Our results indicate that 27% of global forest loss can be attributed to deforestation through permanent land use change for commodity production. The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26%), shifting agriculture (24%), and wildfire (23%). LAND USE AND LAND COVER CHANGE, DRIVERS AND ITS IMPACT: A COMPARATIVE STUDY FROM KUHAR MICHAEL AND LENCHE DIMA OF BLUE NILE AND AWASH BASINS OF ETHIOPIA A Thesis Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Master of Professional Studies By Hussien Ali Oumer. Understanding drivers of changes in land use/land cover (LULC) is essential for modeling future dynamics or development of management strategies to ameliorate or prevent further decline of natural resources.

• Drivers Of Land Use Change Map In India
• Drivers Of Land Use Change Map In The World
• Drivers Of Land Use Change Map In America

Land use change is one of the important aspects of the regional ecological restoration research. With remote sensing (RS) image in 2003, 2007 and 2012, using geographic information system (GIS) technologies, the land use pattern changes in Yimeng Mountain ecological restoration area in China and its driving force factors were studied. Results showed that: (1) Cultivated land constituted the largest area during 10 years, and followed by forest land and grass land; cultivated land and unused land were reduced by 28.43% and 44.32%, whereas forest land, water area and land for water facilities and others were increased. (2) During 2003–2007, forest land change showed the largest, followed by unused land and grass land; however, during 2008–2012, water area and land for water facilities change showed the largest, followed by grass land and unused land.

(3) Land use degree was above the average level, it was in the developing period during 2003–2007 and in the degenerating period during 2008–2012. (4) Ecological Restoration Projects can greatly change the micro topography, increase vegetation coverage, and then induce significant changes in the land use distribution, which were the main driving force factors of the land use pattern change in the ecological restoration area. Land use/cover change (LUCC) is one of the main causes of global change, and it is the issues most closely related to natural and human processes, affecting the sustainable development of cities, societies and people’s daily lives. Facing the current increasingly severe problems of the population-resource-environment balance, the research on LUCC has become the frontier and a hot issue in global change. In 2002, LUCC research has entered into the phase of Land Project, and IGBP(International Geosphere-Biosphere Program) formulated the research emphasis and related scientific problems of Land Project.

The research contents have extended from the global climate change effects to the LUCC process of different scales, the driving mechanism and its influences on environmental resources. Thought that the driving forces of LUCC were climate change and human activities, and so its driving force index should include two types of bio-physical and socio-economical types. Bakkera et al. Studied the abandoned farmland problem of Lesvos island in Greek, and found the soil erosion as a driver of land-use change. Analyzed the quantities, inner structure, types and spatial distribution features of LUCC on the Loess Plateau of east Gansu in the last fifteen years of the 20th century and discovered that the main driving forces of LUCC on the Loess Plateau of east Gansu were natural factors, economic development, population pressure, the adjustment of macro policies, and so on. By selecting the elevation and slope as the index of land use change driving forces, Ye et al. Found that their relationship with land use change was obvious.

Studied land use change dynamics spatial patterns in an ecotone between agriculture and animal husbandry in China and analyzed its driving force; he found that the Tibetan Plateau uplift was the important driving force of climate change in the northern hemisphere of the late Cenozoic and that it had a significant effect on the eastern grassland changes of China. Especially in recent years, it has become the latest trend to research the spatial change rules of LUCC and its driving force factors in different periods of time using the Erdas Imagine remote sensing (RS) and geographic information system (GIS) technologies.

So, some significant achievements of researching the driving mechanisms based on the biophysical factors and socio-economic factors have been reported. In 1999, the Ecological Restoration Projects was initiated in China by restoring forest vegetation on wasteland and cropland, one of the world’s most ambitious conservation set-aside programs, and the nation’s largest ecological restoration project since the 1970 s. The ecological restoration measures mainly include the cropland afforestation, wasteland afforestation, facilitate afforestation, soil conservation tillage, construction of a terrace, comprehensive improvement on gully dam system, etc. The project has ambition to increase area of forest and grassland to 29.8 × 10 6 ha by 2013 (refer to the Annual Reports on the Development of Chinese Forestry (1999–2013) edited by State Forestry Administration, PR China). It is very important to research the land use changes and driving forces of the Ecological Restoration Projects, which can provide a scientific base for the regulation of ecological restoration measures and its beneficial quantitative evaluation. At present, there is a large amount of research on the technologies of the Ecological Restoration Projects and their benefits. However, few studies have characterized the dynamic changes of land use patterns of the Ecological Restoration Projects based on RS and GIS in the study area.

Therefore, it is difficult to accurately answer the questions of ecological restoration mechanisms. Selecting the ecological restoration area of Yimeng Mountain in Shandong Province, a typical hilly area in the northern part of China, the dynamic changes of land use patterns of the hilly ecological restoration area were quantitatively analyzed using Landsat TM RS images from 2003, 2007 and 2012, using RS and GIS technologies, and its driving force factors were discussed in the study area. Change features of land use structure showed that the most widespread land use type during the past 10 years in the study area was cultivated land, accounting for 38.80%–54.21% of the total land use, followed by forest land and grass land, accounting for 16.97%–31.17% and 10.58%–15.35% of the total land use, respectively. The proportion of cultivated land and unused land with respect to the total area were reduced by 28.43% and 44.32%, respectively, whereas the proportions of forest land, grass land, urban village and mining traffic land, water area and land for water facilities were increased by 83.68%, 10.75%, 21.07% and 162.74%, respectively. Land use change rate The land use dynamic degree ( ) of all land use types in the study area were calculated using formula (1). During 2003–2007, the value of forest land was the largest, accounting for 12.42% of the change in total land use, and it indicated that the change amplitude of forest land was the biggest, followed by those of unused land and grass land, accounting for – 5.22% and – 4.73% of the change in total land use, respectively; water areas and land for water facilities, urban village and mining traffic land and cultivated land changed relatively less, accounting for 3.68%, 2.36% and – 2.01% of the change in total land use, respectively.

During 2008–2012, the value of water areas and land for water facilities was the highest, accounting for 24.82% of the change in total land use, and its change amplitude was the biggest, followed by grass land, unused land and cultivated land, accounting for 9.01%, – 4.92% and – 4.09% of the change in total land use, respectively; the change in forest land, urban village and mining traffic land was relatively smaller, accounting for 2.66% and 1.66% of the change in total land use, respectively. Land use change degree The land use degree comprehensive index ( ) in the study was calculated using formula (2) and (3). Is a continuous function whose value interval is between 100, 400, which reflects the land use degree. The larger, the higher of the land use degree.

Showed that the values of in the study area in 2003, 2007 and 2012 were 240.47, 240.89 and 235.06, respectively, which indicated that the land use degree and development intensity in the study area was above the average level. From, was positive (+0.42) during 2003–2007 in the study area, which indicated that the land utilization was in the improving and developing stage; during 2008–2012, was negative (– 5.83), which indicated that the land utilization was in the adjusting stage in the study area. Land use type transformation Feature From and, 70.76% of the mapping unit of land use types was unchanged during 2003–2007. The land use transfer matrix during this period happened mainly between the cultivated land, forest land and grassland; 66.5 km and 22.4 km of the cultivated land transferred to forest land and grassland, accounting for 73.77% and 24.84% of the total transfer area, respectively. Moreover, the unused land also transferred obviously, 5.3 km and 4.4 km of which have transferred into urban village and mining traffic land and forest land, respectively, accounting for 28.00% and 23.73% of the total, respectively. Water areas and land for water facilities as well as urban village and mining traffic land grew relatively less; their main sources were unused land and small amounts of cultivated land.

And showed that 71.47% of the mapping unit of land use types was unchanged during 2008−2012. Similarly, during this period, the land use transfer matrix occurred mainly between the cultivated land, forest land and grassland; 66.6 km 2 and 37.1 km of the cultivated land transferred into forest land and grassland, respectively, accounting for 59.16% and 32.99% of the total, respectively. The total area of the water areas and land for water facilities increased by 21.3 km and mainly came from unused land and some cultivated land.

The area of unused land decreased by 12.6 km, which mainly transferred to forest land and water areas and land for water facilities. The area of urban village and mining traffic land increased by 2.0 km, which mainly comes from unused land and some low-yield cultivated land. Analysis of driving force factors of land use change The calculation results of contribution rate of each principal component showed that the cumulative contribution rate of the first(Y 1) and second (Y 2) principal component factors has exceeded 85.064%, so it showed the explain ability of the 9 driving force factors reached 85.064%, and meet the requirements of analysis.

And the load matrix of principal components was obtained by the maximum variance method.The first (Y 1) and second (Y 2) principal component factor expressions are as follows. Also showed that the driving force factors of the closely related to the first (Y 1) principal component factor were X 4, X 7, X 6, X 3 and X 9, and their correlation coefficients were all above 0.884, which represented the degree of the land development, planting, animal husbandry and forestry development in Yimeng Mountain ecological restoration area. In addition, the correlation coefficients of X 2 and X 1 were also above 0.849, so natural factors, such as terrain slope(X 2) and annual precipitation(X 1) were also the important influencing factors of land use change. Driving force feature of the land use chang e Land use change is a direct manifestation of human effects on the natural environment, whose development is mainly affected by natural and human factors. Natural factors are fundamental to the land use distribution of the ecological environment, which include altitude, landform, gradient, slope direction, soil, vegetation, etc., and human factors which include population, economy, system policy, technical measures, etc. In our study, the results indicated that the social and economic development factors, such as land reclamation rate, per capita amount of stock raising, per capita forest and grass area, per capita grain output and population density, were the main driving force factors in the land use change of Yimeng Mountain ecological restoration area(,Table ), and these driving factors represented the degree of land development, planting, animal husbandry and forestry development in the study area.

Moreover, the natural factors, such as terrain slope and annual precipitation, also were the important influencing factors on the land use change. These results were similar to the results found by Wu et al.

Drivers Of Land Use Change Map In India

Ecological Restoration Project significantly influence on the land use change The above driving force factors(X 2, X 4, X 7, X 6, X 3, X 9) were closely associated with the ecological restoration measures(such as the cropland afforestation, wasteland afforestation, facilitate afforestation, soil conservation tillage, construction of a terrace, comprehensive improvement on gully dam system, etc.) in Yimeng Mountain area. In 2003 (before the implementation of ecological restoration measures), cultivated land accounted for the largest proportion of the land use, accounting for 54.21% of the total, and unused land accounted for 9.86% of the total use, which together account for 64.07% of the land use. During the ten years from 2003 to 2012, the implementation of the Ecological Restoration Project brought a significant decrease in the cultivated and unused land, the proportion of which decreased by 28.43% and 44.32%, respectively, and the transformation from cultivated and unused land into ecological forest land, economic forest land and grassland was the major pattern of land use change. Therefore, the distribution and change of the cultivated and unused land mainly influenced the terrain distribution pattern of land use all over the ecological restoration area. According to the data of Landsat TM RS images from 2003, 2007 and 2012, and the other research results, The cultivated land in the study area is mainly sloped cropland of the hilly area, and the slope is relatively low on the whole. During the ten years since the implementation of the Ecological Restoration Project, the construction of a terrace and the increase in yield per unit of sloped cropland have led to more sloped cropland (cultivated land) transferring to forest land.

Because the reduced sloped cropland was mainly distributed in the lower part of the sloped surface, the average slope of the cultivated land was decreased significantly and the average altitude increased in the study area. During the ten year period, the Ecological Restoration Project, which controlled an area of 75 km and an open forest planting area of 80.5 km, successively developed an area of economic forest grass of 45 km under high standard land preparation, which resulted in the proportion of the forest land and grass land accounting for the total area increasing by 83.68% and 10.75%, respectively. Moreover, the construction of the channel check dam and reservoir in the study area resulted in the proportion of the water area and land for water facilities increasing by 162.21%. Therefore, the implementation of the Ecological Restoration Project greatly changed the landform, slope and vegetation coverage of the study area, which played an important influence on the land use change of Yimeng Mountain ecological restoration area. (1) The land use pattern in the ecological restoration area in Yimeng Mountain has changed significantly during the 10 years of this research. The cultivated land maintained the largest area, followed by forest land and grass land.

Moreover, the proportion of the total area that was cultivated land and unused land decreased by 28.43% and 44.32%, respectively, whereas forest land, grass land, urban village and mining traffic land, and water area and land for water facilities increased by 83.68%, 10.75%, 21.07% and 162.74%, respectively. (2) The analysis of the land use dynamic degree showed that the extent of forest land use change was the largest during 2003–2007, accounting for 12.42% of the change in land use, followed by unused land and grass land. However, the change of water areas and land for water facilities was the largest during 2008–2012, accounting for 24.82% of the change in land use, followed by grass land, unused land and cultivated land. (3) The analysis of the land use degree comprehensive index indicated that the land use and development degree in the study area was higher than the average level and that it was in the developing period during 2003–2007 and in the degenerating period during 2008–2012 and that the transformation of land use types mainly occurred in cultivated land, forest land and grass land.

(4) The effects of human activities on the spatial distribution of land use are the main driving force factors of the land use pattern change, which can lead to great changes over a short period, especially the implementation of Ecological Restoration Projects, which can greatly change the micro topography, reduce the surface slope, increase the vegetation coverage, and then induce significant changes in the spatial distribution of land use in the ecological restoration area of Yimeng Mountain in the northern part of China. The project area setup, observation indicators and test methods were all based on the Specifications for Assessment of Forest Ecosystem Services in China (LY/T 1721–2008), Indicators System for Long-term Observation of Forestry Ecosystems (LY/T 1606–2003) and Observation Methodology for Long-term Forest Ecosystem Research (LY/T 1952–2011). Study area and environmental conditions The experiment was conducted in Tai’an Xintai City and Shandong Province (35°58′-36°08′N,117°27′-117°33′E), located in Yimeng Mountain of south-central Shandong Province in China , where the elevation ranges from 310 m to 413 m. This area has a typical monsoon climate and is located in a warm temperate zone with distinct seasonal changes.

The mean annual precipitation is 798.4 mm, and nearly 70% of the annual precipitation falls between June and September. The average annual evaporation in this region is 1942.6 mm, and the mean annual temperature is approximately 12.0 °C. The soil type in this study is referred to as Brown soil and is similar to the American soil classification of Eutrochrepts; the average soil layer thickness is 20 cm; the soil pH is 6.5–6.9, and it shows higher soil and water loss. So improving the ecological environment management is necessary in the study area. The ecological restoration program of Yimeng Mountain was implemented in 2003, and divided into two parts, each period of five years, namely, 2003–2007 and 2008–2012.

According to the floristic-vegetational analysis results, the vegetation types in the study area belong to the coniferous forests and deciduous broad-leaved forests in the warm temperate zone and to the north China flora of China. Moreover, the coniferous forests include Platycladus orientalis (L.) and Pinus thunbergii Parl.; the deciduous broad-leaved forests include Cotinus coggygria Scop., Robinia pseudoacacia Linn., Prunus Armeniaca Mill., Julans regia Linn., etc.; the shrubs include Vitex negundo Linn. Negundo and Ziziphus jujuba var.spinosa Hu, etc.; the species in the waste grassland are Zoysia japonica Steud., Rubia manjith Roxb. Ex Flem., Themeda japonica Tanaka and Setaria viridis (Linn.) Beauv., etc. Date source Based on the RS software (Erdas Imagine 8.7) and GIS (ArcGIS 9.3) technology, we have processed LANDSAT TM remote sensing images of the three typical periods, in 2003, 2007 and 2012 (Namely: on June 25, 2003; on June 9, 2007; on June 21, 2012. And multispectral image resolution is 30 m, the path/row is 122 /36). In order to reduce the error of image processing, it needed to geometric correction, image sharpening and cloud removal, and drew the plaques to carry out human-computer interaction translations according to the images of hue, saturation, shape, shadow, texture, position, and size.

Then, we made a comprehensive analysis and correction on the translation results using the topographic map, a geological map, a soil map, the present land utilization data and a combined GPS survey of the location. Moreover, according to the national standard of the classification of the present land use situation (GB/T ) and the actual situation in the study area, the land use in the study area was divided into six land use types, namely cultivated land, forest land, grass land, water area and land for water facilities, urban village and mining traffic land and unused land. Methods of the research (1) Land use dynamic degree ( ): To quantitatively describe the range and speed of LUCC, the land use dynamic degree was introduced, the equation to calculate is as follows. Where is the land use dynamic degree of a specific land use type, defined as percent land use change per year; and represent the area under a specific land use type per year, respectively; is time in years. (2) Land use degree comprehensive index ( ): This index mainly reflects the impact of human factors in the land system; to quantitatively measure the intensive land use level, Zhuang et al. Posited the classification principles, the classification values of land use degree classification index , and the expression of.

Where is the land use degree comprehensive index, its value interval is between 100, 400; is the land use degree classification index of the i th class; is the land use degree classification area percentage of the i th class; and is the land use degree classification number. By using the land use degree comprehensive index, we can obtain the land use degree change value; its expression is as follows: where is the land use degree change value; and represent the land use degree comprehensive index of time a and b of the i th class, respectively. If is positive, it would suggest the development period of the regional land use status; if is negative, it would suggest the adjustment or recession period of the regional land use status. (3) Land use transfer matrix: The dynamic transfer matrix can describe the reciprocal transformation between the land use types, which can be used for the hilly ecological restoration area to simulate the process of land use and then form a land use dynamic change matrix table. Combined with the regional influence of Landsat TM data, we can fully explain a period of time during the exchange of various land use types. A study on driving forces of land use and land cover change. Management of Land and Resour.

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Overview Control4 allows for you to easily integrate infra red based audio/visual devices but it does not give you the ability to integrate TCP/IP based devices easily This is where. Can be used to quickly integrate devices not normally integrated within Control4's driver wizard by adding tcp commands to their events page. 4 driver for Modbus RTU. Modbus is a communication protocol which supports large range of devices from home and industrial automation, especially different PLC units, ventilation and heat recovery devices and many others. Usually these have nontrivial functions for which drivers created using the Driver Wizard. The Forecast.io service provides free (upto 1000 calls per day) weather data and forecast. Forecast.io is backed. The Chowmain Forecast.io and OpenWeatherMap drivers brings all of this data into Control4 for use in the home automation system. This is useful for. Control4 Wizard; eggzlot; c4Forums. Control4 driver search. Control4 Driver Wizard Free. All rights reserved. Logitech, the Logitech logo and other Logitech marks are owned by Logitech and may be registered. Get the guaranteed best price on Floor Guitar Effects like the DigiTech RP350 Guitar Multi Effects Pedal at Musician's Friend. Get a low price and free shipping.

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Drivers Of Land Use Change Map In The World

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One of the detailed and useful ways to develop land use classification maps is use of geospatial techniques such as remote sensing and Geographic Information System (GIS). It vastly improves the selection of areas designated as agricultural, industrial and/or urban sector of a region. In Islamabad city and its surroundings, change in land use has been observed and new developments (agriculture, commercial, industrial and urban) are emerging every day. Thus, the rationale of this study was to evaluate land use/cover changes in Islamabad from 1992 to 2012. Quantification of spatial and temporal dynamics of land use/cover changes was accomplished by using two satellite images, and classifying them via supervised classification algorithm and finally applying post-classification change detection technique in GIS. The increase was observed in agricultural area, built-up area and water body from 1992 to 2012. On the other hand forest and barren area followed a declining trend.

The driving force behind this change was economic development, climate change and population growth. Rapid urbanization and deforestation resulted in a wide range of environmental impacts, including degraded habitat quality.

Demographics of Islamabad from 1992 to 2012 The urbanization process has led to chaotic growth in city, deteriorated the living conditions and has worsened the environmental scenario having detrimental impacts on human health. Therefore, it is required to determine the rate and trend of land cover/use conversion for devising a rational land use policy. The study aimed at carrying comparative study/analysis of the LULCC of Islamabad using RS and GIS tools. This aim was achieved through the following objectives (1) to identify and delineate different LULC categories and pattern of land use change in Islamabad from 1992 to 2012 (2) to integrate supervised classification and visual interpretation using GIS and to examine the potential of integrating GIS with RS in studying the spatial distribution of different LULC changes and (3) to identify major driving forces and extent of contribution in LULC change. Data acquisition In order to study LULC changes in a city like Islamabad, two multispectral satellite images of city were acquired for two Epochs; 1992 and 2012. 1992 and 2012 images (LANDSAT) were obtained for the month of October from United States Geological Survey (USGS) , an Earth Science Data Interface and SUPARCO respectively.

Specifications of the satellite data acquired for change analysis are given in Table. In addition to using high-resolution imagery, ancillary data was collected which included ground truth data, aerial photographs and topographic maps. The ground truth data was in the form of reference data points collected using Geographical Positioning System (GPS) from March to October 2012 for 2012 image analysis, used for image classification and accuracy assessment of the results. Image pre-processing and classification Satellite image pre-processing before change detection phenomenon is very important in order to establish a more direct affiliation between the acquired data and biophysical phenomena (Abd El-Kawya et al. Due to acquisition system and platform movements, remotely-sensed data from aircrafts or satellites are generally geometrically distorted. The satellite data were imported into ERDAS 2011 software in an image format for geometric correction.

After the images were geo-referenced, mosaicked and subset on the basis of Area of Interest (AOI). All satellite data were studied by assigning per-pixel signatures and differentiating the land area into five classes on the bases of the specific Digital Number (DN) value of different landscape elements. The delineated classes were Built-up area, Agriculture, Forest, Water and Barren area (Table ). Each class was given unique identity and assigned a particular colour to make them separate from each other. For each of the predetermined land cover/use type, training samples were selected by delimiting polygons around representative sites.

Spectral signatures for the respective land cover types derived from the satellite imagery were recorded by using the pixels enclosed by these polygons. A satisfactory spectral signature is the one ensuring that there is ‘minimal confusion’ among the land covers to be mapped (Gao and Liu ). After that supervised classification was performed by applying maximum likelihood algorithm on the images. It is the type of image classification which is mainly controlled by the analyst as the analyst selects the pixels that are representative of the desired classes.

Classes delineated on the basis of supervised classification To improve classification accuracy and reduction of misclassifications, post-classification refinement was therefore used for simplicity and effectiveness of the method (Harris and Ventura ). Moreover, when using a data having medium-spatial resolution such as that of Landsat mixed pixels are a common problem (Lu and Weng ); especially for the urban surfaces that are a heterogeneous mixture of features mainly including buildings, grass, roads, soil, trees, water (Jensen ). The problem of mixed pixels was addressed by visual interpretation. For the enhancement of classification accuracy and therefore the quality of the land cover/use maps produced, visual interpretation was very important. Thus, local knowledge, reference data, as well as visual analysis, considerably improved the results obtained using the supervised algorithm. Accuracy assessment Accuracy assessment is essential for individual classifications if the classification data is to be useful in change detection (Owojori and Xie ). For the accuracy assessment of land cover maps extracted from satellite images, stratified random method was used to represent different land cover classes of the area.

The accuracy was assessed by using 100 points, based on ground truth data and visual interpretation. The comparison of classification results and reference data was carried out statistically using error matrices. In addition, a non-parametric Kappa test was also performed to measure the extent of classification accuracy as it not only accounts for diagonal elements but for all the elements in the confusion matrix (Rosenfield and Fitzpatrick-Lins ).

Land use/cover change detection Post-classification change detection technique, performed in ArcGIS 10 was employed by the study. Post classification in urban environment has been effectively used by various researchers due to its efficiency in detecting the location, nature and rate of change (Hardin et al. Another technique used to obtain the changes in land cover/use during the specified time period was overlay procedure. A two-way cross-matrix obtained by the application of this was used to describe the key change types in the study area. Cross tabulation analysis was conducted in order to determine the quantitative conversions from a particular category to another land cover category and their corresponding area over the evaluated period on pixel to pixel basis.

Thus, a new thematic layer was also produced from the two five-class maps, containing different combinations of ‘‘from–to’’ change classes. Results and discussion The resulting land use/cover maps of the two periods of 1992 and 2012 shown in Figs. And had an overall map accuracy of 89% for both images by using error/confusion matrix.

This is the commonly employed approach for evaluating per-pixel classification (Lu and Weng ). Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics of 0.89 each. This was reasonably good overall accuracy and accepted for the subsequent analysis and change detection (Lea and Curtis ). Area statistics and percentage of the land use/cover units in 1992–2012 The percentage area of each class in 1992 and 2012 showed that Barren Area had the largest share in 1992 representing 55.35% (49,789 ha) of the total LULC categories assigned.

This class faced a major shift and it was reduced to 1.87% (1678 ha) in 2012. The other class which faced decline during the study period was Forest Area. The area of this class in 1992 was 13.49% (12,136 ha) of the total area and in 2012 it was reduced to 6.82% (6138 ha). The other three classes faced an increase in the total share. The major increment was faced by Built up Area. Its share was increased from 18.09% (16,281 ha) in 1992 to 56.73% (51,039 ha) in 2012. The Agricultural Area was also increased from 11.49% (10,336 ha) in 1992 to 32.23% (29,000 ha) in 2012.

The change in the Water class was not very significant though it was increased during the study period. The share in the total area was 1.57% (1416 ha) in 1992 to 1.75% (1579 ha) in 2012. This study revealed that there was about more than 213% increase of settlement area i.e. From 1992 to 2012 (Table ). This value signified the dramatic land cover change on the category of built up surface exerting an incredible pressure on non-built up surfaces, in particular agricultural lands.

Expansion of the already existing urban fabrics through rapid construction sites of residential units, commercial and industrial units and road networks and pavements port and leisure facilities and other impervious surfaces all combined together led to continuous expansion of built up surfaces in the different corners of the city. Chigbu et al. focused on the analysis of land use and cover changes in Aba Urban area, Nigeria by using medium satellite imageries for change detection. The results revealed that built up area was increased from 21.7 to 36.5%. Another study of Tahir et al. on LULCC in Mekelle city, Ethiopia showed a positive change of 200% in urban area. The urban changes may be associated with population growth as well as industrial development during this period.

Simultaneously, a close relationship of spatial urban expansion was shown with the geometric center of a city and distance from major roads, indicating that most significant drive to urban expansion was road. Deforestation and habitat loss were the major impacts of urbanization on the environment along Islamabad Highway. In this study, it was found that there is approximately 49% decrease in forest cover since last 20 years i.e. From 1992 to 2012. One of the main reasons for the loss of forest to sparse vegetation can be explained by the immense damage caused by wild and accidental fires during the summers, when there is no rain for months and temperature soars up 45 °C and hundreds of hectares turn to cinders.

Conversion of dense forest to agricultural land settlement was also significant. The given data specifically state that the increase in deforestation was mainly due to increase in agricultural use of land but some of the forest areas were shifted to different gardens in the region. If deforestation continues the area is bound to face the negative impact of soil erosion, high temperature and dust storm (Ellis et al.

These negative impacts would further lead to climatic changes and ripple effect would help in an increase of global warming in the future (Siddiqui ). Forest that remained intact during the study period were 50% whereas 8% of the forest land degraded indicating the trend towards the deforestation of forest to sparse vegetation especially along the western margins where most of the reserved forest suffered severe damage. Results indicated that the dense forest present during 1992 along the western margin had been completely changed to sparse vegetation by 2012 due to stone quarries, cultivation, wood cutting for fuel and fodder consumption by the villagers and the facto cement industry which borders the western periphery.

Reports say that the continuous forest destruction in the city is causing a significant loss. The wood biomass declining rate is the second highest in the world and ranges from 4 to 6% per year (UNEP ). Tripathi and Kumar analyzed the LULC dynamics in Takula Block (Uttarakand) by using modern geospatial techniques of remote sensing and GIS and the results revealed that forest decreased by 6.28% from 1999 to 2005. Similarly, Reis investigated the land use and land cover changes in Rize, Turkey by using remote sensing and GIS. The LULCC were analyzed by change detection comparison and the results indicated that the agricultural land increased by 36.2% from 1973 to 2002. The result of classified maps also indicated the increasing rate of Water class share in total area of the city. Water bodies covered only 1416 ha of the study area land in 1992 and increased to 1579 ha in 2012.

This fluctuation may be due to the rain fall in the Monsoon Season. Particularly, in this period, a huge volume of Rawal Lake has declined i.e. 594–478 ha during 20 years.

The total area of Rawal Lake has shown a significant change and it accounted for a percentage of 19.5%. Change detection analysis of Rawal watershed by Butt et al. also reported similar results. The watershed is confronting problems of rapid deforestation and urbanization resulting in gradual land use change. The population growth and increasing number of housing colonies in the catchment area of Rawal Lake are adversely affecting the water regime coming into lake.

The activities, like cutting of trees due to intensive use for household needs and high market value (heating, cooking timber etc.), forest disease and ineffective forest management etc. Are accelerating the rate of deforestation in the watershed area (IUCN; Tanvir et al. The area under vacant land decreased because of increasing population pressure in the core area compelling rich people to move to these vacant lands. This has resulted into the emergence of residential colonies in the outskirts of the city. The Barren class recorded negative change over the years under study (Table ).

Drivers Of Land Use Change Map In America

The results of this study disclosed that the area decreased from 1992 to 2012 and the change was accounted for a percentage of 96.63% in 20 years’ time period. evaluated the LULCC in Mekelle city, Ethopia by using remote sensing and GIS. A negative change of 92.86% was recorded in barren land and all farm lads available in the area were converted into other feature.

Post-classification comparison of the detected change was carried out to produce change maps by using GIS, to comprehend the spatial patterns of change among years. The overlay of LULC maps was to produce the change map (Fig. ) and the ‘from-to’ information given in Table shows that the major observed change during the period of 20 years was from forest and bare soil/rock to agriculture and settlements.

Driving forces LULC changes of Islamabad are governed by a combination of environmental, geographical and socio-economic factors. Although the primary reason for rapid urbanization is population growth, the contribution of other causes such as environmental factors and economic development also need to be assessed. For the evaluation of underlying mechanisms of the changes in LULC, a regression analysis was performed of categories using selected environmental and socio-economic variables (GDP, population, temperature and precipitation), and the results are presented in Table. Socio-economic data, for instance GDP and population values were obtained from the yearly and decadal tables of the Pakistan Bureau of Statistics (Formerly Federal Bureau of Statistics). Regression analysis for land use/cover change and underlying factors The regression analysis of land use parameters in relation to climatic variables, population and economic factors strengthened the influence and role of all these factors in land use conversion pattern in the study area.

The coefficient of determination of 0.873, 0.972, 0.996, 0.991 and 0.996 computed for water body, forest area, agriculture area, built-up area and barren area respectively revealed that 87, 97 and 99% of variance or change in the land use classes in the study area during the specified time period can be explained by the selected underlying factors. The census data indicated the rapid population growth during the study period (Fig. ).

The rapid population growth in urban areas was mainly resulted from migration of rural to urban areas. This increase in population had a plausible effect of increase in pressure on the limited resource-base, and significantly contributed to the expansion of urban land by deforestation and infilling of low-lying areas. Urban growth may have positive or negative impacts on environment but unplanned growth of urban areas always has negative effects. Environmental problems associated with urban growth tend to be analogous in both developing and developed countries.

Urban settlements continue to increase on daily basis so the activities such as construction of buildings, parking lots and roads act as waterproof for the city surface. However, the land has drastically and irreversibly changed from its original state as the conversion from natural landscape to an urban area is an irreversible process. If there is further expansion in urban land it will be a destruction cause of a lot of more precious habitats. The urbanization process operating in the fringe has given rise to typical land use associations where there is side by side development of dynamic and contemporary land use pattern. Serious land use problems such as agricultural land losses and unauthorized urban sprawl have been emerged due to the emergence of this rural urban fringe zone with its complex problems of adjustments in between different ways of life in rural and urban areas. Economic development of Islamabad is another factor contributing to rapid urbanization. The GDP of Islamabad in 1992 was approximately 0.49 billion US dollars, 0.72 billion US dollars in 2002.

Currently, the GDP of the city is 2.11 billion US dollars and share of city in national economy is 1%. The industrialization and economic development has led to higher urbanization rates. This economic activity has also resulted in influx of large number of rural immigrants during the study period. One of the most significant ways that global climate change is predicted to affect economic activity is through its effect on agriculture, since temperature and precipitation are direct inputs to agricultural productions. Climate change has been postulated to affect forest nutrient cycles, agricultural productivity and other processes of ecological significance. For instance, changes in precipitation will cause hydrological fluxes of nutrients to change and could cause changes in productivity, decomposition and nutrient uptake. Similarly increases in temperature could also result in changes in hydrologic fluxes, decomposition, accelerated physiological development, resulting in reduced yield and hastened maturation (Johnson et al.; Bonan ).

Rainfall is the most significant factor in continuous destruction of forests and agricultural lands. In terms of precipitation, higher rainfalls could enhance growing period duration. In some areas of Pakistan high degree of precipitation has enabled higher degree of production and provided more water for irrigation.

However, contrary consequences were observed in some regions due to loss of fertile soils with intense flooding caused by high average rainfalls or hindrance in drying and storage of crops. On the other hand, the declining rate of woody biomass is the second highest in the world. It ranges 4–6% per year (UNEP ). This decline in forest area has been linked with ever changing climatic conditions that have been intensified over the years as a consequence of natural and man-made processes. The regression analysis revealed the impact of temperature and rainfall on the forest area as the average temperature has increased and the precipitation has decreased over the years (Fig. ).

Change in temperature and precipitation of Islamabad from 1992 to 2012 According to the IPCC, an increase in the average global temperature probably leads to changes in atmospheric moisture and precipitation. The impacts of the temperature rises include more intensive rainfall, floods, drying of some rivers in the dry season which were up till now perennial rivers, unpredictable weather such as late start of the rainfall season and or shorter rainy season and frequent events of drought due to low levels of water in the dams. All these are evidently impacts of climate change in Islamabad.

Planners across many sectors will confront the challenge of a changing climate. They will likely adopt a variety of adaptation practices, designed to plan sustainable urbanization, better conserve water supplies, forest and agricultural land and develop alternative strategies for their management. Conclusion In the present study, assessment of LULC and their change detection were carried out using digital image processing techniques. Analysis revealed that urban areas, agricultural areas and water increased during 1992–2012 resulting in substantial reduction of forest area and barren land. The increase in the water was insignificant.

However, the major drinking water reservoir of the city faced a decline by 19.5% during the study period. The conversion of forest and barren land to urban land has caused varied and extensive environmental degradation in the study area and the major negative outcomes associated with the rapid urban development are the growths of slums.

Major driving forces of urban land expansion are population growth and economic development.