Jeganathan CHOCKALINGAM | Birla Institute of Technology, Mesra (Ranchi) India (original) (raw)

Papers by Jeganathan CHOCKALINGAM

Geocarto International, 2021

Journal of Environmental Informatics, 2011

Developing tourism in a hilly region has become a thriving process which induces a complex land-c... more Developing tourism in a hilly region has become a thriving process which induces a complex land-cover dynamics. The study area (Shimla District in Himachal Pradesh) is located in the beautiful Himalayan environment in northern India and was the summer capital of India during the days of British rule. The current study has attempted to understand and model the suitability of three major land use forces (agriculture need, forest ecosystem conservation and human development) using the spatial Analytical Hierarchical Process (AHP). Further, the study utilised a multi-objective conflict analysis (MOCA) to understand and resolve the competition amongst these major land use forces. The cross overlay analysis of the existing land use map and the MOCA-derived map infers that the agricultural and human settlement preference can potentially expand into 26% of the current actual forest area. Around 18% of the potentially suitable area for forest class has already been occupied by non-forest classes. The potential future change zones are located in the middle and western regions/sections of the study area. Since the western region is already developed, more attention needs to be paid in the future to the central region of the study area.

Quantitative Geology and Geostatistics, 2010

Three methods were introduced for estimating the local semivariogram for use in procedures such a... more Three methods were introduced for estimating the local semivariogram for use in procedures such as super-resolution pattern prediction. The first is simply to use a training image to estimate the global semivariogram required. The second method employs a deconvolution-convolution procedure to estimate the local semivariogram. The estimated semivariogram represents proportions and so a further step is required to convert the proportions semivariogram to represent a binary field. The third method is an integration of the first two methods obtained by weighted linear combination across the lags of the semivariograms. The results are evaluated using the known target local semivariogram. The integrated method provides some advantages. The discussion points to problems and potential future improvements on the method.

Decision making in land use planning needs understanding about the pattern of changes. The curren... more Decision making in land use planning needs understanding about the pattern of changes. The current study aims to analyse and predict the land use and land cover change, with the focus on forests, in Shimla District using Bayesian model. Population growth, agricultural-horticulture demands, tourism growth are putting pressure on the valuable forest ecosystem and natural resources of the district. In this study, land cover maps were prepared for the periods 1970s, 1980s and 1990s using remote sensing data. The actual positive changes (i.e., increase in forest) and negative changes (i.e., decrease in forest) derived from the time-series land cover maps were used as apriori evidence in the Bayesian model to derive the statistical weights for various environmental parameters. The environmental parameters were analysed under 4 major group of factors i.e., topographic, land use, landscape, land-water. The probabilistic contribution (i.e., weight) of each attribute under each map was utilis...

Advances in Space Research, 2021

Geoderma, 2020

Abstract Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available f... more Abstract Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available from different space agencies, derived using the active microwave remote sensing, may not be suitable for studies at a local level due to their coarse spatial resolution. Hence, an attempt has been made in this study to estimate the surface soil moisture at high spatial resolution using different high-resolution multispectral imageries from Landsat 8 OLI (Optical Land Imager) and Sentinel 2A MSI (Multi Spectral Instrument), of similar date. The thermal (10.9 µm) and short wave infra-red (SWIR) (2.2 µm) bands of the Landsat image and the SWIR band (2.19 µm) of Sentinel image were used to estimate the soil moisture at a spatial resolution of 30 m and 20 m respectively, whereas the red and near infrared (NIR) bands of both the images were used to estimate the dryness. As the thermal band at high spatial resolution (at 20 m or below) is not available, this study attempted to bridge the gap and integrate multi-sensor and multi-resolution feature space utilizing various scalable procedures so as to estimate soil moisture and dryness. Limitation due to unavailability of the thermal bands in Sentinel multispectral image was overcome through a bridging procedure using Landsat thermal bands derived LST and Sentinel derived NDVI to estimate Sentinel LST at 20 m. The distribution pattern of high-resolution estimated soil moisture in each coarse spatial resolution grid (1 km) was taken into consideration to downscale the available CCI and SMAP soil moisture data. The estimated soil moisture and dryness were upscaled to 1 km and then validated using the downscaled soil moisture products (SMAP and CCI), and a significant correlation having R2 greater than 0.8, 0.5 and 0.4 was observed for the thermal band derived soil moisture, short wave band derived soil moisture, and the red and NIR band derived dryness, respectively. To understand the temporal variability of the soil moisture, data from three different seasons representing extreme soil moisture conditions (winter, summer and monsoon) were analyzed and found that the independently estimated soil moisture had errors in temporal continuity and spatial spread. Hence, the estimated soil moisture was standardized using annual information which resulted in more accurate products, and the SWIR Transformed Reflectance (STR) derived soil moisture was more accurate than LST derived product.

Journal of the Indian Society of Remote Sensing, 2019

The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG... more The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG) and length of greening (LOG), and their rate of change in the western Himalaya in India (Himachal Pradesh) using MODIS NDVI time series data (2001–2015). These metrics were inspected at different stratification levels: state, elevation, climatic zones and bio-geographic provinces. Delayed SOG was observed over 44.87% (P < 0.1), and delayed EOG over 63.3% (P < 0.1) of grassland grids. LOG was shortened in 24.37% (P < 0.1) and extended in 58.04% (P < 0.1) of the grids. At the state level, when statistically significant pixels (SSP) and all the pixels (AP) are used (given as SSP:AP), SOG is delayed by 20.27:6.28 days year−15, while EOG is delayed by 38.02:14.97 days year−15 and LOG is extended by 35.07:8.70 year−15 days. Extended LOG is observed over the temperate and cold arid regions, and shortened LOG is observed over sub-alpine and alpine regions. Variations in SOG and EOG are not uniform across different climatic and bio-geographic regions. However, in the sub-alpine and alpine zones, SOG and EOG followed elevation gradients, i.e. late SOG with early EOG over higher elevations, and early SOG with late EOG over lower elevations. Our study has revealed an interesting pattern of translational phenology (i.e. late SOG and late EOG) of grasslands which hints towards shifting winter period. Overall, it is observed that variations in timing of snowfall and snow cover extent are the reasons for inter-annual variations in the grassland phenology.

Procedia Environmental Sciences, 2011

The spatial variability of remotely sensed image values provides important information about the ... more The spatial variability of remotely sensed image values provides important information about the arrangement of objects and their spatial relationships within the image. The characterisation of spatial variability in such images, for example, to measure of texture, is of great utility for the discrimination of land cover classes. To this end, the variogram, a function commonly applied in geostatistics, has been used widely to extract image texture for remotely sensed data classification. The aim of this study was to assess the increase in accuracy that can be achieved by incorporating univariate and multivariate textural measures of Landsat TM imagery in classification models applied to large heterogeneous landscapes. Such landscapes which difficult to classify due to the large number of land cover categories and low inter-class separability. Madogram, rodogram and direct variogram for the univariate case, and cross-and pseudocross variograms for the multivariate one, together with multi-seasonal spectral information were used in a Random Forest classifier to map land cover types. The addition of spatial variability into multi-seasonal Random Forest models leads to an increase in the overall accuracy of 8%, and to an increase in the Kappa index of 9%, respectively. The increase in per categories Kappa for the textural Random Forest model reached 30% for certain categories. This study demonstrates that the use of information on spatial variability produces a fundamental increase in per class classification accuracy of complex land-cover categories.

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010

Abstract Phenological information can be provided globally using remote sensing based time-series... more Abstract Phenological information can be provided globally using remote sensing based time-series vegetation indices. Basic differences in the data and methods used can yield different results. This study analysed such differences in the phenological information, ...

Indian Forester, 2010

In general, the change detection models can be categorized into two groups. One is spectral chang... more In general, the change detection models can be categorized into two groups. One is spectral change methods and another is post-classification change methods. In the spectral change detection approach, analysis was done to find out difference between the multi-date raw ...

Remote Sensing of Environment, 2012

Several models have been fitted in the past to smooth time-series vegetation index data from diff... more Several models have been fitted in the past to smooth time-series vegetation index data from different satellite sensors to estimate vegetation phenological parameters. However, differences between the models and fine tuning of model parameters lead to potential differences, uncertainty and bias between the results amongst users. The current research assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably. The research used Level 3 Medium Resolution Imaging Spectrometer (MERIS, spatial resolution~4.6 km) Terrestrial Chlorophyll Index (MTCI) data over the years 2004 to 2006 composited at eight day intervals covering the Indian sub-continent. First, the four models were fitted to representative sample time-series of the major vegetation types in India, and the quality of the fit was analysed. Second, the effect of noise on model fitting was analysed by adding Gaussian noise to a standard profile. Finally, the four models were fitted to the whole study area to characterise variation in the quality of model fitting as a function of single and double vegetation seasons. These smoothed data were used to estimate the onset of greenness (OG), a major phenological parameter. The models were evaluated using the root mean square error (RMSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC). The first test (fitting to representative sample time series) revealed the consistently superior performance of the Whittaker and Fourier approaches in most cases. The second test (fitting after the addition of Gaussian noise) revealed the superior performance of the double logistic and Fourier approaches. Finally, when the approaches were applied to the whole study, thus, including vegetation with different phenological profiles and multiple growing seasons (third test), it was found that it was necessary to tune each of the models according to the number of annual growing seasons to produce reliable fits. The double logistic and asymmetric Gaussian models did not perform well for areas with more than one growing season per year. The mean absolute deviation in OG derived from these models was a maximum (3 to 4 weeks) within the dry deciduous vegetation type and minimum (1 week) in evergreen vegetation. All techniques yielded consistent results over the southwestern and northeastern regions of India characterised by tropical climate.

Remote Sensing of Environment, 2010

India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The... more India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in interannual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.

Remote Sensing of Environment, 2012

A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal textur... more A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape, with a large number of land cover categories and low inter-class separability. Five different types of geostatistical textural measure for three different window sizes and three different lags were applied creating a total of 972 potential input variables. Madograms, rodograms and direct variograms for the univariate case and cross-and pseudo-cross variograms for the multivariate case, together with multiseasonal spectral information, were used in a RF classifier to map the land cover types. The pseudo-cross and cross variograms were used specifically to incorporate important seasonal/temporal information. Incorporating multi-scale textural features into the RF models led to an increase in the overall index of 10.71% and, for the most accurate classification, the increase was up to 30% in some classes. The differences in the kappa coefficient for the textural classification models were evaluated statistically using a pairwise Z-test, revealing a significant increase in per-class classification accuracy compared to GLCM-based texture measures. The pseudo-cross variogram between the visible and near-infrared bands was the most important textural features for general classification, and the multi-seasonal pseudo-cross variogram had an outstanding importance for agricultural classes. Overall, the RF classifier applied to a reduced subset of input variables composed of the most informative textural features led to the highest accuracy. Highly reliable classification results were obtained when the 16 most important textural features calculated at single scales (window sizes) were selected and used in the classification. The proposed methodology significantly increased the classification accuracy achieved with a spectral maximum likelihood classifier (ML). The kappa values for the textural RF and ML classifications were equal to 0.92 and 0.83, respectively.

Landscape Ecology, 2010

... 2004), and through the normalized difference vegetation index (NDVI) (Jeyaseelan et al. 2007;... more ... 2004), and through the normalized difference vegetation index (NDVI) (Jeyaseelan et al. 2007; Saikia 2009). ... 1985; Newton 1988; Singh and Singh 1992; Murali and Sukumar 1126 Landscape Ecol (2010) 25:1125–1141 123 Page 3. ...

Journal of the Indian Society of Remote Sensing, 2009

Invasive species have been the focus of environmentalists due to their undesired impact on the ec... more Invasive species have been the focus of environmentalists due to their undesired impact on the ecosystem. Spread of Lantana (Lantana camara L.), an invasive plant species, has been found in diverse geophysical environments causing a threat to the native flora. Various eradication programmes have been attempted such as burning, chemical sprays, bio-control agents and physical plugging mechanism for removing such invasive Nomenclature

Journal of the Indian Society of Remote Sensing, 2009

The study compared forest cover maps derived using coarse resolution vegetation continuous fields... more The study compared forest cover maps derived using coarse resolution vegetation continuous fields (MODIS VCF; 500m resolution) with the maps derived from medium resolution (24m; IRS LISS-III) data. The comparison of VCF, per cent tree cover product, for the years 2000 to 2004 with

International Journal of Remote Sensing, 2010

We have developed a theory of a Josephson junction formed by two tunnel-coupled Bose-Einstein con... more We have developed a theory of a Josephson junction formed by two tunnel-coupled Bose-Einstein condensates in a double-well potential in the regime of strong atom-atom interaction for an arbitrary total number N of bosons in the condensates. The tunnel resonances in the junction are shown to be periodically spaced by the interaction energy, forming a single-atom staircase sensitive to the parity of N even for large N. One of the manifestations of the staircase structure is the periodic modulation with the bias energy of the visibility of the interference pattern in lattices of junctions.

Geophysical Research Letters, 2011

During the last decade two major drought events, one in 2005 and another in 2010, occurred in the... more During the last decade two major drought events, one in 2005 and another in 2010, occurred in the Amazon basin. Several studies have claimed the ability to detect the effect of these droughts on Amazon vegetation response, measured through satellite sensor vegetation indices (VIs). Such monitoring capability is important as it potentially links climate changes (increasing frequency and severity of drought), vegetation response as observed through vegetation greenness, and land-atmosphere carbon fluxes which directly feedback into global climate change. However, we show conclusively that it is not possible to detect the response of vegetation to drought from space using VIs. We analysed 11 years of dry season (July-September) Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and normalised difference vegetation index (NDVI) images. The VI standardised anomaly was analysed alongside the absolute value of EVI and NDVI, and the VI values for drought years were compared with those for nondrought years. Through a series of analyses, the standardised anomalies and VI values for drought years were shown to be of similar magnitude to those for non-drought years. Thus, while Amazon vegetation may respond to drought, this is not detectable through satellite-observed changes in vegetation greenness. A significant long-term decadal decline in VI values is reported, which is independent of the occurrence of drought. This trend may be caused by environmental or noise-related factors which require further investigation.

Geocarto International, 2021

Journal of Environmental Informatics, 2011

Developing tourism in a hilly region has become a thriving process which induces a complex land-c... more Developing tourism in a hilly region has become a thriving process which induces a complex land-cover dynamics. The study area (Shimla District in Himachal Pradesh) is located in the beautiful Himalayan environment in northern India and was the summer capital of India during the days of British rule. The current study has attempted to understand and model the suitability of three major land use forces (agriculture need, forest ecosystem conservation and human development) using the spatial Analytical Hierarchical Process (AHP). Further, the study utilised a multi-objective conflict analysis (MOCA) to understand and resolve the competition amongst these major land use forces. The cross overlay analysis of the existing land use map and the MOCA-derived map infers that the agricultural and human settlement preference can potentially expand into 26% of the current actual forest area. Around 18% of the potentially suitable area for forest class has already been occupied by non-forest classes. The potential future change zones are located in the middle and western regions/sections of the study area. Since the western region is already developed, more attention needs to be paid in the future to the central region of the study area.

Quantitative Geology and Geostatistics, 2010

Three methods were introduced for estimating the local semivariogram for use in procedures such a... more Three methods were introduced for estimating the local semivariogram for use in procedures such as super-resolution pattern prediction. The first is simply to use a training image to estimate the global semivariogram required. The second method employs a deconvolution-convolution procedure to estimate the local semivariogram. The estimated semivariogram represents proportions and so a further step is required to convert the proportions semivariogram to represent a binary field. The third method is an integration of the first two methods obtained by weighted linear combination across the lags of the semivariograms. The results are evaluated using the known target local semivariogram. The integrated method provides some advantages. The discussion points to problems and potential future improvements on the method.

Decision making in land use planning needs understanding about the pattern of changes. The curren... more Decision making in land use planning needs understanding about the pattern of changes. The current study aims to analyse and predict the land use and land cover change, with the focus on forests, in Shimla District using Bayesian model. Population growth, agricultural-horticulture demands, tourism growth are putting pressure on the valuable forest ecosystem and natural resources of the district. In this study, land cover maps were prepared for the periods 1970s, 1980s and 1990s using remote sensing data. The actual positive changes (i.e., increase in forest) and negative changes (i.e., decrease in forest) derived from the time-series land cover maps were used as apriori evidence in the Bayesian model to derive the statistical weights for various environmental parameters. The environmental parameters were analysed under 4 major group of factors i.e., topographic, land use, landscape, land-water. The probabilistic contribution (i.e., weight) of each attribute under each map was utilis...

Advances in Space Research, 2021

Geoderma, 2020

Abstract Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available f... more Abstract Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available from different space agencies, derived using the active microwave remote sensing, may not be suitable for studies at a local level due to their coarse spatial resolution. Hence, an attempt has been made in this study to estimate the surface soil moisture at high spatial resolution using different high-resolution multispectral imageries from Landsat 8 OLI (Optical Land Imager) and Sentinel 2A MSI (Multi Spectral Instrument), of similar date. The thermal (10.9 µm) and short wave infra-red (SWIR) (2.2 µm) bands of the Landsat image and the SWIR band (2.19 µm) of Sentinel image were used to estimate the soil moisture at a spatial resolution of 30 m and 20 m respectively, whereas the red and near infrared (NIR) bands of both the images were used to estimate the dryness. As the thermal band at high spatial resolution (at 20 m or below) is not available, this study attempted to bridge the gap and integrate multi-sensor and multi-resolution feature space utilizing various scalable procedures so as to estimate soil moisture and dryness. Limitation due to unavailability of the thermal bands in Sentinel multispectral image was overcome through a bridging procedure using Landsat thermal bands derived LST and Sentinel derived NDVI to estimate Sentinel LST at 20 m. The distribution pattern of high-resolution estimated soil moisture in each coarse spatial resolution grid (1 km) was taken into consideration to downscale the available CCI and SMAP soil moisture data. The estimated soil moisture and dryness were upscaled to 1 km and then validated using the downscaled soil moisture products (SMAP and CCI), and a significant correlation having R2 greater than 0.8, 0.5 and 0.4 was observed for the thermal band derived soil moisture, short wave band derived soil moisture, and the red and NIR band derived dryness, respectively. To understand the temporal variability of the soil moisture, data from three different seasons representing extreme soil moisture conditions (winter, summer and monsoon) were analyzed and found that the independently estimated soil moisture had errors in temporal continuity and spatial spread. Hence, the estimated soil moisture was standardized using annual information which resulted in more accurate products, and the SWIR Transformed Reflectance (STR) derived soil moisture was more accurate than LST derived product.

Journal of the Indian Society of Remote Sensing, 2019

The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG... more The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG) and length of greening (LOG), and their rate of change in the western Himalaya in India (Himachal Pradesh) using MODIS NDVI time series data (2001–2015). These metrics were inspected at different stratification levels: state, elevation, climatic zones and bio-geographic provinces. Delayed SOG was observed over 44.87% (P < 0.1), and delayed EOG over 63.3% (P < 0.1) of grassland grids. LOG was shortened in 24.37% (P < 0.1) and extended in 58.04% (P < 0.1) of the grids. At the state level, when statistically significant pixels (SSP) and all the pixels (AP) are used (given as SSP:AP), SOG is delayed by 20.27:6.28 days year−15, while EOG is delayed by 38.02:14.97 days year−15 and LOG is extended by 35.07:8.70 year−15 days. Extended LOG is observed over the temperate and cold arid regions, and shortened LOG is observed over sub-alpine and alpine regions. Variations in SOG and EOG are not uniform across different climatic and bio-geographic regions. However, in the sub-alpine and alpine zones, SOG and EOG followed elevation gradients, i.e. late SOG with early EOG over higher elevations, and early SOG with late EOG over lower elevations. Our study has revealed an interesting pattern of translational phenology (i.e. late SOG and late EOG) of grasslands which hints towards shifting winter period. Overall, it is observed that variations in timing of snowfall and snow cover extent are the reasons for inter-annual variations in the grassland phenology.

Procedia Environmental Sciences, 2011

The spatial variability of remotely sensed image values provides important information about the ... more The spatial variability of remotely sensed image values provides important information about the arrangement of objects and their spatial relationships within the image. The characterisation of spatial variability in such images, for example, to measure of texture, is of great utility for the discrimination of land cover classes. To this end, the variogram, a function commonly applied in geostatistics, has been used widely to extract image texture for remotely sensed data classification. The aim of this study was to assess the increase in accuracy that can be achieved by incorporating univariate and multivariate textural measures of Landsat TM imagery in classification models applied to large heterogeneous landscapes. Such landscapes which difficult to classify due to the large number of land cover categories and low inter-class separability. Madogram, rodogram and direct variogram for the univariate case, and cross-and pseudocross variograms for the multivariate one, together with multi-seasonal spectral information were used in a Random Forest classifier to map land cover types. The addition of spatial variability into multi-seasonal Random Forest models leads to an increase in the overall accuracy of 8%, and to an increase in the Kappa index of 9%, respectively. The increase in per categories Kappa for the textural Random Forest model reached 30% for certain categories. This study demonstrates that the use of information on spatial variability produces a fundamental increase in per class classification accuracy of complex land-cover categories.

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010

Abstract Phenological information can be provided globally using remote sensing based time-series... more Abstract Phenological information can be provided globally using remote sensing based time-series vegetation indices. Basic differences in the data and methods used can yield different results. This study analysed such differences in the phenological information, ...

Indian Forester, 2010

In general, the change detection models can be categorized into two groups. One is spectral chang... more In general, the change detection models can be categorized into two groups. One is spectral change methods and another is post-classification change methods. In the spectral change detection approach, analysis was done to find out difference between the multi-date raw ...

Remote Sensing of Environment, 2012

Several models have been fitted in the past to smooth time-series vegetation index data from diff... more Several models have been fitted in the past to smooth time-series vegetation index data from different satellite sensors to estimate vegetation phenological parameters. However, differences between the models and fine tuning of model parameters lead to potential differences, uncertainty and bias between the results amongst users. The current research assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably. The research used Level 3 Medium Resolution Imaging Spectrometer (MERIS, spatial resolution~4.6 km) Terrestrial Chlorophyll Index (MTCI) data over the years 2004 to 2006 composited at eight day intervals covering the Indian sub-continent. First, the four models were fitted to representative sample time-series of the major vegetation types in India, and the quality of the fit was analysed. Second, the effect of noise on model fitting was analysed by adding Gaussian noise to a standard profile. Finally, the four models were fitted to the whole study area to characterise variation in the quality of model fitting as a function of single and double vegetation seasons. These smoothed data were used to estimate the onset of greenness (OG), a major phenological parameter. The models were evaluated using the root mean square error (RMSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC). The first test (fitting to representative sample time series) revealed the consistently superior performance of the Whittaker and Fourier approaches in most cases. The second test (fitting after the addition of Gaussian noise) revealed the superior performance of the double logistic and Fourier approaches. Finally, when the approaches were applied to the whole study, thus, including vegetation with different phenological profiles and multiple growing seasons (third test), it was found that it was necessary to tune each of the models according to the number of annual growing seasons to produce reliable fits. The double logistic and asymmetric Gaussian models did not perform well for areas with more than one growing season per year. The mean absolute deviation in OG derived from these models was a maximum (3 to 4 weeks) within the dry deciduous vegetation type and minimum (1 week) in evergreen vegetation. All techniques yielded consistent results over the southwestern and northeastern regions of India characterised by tropical climate.

Remote Sensing of Environment, 2010

India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The... more India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in interannual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.

Remote Sensing of Environment, 2012

A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal textur... more A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape, with a large number of land cover categories and low inter-class separability. Five different types of geostatistical textural measure for three different window sizes and three different lags were applied creating a total of 972 potential input variables. Madograms, rodograms and direct variograms for the univariate case and cross-and pseudo-cross variograms for the multivariate case, together with multiseasonal spectral information, were used in a RF classifier to map the land cover types. The pseudo-cross and cross variograms were used specifically to incorporate important seasonal/temporal information. Incorporating multi-scale textural features into the RF models led to an increase in the overall index of 10.71% and, for the most accurate classification, the increase was up to 30% in some classes. The differences in the kappa coefficient for the textural classification models were evaluated statistically using a pairwise Z-test, revealing a significant increase in per-class classification accuracy compared to GLCM-based texture measures. The pseudo-cross variogram between the visible and near-infrared bands was the most important textural features for general classification, and the multi-seasonal pseudo-cross variogram had an outstanding importance for agricultural classes. Overall, the RF classifier applied to a reduced subset of input variables composed of the most informative textural features led to the highest accuracy. Highly reliable classification results were obtained when the 16 most important textural features calculated at single scales (window sizes) were selected and used in the classification. The proposed methodology significantly increased the classification accuracy achieved with a spectral maximum likelihood classifier (ML). The kappa values for the textural RF and ML classifications were equal to 0.92 and 0.83, respectively.

Landscape Ecology, 2010

... 2004), and through the normalized difference vegetation index (NDVI) (Jeyaseelan et al. 2007;... more ... 2004), and through the normalized difference vegetation index (NDVI) (Jeyaseelan et al. 2007; Saikia 2009). ... 1985; Newton 1988; Singh and Singh 1992; Murali and Sukumar 1126 Landscape Ecol (2010) 25:1125–1141 123 Page 3. ...

Journal of the Indian Society of Remote Sensing, 2009

Invasive species have been the focus of environmentalists due to their undesired impact on the ec... more Invasive species have been the focus of environmentalists due to their undesired impact on the ecosystem. Spread of Lantana (Lantana camara L.), an invasive plant species, has been found in diverse geophysical environments causing a threat to the native flora. Various eradication programmes have been attempted such as burning, chemical sprays, bio-control agents and physical plugging mechanism for removing such invasive Nomenclature

Journal of the Indian Society of Remote Sensing, 2009

The study compared forest cover maps derived using coarse resolution vegetation continuous fields... more The study compared forest cover maps derived using coarse resolution vegetation continuous fields (MODIS VCF; 500m resolution) with the maps derived from medium resolution (24m; IRS LISS-III) data. The comparison of VCF, per cent tree cover product, for the years 2000 to 2004 with

International Journal of Remote Sensing, 2010

We have developed a theory of a Josephson junction formed by two tunnel-coupled Bose-Einstein con... more We have developed a theory of a Josephson junction formed by two tunnel-coupled Bose-Einstein condensates in a double-well potential in the regime of strong atom-atom interaction for an arbitrary total number N of bosons in the condensates. The tunnel resonances in the junction are shown to be periodically spaced by the interaction energy, forming a single-atom staircase sensitive to the parity of N even for large N. One of the manifestations of the staircase structure is the periodic modulation with the bias energy of the visibility of the interference pattern in lattices of junctions.

Geophysical Research Letters, 2011

During the last decade two major drought events, one in 2005 and another in 2010, occurred in the... more During the last decade two major drought events, one in 2005 and another in 2010, occurred in the Amazon basin. Several studies have claimed the ability to detect the effect of these droughts on Amazon vegetation response, measured through satellite sensor vegetation indices (VIs). Such monitoring capability is important as it potentially links climate changes (increasing frequency and severity of drought), vegetation response as observed through vegetation greenness, and land-atmosphere carbon fluxes which directly feedback into global climate change. However, we show conclusively that it is not possible to detect the response of vegetation to drought from space using VIs. We analysed 11 years of dry season (July-September) Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and normalised difference vegetation index (NDVI) images. The VI standardised anomaly was analysed alongside the absolute value of EVI and NDVI, and the VI values for drought years were compared with those for nondrought years. Through a series of analyses, the standardised anomalies and VI values for drought years were shown to be of similar magnitude to those for non-drought years. Thus, while Amazon vegetation may respond to drought, this is not detectable through satellite-observed changes in vegetation greenness. A significant long-term decadal decline in VI values is reported, which is independent of the occurrence of drought. This trend may be caused by environmental or noise-related factors which require further investigation.