transport simulation and forecasting with the AFWA dust module can be improved in vegetated drylands by calculating the dust emission flux with surface wind friction speed from a drag partition treatment.

Controls on Flood Trends Across the United States

AbstractTrends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls.

Representation of seasonal land use dynamics in SWAT+ for improved assessment of blue and green water consumption

Abstract. In most (sub)-tropical African cultivated regions, more than one cropping season exists following the (one or two) rainy seasons. An additional cropping season is possible when irrigation is applied during the dry season, which could result in three cropping seasons. However, most studies using agro-hydrological models such as the Soil and Water Assessment Tool (SWAT) to map blue and green evapotranspiration (ET) do not account for these cropping seasons. Blue ET is a portion of crop evapotranspiration after irrigation application, while green ET is the evapotranspiration resulting from rainfall. In this paper, we derived dynamic and static trajectories from seasonal land use maps to represent the land use dynamics following the major growing seasons to improve simulated blue and green water consumption from simulated evapotranspiration in SWAT+. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by a spatial mapping of the ET results. Additionally, the SWAT+ blue and green ET were compared with the results from the four remote sensing data-based methods, namely SN (Senay), EK (van Eekelen), the Budyko method, and soil water balance method (SWB). The results show that ET with seasonal representation is closer to remote sensing estimates, giving higher performance than ET with static land use representation. The root mean squared error decreased from 181 to 69 mm yr−1, the percent bias decreased from 20 % to 13 %, and the Nash–Sutcliffe efficiency increased from −0.46 to 0.4. Furthermore, the blue and green ET results from the dynamic SWAT+ model were compared to the four remote sensing methods. The results show that the SWAT+ blue and green ET are similar to the van Eekelen method and performed better than the other three remote sensing methods. It is concluded that representation of seasonal land use dynamics produces better ET results, which provide better estimations of blue and green agricultural water consumption.

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

Impact Classification of Future Land Use and Climate Changes on Flow Regimes in the Yellow River Source Region, China

AbstractTraditional impact assessments of future changes on flow regimes mainly focus on streamflow magnitude and static land use, which are insufficient to capture entire characteristics of flow regime variations and future land use change. In this study, 18 flow regime metrics are adopted to fully characterize the streamflow hydrograph. The future changes are considered, including land use scenario in 2025 predicted by the Cellular Automata‐Markov (CA‐Markov), and climate change scenarios under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP4.5, and RCP8.5) obtained from five general circulation models (GCMs) for the period 2021–2030. Regional impacts of future land use and climate changes on flow regimes in the Yellow River Source Region are simulated and identified using distributed hydrological modeling and spatial classification. Results show that the increases in unused land (14.16%), and decreases in grassland (2.54%), glacier and snow cover (62.85%) are remarkable for the period 1980–2025. The flow regimes will be highly impacted in the source region for the RCP2.6 and RCP8.5 scenarios, but in the middle stream and downstream regions for the RCP4.5 scenario. Both the future land use and climate changes will increase flow magnitude for most regions, but their impacts on other flow regime metrics are not homogeneous. The climate change will play the dominant role in the flow regime variations, while the land use change will highly affect mean pre‐flood runoff, frequency and duration of high flow events, and mean rates of positive and negative changes.

Study on Hydrologic Effects of Land Use Change Using a Distributed Hydrologic Model in the Dynamic Land Use Mode

It is reasonable to simulate the hydrologic cycle in regions with drastic land use change using a distributed hydrologic model in the dynamic land use mode (dynamic mode). A new dynamic mode is introduced into an object-oriented modularized model for basin-scale water cycle simulation (MODCYCLE), a distributed hydrologic model based on sub-watersheds, and the hydrological response unit (HRU). The new mode can linearly interpolate data for the years without land use data and consistently transfer HRU water storage between two adjacent years after a land use data update. The hydrologic cycle simulation of the Sanjiang Plain in China was carried out from 2000 to 2014 in the dynamic mode using land use maps of 2000, 2005, 2010, and 2014. Through calibration and validation, the performance of the model reached a satisfactory level. Replacing the land use data of the calibrated model using that of the year 2000, a comparison model in the static land use mode (static mode) was built (i.e., land use unchanged since 2000). The hydrologic effects of land use change were analyzed using the two models. If the land use pattern remained unchanged from 2000, despite the average annual runoff increasing by 4% and the average annual evapotranspiration decreasing by 4% in this region only, the groundwater storage of the plain areas in 2014 would increase by 4.6 bil. m3 compared to that in 2000, rather than the actual decrease of 4.7 bil. m3. The results show that the fluxes associated with groundwater are obviously more disturbed by land use change in the Sanjiang Plain. This study suggests that the dynamic mode should be used to simulate the hydrologic cycle in regions with drastic land use change, and the consistent transfer of HRU water storage may be considered in the dynamic mode.

Land Use / Land Cover Change in the Western Highlands of Cameroon: Case of the Sabga-Bamunka Area (1980-2020)

Man has greatly influenced the environment through his different activities, technology and dynamism. These modifications are land use/land cover changes. This research describes and analyses the implication of land use dynamics on resources in the Sabga-Bamunka area over time and space from 1980 to 2020. Remote sensing techniques and Geographic Information System were used in describing and analysing land use/cover changes in the Sabga-Bamunka area. Primary data was obtained through field observation in order to confirm observation on satellite imageries. Land use maps were produced using GIS software. Maps were generated to show changes in land use/land cover which were transposed into table and bar graphs to show the magnitude of changes. The analysis of static land use maps of 1980, 2010 and 2020 all showed that there are significant changes observed on forest cover, farmland, grazing land and settlement area. The findings revealed that the study area has experienced a drastic change in land use/land cover during the last forty years. The study area is characterised with decrease in grassland areas, forest and increase in settlement and farmland due to the increasing population which are the main triggering force of land use/land cover changes that has led to the reduction in vegetal cover. Forest decreased from 6 568 hectares (26%) in 1980 to 2 842 hectares (10%) in 2020 indicating a magnitude change of -3726 (-29%) and grass land decreased from 16 434 hectares (64%) in 1980 to 14 585 hectares (53%) in 2020 that is a magnitude change of -1849 (-14%). These decreases gave way to settlements and farmland. Therefore, a reduction in the excessive consumption of fuel wood, the practice of eco-forestry, raising of awareness and a dialogue plate-form are amongst the measures recommended to reduce land use/land cover change in the study area.

A dynamic land use/land cover input helps in picturing the Sahelian paradox: Assessing variability and attribution of changes in surface runoff in a Sahelian watershed

In Sahelian landscapes, land use/land cover (LULC) dynamics and climate variability are already known to affect the water cycle. In its current practice however, hydrological modelling does not account for LULC changes. This issue pertains to rapidly evolving watersheds and might result in critical inaccuracies in the simulated processes. In this study, the Soil and Water Assessment Tool (SWAT) model was used to simulate surface runoff in the small Sahelian watershed of Tougou, which underwent significant LULC changes between 1952 and 2017. Based on rainfall/runoff data acquired from 2004 to 2018, the SWAT model was calibrated under two scenarios: a static land use scenario (SLU) using a single LULC map (in 1999) and a dynamic land use scenario (DLU) integrating 3 LULC maps (1999, 2009 and 2017). The DLU scenario estimated with higher accuracy surface runoff, deep aquifer infiltration and actual evapotranspiration processes. Based on the calibrated parameters, surface runoff was simulated during the historical period 1952–2003 under four scenarios with static LULC maps (in 1952, 1973, 1986 and 1999) opposed to a fifth scenario integrating these LULC maps dynamically. The DLU scenario was found to be more effective at picturing the so-called Sahelian paradox (i.e. the increase in surface runoff despite the decrease in rainfall), reported in the literature for small watersheds in the Sahel. The analysis of variability revealed that fluctuations in surface runoff were both influenced by rainfall and LULC changes. Furthermore, the isolated contributions of climate variability and LULC changes on surface runoff showed that LULC conditions played a dominant role (ηlulc = +393.1%) in the runoff increase over climate (ηcl = −297%) during the historical period. These results highlight the importance of accounting for LULC dynamics in hydrological modelling and advocate the development of integrated modelling frameworks for hydrologists and water resource managers.

Comparative Analysis of Bioenergy Crop Impacts on Water Quality Using Static and Dynamic Land Use Change Modeling Approach

Researchers and federal and state agency officials have long been interested in evaluating location-specific impact of bioenergy energy crops on water quality for developing policy interventions. This modeling study examines long-term impact of giant miscanthus and switchgrass on water quality in the Cache River Watershed (CRW) in Arkansas, United States. The bioenergy crops were simulated on marginal lands using two variants of a Soil and Watershed Assessment Tool (SWAT) model. The first SWAT variant was developed using a static (single) land-use layer (regular-SWAT) and for the second, a dynamic land-use change feature was used with multiple land use layers (location-SWAT). Results indicated that the regular-SWAT predicted larger losses for sediment, total phosphorus and total nitrogen when compared to location-SWAT at the watershed outlet. The lower predicted losses from location-SWAT were attributed to its ability to vary marginal land area between 3% and 11% during the 20-year modeling period as opposed to the regular-SWAT that used a fixed percentage of marginal land area (8%) throughout the same period. Overall, this study demonstrates that environmental impacts of bioenergy crops were better assessed using the dynamic land-use representation approach, which would eliminate any unintended prediction bias in the model due to the use of a single land use layer.

Accounting for Seasonal Land Use Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals

The assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.

Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments

Satellite imagery and climate change projections improve our ability to map and forecast sediment sources and transport pathways at high resolution, which is vital for catchment management. Detailed assessment of temporal and spatial changes in erosion risk are key to forecasting pollutant dispersal, which affects water treatment costs and ecology. Outputs from scenario modeling of the River Derwent catchment, Yorkshire, indicate clear spatial and temporal trends in erosion risk. These trends are not picked up by using traditional methods, which rely on static land use maps. Using satellite-derived maps show that lower resolution traditional land-use maps relatively underestimate erosion risk in terms of location of source areas and seasonal variation in erosion risk. Seasonal variation in agricultural practices can be assessed by incorporating bare land variation into models, which show that erosion risk is relatively overestimated if all agricultural land is assumed to have the same character. Producing seasonal land use maps also allows the assessment of temporal variation in rainfall, which in combination with climate change projections allows for adaptable management plans. The bias in gradient in modeling, which assumes that high gradients result in greater sediment erosion risk, show that traditional models underestimate the contribution of erosion risk in lowland areas. This is compounded by the absence of artificial drainages in topographic rasters, which increases connectivity in lowland areas. By producing end member scenarios, model outputs help to inform where catchment management should be targeted, and whether seasonal interventions should be implemented. This information is vital to communicate with landowners when they implement catchment management practices, such as sediment traps and earth bunds. Adaption of erosion risk modeling practices is urgently needed in order to quantify the impact of artificial interference in which human activity disrupts ‘natural’ sediment source-to sink configurations, such as integrating new pathways and stores due to land use change and management. Furthermore, integrating higher resolution catchment modeling and improved seasonal forecasts of pollutant flux to oceans will permit more effective interventions. This paper highlights single output erosion risk maps are not effective to inform catchment management.

Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara watershed, Ethiopia

Land Use Land Cover (LULC) change significantly affects hydrological processes. Several studies attempted to understand the effect of LULC change on biophysical processes; however, limited studies accounted dynamic nature of land use change. In this study, Soil and Water Assessment Tool (SWAT 2012) hydrological model and statistical analysis were applied to assess the impacts of land use change on hydrological responses such as surface runoff, evapotranspiration, and peak flow in Gummara watershed, Ethiopia. Moreover, the effects of static and dynamic land use data application on the SWAT model performance were evaluated. Two model setups, Static Land Use (SLU) and Dynamic Land Use (DLU), were studied to investigate the effects of accounting dynamic land use on hydrological responses. Both SLU and DLU model setups used the same meteorological, soil, and DEM data, but different land use. The SLU setup used the 1985 land use layer, whereas the DLU setup used 1985, 1995, 2005, and 2015 land use data. The calibration (validation) results showed that the model satisfactorily predicts temporal variation and peak streamflow with Nash Sutcliffe Efficiency (NSE) of 0.75 (0.71) and 0.73 (0.71) in the DLU and SLU setups, respectively. However, the DLU model setup simulated the detailed biophysical processes better during the calibration period. Both model setups equally predicted daily streamflow during the validation period. Better performance was obtained while applying the DLU model setup because of improved representation of the dynamic watershed characteristics such as curve number (CN2), overland Manning's (OV_N), and canopy storage (CANMX). Expansion of agricultural land use by 11.1% and the reduction of forest cover by 2.3% during the period from 1985 to 2015 increased the average annual surface runoff and peak flow by 11.6 mm and 2.4 m3/s, respectively and decreased the evapotranspiration by 5.3 mm. On the other hand, expansion of shrubland by 1% decreased the surface runoff by 1.2 mm and increased the evapotranspiration by 1.1 mm. The results showed that accounting DLU into the SWAT model simulation leads to a more realistic representation of temporal land use changes, thereby improving the accuracy of temporal and spatial hydrological processes estimation.

The implications of land use/cover dynamics on resources development in Tubah sub-division, Cameroon

The environment at various scales is greatly modified through the activities of man and his dynamic science and technology. These modifications essentially constitute land use/cover changes. This paper describes the implications of land use dynamics on resource development in Tubah Sub-division over time and space from 1983 to 2013. This research has made use of remote sensing techniques and Geographic Information System (GIS) in describing land use/cover changes in Tubah Sub-division. Primary data was obtained through field observations and ground truthing in order to confirm observations on satellite imageries. Land use maps were produced using the GIS software packages of Google Earth, ENVI 4.3, Global Mapper 15 and ArcGis 10.2. Maps were generated to show changes in land use/cover which were transposed into tables and bar graphs to show the magnitude of changes, percentage of change and the rate of change. These changes are necessary for economic development and social progress, but often come at a substantial cost on the environment and resources. The analysis of static land use maps of 1983, 2000, and 2013, all pointed to the fact that, there have been significant changes observed on forest cover, farmland, grazing land and settlement land uses.Keywords: Land use/cover, Dynamics, Remote Sensing Techniques, Geographic Information System, Resource Implications, Tubah

Inferring Social Functions Available in the Metro Station Area from Passengers’ Staying Activities in Smart Card Data

The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development.

How complex should models be? Comparing correlative and mechanistic range dynamics models.

Criticism has been levelled at climate-change-induced forecasts of species range shifts that do not account explicitly for complex population dynamics. The relative importance of such dynamics under climate change is, however, undetermined because direct tests comparing the performance of demographic models vs. simpler ecological niche models are still lacking owing to difficulties in evaluating forecasts using real-world data. We provide the first comparison of the skill of coupled ecological-niche-population models and ecological niche models in predicting documented shifts in the ranges of 20 British breeding bird species across a 40-year period. Forecasts from models calibrated with data centred on 1970 were evaluated using data centred on 2010. We found that more complex coupled ecological-niche-population models (that account for dispersal and metapopulation dynamics) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpler models that only account for variation in climate. However, these better forecasts are achieved only if ecological responses to climate change are simulated without static snapshots of historic land use, taken at a single point in time. In contrast, including both static land use and dynamic climate variables in simpler ecological niche models improve forecasts of observed range shifts. Despite being less skilful at predicting range changes at the grid-cell level, ecological niche models do as well, or better, than more complex models at predicting the magnitude of relative change in range size. Therefore, ecological niche models can provide a reasonable first approximation of the magnitude of species' potential range shifts, especially when more detailed data are lacking on dispersal dynamics, demographic processes underpinning population performance, and change in land cover.

Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments

Representations of land use change in hydrologic impact assessment studies mostly rely on static land use information of two points in time, even though the availability of dense time series of land use data allows for the incorporation of dynamic land use changes. We compare the hydrologic impacts of dynamic land use change assessments to those of static land use change assessments. These effects are illustrated with the help of two land use scenarios applied to a hydrologic model of a rapidly developing meso-scale (2036 km2) catchment upstream of Pune, India. The results show that a linear dynamic land use development could be better approximated with the static approach than a non-linear development. An analysis of the impact of the frequency of land use updates indicates that the prediction of non-linear land use change impacts already improves substantially when frequent land use information every five to nine years is used.

The Implications of Land Use/Cover Dynamics on Resources Development in Tubah Sub-Division, Cameroon

The environment at various scales is greatly modified through the activities of man and his dynamic science and technology. These modifications essentially constitute land use/cover changes. This paper describes the implications of land use dynamics on resource development in Tubah Sub-division over time and space from 1983 to 2013. This research has made use of remote sensing techniques and Geographic Information System (GIS) in describing land use/cover changes in Tubah Sub-division. Primary data was obtained through field observations and ground truthing in order to confirm observations on satellite imageries. Land use maps were produced using some GIS software packages such as Google Earth, ENVI 4.3, Global Mapper 15 and ArcGis 10.2. Maps were generated to show changes in Land use/cover which were transpose into tables and bar graphs to show the magnitude of changes, percentage of change and the rate of change. The analysis of static land use maps of 1983, 2000, and 2013, all pointed to the fact that, there have been significant changes observed on forest cover, farmland, grazing land and settlement land uses