In South and Central America, beef cattle ranching has been the dominant driver of forest clearance (e.g. Grainger 1993; Fearnside 2005; Gibbs et al. 2010). During the 1960s, 70s and 80s, road building and financial incentives from the Brazilian government encouraged deforestation of the Amazon to create pasture and cattle ranches (Carvalho et al. 2002; Fearnside 2005). This process often involves clearance of the forest followed by burning to remove any residual trees. Until the 1990s, Brazilian beef was usually sold domestically but in the early 2000s, international demand for Brazilian beef partly drove a spike in deforestation rates between 2002 and 2004 (Nepstad et al. 2006). During the 1970s, an oil embargo prompted rapid expansion of biofuel crop growth in South America, specifically the growth of sugar cane to produce ethanol. In 1977, the Brazilian government mandated that all gasoline must be blended with ethanol. This mandate is still in place and the current minimum blend level is set at 27% ethanol. As well as directly driving forest clearance, the growth of bioenergy crops can indirectly lead to deforestation if it displaces food production which then moves onto forested land.
Despite their significant contribution to both economic and ecological services, dry forests of Ethiopia are currently under severe threats both from anthropogenic and natural calamities (Lemenih et al., 2014). Agriculture, expansion of resettlements, fire, population growth and climatic variation are among the factors that significantly contribute to the decline in size and to the fragmentation of the dry forests of Ethiopia (Eshete et al., 2011; Lemenih et al., 2007). Several of these studies attempted to link the loss in dry forests with human activities but lacks estimates of the spatial extent of the declining woody resources and the major driving forces. In addition, the extent and severity of landuse transition in the ecosystems of the surrounding arid environment is not well studied. During some periods of the dry season, clouds of haze extend along the borders of Sudan and northwestern Ethiopia, which may be rooted from the degradation of the natural woody vegetation of the region. The present study assesses landcover transition using SVM classification algorithms for examining major contributing factors that influence landcover transitions. The SVM classification model works well in spectrally complex landcover categories for enhancing better classification results (Tuia & Camps-Valls, 2011) and understanding of the significant transitions in landcover.
However, observational data on LULCC are not available on the global scale with the required temporal and spatial resolution, consistency, and historical coverage (Verburg et al., 2011). Instead, models are utilized to represent global landuse and produce the required land-usechange time se- ries. Land-use modeling is typically split up into histori- cal backcasting approaches and future scenario modeling. Both forward- and backward-looking models apply a range of different modeling approaches as well as different as- sumptions about drivers and the spatial allocation of land- use changes (National Research Council, 2014; Yang et al., 2014), and they are often initialized with different represen- tations of present-day landuse (Prestele et al., 2016). Thus, even the models within one community (future or historical) do not provide consistent information on landuse and land- usechange over time, and a variety of independent datasets on a spatially explicit or world regional level are provided to the user community (e.g., climate modeling) (see Sup- plement Sect. S1 and Table S1 for examples of the histor- ical data). These historical and future datasets are not con- nected and consistent in the transition period and entail a va- riety of uncertainties (Klein Goldewijk and Verburg, 2013) (Fig. 1). In consequence, these datasets disagree about the amount and the spatial pattern of land affected by human activity. Moreover, varying detail in classification systems, inconsistent definition of individual categories (e.g., forest or pasture), and individual model aggregation techniques, amplify the discrepancies among models (Alexander et al., 2017; Prestele et al., 2016).
(2) Developing systematic approaches to evaluate results of land-usechange models against independent data sources, utilizing the full range of high-resolution satellite data (e.g., the Landsat archive and the European Sentinel satellites), 15
reference data obtained from (sub-)national reporting schemes under international policy frameworks (e.g., Kohl et al., 2015) and innovative methods such as volunteered geographic information and crowd-sourcing (Fritz et al., 2012). Although satellite data is also not directly measured empirical data, but goes through a mathematical conversion process prior to a final land-cover product, it can improve representations of present-day landcover. If not yet possible at the global scale due to the limitations discussed in section 2, we recommend the implementation of regional scale 20
Political-administrative aspects of urbanization have also had a great history. Urban centres in Pakistan emerged, declined or even disappeared with the rise and fall of kingdoms. During British rule, many districts, tehsils and capitals grew fast under political influence. This process has not stopped after independence, and examples are the shifting of capital from Karachi to Islamabad, the interim officiating of Rawalpindi as capital, Abbottabad as, summer capitals of PaktunKhah, division of Karachi into five districts, and currently the upgrading of Chiniot, Nanakana Sahib up to the status of a district. The contribution of industries to employment, in fact is a major force attracting the rural people to the city. Thus the cities of Karachi, Lahore, Faislabad, Multan, Hyderabad, Gujranwala, Sialkot all emerged as big cities due to economic growth and attracted a large number of young people from the small cities and towns. Thus rural-urban migration in the beginning under push- pull factors played a mjor role in the growth of cities. Urban areas in need of manpower for their industrial and manfacturing units pulled in people from rural areas, who were un/under-employed due to farm mechanization. The rapid growth of big cities, has also caused them to expand into the adjoining rural areas in a haphazard and unplanned manner. These newly developed areas being outside the ambit of municipal taxes, having no urban facilities, but cheaper land rates, act as incentives for housing construction. This kind of suburbanization, after a few years put pressure on the cities to extend their municipal limits and include these areas. The current level of urbanization (42%) in Pakistan though not high by global standards is high in South Asia.
One of the common crop modelling applications is the estimation of potential productivity under different assumptions of biophysical constraints [ 44 ]. We use the model (PROMET) (Processes of Mass and Energy Transfer [ 27 , 45 ]). PROMET is an agro-hydrological land surface process model, which contains a mechanistic, bio-physical, dynamic vegetation component to model crop growth and yield formation [ 46 , 47 ]. PROMET allows spatially distributed, raster-based simulations, which model net primary production, evapotranspiration, water balance and yield at different scales, from field to global, as a function of meteorological drivers as well as soil and terrain information. PROMET determines and considers water availability through soil moisture balance, radiation balance and the physiological regulation mechanisms of plant canopies [ 47 , 48 ]. The dynamic crop growth component uses parameters, which represent the sensitivity of the crops to environmental conditions (e.g., temperature, soil suction, nutrient supply) and which determine phenological development and crops reactions to related stresses. Management practices such as crop cultivar selection, sowing date, harvest date and fertilization levels are considered [ 45 ]. PROMET is well parameterized and validated for (but not restricted to) the simulation of the two important Gambella cereal crops, maize and sorghum (see, e.g., Reference [ 27 ]). The required parameters for this paper were either derived from the literature [ 49 ] or determined through comparison with recorded yields in different parts of the globe. The spatial nature of PROMET also allows localizing the potential of cropland expansion through considering biophysical drivers at the local scale, such as climate, soil quality and topography. Simulation of potential yields outside the actual cropland allows determining where an expansion of cropland would potentially be most feasible under the given natural conditions.
The Hisar district, a part of the Indo – Gangetic alluvial plain, is situated between 2853’45” to 2949’15” North latitudes and 7513’15” to 7618’15” East longitudes (Fig.1). It occupies a total area of 3983sq.km. Hisar district comprises of three major physiographic units i.e. Aeolian plain, Older alluvial plain and Chautang flood plain. The district lies in semi-arid region, which is nearly 30 km northeast of the Rajasthan desert. It generally experiences a sub-tropical, continental type of climate. According to 2011 Census, Hisar district recorded a population of 17, 42,815 persons, which made the district 2nd most populous district of the state. The district observed population density of 438 persons per square km. The Population of the district comprised of 931,535 Male and 811,280 female. The district made rapid progress in agricultural production during post Green Revolution period. As a matter of fact the dry climatic conditions of the district necessitated the development of alternative source of water, essential for cultivation of crops.
to 32,087 acres in 2004. During that same time span, the county’s open waters increased from
4,973 acres in 1850 to 11,457 acres in 2004, a 130% increase. The gross amount of wetland loss was primarily due to direct landuse conversion by agricultural LULC, which accounted for 76% of the lost wetlands between 1850 and 2004. Other direct replacements had smaller impacts on wetland loss, with a distant second contributor, urban LULC, accounting for 5% of the wetland loss. Drying and channelization also contributed to wetland loss by conversion to upland forest/ and water LLUC (in the form of canals and reservoirs), contributing 3% and 2% respectively, to wetland area loss However, there are 19,476 acres of wetlands still remaining from the 1850s wetlands. (Tables 7 & 8, Figures 13 & 14).
In general, the results presented in Chapters 2 to 5 showed that the climate is generally warming and landuse/ landcoverchange is occurring rapidly. The synergistic effects of the changing landuse/ landcover and warming climate has placed feed resources at a cross roads in between a warming planet and changing landuse across all eco-environments. The farming and herding households have already sensed this and are trying to developing coping strategies. They are forced to abandon or reduce their dependence on traditional feeds. Grazing-based feed resources are continually reducing to varying degrees across all eco- environments. Conversely, non-grazing based feeds and the associated feed deficit management strategies are becoming increasingly available to livestock and are expected to be the future feed resources. There are concerns, however, as to whether these feed resources are meeting requirements to support productive stock with respect to nutritive value of crop residues, nutrient cycling in croplands, crop expansion to fragile pastoral eco-environments, communal grazing land conversion to private use and a growing livestock population. Land fragmentation and replacement of grazing lands with lands of other use will also aggravate loss of valuable grassland species in the face of a warming climate. These changes call for integrated research and policy interventions for eco-environment/ site specific feed resources development and management strategies that ensure sustainable availability of feeds both in quality and quantity, and conservation of the biodiversity of grasslands.
Landuse and cover data are the basis of investigation on global environmental change and also the key factor in the study of Earth surface activity progress; the exploration in- volves fields like biochemical circle, plant biomass distribu- tion, climatechange and atmospheric circulation. Many re- search results suggested that recent landuse is associated with a large number of industrial and agricultural activities; landuse has experienced a great and fast change in the past 50 years, especially in the width and depth. These changes are usually accompanied by the economic increase and pop- ulation boom as well as the change of production and living (Fuller et al., 2012).
Two different types of surface information are required for use within the CLM; landcover/landuse information and soil description. The landcover/landuse information defines the type of vegetation (e.g., deciduous or evergreen) or landuse (e.g., urban or agricultural) and assigns associated ecological characteristics (e.g., leaf area index) and radiative transfer properties (e.g. aerodynamic roughness length, albedo) to each landcover/landuse category. The classification system implemented for the landcover/landuse categories is the International Geosphere Biosphere Programme (IGBP) system, which consists of seventeen categories (see Table 1). Additionally, the CLM has the capability to divide individual grid cells into fractions, allowing land surface heterogeneities to be included. Other parameters related to the landcover/landuse information include fractional coverage of vegetation.
Integration of these data sources in models allows bridging the gaps in observation across space and time, and permits simulation of processes that are not directly observable. Testing of multiple scenarios may make it possible to separate the influences of different processes (e.g., land management compared to climatechange or weather extremes), as they influ- ence ecosystems and human activities. New methods of analysis, in which entire time series of images can be analyzed at once, provide new possibilities in classifying landcoverchange as it is occurring (Zhu et al. 2012). It is possible that the algorithms used in such analyses could be adapted to analyze joint time series of climatechange, weather extremes, and landcoverchange to separate and investigate the interactions of these variables. Knowledge of such interactions could be included in coupled models of the climate and be used to forecast scenarios of future system behavior (e.g., tipping points). Such forecasts could help identify critical weaknesses in existing planning for mitigation and adaptation. The assess- ment system should include continued contact with groups that represent decision makers for urban and regional planning, agricultural and forest land man- agement, biodiversity conservation, and ecological research, so that the models are sensitive to the types of policy choices that will be needed in the future. The research should be coordinated with national and international campaigns that have complementary interests, such as NEON, GEWEX, and the Global Earth Observation System of Systems (GEOSS). Data sharing among these groups and relevant idea devel- opment should be part of the activities.
Like many other developing countries across the globe, significant land-cover changes have occurred in Ethiopia since the last century. These changes were primarily due to anthropogenic activities, in connection with the population increase and due to landuse changes, including deforestation, over grazing, and improper cultivation of agriculturalland which led to accelerated soil erosion and associate soil nutrient deterioration (FAO, 1986; Hurni, 1993; Gebresamuel et al., 2010; Eleni et al., 2013). Soil degradation in the form of plant nutrient depletion is the major environmental problems in the highlands of Ethiopia. Among Sub-Saharan countries, Ethiopia is the most seriously affected country by land degradation (World Bank, 1998). A change in the land-cover of an area can negatively affect the potential characteristics of the area, and may ultimately lead to degradation and loss of productivity (Zewdu et al., 2014). Land degradation, which is a product of complex interactions of many of the physical and biological variables, reduces the potential capability of soil to produce goods and services. Semi-arid regions are under high pressure to supply the required food for their rapidly increasing populations. Consequent changes in the land-use patterns due to agricultural intensification, together with the harsh climatic conditions including global climatechange, have accelerated land degradation processes, with yield reduction in many parts of the arid and semi-arid Ethiopia (Zewdu et al., 2014).
The rapid industrialization and urbanization of an area requires quick preparation of actual landuse and landcover (LULC) maps in order to detect and avoid overuse and damage of the landscape beyond sustainable development limits. Remote sensing technology fits well for long- term monitoring and assessment of such effects. The aim of this study is to analyze the expansion of urbanization and LULC changes in Sidon City, Lebanon between 1985 and 2015. The study site is a fast growing region. Therefore, monitoring land changes is an important issue in landscape planning and resource management. In this study, an analysis is carried out for LULC changes in Sidon district via satellite imagery and geographic information systems (GIS). The applied methods consist of two major components: remote sensing-based land classification and GIS-based landchange analysis. Seven landcover classes have been detected for evaluating and quantifying the landcover changes for each class within the time frame series from 1985 and 2015. The final results in this study shows the urban expansion and built up areas were clearly detected. Built-up areas took over various unused open areas and in many cases it replaced different agricultural lands. Also results shows the agriculturalland changes where some crop types dominates others in some areas and some has been completely transformed to built-up areas. The result from this research indicates that the civil war and conflict with neigbhouring countries brings decline in agricultural areas, impacting the economic aspect of agro trading and destruction of property and development. Yet, after the conflicts ended, Saidon saw significant increase in urbanisation and development activities at local scale.
In Ethiopia, there are several studies on landuse and landcoverchange that showed the different facets of change. Gete and Hurni (2001) in Dembecha area of Gojjam, Belay (2002) in Derekolli catchment of South Wello, Amare (2007) in Eastern Escarpment of Wello, Nyssen et al (2008) in Wag zone of Amhara Region, Northern Ethiopia, studied landuse and landcover dynamics. Gessesse and Kleman (2007) in Southern part of Ethiopia (Central rift valley), Berhan Gessesse (2010 ) in western part of Ethiopia and Diress et al (2010) in North eastern Afar range lands have also studied LULCC. Most of these studies found cropland has expanded at the expense of woody vegetation cover. But from 1984- 2003, area of forest and shrub land was increased while area of agriculturalland was decreased in Simen Mountains National Park, North western Ethiopia (Menale et al 2011). So that region- specific information of changes in LULCC is essential for land resource management. Though LULCC research is undergoing at different places and at different scale, still a lot more studies are needed to cover the country. The present study investigates the landuse and landcover dynamics in Ameleke watershed in middle catchment of Gidabo River, South Ethiopia. This watershed is inhabited by the Gedeo Guji Oromo ethnic groups. The Gedeo community change the natural forest and grassland to agroforestry while in Guji Oromos’ land charcoal production, expansion of croplands and overgrazing on grass lands and shrub lands are the major landuse problems. Cultivation and grazing of marginal lands has also a desperate effect on the resources of the watershed. In association with this problem, this study tried to document the spatial and temporal landuse and landcover dynamics of Ameleke watershed.
Abstract. Pressure on land resources is expected to increase as global population continues to climb and the world be- comes more affluent, swelling the demand for food. Chang- ing climate may exert additional pressures on natural lands as present-day productive regions may shift, or soil quality may degrade, and the recent rise in demand for biofuels increases competition with edible crops for arable land. Given these projected trends there is a need to understand the global cli- mate impacts of landuse and landcoverchange (LULCC). Here we quantify the climate impacts of global LULCC in terms of modifications to the balance between incoming and outgoing radiation at the top of the atmosphere (radiative forcing, RF) that are caused by changes in long-lived and short-lived greenhouse gas concentrations, aerosol effects, and land surface albedo. We attribute historical changes in terrestrial carbon storage, global fire emissions, secondary organic aerosol emissions, and surface albedo to LULCC us- ing simulations with the Community Land Model version 3.5. These LULCC emissions are combined with estimates of agricultural emissions of important trace gases and min- eral dust in two sets of Community Atmosphere Model sim- ulations to calculate the RF of changes in atmospheric chem- istry and aerosol concentrations attributed to LULCC. With all forcing agents considered together, we show that 40 % (±16 %) of the present-day anthropogenic RF can be at- tributed to LULCC. Changes in the emission of non-CO 2 greenhouse gases and aerosols from LULCC enhance the total LULCC RF by a factor of 2 to 3 with respect to the LULCC RF from CO 2 alone. This enhancement factor also applies to projected LULCC RF, which we compute for four future scenarios associated with the Representative Concen- tration Pathways. We attribute total RFs between 0.9 and 1.9 W m − 2 to LULCC for the year 2100 (relative to a prein- dustrial state). To place an upper bound on the potential of
obtained from pho- tointerpretation of aerial photographs and orthophotographs was used to quantify landcover changes between 1957 and 1996 in a Mediter- ranean middle moun- tain area. Expansion of forested area is clearly the main landcoverchange caused by the abandonment of traditional agricultural activities and by the use of other materials and energy sources instead of forest resources. As a result, about 64% of the area was covered by forest by 1996, where- as in 1957 forests accounted for only 40% of the landcover. Spontaneous afforestation of abandoned fields with Scots pine (Pinus sylvestris L.) in terraced areas and areas of sparse scrub vegetation, coupled with an increase in the density of forest canopies, has been responsible for this expansion of woodland. The influ- ence of physiographic factors in landcoverchange processes in the terraced areas of the catchment was also considered. The results demonstrate that within the terraced areas, north-facing and more elevated steeper slopes are more intensely afforested. However, an accurate analysis of the role played by these factors in landcoverchange cannot be carried out because the pattern of land abandonment is not independent of these physiographic characteristics. Furthermore, field observations at the terrace scale are evidence of the relevant influence of local topography in afforestation dynamics.
At federal level, a “forest development, conservation and utilization proclamation” was issued in 2007 and private forest is recognized in this proclamation. The country’s Climate Resilient Green Economy (CRGE) strategy emphasizes landuse management and enhancement of forest through afforestation and reforestation for reducing Greenhouse Gas Emission (FDRE, 2011). The second national Growth and Transformation Plan (2015- 2020) targets expansion of afforestation and management of lands with natural forests. These policies and strategies recognize both state and private ownership, encourage non-state actors to become involved in forest management and state that ‘any person who develops forest on his land holding or in a state forest area given to him on concession shall be given assurance to his ownership of the forest’ (MARD, 2007). In addition, the CRGE stipulates forestry as one of its four pillars and envisages building on the forest-energy link by improving availability of (cheap) hydroelectricity and improving efficiency in use of biomass energy (FDRE, 2011). Afforestation and reforestation are expected to increase availability of biomass energy while sequestrating additional carbon. Thus, tree plantation has obtained an increased focus also for its link with climatechange.
challenges associated with these diverse approaches and propose actions that can help to mitigate their adverse climatic impacts. PROTOCOLS AND CHAL- LENGES. International pro- tocols, such as the United Na- tions Framework Convention on ClimateChange (UNFCCC) and United Nations Conven- tion to Combat Desertification (UNCCD) are well known for di- rectly addressing the human role in the modification of the climate system. However, they only have an impact when the following ac- tions occur: (i) donors embrace the goals and developing countries and donors work collaboratively to establish appropriate national capabilities and policies that are aligned with the treaty and (ii) developed countries define objec- tives in their national policies that align with the convention goals. Another challenge with these treaties and protocols is that they are typically sector specific. For example, the UNFCCC addresses emissions reductions through focused efforts on forestry and ag- riculture. The UNCCD addresses sustainable development in arid, semiarid, and dry subhumid ar- eas and includes climate-specific objectives (Mattison and Norris 2005; Cowie et al. 2007).
From the point of view of ecosystem disruption, the greater amount of CILCC than LULCC would suggest that CILCC would cause more disruption in all three of the RCP scenarios considered here. However, habitat destruction, particularly conversion of land to agriculturaluse, is thought to be the most important driver of biodiversity loss, with climatechange less important [Hassan et al., 2005]. Since the CILCC is only slightly higher than the amount of LULCC in RCP2.6 and RCP4.5, it is possible that LULCC may have a bigger impact on biodiversity in these scenarios. For RCP8.5, CILCC would likely still be a larger impact on biodiversity, since the total area affected by CILCC is more than double than from LULCC. As well as the extent of the impact, the duration also should be taken into account. After stabilization of the forcing, the effects of LULCC drop off, whereas the CILCC continues as the vegetation reaches equilibrium. The CILCC is likely to continue well beyond 2100 for decades or even centuries after the forcing has stabilized [Jones et al., 2010; Liddicoat et al., 2013]. Comparing the disruptive impact, CILCC could be a more serious challenge than LULCC, particularly in RCP8.5, because of the longevity and quantity of impact, even if the severity is lower. The important role of CILCC in terrestrial carbon changes highlights how critical it is to reduce the uncertainty