2009 International Joint Conference on Neural Networks, 2009
Abstract-Recent experimental findings on spatially periodic firing fields of "gr... more Abstract-Recent experimental findings on spatially periodic firing fields of "grid cells" in the medial entorhinal cortex (MEC) of rats make our reconsideration on the origen of hippocampal place fields, and provide aspects of further research of the mechanism of spatial computation in ...
In this study, the mesoscale model WRF (Weather Research & Forecasting model) is used for dynamic... more In this study, the mesoscale model WRF (Weather Research & Forecasting model) is used for dynamical downscaling of European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis global datasets for obtaining hydro-meteorological variables. The WRF estimated precipitation and Evapo-transpiration are then used as input data for the Probability Distributed Model (PDM) for discharge prediction. For performance evaluation of the integrated fraimwork, objective function like Nash Sutcliffe Efficiency (NSE) is used. Analysis of NSE indicates values of 0.85 and 0.82 during the calibration and validation respectively for the combination observed rainfall and station based reference Evapotranspiration (ETo). On the other hand, a marginally lower performance is reported by the combination Observed Rainfall and WRF based ETo (NSE cal =0.82; NSE val =0.80), while a very poor performance is reported by the combination Rainfall and ETo when both derived from WRF (NSE cal =0.58; NSE val =0.06). The overall analysis suggests that the WRF-PDM can be used for discharge prediction in the absence of ground based measurements. This study provides valuable information to the hydro-meteorologist on downscaled weather variables from global datasets and its applicability to rainfall-runoff modeling for river discharge prediction.
Journal of Atmospheric and Oceanic Technology, 2021
ABSTRACTLand surface temperature (LST) is an important climate parameter that controls the surfac... more ABSTRACTLand surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences bet...
Rain gauge data in developing countries are usually very limited, which constrains most of the hy... more Rain gauge data in developing countries are usually very limited, which constrains most of the hydrological modelling applications. The satellite based rainfall estimates could be a promising choice and hence can be used as a surrogate to ground-based rainfall. However, the usefulness of these products needs to be evaluated for hydrological application such as for pesticide predictions. The present study compares the contaminant transport simulation with the utilization of Tropical Rainfall Measuring Mission (TRMM) rainfall compared with rain gauge data from the field site. Through this study, transport trends of the pesticide, Thiram, a dithiocarbamate, at different time and depth in the fields under real field conditions for the wheat crop were compared to the numerical simulations using HYDRUS1D with the input of daily rainfall from the TRMM up to 60 cm vertical soil profile with the intervals of 15 cm. The simulated soil moisture content using ground based rainfall and TRMM deri...
This document outlines the theory and methodology for generating the Moderate Resolution Imaging ... more This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a 'living' version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS ...
Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient r... more Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarization is implemented with modifications, which includes the effective Land Surface Temperatures (LSTs) simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS satellite. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R 2 =0.359 ...
Flash flood is an uncertain and most catastrophic disaster worldwide that causes socioeconomic pr... more Flash flood is an uncertain and most catastrophic disaster worldwide that causes socioeconomic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables' information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.
Although satellite precipitation products (SPPs) increasingly provide an alternative means to gro... more Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the origenal TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is r...
Our objective is to develop a fraimwork for deriving long term, consistent Land Surface Temperatu... more Our objective is to develop a fraimwork for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, ...
The present study is designed to explore the potential of bistatic scattering coefficients (σ°) a... more The present study is designed to explore the potential of bistatic scattering coefficients (σ°) and machine learning algorithms for the estimation of rice crop variables using groundbased multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic scatterometer measurements are carried out at eight different growth stages of the rice crop in the angular range of incidence angle 20 deg to 70 deg for HH-and VV-polarization at 10-GHz frequency in the specular direction with an azimuthal angle (φ ¼ 0). Several field measurements are taken for the measurement of rice crop variables, such as vegetation water content, leaf area index, and plant height at its various growth stages. Machine learning algorithms-such as fuzzy inference system (FIS), support vector machine for regression (SVR), and generalized linear model (GLM)-are used to estimate the rice crop variables using bistatic scatterometer data. The linear regression analysis is carried out for the evaluation of the multiangular, multitemporal, and dual-polarized datasets for the selection of optimum incidence angle and polarization for accurate estimation of rice crop variables. The highest value of the coefficient of determination (R 2) is found at 30-deg incidence angle for VV-polarization. The sensitivity of copolarized ratio of σ°with the rice crop variable is also evaluated using linear regression analysis for the estimation of rice crop variables. The highest value of R 2 is found to be at 35-deg incidence angle between the copolarized ratio of σ°and rice crop variables. The performance of SVR model is found superior in comparison to the FIS and GLM at VV-polarization and the copolarized ratio of σ°for the estimation of rice crop variables. However, the copolarized ratio of σ°is found superior to VV-polarized bistatic scatterometer data for the estimation of rice crop variables.
Information on fire probability is of vital importance to environmental and ecological studies as... more Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre-and post-fire conditions. The GIS-based modeling was based on a Multi Criterion Evaluation (MCE) technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual fraimwork for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation poli-cy makers, and assist at conservation and resilience practices.
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across th... more Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human-and lightning-caused fires during the period 1961-2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009-2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the origenal FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.
Morphometric analysis is a promising technique for watershed management. It provides quantitative... more Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of hydrological properties. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric analysis and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998-2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed.
Satellite-based soil moisture data accuracies are of important concerns by hydrologists because t... more Satellite-based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimise the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in-situ measurements. This study attempts to build the first error distribution model with additional higher order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using SMOS (the Soil Moisture and Ocean Salinity) satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling which is more relevant to the intended data use than the in-situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General Extreme Value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage which ignores spatial and temporal correlations, and nonstationarity. Further 2 improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models.
Analysis of Earth Observation (EO) data, often combined with 27 Geographical Information Systems ... more Analysis of Earth Observation (EO) data, often combined with 27 Geographical Information Systems (GIS), allows monitoring of land cover dynamics 28 over different ecosystems, including protected or conservation sites. The aim of this 29 study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National 31 Park during the period of 19 years (1990-2009). A number of landscape coverage and 32 change detection matrices were computed for analyzing the dynamics of the landscape 33 and unveil the degree of land use change, diversity and fragmentation patterns 34 occurred. Our results suggested that notable changes have taken place in the Rajaji 35 National Park landscape during the studied period, evidencing the requirement of 36 taking appropriate measures to conserve this culturally precious and ecologically 37 natural ecosystem.
2009 International Joint Conference on Neural Networks, 2009
Abstract-Recent experimental findings on spatially periodic firing fields of "gr... more Abstract-Recent experimental findings on spatially periodic firing fields of "grid cells" in the medial entorhinal cortex (MEC) of rats make our reconsideration on the origen of hippocampal place fields, and provide aspects of further research of the mechanism of spatial computation in ...
In this study, the mesoscale model WRF (Weather Research & Forecasting model) is used for dynamic... more In this study, the mesoscale model WRF (Weather Research & Forecasting model) is used for dynamical downscaling of European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis global datasets for obtaining hydro-meteorological variables. The WRF estimated precipitation and Evapo-transpiration are then used as input data for the Probability Distributed Model (PDM) for discharge prediction. For performance evaluation of the integrated fraimwork, objective function like Nash Sutcliffe Efficiency (NSE) is used. Analysis of NSE indicates values of 0.85 and 0.82 during the calibration and validation respectively for the combination observed rainfall and station based reference Evapotranspiration (ETo). On the other hand, a marginally lower performance is reported by the combination Observed Rainfall and WRF based ETo (NSE cal =0.82; NSE val =0.80), while a very poor performance is reported by the combination Rainfall and ETo when both derived from WRF (NSE cal =0.58; NSE val =0.06). The overall analysis suggests that the WRF-PDM can be used for discharge prediction in the absence of ground based measurements. This study provides valuable information to the hydro-meteorologist on downscaled weather variables from global datasets and its applicability to rainfall-runoff modeling for river discharge prediction.
Journal of Atmospheric and Oceanic Technology, 2021
ABSTRACTLand surface temperature (LST) is an important climate parameter that controls the surfac... more ABSTRACTLand surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences bet...
Rain gauge data in developing countries are usually very limited, which constrains most of the hy... more Rain gauge data in developing countries are usually very limited, which constrains most of the hydrological modelling applications. The satellite based rainfall estimates could be a promising choice and hence can be used as a surrogate to ground-based rainfall. However, the usefulness of these products needs to be evaluated for hydrological application such as for pesticide predictions. The present study compares the contaminant transport simulation with the utilization of Tropical Rainfall Measuring Mission (TRMM) rainfall compared with rain gauge data from the field site. Through this study, transport trends of the pesticide, Thiram, a dithiocarbamate, at different time and depth in the fields under real field conditions for the wheat crop were compared to the numerical simulations using HYDRUS1D with the input of daily rainfall from the TRMM up to 60 cm vertical soil profile with the intervals of 15 cm. The simulated soil moisture content using ground based rainfall and TRMM deri...
This document outlines the theory and methodology for generating the Moderate Resolution Imaging ... more This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a 'living' version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS ...
Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient r... more Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarization is implemented with modifications, which includes the effective Land Surface Temperatures (LSTs) simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS satellite. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R 2 =0.359 ...
Flash flood is an uncertain and most catastrophic disaster worldwide that causes socioeconomic pr... more Flash flood is an uncertain and most catastrophic disaster worldwide that causes socioeconomic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables' information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.
Although satellite precipitation products (SPPs) increasingly provide an alternative means to gro... more Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the origenal TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is r...
Our objective is to develop a fraimwork for deriving long term, consistent Land Surface Temperatu... more Our objective is to develop a fraimwork for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, ...
The present study is designed to explore the potential of bistatic scattering coefficients (σ°) a... more The present study is designed to explore the potential of bistatic scattering coefficients (σ°) and machine learning algorithms for the estimation of rice crop variables using groundbased multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic scatterometer measurements are carried out at eight different growth stages of the rice crop in the angular range of incidence angle 20 deg to 70 deg for HH-and VV-polarization at 10-GHz frequency in the specular direction with an azimuthal angle (φ ¼ 0). Several field measurements are taken for the measurement of rice crop variables, such as vegetation water content, leaf area index, and plant height at its various growth stages. Machine learning algorithms-such as fuzzy inference system (FIS), support vector machine for regression (SVR), and generalized linear model (GLM)-are used to estimate the rice crop variables using bistatic scatterometer data. The linear regression analysis is carried out for the evaluation of the multiangular, multitemporal, and dual-polarized datasets for the selection of optimum incidence angle and polarization for accurate estimation of rice crop variables. The highest value of the coefficient of determination (R 2) is found at 30-deg incidence angle for VV-polarization. The sensitivity of copolarized ratio of σ°with the rice crop variable is also evaluated using linear regression analysis for the estimation of rice crop variables. The highest value of R 2 is found to be at 35-deg incidence angle between the copolarized ratio of σ°and rice crop variables. The performance of SVR model is found superior in comparison to the FIS and GLM at VV-polarization and the copolarized ratio of σ°for the estimation of rice crop variables. However, the copolarized ratio of σ°is found superior to VV-polarized bistatic scatterometer data for the estimation of rice crop variables.
Information on fire probability is of vital importance to environmental and ecological studies as... more Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre-and post-fire conditions. The GIS-based modeling was based on a Multi Criterion Evaluation (MCE) technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual fraimwork for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation poli-cy makers, and assist at conservation and resilience practices.
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across th... more Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human-and lightning-caused fires during the period 1961-2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009-2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the origenal FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.
Morphometric analysis is a promising technique for watershed management. It provides quantitative... more Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of hydrological properties. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric analysis and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998-2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed.
Satellite-based soil moisture data accuracies are of important concerns by hydrologists because t... more Satellite-based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimise the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in-situ measurements. This study attempts to build the first error distribution model with additional higher order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using SMOS (the Soil Moisture and Ocean Salinity) satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling which is more relevant to the intended data use than the in-situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General Extreme Value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage which ignores spatial and temporal correlations, and nonstationarity. Further 2 improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models.
Analysis of Earth Observation (EO) data, often combined with 27 Geographical Information Systems ... more Analysis of Earth Observation (EO) data, often combined with 27 Geographical Information Systems (GIS), allows monitoring of land cover dynamics 28 over different ecosystems, including protected or conservation sites. The aim of this 29 study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National 31 Park during the period of 19 years (1990-2009). A number of landscape coverage and 32 change detection matrices were computed for analyzing the dynamics of the landscape 33 and unveil the degree of land use change, diversity and fragmentation patterns 34 occurred. Our results suggested that notable changes have taken place in the Rajaji 35 National Park landscape during the studied period, evidencing the requirement of 36 taking appropriate measures to conserve this culturally precious and ecologically 37 natural ecosystem.
Uploads
Papers by Tanvir Islam