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Introduction
Land cover and land use (LCLU) changes induced by human and natural processes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. Studies have revealed changes in water cycling process between land and atmosphere due to the large scale land cover changes, affecting the local to regional climate. Geo spatial technologies such as remote sensing (RS) and Geographic Information Systems (GIS) are very effective in measuring, monitoring and predicating the land use/cover changes. Timely information with higher accuracy of landuse (LU), Land cover (LC) changes is crucial for long-term planning, economic development, and sustainable management [1] of natural resources. The analysis of temporal remote sensing data helps in understanding the land cover changes and its impact on the environment. The thermal infrared bands of remote sesning data of space borne sensors help to retrieve Land Surface Temperature (LST). Land surface temperature is the measure of the heat emision from land surface due to various activities associated with the land surface. Increase in paved land cover is an indication of concentrated human activities, which often leads to increased LST’s [2]. Increased LST in certain urban pockets in comparison to its surroundings consequent to the increase in paved surfaces is known as urban heat island (UHI) phenomenon [3,4].
Detection of the thermal charecteristics of land surface using remote sensing data of space borne sensors and the analysis of land surface temperature (LST) has been reported earlier [5]. Spatio-temporal data were used to develop models of land surface atmosphere exchange, and to analyze the relationship between temperature and land use and land cover (LULC) in urban areas [6] highlighting the relationship between LST and surface characteristics such as vegetation indices [7, 8, 9]. Also, the studies reveal the effect of biophysical factors on LST by using vegetation fraction instead of qualitative LULC classes [9, 10, 11]. The vegetation index–LST relationship has also been used to retrieve surface biophysical parameters [7] to extract sub-pixel thermal variations [12], and to analyze land cover dynamics [13]. Many investigators have observed a negative relationship between vegetation index and LST, leading to further research into two major pathways, namely, statistical analysis of the relationship and the temperature/vegetation index (TVX) approach. TVX is a multi-spectral method of combining LST and a vegetation index (VI) to monitor their associations.
Land surface and atmospheric temperatures rise by various anthropogenic activities like increased land surface coverage by artificial materials, energy consumption, which have a high heat capacity and conductivity, and is also associated with the decreases in vegetation and water surfaces, which are the major factors that reduce surface temperature through evapo-transpiration [14]. Temperatures can be monitored through space borne remote sensing(rs) sensors, which account for the top of the atmosphere (TOA) radiances in the thermal infrared (TIR) region. TOA radiance is the net radiance of the emitted radiance from the earth’s surface upwelling radiance from the atmosphere, and downwelling radiance from the sky. The brightness temperatures (also known as blackbody temperatures) can also be derived from the TOA radiance [15]. These brightness temperatures account for the various properties of the land surface, the amount and nature of vegetation cover, the thermal properties and moisture content of the soil [16]. However, lack of knowledge of spectral emissivity can introduce an error which ranges from 0.2 to 1.2k for mid-latitude summers and 0.8 to 1.4k for the winter conditions for an emissivity of 0.98 and at the ground height of 10km [15]. Two approaches have been developed to recover LST from multispectral TIR imagery [17] as on date. The first approach utilises the radiative transfer equation to correct the at sensor radiance to surface radiance, followed by an emissivity model to separate the surfaces radiance into temperature and emissivity [16]. The second approach applies the split window technique for sea surfaces to land surfaces. Assuming that the emmisivity in the channels used for the split window is similar [15]. TIR region corresponding to 8-14 µm in the electromagnetic spectrum is being used for quantifying the thermal urban environment. Data from space-borne remote sensors (Landsat series satellites) are one of the most widely used for environmental studies. Landsat thematic mapper is composed by seven bands, six of them in the visible and near infrared, and only one band located in the thermal infrared region (with an effective wavelength of 11.457 µm) is used for LST retrieval. Availability of one thermal band might be stated as a disadvantage/limitation in order to obtain LST as it does not allow the application of a split-window method [18] neither a temperature/emissivity separation method [19, 20] to obtain information about the emissivity spectrum of natural surfaces.
The objective here is to investigate the Land Surface Temperature with Land use dynamics to understand the Urban Heat Island phenomenon in Himachal Pradesh considering Multi -senor, Multi-resolution and temporal RS data acquired through space borne sensors. This involved;
- Temporal LU change analysis (during 1989 and 2005)
- Computation of LST and NDVI (Normalised Difference Vegetation Index) from Landsat TM (1989) and Landsat ETM (2000) and Landsat ETM+ (2005) Data;
- Investigation of the role of NDVI in LST.
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