Journal Of Environment

REMOTE SENSING (RS) AND GEOGRAPHICAL INFORMATION SYSTEMS (GIS) USAGE FOR CLIMATE CHANGE EFFECTS

Gül USLU*, Ayşenur USLU**

*Dr., Ondokuz Mayıs UnıversıtyDepartment Of Geomatıcs Engıneerıng, Samsun, TURKEY, gulluslu@gmail.com

**Dr., Sinop University, Health School, Occupation Health and Safety Department, Sinop, TURKEY, auslu@omu.edu.tr

ABSTRACT:

Global warming becomes a critical and dominant global environmental problem in the last three decades, resulting from global climate change. The subject of climatic change has received increasing attention especially in scientific literature and in the popular press. The earth's climate is dynamic. An understanding of its variability is vital in today's advanced society. Climate change will mainly stand out from sea level rise and more frequent and intense rainfall as well as extended dry periods. GIS with updated data helps people to know what happens in our planet, how climate takes place and where impacts of climate change affect people. Remote sensing was also identified as an institutional technology. Tying remote sensing technologies and data with GIS is a powerful combination of understanding spatial patterns in the earth’s changing surface. RS and GIS are applicable in climate change analysis and adaptation. Using GIS and Remote sensing techniques allows us to utilize complex statistical methods to view trends and change at specific times or over periods of times. The aim of this study is to describe terms such as climate change, remote sensing (RS) and geographical information systems (GIS) and to give examples about climate change with the application of RS and GIS and to explain the role of RS and GIS in climate change management.

Key words: Global Warning, Climate Change, Remote Sensing, Geographical Information System.

1. INTRODUCTION

A critical and perhaps the dominant global environmental problem in the last three decades is global warming, resulting from global climate change (Scholz and Hasse, 2008). Global Warming is an increase in Earth’s average surface temperature, due mostly to the release of greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4), water vapour, Nitrous oxide NO2, chloro-floro-carbons (CFCs), among others into the atmosphere by human-fuelled activities such as increased fossil fuel consumption (Benson, 2008). Increases in the atmospheric concentrations of these gases cause Earth to warm. Human activities, especially the burning of fossil fuels since the start of the Industrial Revolution, have increased atmospheric CO2 concentrations by about 40%, with more than half the increase occurring since 1970. Since 1900, the global average surface temperature has increased by about 0.8 °C (1.4 °F) (Cicerone and Nurse, 2010). Global warming is a term often used interchangeably with the term “climate change,” but they are not the same thing. Global warming refers to an average increase in the temperature of the atmosphere near the Earth’s surface. Global warming is just one aspect of global climate change, albeit a very important one. Climate change refers to any significant change in measures of climate (such as temperature or precipitation) lasting for an extended period (decades or longer). Climate change may result from natural factors and processes or from human activities (USEPA, 2012).

Climate change may have serious impacts on society, for example on coastal areas through sea level rise (Warrick et al., 1993), on agricultural areas because of shifts in growing seasons and changes in water availability (Mearns et al., 1997) and in river deltas because of both sea level rise and an increased occurrence of flooding events (Jacobs et al., 2000). So human survival is threatened.

Remote Sensing (RS) and Geographic Information System (GIS) have found wide application areas in climate change analyses and adaptation. RS is the process of acquiring data on characteristics of the Earth’s surface by a device (sensor) that is not in contact with the objects being measured (Janssen, 2004) (Figure 1). GIS is the information tied to the earth’s surface (including the zones immediately adjacent to the surface, subsurface, oceans, and atmosphere) (Longley et al., 1999). RS enables the acquisition of

1

Journal Of Environment

large-scale comprehensive datasets whereas GIS provides a means of displaying, overlaying, combining data from other sources and analysing the data (Chapman and Thornes, 2003).

A range of approaches have been deployed in assessing the potential impacts of climate change on coastal wetlands including the use of Geoinformatics tools. This involves the application of geographic information science (GIS) and remote sensing techniques in assessing the potential impacts of climate change. GIS is a computer system comprised of hardware, software, and data which allows the user to layer different types of information together provided the data have a common geographic location (Badurak, 2000).

GIS became well-established as a technology in the 1980s and 1990s. In 1982, IBM introduced its PC (personal computer) and within a few years, some large and desktop GIS packages such as Arc/Info and IDRISI made a transition to the microcomputer. In the 1990s, GIS stretched far beyond its origin in mapping science to encompass disciplines such as geology, archeology, epidemiology, and criminal justice. In addition, it became fully integrated with global positioning system (GPS) (Clarke, 2003). GIS is a very important tool in impact assessment because it facilitates the analysis of multiple layers of data and allows statistical analysis of multiple factors while maintaining their spatial representation. It also facilitates the collection of future relevant data and complex analyses.

Remote sensing is the discipline of deriving information about the earth’s land and water surfaces using images acquired from an overhead perspective, by using electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the earth’s surface (Campbell, 2006). For climate change analysis, RS is a required tool for up-to-date environmental data acquisition both at local and synoptic levels. Scientists are now using satellite instruments to locate sinks and sources of CO2 in the ocean and land (Science, 2007). GIS on the other hand has a very important role to play in environmental monitoring and modelling for combining distributed field-based measurements and remotely sensed data (Larsen, 1999). Chapmann and Thornes (2003) submit that climatological and meteorological phenomena are naturally spatially variable, and hence GIS represents a useful solution to the management of vast spatial climate datasets for a wide number of applications.

2. RS AND GIS USAGE

A. Ice/Glacier Monitoring

The world-wide retreat of many glaciers during the past few decades is frequently mentioned as a clear and unambiguous sign of global warming (Oerlemans, 2006).Climate change induces disturbance in glacier and permafrost equilibrium and can shift hazard zones beyond historical knowledge. In addition, human settlements and activities increasingly extend towards endangered zones in many regions. As a result, historical data alone are not sufficient any more for hazard assessments and have to be combined with new observation and modelling approaches.

Recent evidence of rapid glacier recession in many regions of the world has spurred interest in glacier monitoring (Meier et al. 2003). An understanding of glacier behavior is vital because the fluctuating mass of ice impacts global sea rise, regional sources of water, mountain ecosystems, and are sources of paleoclimate data (Linsbauer et al. 2009). Remote sensing, Digital Elevation Models (DEM) and Geographic Information Systems (GIS) play an important role for glacier inventorying and monitoring work. Coupled together, these interfaces provide an invaluable environment to store data, illustrate glacier fluctuations, and quantify changes (Kaab et al., 2006).

Glaciers are natural indicators of local andglobal climatic change because variations in temperature and precipitation influence the annual mass balance of the glacier (Racoviteanu et al. 2009). Glacier geologists and glaciologists study historical extents of glaciers to current extents in order to understand how climate affects their behavior, and to gain insight in how glaciers may respond to climate change in the future. These changes are often quantified, analyzed, and stored using geographic information systems.

There are three main applications of glacier mapping in GIS.

1- Mapping glaciers in GIS allows a researcher to visualize and quantify glacier change over a historical period.

2- GIS offers a dynamic interface to create an inventory of glaciers in an entire region.

2

Journal Of Environment

3- The use of GIS with a DEM allows one to extract information on various properties of the glacier, such as volume, shear stress, surface topography, or subsurface topography.

Ye et al. (2006) studied glacier variations in the Geladandong mountain region of central Tibet using RS and GIS technics. Data from Landsat images at three different times, 1973–76, 1992 and 2002 were compared with glacier areas digitized from a topographic map based on aerial photographs taken in 1969. Findings showed that there was accelerated glacier retreat in recent years, attributable to increase in summer air temperature.

Peduzzi et al. (2010) utilized a combination of satellite imagery, DEM’s, and field techniques to estimate the change volume of the Nevado Coropuna glacier in Peru from 1955 to 2008. Field techniques included measuring ice thickness using a Ground Penetrating Radar (GPR). Thickness was modelled for the entire glacier in areas where no measurements were taken using multi-regression analysis. By combining modelled thickness with glacier extent and a DEM, the researchers successful quantified changes in ice volume from 1955 to 2008.

Landsat-7 data provide an effective continuation of this glacier monitoring capability. Figure 2 illustrates this for the Pasterze Glacier, Hohe Tauern, Austria. Superimposed on a Landsat-7 image of this glacier, collected on September 6, 1999, are the margins of the glacier tongue extracted from two earlier Landsat images (Bayr, Hall, & Kovalick, 1994; Hall, Williams, & Bayr, 1992).

B.Vegetation Change Monitoring

Classifying and mapping vegetation is an important technical task for managing natural resources as vegetation provides a base for all living beings and plays an essential role in affecting global climate change, such as influencing terrestrial CO2 (Xiao et al. 2004). Vegetation mapping also presents valuable information for understanding the natural and man-made environments through quantifying vegetation cover from local to global scales at a given time point or over a continuous period. It is critical to obtain current states of vegetation cover in order to initiate vegetation protection and restoration programs (Egbert et al. 2002; He et al. 2005). Traditional methods (e.g. field surveys, literature reviews, map interpretation and collateral and ancillary data analysis), however, are not effective to acquire vegetation covers because they are time consuming, date lagged and often too expensive. The technology of remote sensing has been determined to be a cost-effective and practical to study vegetation cover changes, especially over large areas (Langley et al. 2001; Nordberg and Evertson 2003).

Monitoring of vegetation change using remote sensing is providing an improved understanding of the health and condition of vegetation as well as rates of conversion of natural vegetation to other land uses. The first vegetation maps made with the help of remote sensing were based on the visual interpretation of aerial photographs. (Lillesand etal., 2008). With the advent of the Landsat programme in 1972, there was an immediate interest in the potential for mapping vegetation over larger areas in more efficient manner than traditional air photo interpretation. Active research and several approaches are applied for using digital satellites images for mapping vegetation.

C.Distribution of Greenhouse Gases

A greenhouse gas is a gas in an atmosphere that absorbs and emits radiation within the thermal infrared range. This process is the fundamentalcause of greenhouse effect. The primary greenhouse gases in the Earth’satmosphere are water vapour, carbon dioxide (CO2), methane (CH4), nitrous oxide, and ozone. Since the beginning of the industrial Revolution, the burning of fosil fuels has contributed to 40% increase in the concentration of carbon dioxide in the atmosphere from 280ppm (parts per million) to 397ppm. The 2007 fourth Assessment Report compiled by the

Intergovernmental panel on climate change (IPCC) (AR4) noted that “changes in atmospheric concentrations of greenhouse gases and aerosols, land cover and solar radiation alter the energy balance of the climate system”.

Carbon dioxide (CO2) and Methane (CH4) are the two major important anthropogenic greenhouse gases and contribute to global warming. Satellite measurements of vertical columns or column averaged mixing ratios of CO2 and CH4 have the potential to improve this situation. This is because satellites produce large amounts of data which cover the entire Earth in short time periods.

3

Journal Of Environment

The periodic monitoring of methane gas emissions from agricultural activities in US using GIS is indispensable in the formulation of policies. As a decision support tool, it is essential in tracking the threats of greenhouse gas emission at both national and state level. The GIS mapping highlighted the spatial distribution of rice production and climate change prediction and the surround ecology of the state as well. GIS technique as used here in contributing to the literature stands as a relevant decision support tool that pinpoints high risk areas and counties vulnerable to the concentration of methane gas in the atmosphere. This involved the generation of maps that identified rice producing counties known for methane gas emission, visualizing counties and natural areas prone to greenhouse gas concentration. In these settings, not only help focus the scope of GIS and environmental planning with records of change in affected areas, but it furnished information on pace at which rice farming activities can influence climate change (Edmund et al., 2012 ).

D.Flood Monitoring

Recently, the advancement in computer-aided technology has been extensively used in formulating models used for flood calculation and hazard analysis. GIS is used to visualize the results of the flood phenomenon and do vulnerability or risk analysis and hazard assessment in a 1D, 2D, or 3D approach. Remote sensing can be used to validate the model results by comparing the flood inundation area. Remote sensing images are used as base maps for flooded areas since they can be acquired in all weather condition and at any time (Duan et al., 2009).

Townsend and Walsh (1998) is one example of many studies that have used RS and GIS in predicting floods. The objective of this study was to compare the flood detection abilities of RS data as well as GIS based models. For RS data, Synthetic Aperture Radar (SAR) images were derived from multispectral satellites and then analyzed using Normalized Difference Vegetation Index (NVDI) to derive possible flood inundation. On the other hand DEMs were used to generate GIS models representing potential wetness and potential flood inundations. Their findings indicated that both SAR images and GIS models obtained comparable results of possible flood inundation.

In the study of watershed modelling and flood prediction, Konadu and Fosu (2009) used a vector based GIS and DEM in order to delineate watershed boundaries and predict areas of possible inundation during a flood event in the city of Accra. In this study digital contours obtained from the topographic map of the study area were utilized for DEM generation. The DEM generated provided the terrain representation and information in terms of the direction in which water that enters into an area will flow. Through the use of GIS, raster analyses were performed to generate data on flow direction, flow accumulation, stream definition, stream segmentation and watershed delineation. These data were then useful in developing a vector representation of catchments and drainage lines. Flood extent was then simulated based on the derived drainage lines, their depth and capacity to hold rainfall run-off. The flood level contours derived for the selected flood water levels of 0.5, 1, 1.5 or 2 meters were used to indicate areas that could face possible inundation in the event of any flood.

Several other studies (De Roo et al., 2000; De Smedt et al., 2002; Usul and Turan, 2006; Rahman, 2006; Batelaan et al., 2007; Chormanski et al., 2008; Stancalie et al., 2009; Kabir et al., 2011) have also demonstrated the potential of different GIS based hydrological models to simulate/ predict flood.

A study by Samarasinghea et al. (2010) is a typical example in which RS and GIS techniques have successfully been used in flood risk analysis and mapping. In this Study, RS satellite imageries acquired during a dry season and also during flooding were analyzed to derive flood extent. Their results indicated that flood extent derived from RS data were comparable with those obtained when one of the flood inundation models, Hydrologic Engineering Centres River Analysis System (HEC-RAS model) was used and based on this, they were able to validate their model. The study also made use of GIS data and functions for flood simulation and flood risk mapping. They were able to obtain the flood risk map by map multiplication in ArcGIS environment of flood hazard and flood vulnerability maps. By overlaying vulnerability and flood harzard values with the polygon map of the area, Samarasinghea et al. (2010) were able to map out zones with low, moderate and high flood risk. Usul and Turan (2006) have also used GIS in their study in order to map out flooded areas. Apart from its usefulness in flood hazard mapping, Usul and Turan (2006) report that using GIS tools have added advantages as it enables determination of basin characteristics, easy manipulation of conditions of river components to suit any river size as well as enabling the end-user to have a bigger and more complete picture of what is likely to happen in a watershed during and after a flood.

4

Journal Of Environment

In the field of flood survey, radar images is used in many cases because it is not influenced by clouds. The pictorial radar material is taken, filter interference (caused by interfere phenomenon) and cover each other by multi-time principle or cover other multi spectrum images. Because of sensitivity to water, flood area that is received from radar images, is used as a pellicle to cover other images and it reappear very clear (Figure 3) (Pham, 2004).

(a) (b)

Figure 3. (a) LANDSAT-ETM satellite picture taken in 1/1999, (b) LANDSAT-ETM satellite picture covered by RADASAT satellite picture that was taken at the time of the flood on November 6th 1999. The dark purple areas represent the flooded areas.

E.Erosion Monitoring and Control

Assessing the soil erosion rate is essential for the development of adequate erosion prevention measures for sustainable management of land and water resources. Geographic Information System (GIS) technologies are valuable tools in developing environmental models through their advance features of data storage, management, analysis, and display (Burrough et al., 1998). The Remote Sensing (RS) technology has been used to provide the land use/cover information by using digital image processing techniques (Yuksel et al., 2008). There have been many studies on modeling soil erosion by utilizing RS and GIS technologies (Rode, Frede, 1998; Yuksel et al. 2008)

Especially, generating accurate erosion risk maps in GIS environment is very important to locate the areas with high erosion risks (Mitasova et al. 1996) and to develop adequate erosion prevention techniques (Vrieling, 2002). Sazbo et al., (1998) conducted a study where RS and GIS technologies were successfully used for land degradation and erosion mapping. Another study by Bojie et al. (1995) also indicated that GIS analysis provide satisfactory results in developing erosion surveys and risk maps by using GIS data layers such as DEM, slope, aspect, and land use.

The most common empirical erosion prediction models, integrating with RS and GIS, are Revised Universal Soil Loss Equation (RUSLE), The Water Erosion Prediction Project (WEPP), and COoRdination of INformation on the Environment (CORINE), which can be used for erosion risk mapping (Yuksel et al., 2008).

F.Agriculture

Lal (2009) averred that soil quality, water availability or drought stress and climate change are three biophysical factors which need to be addressed for food security in the face of climate change. RS and GIS have been found useful for soil characteristics mapping, agro-climatic assessment, land use suitability for crop production, irrigation management, precision farming, crop type mapping, crop condition assessment, among others.

Satellite data provide a real-time assessment of crop condition. Ines et al. (2006) combined Landsat7 ETM+ images and derived distributed data such as sowing dates, irrigation practices, soil properties, depth to groundwater and water quality as inputs in exploring water management options in Kaithal, Haryana, India during 2000–2001 dry season. Results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently (Enıolorunda, 2014).

5

Journal Of Environment

G. Drought

As drougth definitions are region specific, reflecting differences in climatic characteristics as well as incorporating different physical, biological and socio-economic variables, it is usually difficult to transfer definitions derived for one region to another.

Meteorologists generally define drought as a prolonged period of dry weather caused by a lack of precipitation that results in a serious water shortage for some activity, population, or ecological system. Drought can also be thought of as an extended imbalance between precipitation and evaporation (USEPA, 2012).

According to Mc Mohan and Diaz Arena(1982), “Drought is period of abnormally dry weather sufficiently fort he lack of precipitation to cause a serious hydrological imbalance and carries connotations of a moisture deficiency with respect to man’s usage of water.

According to Hangman (1984), “Drougth is considered by many to be the most complex but least understood of all natural hazards affecting more people than any other hazard” (Wilhite, 2000)

The impacts of a drought can be economic, environmental or social. Direct impacts include reduce crop, rangeland, and forest productivity, increased fire hazard, reduced water levels, increased livestock and wildlife mortality rates, and damage to wildlife and fish habitat. The consequences of these direct impacts illustrate indirect impacts. For example, a reduction in crop, rangeland and forest productivity may result in reduced income for farmers and agribusiness, increased priced for food timber, unemployement, reduced tax revenues because of expenditures, foreclosures on bank loans to farmers and business, migration and disaster relief programs.

Traditionally, there are several operational indices in drought assessment and monitoring that are based on rainfall data. These indices are often not easily accessible, nor are they tailored to be conveniently understood by decision makers (Ji & Peters, 2003). The common approach that is used to derive the necessary information is the application of climatic drought indices, such as the Palmer Drought Monitoring Index, which has been widely used by the U.S. Department of Agriculture (Jain et al., 2009). Another popular climatic drought index is the Standardized Precipitation Index (SPI) that was developed by McKee et al. (1993), which can identify data on emerging drought months for regional and global applications. Mishra and Desai (2005) have adopted the SPI for parts of India and have used that data to compile a drought severity area frequency curve. These drought severity and monitoring indices are based on point data that are measured at the different meteorological stations located in a wide area. In remote areas where there is not a dense network of stations, extrapolation of rainfall observation from nearby stations is commonly used, resulting in high uncertainty about its usefulness for real-time rescue missions.

The concept of identifying and modeling drought as an object is new (Rulinda et al., 2010). Rulinda et al. (2010) further indicate that "a next step in drought modeling is an approach focusing on spatial object and this kind of object can be built from different temporal resolution images". In remote sensing, objects are identified and subsequently classified on the basis of pixel information, and the objects are subsequently tracked in time, during which, their behavior may be governed by external factors that must also be identified and quantified (Stein et al., 2009).

3. CONCLUSION

Climate change and food security are important issues in the world. Space technology, remote sensing satellites in particular, are very beneficial for observations related to climate change parameters, impacts and mitigation actions, as well as adaptation to climate change.

The satelite or remote sensing tecniques can be used for monitoring the current stuation- before, during or after disaster. They can be used to provide baseline data which future change can be compared while the GIS tecniques provide a suitable framework for integrating and analysing the many types of data sources required for disaster monitoring.

In this study, snow/glacier monitoring, distribution of greenhouse gases, erosion monitoring and control, agriculture, flood monitoring and drought subjects are considered. This review has shown from all cited examples that climate change will be less understood or managed without the application of RS and GIS.

6

Journal Of Environment

As the earth’s system is continuously heating up by the year due to anthropogenic emission of greenhouse gasses, more and more researches are being expanded into discovering efficient ways of sequestering CO2

from the atmosphere as the only way to prevent global warming. However, in the interim, climate change analysis will continue to benefit from the plethora of data volume generated by air and space vehicles and the sustained advancement in space and computer technology, thereby making RS and GIS plays a crucial role in tracing the trajectories of climate change and its effects for human survival.

REFERENCES

AGAGE (Advanced Global Atmospheric Gases Experiment). 2011. ALE/GAGE/ AGAGE data base. Accessed November 2011. http://agage.eas.gatech.edu/data.htm.

Badurak, C. 2000. Managing GIS in academic libraries. WAML Information Bulletin 31(2): 110-114.

Batelaan, O., J. Chormanski, F. Canters and T. Van de Voorde, 2007. Improved distributed runoff modelling of urbanised catchments by integration of multi-resolution remote sensing. Proceeding of the Geoscience and Remote Sensing Symposium, IGARSS 2007, IEEE 23-28 July 2007, pp: 5021- 5024

Bayr, K. J., Hall, D. K., & Kovalick, W. M. (1994).Observations on glaciers in the eastern Austrian Alps using satellite data. International Journal of Remote Sensing, 15 (9), 1733 – 1742.

Benson N. (2008): Climate Change, Effects. In Philander S. G. (ed): Encyclopaedia of Global Warming and Climate Change, Vol. 1-3, Sage Publications, Inc.

Bindschadler, R., Dowdeswell, J., ,Hall, D., Winther, J.G., 2001, Glaciological applications with Landsat-7 imagery: Early assessments, Remote Sensing of Environment 78 (2001) 163 – 179.

Bojie, F.; Xilin, W.; Gulinck, H. Soil erosion types in the Loess Hill and Gully area of China. J. Environ. Sci. Eng. 1995, 7, 266-272.

Burrough, P.A; McDonnell, R.A. Principles of Geographic Information Systems; Oxford Science Publications: New York, USA, 1998; 356 p.

Campbell, J. B. (2006). Introduction to remote sensing (4 ed.): Guilford Press, New York, USA.

Chapman L. and Thornes J. E. (2003): The use of geographical information systems in climatology and meteorology. Climate and Atmospheric Research Group, School of Geography and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK. Available at http:// citeseerx.ist.psu.edu/viewdoc/ download?doi =10.1.1.109.5160&rep = rep1&type= pdf.

Chapman L. and Thornes J. E. (2003): The use of geographical information systems in climatology and meteorology. Climate and Atmospheric Research Group, School of Geography and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK. Available at http:// citeseerx.ist.psu.edu/viewdoc/ download?doi =10.1.1.109.5160&rep = rep1&type= pdf.

Chormanski, J., T. Van de Voorde, T. Deroeck, O. Batelaan and F. Canters, 2008. Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover. Sensors, 8: 910-932.

Clarke, C. K. (2003). Getting Started with Geographic Information Systems (4 ed.): Pearson Education, Inc., Upper saddle River, New Jersey 07458.

De Roo, A.P.J., C.G. Wesseling and W.P.A. Van Deursen, 2000.Physically based river basin modeling within a GIS: The LISFLOOD model.Hydrol. Process, 14:1981-1992.

De Smedt, F., Y.B. Liu and S. Gebremeskel, 2000. Hydrologic Modeling on A Catchment Scale using GIS and Remote Sensed Land use Information. In: Brebbia, C.A. (Ed.), Risk Analysis II. WTI Press, Southampton, Boston, pp: 295-304

Distribution of Greenhouse Gases, www.abcnews.com/sections/us/global106.html, 2015

Duan M., Z. Jixian, L. Zhengjun, A. Aekakkararungroj. 2009. “Use of Remote Sensing and GIS for Flood Hazard Mapping in Chiang Mai Province, Northern Thailand.” Paper presented at the International Conference on Geo-spatial Solutions for Emergency Management and the 50th Anniversary of the Chinese Academy of Surveying and Mapping, Beijing, China, September 14-16.

7

Journal Of Environment

Edmund C. Merem, Chandra Richardson, Corney Romarno, Joan Wesley and Yaw Twumasi (2012).Using GIS to assess the contribution of farming Activities towards climate change in the state of Mississppi.British journal of Environmental and climate change. 2 (2): 113-136-2012 www.scciencedomain.org

Egbert SL, Park S, Price KP,. (2002). Using conservation reserve program maps derived from satellite imagery to characterize landscape structure. Comput Electron Agric 2002; 37:141-56.

Enıolorunda N.B., 2014, Climate Change Analysis and Adaptation: The Role of Remote Sensing (Rs) and Geographical Information System (Gis), International Journal Of Computational Engineering Research (ijceronline.com) Vol. 04 Issue. 01, pp. 41-51

Hall, D. K., Williams, R. S. Jr., & Bayr, K. J. (1992). Glacier recession in Iceland and Austria.Eos, 73 (12), 129, 135, and 141.

He C, Zhang Q, Li Y, et al., (2005). Zoning grassland protection area using remote sensing and cellular automata modeling—a case study in Xilingol steppe grassland in northern China. J Arid Environ 2005;63:814-26

Ines A. V.M., Honda K., Das Gupta A., Droogers P. and Clemente R. S. (2006): Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. Agricultural Water Management, 83, 221-232.

Jacobs, P., Blom, G. and Linden, G. van der, 2000. Climatological changes in storm surges and river discharges: The impact on flood protection and salt intrusion in the Rhine-Meuse delta. In: J. Beersma, M. Agnew, D. Viner and M. Hulme (Eds.), Climate scenarios for water-related and coastal impacts. Proc. ECLAT-2 Workshop Report No.3 KNMI, De Bilt, the Netherlands, 35-48.

Jain, S. K., Keshri, R., Goswami, A., Sarkar, A., & Chaudhry, A. (2009). Identification of droughtvulnerable areas using NOAA AVHRR data. International Journal of Remote Sensing, 30(10), 2653- 2668.

Janssen L. L. F. (2004): Introduction to remote sensing. In Kerle N., Janssen L. L.F. and Huurneman G.C. (eds): Principles of Remote Sensing, ITC, Netherlands.

Ji, L.,& Peters, A. J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87(1), 85-98.

Kaab A., Huggel C. and Fischer L. (2006): Remote sensing technologies for monitoring climate change impacts on glacier- and permafrost-related hazards. 2006 ECI Conference on Geohazards, Lillehammer, Norway.

Kabir, A., M. Mahdavi, A. Bahremand and N. Noora, 2011. Application of a geographical information system (GIS) based hydrological model for flow prediction in Gorganrood river basin, Iran. Afr. J. Agric. Res., 6: 35-45.

Lal R. (2009): Soils and Food Sufficiency: A Review. In Lichtfouse E., Navarrete M.,Debaeke P., Souchère V. and Alberola C. (eds): Sustainable agriculture, Springer, New York.

Langley SK, Cheshire HM, Humes KS., (2001). A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. J Arid Environ2001;49:401-11.

Larsen L. (1999): GIS in environmental monitoring and assessment. In Longley P. A., Goodchild M. F., Maguire D. J. and Rhind D. W. (eds) Geographical Information Systems, Vol. 1, 2nd Edition, John Wiley & Sons, Inc.

Lillesand, M. T., KIefer, W. R. and Chipman, N, J. (2008). Remote sensing and image interpretation (6th ed). John Wiley and Sons, Inc, New York.

Linsbauer, A., Paul, F, Hoelzle, M., Frey, H. and Haeberli, W. (2009): The Swiss Alps without glaciers – a GIS-based modelling approach for reconstruction of glacier beds. Proceedings of Geomorphometry 2009. Zurich, Switzerland, 243-247.

Longley P. A., Goodchild M. F., Maguire D. J. and Rhind D. W. (1999): Epilogue: seeking out the future. In Longley P. A., Goodchild M. F., Maguire D. J. and Rhind D. W. (eds) Geographical Information Systems, Vol. 1, 2nd Edition, John Wiley & Sons, Inc.

8

Journal Of Environment

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Paper presented at the Eighth Conference on Applied Climatology

Mearns, L.O., Rosenzweig, C. and Goldberg, R., 1997. Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Climatic Change, 35, 367-396.

Meier, Mark F., Dyurgerov, Mark B., McCabe, Gregory J. (2003). The Health of Glaciers: Recent Changes in Glacier Regime. Climate Change, 59: 123-135.

Mishra, A. K.,& Desai, V. R. (2005). Spatial and temporal drought analysis in the Kansabati river basin, India. International Journal of River Basin Management, 3(1), 31-41.

Mitasova, H.; Hofierka, J.; Zlocha, M.; Iverson, 1996, R.L. Modeling topographic potential for erosion and deposition using GIS, International Journal of Geographical Information Science 10, 5, 629-641.

Nordberg ML, Evertson J., (2003). Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM and ETM+ imagery.Land Degradation & Development 2003;16:139-149.

Oerlemans J. (2006): Extracting a Climate Signal from 169 Glacier Records. Science, vol. 308, pp. 675-677.

Peduzzi P., Herold C., Silverio W., 2010. Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques: the case of Nevado Coropuna (Peru), The Cryosphere, 4, 313–323.

Pham, V.H., 2004, Use of Remote Sensing and GIS Technology for Monitoring and Assessment of Flooding Status at the Coastal Zone in the Central Part of Vietnam, 3rd FIG Regional Conference Jakarta, Indonesia, October 3-7.

Racoviteanu, A. E., Paul, F., Raup, B., Khalsa, S. J. S., and Armstrong, R., 2009: Challenges and recommendations in mapping of glacier parameters from space: results of the 2008 Global Land Ice Measurements from Space (GLIMS) workshop, Boulder, Colorado, USA. Annals of Glaciology, 50(53): 53–69

Rahman, M.Z.B.A., 2006. Digital surface model (DSM) construction and flood hazard simulation for Development Plans in Naga City, Philippines. GIS Develop. Malaysia, 1(3): 15-20.

Rode, M.; Frede, H.G. Modification of AGNPS for agricultural land and climate conditions in central Germany. J. Environ. Qual. 1997, 26, 165-172.

Rulinda, C. M., Bijker, W., & Stein, A. Image mining for drought monitoring in eastern Africa using Meteosat SEVIRI data. International Journal of Applied Earth Observation and Geoinformation, 12(Supplement 1), S63-S68.

Samarasinghea, S.M.J.S., H.K. Nandalalb, D.P. Weliwitiyac, J.S.M. Fowzed, M.K. Hazarikad and L. Samarakoond, 2010. Application of remote sensing and GIS for flood risk analysis: A case study at Kalu-Ganga River, Sri Lanka. Int. Arch. Photogr. Remote Sens. Spatial Inform. Sci., Vol. 38, Part 8, Kyoto Japan 2010

Sazbo, J., Pasztor, L.; Suba, Z.; Varallyay, G. Integration of remote sensing and GIS techniques in land degradation mapping. Proceedings of the 16th International Congress of Soil Science, Montpellier, France, August 1998; pp. 63-75.

Scholz F. and Hasse U. (2008): Permanent wood sequestration: The solution to the Global Carbon dioxide problem. Chemsuschem 1, 381-384.

Science (2007): How Satellites Help Us Understand Earth's Carbon Cycle. Available on-line: www.science20.com/news/how_satellites_help_us_understand_earths_carbon_cycle.

Stancalie, G.H., V. Craciunescu and A., Irimescu, 2009. Spatial Data Integration for Emergency Services of Flood Management. In: Jones, J.A.A. et al. (Eds.), Threats to Global Water Security. 2009: 155-165.

Stein, A., Hamm, N. A. S., & Ye, Q. (2009). Handling uncertainties in image mining for remote sensing studies. International Journal of Remote Sensing, 30(20), 5365 - 5382.

9

Journal Of Environment

Townsend, P.A., and S.J. Walsh, 1998. Modeling floodplain inundation using an integrated GIs with radar and optical remote sensing, Geomorphology, 21(3-4):295-312.

United States Environmental Protection Agency (USEPA), 2012, Climate Change Indicators in the United States

Usul, N.And B. Turan, 2006.Flood forecasting and analysis within the Ulus Basin, Turkey, using geographic information systems. Nat. Hazards, 39: 213-229

Vrieling, A.; Sterk G.; Beaulieu, N. Erosion risk mapping; a methodological case study in the Colombian Eastern Plains. Journal of Soil and Water Conservation 2002, 57, 3, 158-163.

Warrick, R.A., Barrow, E.M. and Wigley, T.M.L. (Eds.), 1993. Climate and sea level change: observations, projections and implications. Cambridge Univ. Press, Cambridge, 424 pp.

Xiao, X.M., Zhang Q. and Braswell B., (2004), Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens Environ., 91, pp 256–70.

Ye, Q.H., Kang, S.C., Chen, F., & Wang, J.H., 2006a. Monitoring glacier variations on Geladandong mountain, central Tibetan Plateau, from 1969 to 2002 using remotesensing and GIS technologies. Journal of Glaciology. 52(179), 537-545.

Yuksel, A.; Akay A.E.; Gundogan, R.; Reis, M.; Cetiner, M. Application of GeoWEPP for Determining Sediment Yield and Runoff in the Orcan Creek Watershed in Kahramanmaras, Turkey. Sensors 2008, 8, 1222-1236.

Yuksel, A.; Akay, A.E.; Gundogan, R. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project. Sensors 2008, 8, 1237-1251

10