Future Land Use Cover Change Research Articles

Projection of urban settlement in eco-sensitive hilly areas and its impact on peak runoff

Environmental degradation due to the rapid growth of urban sprawl to eco-sensitive areas emphasizes the need of future projection of urban settlement in eco-sensitive areas. As a promising step towards the eco-friendly urban development, this paper projects the urban settlement in hills of Guwahati city by using a well-validated urban settlement assessment model of the study area. Future values of the socio-economic, demographic and geographical parameters incorporated in the model have been determined with reference to the “Master Plan for Guwahati Metropolitan Area-2025”. As per this model projection, by 2025, average 25.45% of the hilly area of Guwahati city is going to have an unplanned urban settlement, whereas, in 2011, it was only 14.33%. Future landuse land cover change data of the hills have been generated by spatially distributing the projected urban settlement depending on the grid-wise elevation in hills. This type of projection, done on the basis of future socio-economic and demographic condition derived from the master plan of a city can be used for environmental impact assessment of the plan. Finally, by using Rational Method, the impact of the future urban settlement on peak run-off generation has been evaluated for a watershed in the Fatasil hill of Guwahati city. The hydrological study has shown an alarming result with a 20.75% increase in peak run-off due to the conversion of additional 14.5% of the watershed area to urban settlement.

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

Forecasting landscape changes is vital for developing and implementing sustainable urban planning. Presently, apart from lowland coastal cities, mountain cities (i.e., hill stations) are also facing the negative impacts of rapid urbanization due to their economic and social importance. However, few studies are addressing urban landscape changes in hill stations in Asia. This study aims to examine and forecast landscape changes in the rapidly urbanizing hill station of Nuwara Eliya, Sri Lanka. Landsat data and geospatial techniques including support vector machines, urban–rural gradient, and statistical analysis were used to map and examine the land use/land cover (LULC) change in Nuwara Eliya during the 1996–2006 and 2006–2017 periods. The multilayer perceptron neural network-Markov model was applied to simulate future LULC changes for 2027 and 2037. The results show that Nuwara Eliya has been directly affected by rapid urban development. During the past 21 years (1996–2017), built-up areas increased by 1791 ha while agricultural land declined by 1919 ha due to augmented urban development pressure. The pressure of urban development on forest land has been relatively low, mainly due to strict conservation government policies. The results further show that the observed landscape changes will continue in a similar pattern in the future, confirming a significant increase and decrease of built-up and agricultural land, respectively, from 2017 to 2037. The changes in agricultural land exhibit a strong negative relationship with the changes in built-up land along the urban–rural gradient (R2 were 0.86 in 1996–2006, and 0.93 in 2006–2017, respectively). The observed LULC changes could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea. Further, unplanned development could cause several environmental issues. The study is important for understanding future LULC changes and suggesting necessary remedial measures to minimize possible undesirable environmental and socioeconomic impacts.

Modeling the effects of land cover change on sediment concentrations in a gold-mined Amazonian basin

Land use/land cover change (LUCC) combined with gold mining activity (GMA) have reportedly affected the water quality of Amazonian rivers by intensifying surface soil erosion and increasing rivers’ sediment concentration (SC). However, the role of LUCC on river siltation in comparison to that caused by GMA has not been assessed, and predictions of SC in the rivers accounting for future LUCC are scarce. This study applied a sediment modeling approach on Crepori Basin, located at eastern Amazon Basin, to simulate the impacts of past and future LUCC on SC in the river, comparing their impacts to those caused by GMA. Between 1973 and 2012, the expansion of deforested areas in the region had increased sheet erosion-driven SC in the river, especially during the high-water season, by up to 73.3%. LUCC projections for 2050 suggest future increases in sheet erosion-driven SC during the high-water season by more than three times of that caused by the land cover scenario of 1998–2012. Comparison between SC driven by sheet erosion to that caused by GMA has also shown that during the high-water and the low-water seasons, respectively, only about 14% and 6% of the total SC in the Crepori River resulted from laminar soil erosion, with the remaining proportion resulting from GMA.

Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987–2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas.

Effect of land use-land cover change on the regimes of surface runoff-the case of Lake Basaka catchment (Ethiopia).

In this study, the effect of land use-land cover change (LULCC) on surface (direct) runoff was estimated for Lake Basaka catchment using the soil conservation services-curve number model in the geospatial information system (ArcInfo), assisted by remote sensing. The result indicated that Lake Basaka catchment experienced a significant LULCC. About 86% of forest coverage and 46% of grasslands were lost over the study period (1973-2015), which were shifted to open bushy woodlands, farms, lake water and wetlands. The runoff responses were observed to be increasing since 1970s, especially after the inception of large-scale irrigation schemes to the region. The highest increase of surface runoff was observed to occur after mid-1980s, which is in line with the significant LULCC and the corresponding increment of lake level in that period. The reduction in vegetation cover has resulted in an increase of runoff coefficient (rc) from 0.07 in the 1960s to about 0.23 in 2000s. The sensitivity analysis result indicated that about 70% of the increase runoff rate in the lake catchment is attributed to LULCC, and the remaining proportion is due to rainfall. However, the effect of extreme rainfall on runoff process could not be underemphasized since it has significant impact especially during extreme events (observed rc of 0.33 in 2008). Overall, when predicting the runoff response of the lake catchment, it is importance to take into account possible future LULCC and evolution.

Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

Abstract Over the years, Osun drainage basin has witnessed tremendous increase in population, and urbanization that have changed the landscape of the area. This study evaluated the spatio-temporal pattern of land use/land cover change (LULC) in the study area, and made hydrological inferences. Landsat imageries were acquired from USGS-EROS satellite image database for the period 1984, 2000 and 2015, while the Digital Elevation Model (DEM) was obtained from Shuttle Radar Topography Mission (SRTM) of the National Aeronautics and Space Agency (NASA). Supervised image classification using the Maximum Likelihood Algorithm in Erdas Imagine was adopted to classified the land use/land cover of the study area into seven classes. Elevation, aspect and slope of the study area were processed from DEM using ArcGIS. Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS was used to simulate the basin future LULC change, using change driving factors of population, elevation, aspect and slope of the study area. There was about 234% increase in built up areas and 89.22% in crop/shrubs between 1984 and 2015. The most significant decrease in LULC occurred in forest (58.75%) and wetland (84.69%) during this period. The predicted future LULC change suggests that only about 12% of the basin will remain under forest cover by the year 2046. The results underscored the increasing anthropogenic activities in the basin that influenced recharge rate, surface runoff, incidences of soil erosion, etc., in Osun drainage basin. The planting of the lost native trees was recommended for the sustainability of the basin’s ecosystem.

Dramatic Loss of Agricultural Land Due to Urban Expansion Threatens Food Security in the Nile Delta, Egypt

Egypt has one of the largest and fastest growing populations in the world. However, nearly 96% of the total land area is uninhabited desert and 96% of the population is concentrated around the River Nile valley and the Delta. This unbalanced distribution and dramatically rising population have caused severe socio-economic problems. In this research, 24 land use/land cover (LULC) maps from 1992 to 2015 were used to monitor LULC changes in the Nile Delta and quantify the rates and types of LULC transitions. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion over the 24 year period at an average rate of 3108 ha year−1, whilst 206,100 hectares of bare land was converted to agricultural land (New Lands) at an average rate of 8588 ha year−1. A Cellular Automata-Markov (CA-Markov) integrated model was used to simulate future alternative LULC change scenarios. Under a Business as Usual scenario, 87,000 hectares of land transitioned from agricultural land to urban areas by 2030, posing a threat to the agricultural sector sustainability and food security in Egypt. Three alternative future scenarios were developed to promote urban development elsewhere, hence, with potential to preserve the fertile soils of the Nile Delta. A scenario which permitted urban expansion into the desert only preserved the largest amount of agricultural land in the Nile Delta. However, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density resulted in almost the same area of agricultural land being preserved. The alternative future scenarios are valuable for supporting policy development and planning decisions in Egypt and demonstrating that continued urban development is possible while minimising the threats to environmental sustainability and national food security.

What Is Left for Our Next Generation? Integrating Ecosystem Services into Regional Policy Planning in the Three Gorges Reservoir Area of China

Land use/Land cover (LULC) changes as a result of policy planning influence ecosystem structures, processes, and functions, which are the basis for providing a wide range of ecosystem services (ES). There is an increasing consensus about the importance of integrating ES into ecological policy but quantifying the potential impacts of different policy on ES has proven difficult. We designed a remote sensing, geographic information system and scenario analysis-based approach to estimate and analyze the relationship between ES (soil conservation and carbon sequestration) and ecological policy designed to improve human welfare in the Chongqing municipality in the upper reaches of the Three Gorges Reservoir Area, China; a densely populated, highly modified watershed with serious soil erosion and flood hazard. Three alternative scenarios in 2050 were modeled for the Three Gorges Reservoir Area watershed. The model GEOMOD was used to predict future LULC changes due to policy planning. The ES models (Universal Soil Loss Equation model and Carnegie-Ames-Stanford Approach model) were designed to inform decisions, with an aim to align economic forces with conservation. We examine policy effectiveness by comparing scenarios for 2050 (Scenario1: Maintain current policy with no considerations of ES; Scenario2: Integrate ES into policy planning; Scenario3: Integrate ES into policy planning in view of the need of local people). Scenario-based LULC change analysis revealed that if the current afforestation policy continues (scenario 1), total ES would be further increased in 2050 due to expansion of forest cover. However, by targeting policy to improve ES provision (scenarios 2 and 3), ecological risks of soil loss can be significantly reduced and carbon sequestration enhanced. Scenario 3, thus, provided the best future environmental development scenario considering the need of local people in each region for ES. This scenario will theoretically help the Three Gorges Dam to harvest more ecological benefits through improvements in soil conservation and carbon sequestration. This study highlights the observation that including ES in policy planning and has a great potential to generate opportunities to maximize ES. This study highlights that including ES in policy planning has a great potential to generate opportunities to maximize ES. Hence, there is a need to encourage proper implementation of ecological policy to maintain and improve ES.

An integrating approach of cellular automata and ecological network to predict the impact of land use change on connectivity

As a primary concern in biodiversity conservation, understanding the impact of land use/cover change (LUCC) on functional connectivity has the overarching significance. Yet, LUCC simulation models used in previous studies are insufficient to tackle the complex and non-linear interactions of land-use components, and thus may not be able to provide the accurate impact assessments. This paper aimed to fill this gap and to find a solution that avoids significant connectivity loss in the future LUCC. The artificial neural network (ANN) based cellular automata (CA) and graph theory were combined, to explore potential changes in functional connectivity under alternative urban expansion scenarios. Results show that, both urban land and forested land will increase in all of future scenarios; however, functional connectivity of each species will still decrease except under a population change based scenario. The increase in forested land and the contrary connectivity loss proved the strong barrier effect caused by urban expansion. The impacts vary by species: small forest mammals might benefit more from the gain in the stepping-stone-like patches, while the intra-patch connectivity loss is the primary threat for large terrestrial mammals. Finally, we identified the key connecting patches (i.e., stepping-stones) and suggested that those patches should be prioritized for protection.

A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change

Despite recent advances in quantifying land-use/cover change (LUCC) transition potential, transition rules are often not transparent and uncertainty is rarely made explicit. Here, we introduce DoTRules—a dictionary of trusted rules—as a transparent alternative to calculate transition potential in cellular automata models. Rules relate LUCC variables to the observed historical changes. Shannon entropy is calculated to assess the uncertainty of each rule, and the most trusted rules are used to project future LUCC. DoTRules produces rule-level uncertainty estimates, which can be mapped. In a case study of the Ahvaz region of Iran, the overall accuracy of LUCC simulation calibrated using DoTRules was very similar to simulations calibrated with the state-of-the-art random forest, but DoTRules provides a more transparent approach where transition rule information and uncertainty can be readily accessed and interpreted. The results demonstrate that DoTRules has potential to derive new insights into LUCC processes.

Scale-dependent impacts of urban and agricultural land use on nutrients, sediment, and runoff

We coupled a spatially-explicit land use/land cover (LULC) change model, Dinamica EGO, (Environment for Geoprocessing Objects), with the Chesapeake Bay Watershed Model (CBWM) to project the impact of future LULC change on loading of total nitrogen (TN), total phosphorous (TP) and total suspended solids (TSS) as well as runoff volume in the watersheds surrounding Virginia's Shenandoah National Park in the eastern United States. We allowed for the dynamic transition of four LULC classes, Developed, Forest, Grasses (including both pasture and hayfields) and Crops. Using 2011 as a baseline scenario and observed differences in LULC between 2001 and 2011, we estimated the temporal and spatial patterns of LULC change as influenced by physiographic and socio-economic drivers 50 years in the future (2061). Between transitions of the four LULC classes, the greatest absolute change occurred between the gain in total Developed land and loss in total Forest. New Developed land was driven primarily by distance to existing Developed land and population density. Major findings on the effect of LULC change on watershed model outputs were that: the impact of LULC change on pollutant loading and runoff volume is more pronounced at finer spatial scales; increases in the area of Grasses produced the greatest increase in TP loading, while loss of Forest increased TN, TSS, and runoff volume the most; and land-river segments with a greater proportion of Developed or a smaller proportion of Forest in the 2011 scenario experienced a greater change in runoff than other land-river segments. Results of this study illustrate the potential impact of projected LULC change on nutrient and sediment loads which can adversely impact water quality. Studies like this contribute to a broader understanding of how ecosystem services such as fresh water respond to LULC change, information relevant to those in planning and watershed management.

Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran.

The present study was conducted, based on scenario modeling approach, in the Do-hezar and Se-hezar forested landscape in the Mazandaran Province in Northern Iran in order to detect spatial-temporal changes of carbon storage and sequestration in four different carbon pools, i.e., aboveground and belowground biomasses, dead organic matter, and organic soils. For this purpose, firstly, the changing trend of land use/land cover (LULC) was detected by analyzing and comparing remotely sensed data of the landscape during the period of 1984-2016. Then, the impacts of future LULC changes on carbon storage and sequestration were predicted and valued using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model under two future plausible scenarios of business as usual (BAU) and balanced development (BD). According to the results of BAU scenario, continuation of the current trend will lead to a significant reduction in the carbon sequestration and a huge amount of social cost due to the loss of carbon stored in the landscape and its release to the atmosphere. The BD scenario which refers to the principled and under control development of human settlements simultaneously with forest conservational and restoration activities, could potentially reverse the downtrend of carbon sequestration service and avoid future socioeconomic costs, hence add to the economic value of the forest landscape in terms of providing a better sink for carbon storage. The results of this research can facilitate the quantitative and accurate assessment of carbon storage and sequestration relying on more precise biophysical and economic data as well as provide insight for effective land-use planning.

Impact of water demand on hydrological regime under climate and LULC change scenarios

The present study focuses on an assessment of the impact of future water demand on the hydrological regime under land use/land cover (LULC) and climate change scenarios. The impact has been quantified in terms of streamflow and groundwater recharge in the Gandherswari River basin, West Bengal, India. dynamic conversion of land use and its effects (Dyna-CLUE) and statistical downscaling model (SDSM) are used for quantifying the future LULC and climate change scenarios, respectively. Physical-based semi-distributed model Soil and Water Assessment Tool (SWAT) is used for estimating future streamflow and spatiotemporally distributed groundwater recharge. Model calibration and validation have been performed using discharge data (1990–2016). The impacts of LULC and climate change on hydrological variables are evaluated with three scenarios (for the years 2030, 2050 and 2080). Temperature Vegetation Dyrness Index (TVDI) and evapotranspiration (ET) are considered for estimation of water-deficit conditions in the river basin. Exceedance probability and recurrence interval representation are considered for uncertainty analysis. The results show increased discharge in case of monsoon season and decreased discharge in case of the non-monsoon season for the years 2030 and 2050. However, a reverse trend is obtained for the year 2080. The overall increase in groundwater recharge is visible for all the years. This analysis provides valuable information for the irrigation water management framework.

MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA USING MULTI-TEMPORAL LANDSAT DATA

Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making. The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR). The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR

Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran)

Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and low-density forests were degraded during 1990–2014, respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.

Scenario modelling of land use/land cover changes in Munessa-Shashemene landscape of the Ethiopian highlands

Models under a set of scenarios are used to simulate and improve our understanding of land use/land cover (LULC) changes, which is central for sustainable management of a given natural resource. In this study, we simulated and examined the possible future LULC patterns and changes in Munessa-Shashemene landscape of the Ethiopian highlands covering four decades (2012–2050) using a spatially explicit GIS-based model. Both primary and secondary sources were utilized to identify relevant explanatory variables (drivers) and LULC datasets for the model. Three alternative scenarios, namely Business As Usual (BAU), Forest Conservation and Water Protection (FCWP) and Sustainable Intensification (SI) were used. The simulated LULC map of 2012 was compared with the actual for model validation and showed a good consistency. The results revealed that areas of croplands will increase widely under the BAU scenario and would expand to the remaining woodlands, natural forests and grasslands, reflecting vulnerability of these LULC types and potential loss of associated ecosystem service values (ESVs). FCWP scenario would bring competition among other LULC types, particularly more pressure to the grassland ecosystem. Hence, the two scenarios will result in severe LULC dynamics that lead to serious environmental crisis. The SI scenario, with holistic approach, demonstrated that expansion of croplands could vigorously be reduced, remaining forests better conserved and degraded land recovered, resulting in gains of the associated total ESVs. We conclude that a holistic landscape management, i.e. SI, is the best approach to ensure expected production while safeguarding the environment of the studied landscape and elsewhere with similar geographic settings. Further study is suggested to practically test our framework through a research for development approach in a test site so that it can be used as a model area for effective use and conservation of our natural resources.

Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region

Monitoring the impact of current Land Use/Land Cover (LULC) management practices on future Ecosystem Services (ESs) provisioning has been emphasized because of the effect of such practices on ecological sustainability. We sought to model and predict the impacts of future LULC changes on subsequent changes in Ecosystem Service Value (ESV) in fragile environments undergoing complex LULC changes, Su-Xi-Chang region. After mapping and classifying the LULC for the years 1990, 2000, and 2010 using GIS and remote sensing, a Cellular Automata (CA)–Markov model was employed to model future LULC changes for the year 2020. ESV was predicted using the projected LULC data and the modified ES coefficients adopted by Xie et al. (2003). The projected results of the changes in LULC reveal that construction land expanded extensively, mainly at the expense of farmland, wetland, and water bodies. The predicted results of the ESVs indicate that water bodies and farmland are the dominant LULC categories, accounting for 90% of the total ESV. Over the study period, ESVs were diminished by 7.3915 billion CNY, mostly because of the decrease in farmland, water bodies, and wetland. A reasonable land use plan should be developed with an emphasis on controlling construction land encroachment on farmland, wetlands, and water bodies. The rules of ecological protection should be followed in LULC management to preserve ecological resources.

Climate change versus land-use change—What affects the mountain landscapes more?

Land use and climate change are both strong drivers of landscape transformation. Using a representative valley of the Central Alps (Stubai Valley, Tyrol, Austria) we assess (1) the historical and likely future spatial patterns of land use/land cover (LULC), (2) the influence of temperature increase on the LULC distribution, and (3) the speed at which these changes will occur. Based on the historical landscape development and spatially explicit models, the effects of various land use and climate scenarios were modelled. Employing a pan-Alpine model, we were able to detect the temporal trajectory of spatial reforestation. The results show that land-use changes that already occurred during the last decades are responsible for the main future LULC changes (by secondary succession). Only an extreme land abandonment scenario and extreme climate scenarios (5K temperature increase) would bring about similar changes in LULC distribution and expansion of the forested areas. While alpine grasslands, alpine pioneer formation and glaciers would shrink drastically, especially deciduous forests would spread. To a considerable degree, such changes might take place over the next 300 years. By contrast, the increase in forest areas triggered by temperature changes would be slower and longer termed (up to 700–800 years).The effects and intensity of land-use change in the investigated valley, that is comparable to many regions in the Alps, will be at least equally severe and responsible for transformation of the landscape as those of a projected temperature increase.

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multi-temporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar

Urban population growth has been accelerating in the developing countries and this exerts tremendous pressure on available land resources in major cities and urban centres. Greater Doha, which is a metropolis area where 78% of the country’s population live, has witnessed a substantial population growth and a large urban expansion. This study is the first of its kind in the State of Qatar and it aims to provide an understanding of the land use/land cover (LULC) change and its spatio-temporal pattern in Greater Doha between the years 1997 and 2010. Parcel data and census statistics from these two dates were used in the analysis. LULC change statistics indicate a very large increase of 289% in the built-up areas and a larger increase of 426% of recreational spaces which shows a progressive planning approach towards improving living standards in Greater Doha. As a result of this development demands, the open spaces have been substantially reduced from 77% to 25%, population density increased in many parts of the metropolis and high-rise building started to dominate the skylines in certain parts of the metropolis. MARKOV model was used to generate future LULC change scenarios for the year 2020. The analysis predicts a 20% increase of built-up areas which will take place in the open spaces, and this modest increase may be explained by model constraints resulting from limited availability of land within the current boundaries of Greater Doha for future development.