Markand Oza - Academia.edu (original) (raw)

Papers by Markand Oza

Proceedings of the Fourth ACM IKDD Conferences on Data Sciences

Sustainable land management is one of the crucial aspects that need to be considered in order to ... more Sustainable land management is one of the crucial aspects that need to be considered in order to protect the resources for future generations. Understanding of land cover changes that occurred during the past decade is necessary to formulate policies and actions for sustainable land management. Land cover changes affect ecosystem and global climate. These changes transform natural habitats of plant and animal species and also modify air temperature and near surface moisture content which leads to many drastic changes in climate. Land cover change study helps climate and ecosystem scientists in understanding role of land cover changes in bringing climate and ecosystem changes. This paper has used segmentation-based data mining approach on MODIS NDVI (Normalized Difference Vegetation Index) data for understanding of land cover changes in states of Gujarat and Rajasthan. Data smoothing using Savitzky-Golay filtering method has been performed before applying algorithm for land cover change detection. Algorithm is able to identify the time point of change along with type of land cover change. The findings show a lot of industrialization and urbanization in Surat and its satellite towns. Other major cities such as Ahmedabad and Jaipur have shown urban growth towards periphery over a period of time. Although agricultural area has got reduced due to urban growth but barren land has got converted into agricultural area as irrigation facility has improved over time due to emergence of Narmada Canal Network.

Journal of the Indian Society of Remote Sensing

Land degradation is one of the major threats faced globally, affecting arid and other dryland are... more Land degradation is one of the major threats faced globally, affecting arid and other dryland areas severely. In such areas, biomass productivity is a function of precipitation to a large extent. This paper discusses methodology developed to generate land productivity dynamics assessment map using vegetation precipitation relationship residuals as proxy parameter to land degradation using long-term time series of satellite-derived Normalised Vegetation Difference Index and climate model-based precipitation data. This developed methodology rationally utilises information retrieved from statistical methods as well as qualitative measures. Application of developed methodology indicates that 62.54% of total land area of Rajasthan state is showing symptoms of increasing land productivity as compared to 2.35% area of decreasing land productivity.

2019 6th Swiss Conference on Data Science (SDS), 2019

Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parame... more Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parameter optimization. In this paper a generalized deep convolutional hybrid network model is proposed, named Reception that not only can tackle problem of solving optimal kernel size but also have goodness of both ResNet and Inception. The proposed Reception module, compliments the learning of filters having small and large receptive fields. This allows the network to extract the tiniest of details as well as the broadest of shapes. Although this strategy increases the width of the network and the number of parameters, the depth requirement of the network reduces significantly. Moreover, the number of parameters are kept in line using a carefully crafted design. The model when used for classifying ships in satellite images achieves a mean test accuracy of 98.56% with standard deviation of 0.14 in 5-fold cross validation and F1-score of 0.99.

Journal of Spatial Science, 2016

a a crop inventory and agro-ecosystems Division, Space applications centre (iSro), ahmedabad, ind... more a a crop inventory and agro-ecosystems Division, Space applications centre (iSro), ahmedabad, india; b atmospheric Science Division, Space applications centre (iSro), ahmedabad, india

GIScience & Remote Sensing, 2015

Highlights In-season agricultural area tracking at regular interval from geostationary satellite.... more Highlights In-season agricultural area tracking at regular interval from geostationary satellite. Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

Journal of Geomatics

Development of reliable crop yield models with minimal ground data is a major thrust area for agr... more Development of reliable crop yield models with minimal ground data is a major thrust area for agricultural planning which encompasses managing agricultural inventory for ensuring food security. Remote sensing technology provides a systematic and reliable data source required for study of vegetation development. Exploiting temporal behavior in an agricultural environment is very informative as it provides a link for quantitative assessment of plant state and growing conditions to final grain yield. The present study aims at using multi-date MODIS data for wheat yield modelling over North-west India during 2005-06 rabi season. Spectral crop growth curves such as quadratic, cubic and power-exponential were fitted. The data set was grouped in to two strata namely, high yield (yield > 3 t/ha) and low yield (yield < 3 t/ha) strata. A correlation analysis of the model estimated peak NDVI (called Gmax) and wheat yield was carried out. In-sample cross validation using leave-one-out met...

ABSTRACT The temporal spectral profile based analytical method has been used to determine the eme... more ABSTRACT The temporal spectral profile based analytical method has been used to determine the emergence day of a given crop. It is well known that, the Spectral profile of Normalized Difference Vegetation Index (NDVI) rises, peaks and declines as the crop grows with time. In contrast to this, the spectral profile of Short Wave Infrared - Water Stress Index (SWIR-WSI) decreases as the crop emerges, reaches minimum value and then rises as the crop senescence. Based on this temporal behaviour, a novel analytical approach of estimating the spectral emergence and maturity dates of wheat crop, using multi-temporal NDVI growth profile in conjunction with SWIR-WSI growth profile is proposed. This analytical approach relies on the hypothesis that, the temporal spectral pattern of NDVI and SWIR-WSI intersect and points of intersection correspond to the spectral emergence and maturity stages of the crop. The NDVI and SWIR-WSI were derived from the Advanced Wide Field Sensor (AWiFS) data from Resourcesat (IRS-P6) satellite data of 12-different dates during wheat growing season of 2006-07. The spectral profiles of NDVI and SWIR-WSI were fitted using quadratic equations with least-square method. The points of intersection of these two quadratic equations were computed. The first point of intersection occurs at the time of spectral emergence of the crop. It was observed that the first intersection occurred during 13-November to 21-November, 2006. Similarly, the second point of intersection should occur at the time of crop maturity. It was observed that the second intersection occurred between 16-April and 26-April, 2007. These observations agree with the general wheat cultural practices reported in different districts of Haryana State.

Agriculture and Hydrology Applications of Remote Sensing, 2006

Crop acreage estimation in hilly regions is till date a challenge for the remote sensing communit... more Crop acreage estimation in hilly regions is till date a challenge for the remote sensing community due to the problems of undulating topography, inaccessibility to vast areas, smaller field size, practice of shifting cultivation, accounting for area falling under hill shades and valleys. Remote sensing alone may not be able to provide reliable estimate of crop acreage in these areas.

Journal of the Indian Society of Remote Sensing, 2013

Journal of the Indian Society of Remote Sensing, 1989

Journal of the Indian Society of Remote Sensing, 1993

Communications in Statistics - Theory and Methods, 1986

Random coefficient polynomial regression model has been considered for prediction purpose when th... more Random coefficient polynomial regression model has been considered for prediction purpose when there is uncertainty about the degree of the polynomialo Expressions for mean square errors of two predictors based on simple estimators have been derived and their perfomaiices have been compared when parameters are estimated from the sample. A modified predictor has also been suggested when parameters in the predicting equations are to be estimated from the sample. Perform-ance ofseveral predictors haife been compared by cross validation technique from a real set of data.

Agricultural Economics Research Review, 2014

The study has analysed changes in climate variables, viz. temperature and rainfall during the per... more The study has analysed changes in climate variables, viz. temperature and rainfall during the period 1969-2005 and has assessed their impact on yields of important food crops. A significant rise was observed in mean monthly temperature, but more so during the post-rainy season. The changes in rainfall, however, were not as significant. While an increase in maximum temperature was found to have an adverse effect on the crop yields, a similar increase in minimum temperature had a favourable effect on yields of most crops, but it was not sufficient to fully compensate the damages caused by the rise in maximum temperature. Pigeonpea, rice, chickpea and wheat were more vulnerable to rise in temperature. Rainfall had a positive effect on most crops, but it could not counterbalance the negative effect of temperature. The projections of climate impacts towards 2100 have suggested that with significant changes in temperature and rainfall, the rice yield will be lower by 15 per cent and wheat yield by 22 per cent. Coarse cereals will be affected less, while pulses will be affected more than cereals. If the changes in climate are not significant, damages to crops will be smaller. In the short-run too climate impacts will not be so severe.

Current Science, 2015

A study was taken up to identify annual changes in temperature at a scale of 1  1. For this st... more A study was taken up to identify annual changes in temperature at a scale of 1  1. For this study, daily (maximum and minimum) temperature data for 45 years (1969-2013) at a grid size of 1  1, prepared by the India Meteorological Department, Pune were used. The identification of change was based on statistical trend analysis. From the analysis, it can be concluded that the dominant tendency over the India land mass is of warming, and colder months of the year show more warming. Analysis of temperature difference (TD) brought out the existence of contiguous and large spatial clusters of shrinking and expanding TD. Further analysis is required to factor the variability in temperature due to anthropogenic changes.

Journal of Geomatics

In India, the procedure developed under Forecasting Agricultural output using Space, Agro meteoro... more In India, the procedure developed under Forecasting Agricultural output using Space, Agro meteorology and Land based observations (FASAL) Project is now accepted as operational for making multiple in-season crop production forecasts. Till recently, the processing was done using commercially available software. SAC took the initiative to provide automation intensive software solutions by developing an in-house geospatial software solution for crop production forecasting with a mandate to build from only fresh developments and free and open source software. This effort has culminated into a geospatial software framework called FASALSoft. This paper brings out details on the software framework realised by amalgamating and adopting available open source geospatial tools and fresh software developments which can perform the required image processing and geospatial operations in an effective way for a national level crop forecast tasks using single date optical data, multi-temporal optica...

India has one of the best systems in the world to collect, collate and compile data on crop produ... more India has one of the best systems in the world to collect, collate and compile data on crop production. Some of the limitations of conventional methods are timeliness (or lack of it) and quality of the statistics. The final estimates are worked out some 6 – 9 months after the end of the season. This makes these statistics unusable for planning and management purposes. Hence, there is considerable scope of improvement in the conventional system. The remote sensing based methodology developed to gather agricultural information is described in this paper. Such methods circumvent some of the limitations of conventional approach.

Journal of Geomatics

Changing rainfall has significant effect on water resources, agricultural output and hence econom... more Changing rainfall has significant effect on water resources, agricultural output and hence economy. To understand the variability in rainfall, a spatio-temporal analysis of Indian summer monsoon rainfall was taken up. The objective for the present analysis was to identify trends in amount of Indian summer monsoon at various spatial scales. Daily gridded rainfall data (1 deg. X 1 deg. spatial resolution) for the period 1951-2010 corresponding to monsoon season and monthly rainfall data at meteorological sub-division level for 1901 - 2010 were analysed. From the gridded data, a series of rainfall at agroclimatic regions was constructed. The analysis was based on linear trend analysis. Both parametric and non-parametric methods were used. From statistical analysis of data it was concluded that there is a decreasing trend in all-India Indian summer monsoon rainfall. Northeast India is one big cluster having highly decreasing trend. Also, there is a strong agreement between gridded and m...

This paper describes the methodology developed and implemented for Remote Sensing DataBase (RSDB)... more This paper describes the methodology developed and implemented for Remote Sensing DataBase (RSDB) preparation from multitemporal and multi-sensor data from Indian earth observation satellites. The characteristics of the RSDB prepared over parts of Indo-Gangetic Basin and Central India (IGB&CI) are summarised. An approach for extraction of long term (1988-89 to 2004-05) Land Use / Land Cover (LULC) changes using RSDB over selected sites is demonstrated. Design of analysis framework and selection of a suitable projection scheme with optimization of scale factors has been discussed. The major components of procedure are multi-date RS data geo-referencing, radiometric normalization, top of atmosphere (TOA) reflectance images preparation, multi-image compositing and mosaicing. An RSDB for three rabi (winter) seasons i.e. 1988-89 (226 IRS 1A LISS-II quadrants), 1993-94 (251 IRS 1B LISS-II quadrants) and 2004-05 (three bi-monthly sets, 170 IRS P6 AWiFS quadrants) has been prepared over IGB...

Proceedings of the Fourth ACM IKDD Conferences on Data Sciences

Sustainable land management is one of the crucial aspects that need to be considered in order to ... more Sustainable land management is one of the crucial aspects that need to be considered in order to protect the resources for future generations. Understanding of land cover changes that occurred during the past decade is necessary to formulate policies and actions for sustainable land management. Land cover changes affect ecosystem and global climate. These changes transform natural habitats of plant and animal species and also modify air temperature and near surface moisture content which leads to many drastic changes in climate. Land cover change study helps climate and ecosystem scientists in understanding role of land cover changes in bringing climate and ecosystem changes. This paper has used segmentation-based data mining approach on MODIS NDVI (Normalized Difference Vegetation Index) data for understanding of land cover changes in states of Gujarat and Rajasthan. Data smoothing using Savitzky-Golay filtering method has been performed before applying algorithm for land cover change detection. Algorithm is able to identify the time point of change along with type of land cover change. The findings show a lot of industrialization and urbanization in Surat and its satellite towns. Other major cities such as Ahmedabad and Jaipur have shown urban growth towards periphery over a period of time. Although agricultural area has got reduced due to urban growth but barren land has got converted into agricultural area as irrigation facility has improved over time due to emergence of Narmada Canal Network.

Journal of the Indian Society of Remote Sensing

Land degradation is one of the major threats faced globally, affecting arid and other dryland are... more Land degradation is one of the major threats faced globally, affecting arid and other dryland areas severely. In such areas, biomass productivity is a function of precipitation to a large extent. This paper discusses methodology developed to generate land productivity dynamics assessment map using vegetation precipitation relationship residuals as proxy parameter to land degradation using long-term time series of satellite-derived Normalised Vegetation Difference Index and climate model-based precipitation data. This developed methodology rationally utilises information retrieved from statistical methods as well as qualitative measures. Application of developed methodology indicates that 62.54% of total land area of Rajasthan state is showing symptoms of increasing land productivity as compared to 2.35% area of decreasing land productivity.

2019 6th Swiss Conference on Data Science (SDS), 2019

Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parame... more Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parameter optimization. In this paper a generalized deep convolutional hybrid network model is proposed, named Reception that not only can tackle problem of solving optimal kernel size but also have goodness of both ResNet and Inception. The proposed Reception module, compliments the learning of filters having small and large receptive fields. This allows the network to extract the tiniest of details as well as the broadest of shapes. Although this strategy increases the width of the network and the number of parameters, the depth requirement of the network reduces significantly. Moreover, the number of parameters are kept in line using a carefully crafted design. The model when used for classifying ships in satellite images achieves a mean test accuracy of 98.56% with standard deviation of 0.14 in 5-fold cross validation and F1-score of 0.99.

Journal of Spatial Science, 2016

a a crop inventory and agro-ecosystems Division, Space applications centre (iSro), ahmedabad, ind... more a a crop inventory and agro-ecosystems Division, Space applications centre (iSro), ahmedabad, india; b atmospheric Science Division, Space applications centre (iSro), ahmedabad, india

GIScience & Remote Sensing, 2015

Highlights In-season agricultural area tracking at regular interval from geostationary satellite.... more Highlights In-season agricultural area tracking at regular interval from geostationary satellite. Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

Journal of Geomatics

Development of reliable crop yield models with minimal ground data is a major thrust area for agr... more Development of reliable crop yield models with minimal ground data is a major thrust area for agricultural planning which encompasses managing agricultural inventory for ensuring food security. Remote sensing technology provides a systematic and reliable data source required for study of vegetation development. Exploiting temporal behavior in an agricultural environment is very informative as it provides a link for quantitative assessment of plant state and growing conditions to final grain yield. The present study aims at using multi-date MODIS data for wheat yield modelling over North-west India during 2005-06 rabi season. Spectral crop growth curves such as quadratic, cubic and power-exponential were fitted. The data set was grouped in to two strata namely, high yield (yield > 3 t/ha) and low yield (yield < 3 t/ha) strata. A correlation analysis of the model estimated peak NDVI (called Gmax) and wheat yield was carried out. In-sample cross validation using leave-one-out met...

ABSTRACT The temporal spectral profile based analytical method has been used to determine the eme... more ABSTRACT The temporal spectral profile based analytical method has been used to determine the emergence day of a given crop. It is well known that, the Spectral profile of Normalized Difference Vegetation Index (NDVI) rises, peaks and declines as the crop grows with time. In contrast to this, the spectral profile of Short Wave Infrared - Water Stress Index (SWIR-WSI) decreases as the crop emerges, reaches minimum value and then rises as the crop senescence. Based on this temporal behaviour, a novel analytical approach of estimating the spectral emergence and maturity dates of wheat crop, using multi-temporal NDVI growth profile in conjunction with SWIR-WSI growth profile is proposed. This analytical approach relies on the hypothesis that, the temporal spectral pattern of NDVI and SWIR-WSI intersect and points of intersection correspond to the spectral emergence and maturity stages of the crop. The NDVI and SWIR-WSI were derived from the Advanced Wide Field Sensor (AWiFS) data from Resourcesat (IRS-P6) satellite data of 12-different dates during wheat growing season of 2006-07. The spectral profiles of NDVI and SWIR-WSI were fitted using quadratic equations with least-square method. The points of intersection of these two quadratic equations were computed. The first point of intersection occurs at the time of spectral emergence of the crop. It was observed that the first intersection occurred during 13-November to 21-November, 2006. Similarly, the second point of intersection should occur at the time of crop maturity. It was observed that the second intersection occurred between 16-April and 26-April, 2007. These observations agree with the general wheat cultural practices reported in different districts of Haryana State.

Agriculture and Hydrology Applications of Remote Sensing, 2006

Crop acreage estimation in hilly regions is till date a challenge for the remote sensing communit... more Crop acreage estimation in hilly regions is till date a challenge for the remote sensing community due to the problems of undulating topography, inaccessibility to vast areas, smaller field size, practice of shifting cultivation, accounting for area falling under hill shades and valleys. Remote sensing alone may not be able to provide reliable estimate of crop acreage in these areas.

Journal of the Indian Society of Remote Sensing, 2013

Journal of the Indian Society of Remote Sensing, 1989

Journal of the Indian Society of Remote Sensing, 1993

Communications in Statistics - Theory and Methods, 1986

Random coefficient polynomial regression model has been considered for prediction purpose when th... more Random coefficient polynomial regression model has been considered for prediction purpose when there is uncertainty about the degree of the polynomialo Expressions for mean square errors of two predictors based on simple estimators have been derived and their perfomaiices have been compared when parameters are estimated from the sample. A modified predictor has also been suggested when parameters in the predicting equations are to be estimated from the sample. Perform-ance ofseveral predictors haife been compared by cross validation technique from a real set of data.

Agricultural Economics Research Review, 2014

The study has analysed changes in climate variables, viz. temperature and rainfall during the per... more The study has analysed changes in climate variables, viz. temperature and rainfall during the period 1969-2005 and has assessed their impact on yields of important food crops. A significant rise was observed in mean monthly temperature, but more so during the post-rainy season. The changes in rainfall, however, were not as significant. While an increase in maximum temperature was found to have an adverse effect on the crop yields, a similar increase in minimum temperature had a favourable effect on yields of most crops, but it was not sufficient to fully compensate the damages caused by the rise in maximum temperature. Pigeonpea, rice, chickpea and wheat were more vulnerable to rise in temperature. Rainfall had a positive effect on most crops, but it could not counterbalance the negative effect of temperature. The projections of climate impacts towards 2100 have suggested that with significant changes in temperature and rainfall, the rice yield will be lower by 15 per cent and wheat yield by 22 per cent. Coarse cereals will be affected less, while pulses will be affected more than cereals. If the changes in climate are not significant, damages to crops will be smaller. In the short-run too climate impacts will not be so severe.

Current Science, 2015

A study was taken up to identify annual changes in temperature at a scale of 1  1. For this st... more A study was taken up to identify annual changes in temperature at a scale of 1  1. For this study, daily (maximum and minimum) temperature data for 45 years (1969-2013) at a grid size of 1  1, prepared by the India Meteorological Department, Pune were used. The identification of change was based on statistical trend analysis. From the analysis, it can be concluded that the dominant tendency over the India land mass is of warming, and colder months of the year show more warming. Analysis of temperature difference (TD) brought out the existence of contiguous and large spatial clusters of shrinking and expanding TD. Further analysis is required to factor the variability in temperature due to anthropogenic changes.

Journal of Geomatics

In India, the procedure developed under Forecasting Agricultural output using Space, Agro meteoro... more In India, the procedure developed under Forecasting Agricultural output using Space, Agro meteorology and Land based observations (FASAL) Project is now accepted as operational for making multiple in-season crop production forecasts. Till recently, the processing was done using commercially available software. SAC took the initiative to provide automation intensive software solutions by developing an in-house geospatial software solution for crop production forecasting with a mandate to build from only fresh developments and free and open source software. This effort has culminated into a geospatial software framework called FASALSoft. This paper brings out details on the software framework realised by amalgamating and adopting available open source geospatial tools and fresh software developments which can perform the required image processing and geospatial operations in an effective way for a national level crop forecast tasks using single date optical data, multi-temporal optica...

India has one of the best systems in the world to collect, collate and compile data on crop produ... more India has one of the best systems in the world to collect, collate and compile data on crop production. Some of the limitations of conventional methods are timeliness (or lack of it) and quality of the statistics. The final estimates are worked out some 6 – 9 months after the end of the season. This makes these statistics unusable for planning and management purposes. Hence, there is considerable scope of improvement in the conventional system. The remote sensing based methodology developed to gather agricultural information is described in this paper. Such methods circumvent some of the limitations of conventional approach.

Journal of Geomatics

Changing rainfall has significant effect on water resources, agricultural output and hence econom... more Changing rainfall has significant effect on water resources, agricultural output and hence economy. To understand the variability in rainfall, a spatio-temporal analysis of Indian summer monsoon rainfall was taken up. The objective for the present analysis was to identify trends in amount of Indian summer monsoon at various spatial scales. Daily gridded rainfall data (1 deg. X 1 deg. spatial resolution) for the period 1951-2010 corresponding to monsoon season and monthly rainfall data at meteorological sub-division level for 1901 - 2010 were analysed. From the gridded data, a series of rainfall at agroclimatic regions was constructed. The analysis was based on linear trend analysis. Both parametric and non-parametric methods were used. From statistical analysis of data it was concluded that there is a decreasing trend in all-India Indian summer monsoon rainfall. Northeast India is one big cluster having highly decreasing trend. Also, there is a strong agreement between gridded and m...

This paper describes the methodology developed and implemented for Remote Sensing DataBase (RSDB)... more This paper describes the methodology developed and implemented for Remote Sensing DataBase (RSDB) preparation from multitemporal and multi-sensor data from Indian earth observation satellites. The characteristics of the RSDB prepared over parts of Indo-Gangetic Basin and Central India (IGB&CI) are summarised. An approach for extraction of long term (1988-89 to 2004-05) Land Use / Land Cover (LULC) changes using RSDB over selected sites is demonstrated. Design of analysis framework and selection of a suitable projection scheme with optimization of scale factors has been discussed. The major components of procedure are multi-date RS data geo-referencing, radiometric normalization, top of atmosphere (TOA) reflectance images preparation, multi-image compositing and mosaicing. An RSDB for three rabi (winter) seasons i.e. 1988-89 (226 IRS 1A LISS-II quadrants), 1993-94 (251 IRS 1B LISS-II quadrants) and 2004-05 (three bi-monthly sets, 170 IRS P6 AWiFS quadrants) has been prepared over IGB...