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Introduction
Land use land cover (LULC) information is essential for managing natural resources and monitoring of environmentalchanges. Exchanges of energy take place between the biophysical properties of the Earth (ex: vegetation) with the atmosphere. These exchanges are influenced by properties of the land, such as the overlying vegetation, the underlying soils and land management practices. Changes in these systems show adverse effect on environment by surface energy imbalance and through biogeochemical interactions that affect the carbon cycle [1]. Thus LULC changes altering the local climate are the drivers for global climate change. Increasing concentrations of greenhouse gases due to the natural and anthropogenic factors alters temperature trends [2, 3]. Landscape dynamics involving LULC changes have contributed to the increase in land surface temperature (LST).The different land use types indicate the variability with different land surface temperatures (LST) [4, 5]. The increasing evidence provides a base that non radiative forces such as LULC change also be major factors contributing to climate change. The climate is altered due to changes in Land use and land cover. There has been an increase in surface temperature due to alterations and conversions of vegetated surfaces to impervious surfaces [6]. These changes affect the absorption of solar radiation, surface temperature, evaporation rates, storage of heat, wind turbulence and can drastically alter the conditions of the near-surface atmosphere. The clouds, land use, land surface temperature, exchanges of energy and moisture are referred to be the key indicators considered to explain the global climate change, which vary rapidly in time and space. These parameters will drive in precise measures of radiation budget, heat balance for climate models [7]. Deforestation has been the major driver in tropics [8] and subtropics [9]. The alteration of landscapes, primarily the conversion of forests to agriculture or pasture, changes the partitioning of solar insolation into its sensible and latent turbulent heat forms. The intensified LULC changes induces prolonged drought, which can trigger large-scale landscape changes through vegetation mortality from water stress [10] and land surface conditions for decades [11, 12]. The mortality of over story trees will rapidly alter ecosystem type and associated ecosystem properties at a regional-scale.
Thus, to mitigate the impacts of changes in climate, it is essential to monitor LULC changes at appropriate scales.
The investigation of environmental energy flows by thermal infrared measures of surface temperatures has been in practice since 1960’s [13]. A higher level of latent heat exchange was found with more vegetated areas, while sensible heat exchange was more favored by sparsely vegetated areas. This evidence on latent heat exchange encouraged many researchers to focus on deriving LST and LULC relationship [14]. Thermal infrared (TIR) remote sensing techniques for analyzing land surface temperature (LST) patterns and its relationship with surface characteristics, assessing urban heat island (UHI), and relating LSTs with surface energy fluxes to characterize landscape properties, patterns, and processeshave been well applied in urban climate and environmental studies [4, 15, 16, 17]. However, there is limited research in complicated landscapesdue to surface heterogeneity and characteristic of forests, owing to difficulties associated with sampling and quantifications. Also, there are gaps in the quantitative measures of the surface temperature heterogeneity at scales which can be recognized on the ground. The LST measures with multi temporal data at micro scale is also not addressed. The availability of remote sensing data acquired through space borne satellites (RS) and Geographic Information System (GIS) aid in the analysis of spatio-temporal dynamics of the Earth’s features which help in managing natural resources and assessment of environmental changes [18]. Remote sensing through thermal scanning of entire surface types (plants and soils), simultaneously, express their temperature responses comparable to the atmospheric and radiant inputs [19,20]. An Earth surface feature emits thermal radiation at different wavelengths depending on their emissivity(ε). Emissivity is defined as the ratio of the spectral radiant emittance of a grey body to that emitted by a blackbody at the same temperature [21]. Accurate land surface emissivity values aid in reliable inferences among different land covers for retrieving LST from thermal infrared (TIR) data [6, 22, 23]. Land surface temperature and emissivity are prime variables to determine the amount of thermal infrared energy radiated from the Earth's surface according to Planck's law. These variables will provide information of many different types of Earth surface processes, surface-atmosphere interactions and evapotranspiration [24].
Estimation of LST using emissivity will provide more accurate estimation with appropriate calibration of atmospheric contamination by a separation of surface emissivity and temperature from radiance at ground level and atmospheric corrections [25, 26]. Earlier studies have highlighted the advantage of using TM and ETM thermal band data of Landsat sensors for deriving temperature [6, 27, 28]. Emissivity and LST were found to be well correlated to the measured LST [29,30]. LST derivation from the normalized difference vegetation index (NDVI) is used to derive the negative correlation between for LST downscaling purposes [31]. The NDVI cannot explain all the variation in LST and the vegetation fraction from ETM+ images is provided a stronger negative correlation than the one between LST & NDVI [29, 32]. Central Western Ghats has been experiencing large scale land use changes due to many unplanned developmental activities evident from barren hilltops, conversion of perennial streams to seasonal streams, decreased flow in streams, etc. The objective of the current study is to understand LULC changeswith temperature trends using multi-resolution remote sensing data. This involves,
- measuring the relationship between surface biophysical parameters (land use / land cover and sub-pixel thermal variations (Land Surface Temperature),
- determine the role land use dynamics in temperature changes through time and space.
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