Land-cover classification is one of the most important and typical
applications of remote sensing data (Forster 1985; Green 1994; Jensen
and Cowen 1999). ‘Land cover’ corresponds to the physical condition of
the ground surface, for example, forest, grassland, cropland, urban, and
water; while ‘land use’ reflects human activities such as the use of
the land, for example, industrial, residential, recreational, and
agricultural. Land-cover refers to features of land surface, which may
be natural, semi-natural, managed, or manmade. They are directly
observable by a remote sensor. Whereas, land-use refers to activities on
land, or classification of land according to how it is being used. Not
always directly observable, inferences about land-use can often be made
from land-cover. A reason for developing and maintaining a land-cover
inventory is to provide a consistent view of the stock and state of our
natural and built resources as they change through time. Land-use
denotes the human employment of the land, so that a change in land-use
may involve a shift to a different type of use, for instance, from
farming to residential, or a change in intensity of use. A land-use
class is composed of several land-covers, for example, a residential
area not only contains built-up but also contains trees, grasslands,
water bodies, and other land-cover classes. The properties measured with
remote sensing techniques relate to land-cover, from which the land-use
can be inferred, particularly with ancillary data or a priori knowledge
(Bhatta 2008).
Socioeconomic considerations are important to describe the relationship between economic activity and social life. These may affect patterns of consumption, the distribution of incomes and wealth, the manner in which people behave, and the overall quality of life. The goal of socioeconomic study is generally to bring about socioeconomic development, usually in terms of improvements in metrics such as gross domestic product, life expectancy, literacy, levels of employment, sex ratio, etc. Census activities study and analyze these patterns effectively.
Both land-use and land-cover are linked to socioeconomic variables in a complex way; for example, migration from agricultural employment to industrial employment causes urban expansion by changing the land-cover as well as land-use patterns; economic growth creates demand for new housing or more housing space for individuals resulting in urbanization (Bhatta 2009). These are direct impact of socioeconomic variable on the land-use/land-cover (LULC), however, a change in land-use or land-cover can also be a driving factor to socioeconomic changes; for example, government policy to convert a large agricultural land into industry will force a socioeconomic change for the neighbouring residents. Changes to the landscape are occurring every day with significant implications for taxation, quality of life, water quality, agricultural viability, wildlife habitat, and social equity (Maher 2006).
Each land-cover has its own influence within its neighbours, for example, trees and other vegetations provide many benefits, including habitat for a variety of plants and animals, recreation, protection of watersheds from erosion, and timber products (Iverson 1994; Bonsignore 2003; Uy and Nakagoshi 2008). Vegetation within the urban landscape also provide additional benefits less commonly associated with rural vegetation; for example, green spaces sequester CO2 (Nowak 1993; Nowak and Crane 2002; McHale et al. 2007) and produce O2 (Jo 2002), they reduce air pollution (Yang et al. 2005) and noise (Fang and Ling 2003), regulate microclimates, reduce the heat island effect in cities (Shin and Lee 2005), affect house prices (Kong et al. 2007), maintain diversity and corridors into urban habitat islands (Zalewski 1994; Blair 1996; Zipperer et al. 1997), they have recreational and social values (Tarrant and Cordell 2002), and they mask unpleasant urban views. Yet the data on urban vegetation resources are limited for the cities in many developing countries (Angel et al. 2005).
In most of the developed countries, for many urban areas, they may have a very good temporal inventory on urban vegetation resources; but in developing countries, inventories of urban land-cover typically have focused on classifying man-made features to the exclusion of data on the wild vegetation and scattered vegetation within these complex landscapes. For example, undeveloped land in urban settings often is labeled as ‘vacant land’ and housing areas as ‘residential’ without mentioning the tree and herbaceous cover in these areas (Iverson and Cook 2000). Grasslands, pastures, and bare grounds are collectively identified as open space; although they never play same role in ecological and environmental protection. In case of agriculture, only large agricultural fields are generally identified ignoring residential agricultures. Similar tendency can also be seen in other instances as well; for example, water class is commonly identified ignoring the open-drainage water, although they have similar ecological and environmental impacts as for other open water bodies.
The use of satellite imagery to map the land-cover has met with varying degrees of success (Quattrochi 1983; Toll 1984; Duggin et al. 1986; Haack et al. 1987; Sadowski et al. 1987; Foresman et al. 1997; Iverson and Cook 2000; Yunhao et al. 2006; Olthof and Fraser 2007; Gjertsen 2007; Carrão et al. 2008; Herold et al. 2008). A number of regional- to national- to global-scale land-cover databases were developed that led to the effective use of land. As a result, there is a strong reliance on remote-sensing and land-cover databases for multi-scale environmental and ecological studies. Land-cover classification and identification of transition between these classes, from remote sensing imageries, is an essentially performed operation in the recent years.
However, in the past, there were a large number of technical reasons that have precluded detailed land-cover being derived from remotely sensed data, especially from satellite imagery. Some of the major reasons are the unavailability of high-resolution satellite sensors, excessive cost of high-resolution aerial photographs, and political restrictions on procuring high-resolution imageries to the non-military researchers in many developing countries. As a result, most of the studies were carried out using low or medium resolution satellite imageries, which are not reliable enough in consideration of urban heterogeneity. Buchan and Hubbard (1986) reported that even the 20 m resolution of multispectral SPOT image is insufficient for mapping the heterogeneity of some inner city-space because of the heterogeneous nature of the land-cover classes within a small transect. A recent study shows that LISS–IV image of 5.8 m resolution also suffers from 15–20% overall confusion towards urban landscape classification due to mixed pixel and mixed class (Bhatta 2007). Misclassification in land-use and land-cover in the prediction of landscape dynamics may cause serious negative impacts (Fang et al. 2006).
As a result of using low or medium resolution imageries for land-cover classification, many municipal governments in developing countries lack the necessary data to manage and/or protect their urban vegetations, water bodies, wetlands, grasslands, and agricultural lands against the pressures of continued development. There is a need to emphasize for additional information on the quantity, quality, and functionality of land-cover classes in the urban and urbanizing areas. These areas are especially important, because population density is extremely high in these areas and increasing in a very high rate.
Socioeconomic considerations are an integral part of ecosystem management, particularly as it relates to urbanizing regions (Whitney and Adams 1980; Jones et al. 1995). New and innovative methods for merging ecology and socioeconomics are the central to studies of urban to rural transects (McDonnell and Pickett 1990; Grove and Burch 1997; McDonnell et al. 1997; Zipperer et al. 1997; Gomiero et al. 1999; Zhang et al. 2006; Uy and Nakagoshi 2008). Satellite imageries can effectively be used with socioeconomic measures to determine the relationship between population density and forest fragmentation (Vogelmann 1995) or to categorize urban regions by degrees of population crowding (Weber and Hirsch 1992). Gatrell and Jensen (2008) cite several references to describe how remote sensing data have been used in urban areas, and highlight some research areas where remote sensing may continue to aid urban geographical inquiry and some potential pitfalls. They conclude that remote sensing has a critical role to play in the analysis of the interactions that occur between people and urban environments that may help shape our understanding of humans and the environment in which they live. Although, literature on correlation analysis between remote sensing data and socioeconomic variables are limited, some successful attempts should be appreciated; for example, normalized difference vegetation index (NDVI) to establish links with key socio-economic variables (Heynen and Lindsey 2003; Lafary et al. 2008), urban canopy dynamics (as measured by Leaf Area Index (LAI)) to predict observed socioeconomic conditions in cities (Jensen et al. 2004). Based on the foundations of urban remote sensing, a new collection of researchers have begun to ask novel questions concerning the socio-spatial implications of the observed interactions between the built and natural environments with respect to observed socioeconomics as well as related implications on public policy (Jensen and Cowen 1999, Gatrell and Jensen 2002, Heynen and Lindsey 2003, Jensen et al. 2004, Malczewski and Poetz 2005, Mennis and Jordan 2005). Specifically, researches have evaluated how urban conditions and associated socio-demographics correlate at multiple scales. More recently, researchers have begun to link observed quality of life indicators with remotely sensed data to construct a better understanding of basic human–environment interactions. However, research on linking remote sensing imageries with socioeconomics is still in its infancy; a lot of hurdles are yet to overcome.
References
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ALSTON, M., and BOWLES, W., 2003, Research for Social Workers: An Introduction to Methods, 2nd ed. Routledge, pp. 334.
ANGEL, S., SHEPPARD, S.C., and CIVCO, D.L., 2005, The Dynamics of Global Urban Expansion. Transport and Urban Development Department. The World Bank, Washington D.C., pp. 1–200. Available online at: http://www.citiesalliance.org/doc/resou ... pt2005.pdf (last accessed 13 February 2008).
BHATTA, B., 2007, Quantification of confusion in LISS–III & LISS–IV data for urban land-cover classification. In the Proceedings of National Conference on High Resolution Remote Sensing & Thematic Applications, Kolkata, 18–20 December, Indian Society of Remote Sensing, p. 58.
BHATTA, B., 2008, Remote Sensing and GIS. Oxford University Press, pp. 872.
BHATTA, B., 2009, Analysis of urban growth pattern using remote sensing and GIS : a case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733–4746.
BLAIR, R.B., 1996, Land use and avian species diversity along an urban gradient. Ecological Applications, 6, 506–519.
BONSIGNORE, R.E., 2003, The diversity of green spaces. Design Center for American urban Landscape. Design Brief, number 2/August. Design Centre for the American Urban Landscape, University of Minnesota, Minneapolis. Available at /http://www.designcenter.umn.edu/reference_ctr/ publications/pdfs/db2.pdf (accessed 15 April 2007).
BUCHAN, G.M., and HUBBARD, N.K., 1986, Remote sensing in land-use planning: an application in west central Scotland using SPOT-simulation data. International Journal of Remote Sensing, 7, 767–777.
BUNTING, S.W., 2007. Confronting the realities of wastewater aquaculture in peri-urban Kolkata with bioeconomic modelling. Water Research, 41(2), 499–505
CARRÃO, H., GONÇALVES, P., and CAETANO, M. 2008, Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment, 112(3), 986–997.
COHEN, L., MANION, L., and MORRISON, K.R.B., 2000, Research Methods in Education, 5th ed. Routledge, pp. 446.
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FANG, C.F., and LING, D.L., 2003, Investigation of the noise reduction provided by tree belts. Landscape and Urban Planning, 63, 187–195.
FANG, S., GERTNER1, G., WANG, G., and ANDERSON, A., 2006, The impact of misclassification in land use maps in the prediction of landscape dynamics. Landscape Ecology, 21, 233–242
FORESMAN, T.W., PICKETT, S.T.A., and ZIPPERER, W.C., 1997, Methods for spatial and temporal land use and land cover assessment for urban ecosystems and application in the greater Baltimore–Chesapeake region. Urban Ecosystems, 1, 201–216.
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Socioeconomic considerations are important to describe the relationship between economic activity and social life. These may affect patterns of consumption, the distribution of incomes and wealth, the manner in which people behave, and the overall quality of life. The goal of socioeconomic study is generally to bring about socioeconomic development, usually in terms of improvements in metrics such as gross domestic product, life expectancy, literacy, levels of employment, sex ratio, etc. Census activities study and analyze these patterns effectively.
Both land-use and land-cover are linked to socioeconomic variables in a complex way; for example, migration from agricultural employment to industrial employment causes urban expansion by changing the land-cover as well as land-use patterns; economic growth creates demand for new housing or more housing space for individuals resulting in urbanization (Bhatta 2009). These are direct impact of socioeconomic variable on the land-use/land-cover (LULC), however, a change in land-use or land-cover can also be a driving factor to socioeconomic changes; for example, government policy to convert a large agricultural land into industry will force a socioeconomic change for the neighbouring residents. Changes to the landscape are occurring every day with significant implications for taxation, quality of life, water quality, agricultural viability, wildlife habitat, and social equity (Maher 2006).
Each land-cover has its own influence within its neighbours, for example, trees and other vegetations provide many benefits, including habitat for a variety of plants and animals, recreation, protection of watersheds from erosion, and timber products (Iverson 1994; Bonsignore 2003; Uy and Nakagoshi 2008). Vegetation within the urban landscape also provide additional benefits less commonly associated with rural vegetation; for example, green spaces sequester CO2 (Nowak 1993; Nowak and Crane 2002; McHale et al. 2007) and produce O2 (Jo 2002), they reduce air pollution (Yang et al. 2005) and noise (Fang and Ling 2003), regulate microclimates, reduce the heat island effect in cities (Shin and Lee 2005), affect house prices (Kong et al. 2007), maintain diversity and corridors into urban habitat islands (Zalewski 1994; Blair 1996; Zipperer et al. 1997), they have recreational and social values (Tarrant and Cordell 2002), and they mask unpleasant urban views. Yet the data on urban vegetation resources are limited for the cities in many developing countries (Angel et al. 2005).
In most of the developed countries, for many urban areas, they may have a very good temporal inventory on urban vegetation resources; but in developing countries, inventories of urban land-cover typically have focused on classifying man-made features to the exclusion of data on the wild vegetation and scattered vegetation within these complex landscapes. For example, undeveloped land in urban settings often is labeled as ‘vacant land’ and housing areas as ‘residential’ without mentioning the tree and herbaceous cover in these areas (Iverson and Cook 2000). Grasslands, pastures, and bare grounds are collectively identified as open space; although they never play same role in ecological and environmental protection. In case of agriculture, only large agricultural fields are generally identified ignoring residential agricultures. Similar tendency can also be seen in other instances as well; for example, water class is commonly identified ignoring the open-drainage water, although they have similar ecological and environmental impacts as for other open water bodies.
The use of satellite imagery to map the land-cover has met with varying degrees of success (Quattrochi 1983; Toll 1984; Duggin et al. 1986; Haack et al. 1987; Sadowski et al. 1987; Foresman et al. 1997; Iverson and Cook 2000; Yunhao et al. 2006; Olthof and Fraser 2007; Gjertsen 2007; Carrão et al. 2008; Herold et al. 2008). A number of regional- to national- to global-scale land-cover databases were developed that led to the effective use of land. As a result, there is a strong reliance on remote-sensing and land-cover databases for multi-scale environmental and ecological studies. Land-cover classification and identification of transition between these classes, from remote sensing imageries, is an essentially performed operation in the recent years.
However, in the past, there were a large number of technical reasons that have precluded detailed land-cover being derived from remotely sensed data, especially from satellite imagery. Some of the major reasons are the unavailability of high-resolution satellite sensors, excessive cost of high-resolution aerial photographs, and political restrictions on procuring high-resolution imageries to the non-military researchers in many developing countries. As a result, most of the studies were carried out using low or medium resolution satellite imageries, which are not reliable enough in consideration of urban heterogeneity. Buchan and Hubbard (1986) reported that even the 20 m resolution of multispectral SPOT image is insufficient for mapping the heterogeneity of some inner city-space because of the heterogeneous nature of the land-cover classes within a small transect. A recent study shows that LISS–IV image of 5.8 m resolution also suffers from 15–20% overall confusion towards urban landscape classification due to mixed pixel and mixed class (Bhatta 2007). Misclassification in land-use and land-cover in the prediction of landscape dynamics may cause serious negative impacts (Fang et al. 2006).
As a result of using low or medium resolution imageries for land-cover classification, many municipal governments in developing countries lack the necessary data to manage and/or protect their urban vegetations, water bodies, wetlands, grasslands, and agricultural lands against the pressures of continued development. There is a need to emphasize for additional information on the quantity, quality, and functionality of land-cover classes in the urban and urbanizing areas. These areas are especially important, because population density is extremely high in these areas and increasing in a very high rate.
Socioeconomic considerations are an integral part of ecosystem management, particularly as it relates to urbanizing regions (Whitney and Adams 1980; Jones et al. 1995). New and innovative methods for merging ecology and socioeconomics are the central to studies of urban to rural transects (McDonnell and Pickett 1990; Grove and Burch 1997; McDonnell et al. 1997; Zipperer et al. 1997; Gomiero et al. 1999; Zhang et al. 2006; Uy and Nakagoshi 2008). Satellite imageries can effectively be used with socioeconomic measures to determine the relationship between population density and forest fragmentation (Vogelmann 1995) or to categorize urban regions by degrees of population crowding (Weber and Hirsch 1992). Gatrell and Jensen (2008) cite several references to describe how remote sensing data have been used in urban areas, and highlight some research areas where remote sensing may continue to aid urban geographical inquiry and some potential pitfalls. They conclude that remote sensing has a critical role to play in the analysis of the interactions that occur between people and urban environments that may help shape our understanding of humans and the environment in which they live. Although, literature on correlation analysis between remote sensing data and socioeconomic variables are limited, some successful attempts should be appreciated; for example, normalized difference vegetation index (NDVI) to establish links with key socio-economic variables (Heynen and Lindsey 2003; Lafary et al. 2008), urban canopy dynamics (as measured by Leaf Area Index (LAI)) to predict observed socioeconomic conditions in cities (Jensen et al. 2004). Based on the foundations of urban remote sensing, a new collection of researchers have begun to ask novel questions concerning the socio-spatial implications of the observed interactions between the built and natural environments with respect to observed socioeconomics as well as related implications on public policy (Jensen and Cowen 1999, Gatrell and Jensen 2002, Heynen and Lindsey 2003, Jensen et al. 2004, Malczewski and Poetz 2005, Mennis and Jordan 2005). Specifically, researches have evaluated how urban conditions and associated socio-demographics correlate at multiple scales. More recently, researchers have begun to link observed quality of life indicators with remotely sensed data to construct a better understanding of basic human–environment interactions. However, research on linking remote sensing imageries with socioeconomics is still in its infancy; a lot of hurdles are yet to overcome.
References
AHERN, J., 1999, Spatial concepts, planning strategies and future scenarios: a framework method for integrating landscape ecology and landscape planning. In: Landscape Ecological Analysis: Issues and Applications, Klopatek, J., Gardner, R. (Eds.), Springer, New York, pp. 175–2001.
ALSTON, M., and BOWLES, W., 2003, Research for Social Workers: An Introduction to Methods, 2nd ed. Routledge, pp. 334.
ANGEL, S., SHEPPARD, S.C., and CIVCO, D.L., 2005, The Dynamics of Global Urban Expansion. Transport and Urban Development Department. The World Bank, Washington D.C., pp. 1–200. Available online at: http://www.citiesalliance.org/doc/resou ... pt2005.pdf (last accessed 13 February 2008).
BHATTA, B., 2007, Quantification of confusion in LISS–III & LISS–IV data for urban land-cover classification. In the Proceedings of National Conference on High Resolution Remote Sensing & Thematic Applications, Kolkata, 18–20 December, Indian Society of Remote Sensing, p. 58.
BHATTA, B., 2008, Remote Sensing and GIS. Oxford University Press, pp. 872.
BHATTA, B., 2009, Analysis of urban growth pattern using remote sensing and GIS : a case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733–4746.
BLAIR, R.B., 1996, Land use and avian species diversity along an urban gradient. Ecological Applications, 6, 506–519.
BONSIGNORE, R.E., 2003, The diversity of green spaces. Design Center for American urban Landscape. Design Brief, number 2/August. Design Centre for the American Urban Landscape, University of Minnesota, Minneapolis. Available at /http://www.designcenter.umn.edu/reference_ctr/ publications/pdfs/db2.pdf (accessed 15 April 2007).
BUCHAN, G.M., and HUBBARD, N.K., 1986, Remote sensing in land-use planning: an application in west central Scotland using SPOT-simulation data. International Journal of Remote Sensing, 7, 767–777.
BUNTING, S.W., 2007. Confronting the realities of wastewater aquaculture in peri-urban Kolkata with bioeconomic modelling. Water Research, 41(2), 499–505
CARRÃO, H., GONÇALVES, P., and CAETANO, M. 2008, Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment, 112(3), 986–997.
COHEN, L., MANION, L., and MORRISON, K.R.B., 2000, Research Methods in Education, 5th ed. Routledge, pp. 446.
Directorate of Census, 2001, Census Report – 2001, Govt. of India. Table-4, Table-13, and Table-B21.
DUGGIN, M.J., ROWNTREE, R., EMMONS, M., HUBBARD, N., ODELL, A.W., SAKHAVAT, H., and LINDSAY, J., 1986, The use of multidate multichannel radiance data in urban feature analysis. Remote Sensing of the Environment, 20, 95–105.
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