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
Complex landuse and landcoverchange (LULCC) processes modify ecosystems' ability to store and sequester carbon and regulate the climate, resulting in thermally uncomfortable climates and even more carbon emissions in an unchecked cycle. The value of potential loss of such climate ecosystem services remains understudied in urbanization planning and development. Using ecosystem modeling, this research quantifies potential changes of carbon storage and sequestration for a case of future LULCC in a tropical country by building an initial baseline carbon account of the existing forest. This study looked at a unique case of planned local-scale LULCC in Singapore where a secondary forest, Punggol Forest, is slated for conversion into a mixed-use residential neighborhood, Punggol Eco-Town. Carbon accounting is conducted to determine the carbon footprint of the LULCC, specifically for carbon storage and rate of carbon sequestration, using a sampled tree inventory with primary data collection. The results suggest that considerations of urban tree species selection in urban forestry are important in planning in order to reduce climate ecosystem services loss as a result of development. It is also a first step in using urban forestry tools for carbon accounting in decision-making for urban planning.
The landuse/cover information helps in understanding status of use of resources and in monitoring, modeling and analyzing environmental change (Krishna et al., 2001). The landuse/landcover are, as such, dynamic in nature as both, its value and pattern change from one particular point of time to another and from one geographical area to another, with varying efficiencies, abilities, priorities and needs (Bisht and Tiwari,1996). It is directly related with the level of techno-economic advancement of civilization of its inhabitants (Whyte, 1961). The landuse/landcover changes are the results of many interacting processes and each of these operates over a range of scales in space and time (Verburg et al., 2003). The problem with landusechange is that role players do not always consider the agricultural, cultural, demographic and socio-economic characteristics of an area before the land is actually developed, resulting in unsuitable landuse changes (Lockeretz 1988; Werner 1993). Remote sensing and Geographical Information System provide an accurate tool for environmental monitoring (Malaviya et al., 2009). Remotely sensed data can be collected at multiple scales and at multiple times, thereby, offering the opportunity for analysis of previous phenomenon synoptically from local to global scales throughout the time (Reddy, 2004). The given observations are the testimony of the fact that the landuse and landcoverchange has emerged as a central theme in evolving strategies for the management of natural resources and monitoring the environmental change.
Landuse and landcoverchange (LUCC) has been recognized as an important driver of environmental change on all spatial and temporal scales (Turner et al., 1994). LUCC contributes significantly to earth atmosphere interactions, forest fragmentation, and biodiversity loss. It has become one of the major issues for environmental change monitoring and natural resource management. In Australia, modification of landcover since European settlement has largely been due to land clearing and weed invasion, as well as to some natural disturbances such as bushfire. In the Strzelecki Ranges, located in south eastern Victoria, the wide scale of land clearing, subsequent agricultural abandonment and fires have all resulted in severe landscape disturbance in the Ranges. Landuse and landcover have undergone further significant changes with the establishment of large scale plantations in the area over the last four decades. However, details of landuse and landcoverchange and its influence on the rainforest in this area is unknown.
Landuse and landcoverchange through inappropriate agricultural practices and high human and livestock population pressure have led to severe land degradation in the Ethiopian highlands. This has led to further degradation such as biodiversity loss, deforestation, soil erosion and soil quality. Agricultural and economic growth in Ethiopia is constrained by the deteriorating natural resource base, especially in the highlands where 80% of the population lives. This threat stems from the depletion and degradation of the vegetation cover of the country. Loss of biodiversity is associated with landuse/landcover changes that are related to a range of biophysical and socio-economic drivers. The implications of these changes suggest that the landuse/cover changes have skewed to the rampant conversion of areas once covered with vegetation to cultivation without adequate use of soil and water conservation and rehabilitation practices. Understanding of the driving forces of landuse and landcoverchange (LULC C) is essential for effective sustainable land resource management. Change in LULC can also negatively affect the potentialuse of an area and may ultimately lead to land degradation. Improving the understanding of landuse and landcover dynamics can help in projecting future changes in landuse and landcover and to instigate more appropriate policy interventions for achieving better land management.
is at its minimum from July to September, however future minimal values occur earlier in the year near April to June.
8.3 Model Response to LandUseLandCoverChange Only
Figure 17 shows the average monthly flow and sediment loading under future LULC change only for each carbon emission scenario compared to the baseline. Runoff is virtually unaltered by the changes made to landuse; future quantities match the present baseline with monthly differences equaling no more than ± 20 cms, occurring from August – October. Sediment loading is more significantly affected, with monthly averages differing from the baseline ± 185 tonnes / day (Figure 17(b)). Further, a distinct response to the specific carbon scenarios is more easily identifiable. A2 predicts an increase in loading for all months. The largest increase (33%) occurs in March. A1B produces similar monthly averages compared to the baseline, and B1 results in a decrease in sediment loading. The largest deviation between the baseline and B1 also occurs in March, when the sediment loading is projected to reduce by 18%. Over the course of the entire 30 year simulation, the sediment percent change for A2, A1B, and B1 from the baseline are +43.8, -0.4, and -20.8, respectively. Percent change for runoff is < 1% for all three scenarios. In general, the seasonal fluctuations of future streamflow and sediment loading remain consistent with present day conditions.
Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-useland-coverchange (LUCC) studies. This is due to the increased awareness by governments, NGOs and Indus- try that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This pa- per examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected stud- ies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important eve- ryday applications.
Change detection has become a major application and essential for monitoring landuse and landcover changes. Remote sensing and geographic information system (GIS) have been recognize as low cost efficient tools, which have been widely used, to detect and develope landuse and landcoverchange information over large areas (Lunetta, 2006). Over the last few decades, remote sensing techniques has been discussed and applied by numerous scholars in landuse and landcoverchange detection studies (Mukherjee, 1987; Quarmby & Cushine, 1989; Lambin & Strahler, 1993; Roberts et al., 1998; Mas, 1999; Hayes & Sader, 2001; Rogan et al., 2002; Walker, 2003; Deszo 2004; Woodcock & Ozdogan, 2004; Rhemtulla, Mladenoff & Clayton, 2007; Fan 2007; Wilkinson, Parker & Evans, 2008; Narimah, 2010; Panahi 2010; Praveen & Jayarama, 2013; Sumayyah & Zullyadini, 2016; Nur Hakimah & Lam, 2016; Lam & Hay, 2017; Zurinah Tahir & Jalaludin Abdul Malek, 2017; Nur Syabeera & Firuza, 2019). remote sensing applications and GIS are capable of generating fast and accurate information of the spatial distribution of landuse and landcover over large area (Zsuzsanna, Bartholy, Pongracz & Barcza, 2005; Rogana & Chen, 2004; Guerschman et al., 2003; Carlson & Azofeifa, 1999).
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.
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 agricultural land 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.
Land is major source of the human being and also the pressure of the population and increasing variety of demand on land resources exert pressure and strain on the available resources. The present study area part of the central Tamil Nadu coast, between Vedaraniiyam and Adirampattenam carried Various landuse characters and land forms with several years for the study of landuse and landcover analysis. The Remote sensing techniques used for the present landuse and landcoverchange study
In recent years, the researches on the applications of thermal remote sensing of urban areas were mainly in following respects: study on the land surface temperature or the spatial structure of urban thermal pattern and their relationship with surface parameters; urban surface energy balances and fluxes[8,10]; the relationship between atmospheric temperature and land surface temperature. Some studies examined the effect of landuse/landcoverchange on LST[6-7,15], which was found to be positively correlated with impervious surface .Some studies
SWAT allows users to adjust CO2 concentration, weather parameters (e.g., temperature, precipita- tion, radiation and humidity), and landuse, and includes approaches describing how those parameters affect plant growth, ET, snow, and runoff generation. SWAT has been found to be suitable for large basins such as the Brahmaputra, and has often been used as a tool to investigate climate and landusechange effects on freshwater availability around the world ( Abbaspour et al., 2009; Gosain et al., 2006; Jha et al., 2006; Montenegro and Ragab, 2010; Rossi et al., 2009; Schuol et al., 2008; Siderius et al., 2013 ). The primary goal of this study was to assess long-term patterns of freshwater availability in the Brahmaputra basin under climate and landuse and landcoverchange scenarios. To fulﬁll the goal, we calibrated the model using the sequential uncertainty ﬁtting II (SUFI2) algorithm ( Abbaspour et al., 2004 ). We then quantiﬁed the sensitivity of the hydrological variables such as total water yield, soil water content, ET, streamﬂow, and groundwater recharge to a group of various climatechange scenarios including changes in CO 2 concentration, temperature, and precipitation. We assessed the long-term patterns in the hydrological variables with Phase 3 of the Coupled Model Intercompari- son Project (CMIP3) downscaled precipitation and downscaled Integrated Model to Assess the Global Environment (IMAGE) landusechange scenarios for the 21st century under the A1B and A2 scenarios ( Nakicenovic and Swart, 2000 ). In brief, the A1B storyline assumes a future world of very rapid eco- nomic growth, low population growth, and rapid introduction of new and more efﬁcient technology with the development balanced across fossil fuel and non-fossil fuel energy sources. In contrast, the A2 storyline assumes a very heterogeneous world where population growth is high, economic devel- opment is primarily regionally oriented, and per capita economic growth and technological change are more fragmented and slower than in A1B.
Higher-resolution landcover characteristics and landcoverchange data are needed to detect distur- bances or landcover transformations from local to national scales and to evaluate the specific cover types that are affected by weather and climate variability. Local case studies may be needed to document and determine the pace of LULCC for climate assess- ment. A Landsat-scale (30-m resolution) dataset, such as the USGS-led National LandCover Database (NLCD), is suited to this application (Fry et al. 2011). However, an operational assessment would require landcover information at a more frequent interval than NLCD (updated every 5 years), and in order to be more relevant for assessing the connec- tions between LULCC and climate, the detection of LULCC as it is occurring is an appropriate goal. The planned U.S. Forest Service (USFS)–USGS LandCover Monitoring System concept that would provide Landsat-scale annual landcover disturbance data is a stronger, potential long-term candidate (Lebow et al. 2012). Finally, geospatial landuse data are needed to understand local- to national-scale social and eco- nomic impacts and mitigation opportunities. Sectoral products, such as USFS Forest Inventory and Analysis (FIA) data and National Agricultural Statistics Service (NASS) data from the U.S. Department of Agriculture (USDA), are useful, but the absence of spatially explicit national landuse data remains an issue.
Abstract. Landuse and coverchange is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important ap- proach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of landuse using re- mote sensing technology and analyzes historical space–time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source informa- tion and creates a convenient and efficient high-precision landuse computer automatic classification method which meets the application requirements and is suitable for com- plex landform of the studied area. This work analyzes the histological space–time characteristics of landuse and coverchange in the Qiantang River basin in 2001, 2007 and 2014, in order to (i) verify the feasibility of studying landusechange with remote sensing technology, (ii) accurately un- derstand the change of landuse and cover as well as histori- cal space–time evolution trend, (iii) provide a realistic basis for the sustainable development of the Qiantang River basin and (iv) provide a strong information support and new re- search method for optimizing the Qiantang River landuse structure and achieving optimal allocation of land resources and scientific management.
In addition to utilizing data from existing in situ and spaceborne climate monitoring platforms, new in situ monitoring networks need to be estab- lished in regions where rapid LULCC is currently underway. This effort could be undertaken in se- lected areas such as the Amazonia, Costa Rican cloud forests, Southeast Asian tropical forests, and near rapidly growing urban and agricultural areas and then expanded to other regions. This effort could consider collaborating with existing coordinated national and international efforts [e.g., Flux Network (FLUXNET; Baldocchi et al. 2001)]. Mitigation of the diverse range of effects on climate from LULCC can also begin with existing local policies and practices of land management devised for conservation efforts. For example, in the United States and China, there are certain government policies [e.g., Grain for Green Project (Fan et al. 2014)] that encourage farmers from selected re- gions to adopt conservation practices that may also
Analyzing the impact of LULC change on UHI needs to consider urbanization effects. Urbanization leads to the expansion of built-up and impervious surface that intensify UHIs (Chun and Guldmann, 2014). Previous studies applied different methods such as diurnal temperature range (DTR) (Wang et al., 2007; Mohan and Kandya, 2015), landusechange trajectories (Feng et al., 2013), or a surface urban heat island index (SUHI) (Dihkan et al., 2015) to quantify the effects of urbanization on UHI. These studies were successful in demonstrating the contribution of urban growth to the UHI effect as well as investigating the differences in UHI between urban and rural areas. However, applying these methods could not provide insight into the effect of urban development types on UHI. Urban growth can occur in different ways, such as including infill, extension, or leapfrog development (Angel et al., 2012). It is crucial to examine how UHI is affected by different spatial patterns of urban growth. For urban planners, understanding which kinds of urban expansion exacerbate or mitigate impacts on UHIs can contribute significantly to UHI mitigation strategy.
these simulations: secondary LULCC and negative emissions using bioenergy with carbon capture and storage (BECCS). The carbon changes from secondary landuse changes (for instance natural to managed forest, which is not accounted for in this model) can be substantial and may account for more carbon emissions than primary landuse changes [Shevliakova et al., 2009; Hurtt et al., 2011; Lawrence et al., 2012]. Similarly, BECCS for the RCP2.6 scenario could give negative emissions of between 43.8 and 160.6 GtC [Kato and Yamagata, 2014]. According to those projections, the potential of BECCS is likely to be bigger than the net land carbon change in any of the three RCPs considered here (8, 101, or 83 GtC for the three RCPs, respectively; see Figure 5a). Therefore, the lack of representation of secondary LULCC and BECCS is a considerable limitation to this study. It is also notable that the total land carbon change (including
cases where the class variations per unit area are high , greater number of categories may reduce the interpretation clarity . Also define the number of iterations and the convergence threhol limits . The process output is generated when either of these criteria is met . The specified maximum number of iterations was 30 and the convergence threshold was specified at 0.98.The generated outpur resembles the reference satellite image subset since the colour scheme option chosedn was approximate tru colour. In case the colour scheme option chosen was preudo colour , the generated outpur layer would appear in shades of grey . Image interpreation is easier with the approximate true colour option. Geo linking the satellite image subset with the generated output layer helps identify classes more accurately. For the software generated classes , type and colour definition is done using the keyboard after clicking on well defined landuse class samples on the generated output layer.
Oleson, K. W., D. M. Lawrence, G. B. Bonan, M. G. Flanner, E. Kluzek, P. J. Lawrence, S. Levis, S. C. Swenson, and P. E. Thornton, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR, 266 pp. [Available online at http://www.cesm.ucar.edu/models/ccsm4.0/clm/CLM4_Tech_Note.pdf] Oleson, K.W., Bonan, G.B., Levis, S. et al., 2004. Climate Dynamics 23: 117.