T. R. Kiran Chand

Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data

Changes in electric power consumption patterns of a country over a period of time reflect on its socio‐economic development and energy utilization processes. In the present study, we characterized spatial and temporal changes in electric power consumption patterns over India during 1993 to 2002, using ‘night‐time lights’ data given by the Defense Meteorological Satellite Program–Operational Line Scan System (DMSP‐OLS) over the Indian region. The OLS operates in two bands: visible (0.5–0.9 µm) and thermal (10.5–12.5 µm) and has a unique capability of picking up faint sources of visible–near infrared emissions (lights) at night on the Earths surface including cities, towns and villages with a DN value ranging from 1 to 63. Night‐time light images for cloud‐free dates given by the DMSP‐OLS from 1993 to 2002 were segregated into respective years and were integrated to generate one ‘Stable light image’ per year. Changes in light scenarios over the Indian region in the decadal time frame were studied using stable lights datasets from 1993 to 2002. Information on changes in the light scenarios was integrated with demographic data to characterize developments in major cities and states of India. Results of the study suggested an increase in population by 170 million and power consumption from 44962 million kWh to 306355 million kWh over the country during 1993–2002, which was associated with an overall increase in number of night‐time lights of up to 26% in all states, indicating development in electric power consumption patterns. Correlation analysis between increase in population to the increase in night‐time lights and electric power consumption showed a coefficient of determination, R 2, of 0.59 and 0.56 respectively. Increase in light intensities along the peripheries of major Indian cities was observed, which indicated increased stress on the cities and corresponding development in power consumption patterns during the decadal time frame. Certain states, however, showed a decrease in night‐time lights in some areas, which are primarily attributed to the decreased economic growth trend and poverty and accounted to the scatter observed in the correlation analysis. Results are discussed in the paper.

Active forest fire monitoring in Uttaranchal State, India using multi-temporal DMSP-OLS and MODIS data

This paper gives an account of day–night active forest fire monitoring conducted over the sub‐tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence Meteorological Satellite Program – Operational Line Scan system (DMSP‐OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May–June 2005 and daily datasets of DMSP‐OLS (night‐time) and selected cloud‐free MODIS (daytime) datasets were used in mapping active fire locations. DMSP‐OLS collects data in visible (0.5 to 0.9 µm) and thermal (10.5 to 12.5 µm) bands and detects dim sources of lighting on the earths surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP‐OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night‐time rather than daytime. Validation of satellite‐derived fires with ground data showed a high degree of spatial correlation.

Studies on urban heat islands using envisat AATSR data

Urbanization has significant effects on local weather and climate and among these effects one of the most familiar is the urban heat island, for which the temperatures of the central urban locations are several degrees higher than those of nearby rural areas of similar elevation. Satellite data provides important inputs for estimating regional surface albedo and evapotranspiration required in the studies related to surface energy balance. Present study describes the analysis of day and night ENVISAT-AATSR satellite data for Urban heat island and surface thermal inertia. Field campaigns have been conducted in synchronous with the satellite data over pass for validating the surface temperature estimated from AATSR data. Satellite derived surface temperature values are within ±1° C from ground measured values. Heat island formations in urban regions of Hyderabad and environs can be clearly seen in the night time data with core urban regions showing high temperatures. Apparent thermal inertia derived from AATSR day and night data sets have shown typical variations over urban regions.

Emissions from grassland burning in Kaziranga National Park, India – analysis from IRS-P6 AWiFS satellite remote sensing datasets

Biomass burning from vegetation fires is an important source of greenhouse gas emissions. In this study, we quantify biomass burning emissions from grasslands from the highly sensitive Kaziranga National Park, Assam, Northeast India. Most of the fires in the park are ‘controlled burning fires’ set by the park officials for management purposes. We evaluated the short-term impacts of fires and the resulting air pollution through integrating biomass burnt information from satellite remote sensing datasets. IRS-P6 Advanced Wide Field Sensor (AWiFS) data during March and April corresponding to dry season were evaluated to delineate the burnt areas. These burnt area estimates were then integrated with biomass data and emission factors for quantifying the greenhouse gas emissions. Results suggested that of the total study area of 37,822 ha, nearly 3163.282 ha has been burnt during March, 2005. Within one month, the burnt area increased to 7443.92 ha by April, i.e., from 8.36% to 19.68%. In total, biomass burning from the grasslands contributed to 29.65 Tg CO2, 1.19 Tg CO, 0.071 Tg NOx, 0.042 Tg CH4, 0.0625 Tg total non-methane hydrocarbons, 0.152 Tg of particulate matter, and 0.062 Tg of organic carbon and 0.008 Tg of black carbon during April. The importance of ‘fire’ as a management tool for maintaining the wildlife habitat has been highlighted in addition to some of the adverse affects of air pollution resulting from such management practices. The results from this study will be useful to forest officials as well as policy makers to undertake some sustainable forest management practices to maintain an ideal habitat for Kazirangas wildlife.

Modeling potential forest fire danger using modis data

Generation of fire danger maps play a vital role in forest fire management like forest fire research, locating lookout towers, risk assessment and for various other simulation studies. The present study addresses remote sensing and GIS applications in generating fire danger maps for tropical deciduous forests. Fire danger variables such as fuel type, topography, temperature, and relative humidity have been used in modeling fire danger. Information on local climate patterns and past fire records has been used to derive fire frequency map of the study area. Intermediate indices were derived using multiple regressions, where fire frequency data is taken as dependent variable. Results indicate that forests near human settlements are more vulnerable to forest fires.

Forest fires monitoring using envisat-aatsr data

Forests over Indian region are fire prone during summer season and effective means for monitoring such events is important. Satellite data with its repetitive and wide area coverage provides data sets required for monitoring such events. The advances in sensor technology and multi-satellite systems have improved capability for monitoring such events. The present study addresses forest fires monitoring using night time data sets of ENVISAT-AATSR data over Indian Region. The results of the study indicated that region specific algorithms are required for forest fire detection as soils in tropical regions have higher temperatures during night time.

Crop surface temperature estimation in irrigated command areas using MODIS satellite data

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various split‐window algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi‐resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite‐derived CST. CST values obtained using the Sobrino method have been found to be closer to the ground‐measured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well‐grown crops.

Surface Temperature Variation with Vegetation Phenology over Deciduous Forests – A Study over Nagarjunasagar Srisailam Tiger Reserve (NSTR) Using ENVISAT ‐ AATSR Data

Abstract Combined analysis of reflective and thermal data recorded by coarse resolution and high repetitive satellites provide a useful means to study seasonal vegetation characteristics and other related phenological parameters. ENVISAT Advanced Along Track Scanning Radiometer (AATSR) data for different seasons from January 04 to April 04 has been analyzed to derive the Normalized Difference Vegetation Index (NDVI) and Surface Temperature (ST) images for the forest regions of Nagarjunasagar Srisailam Tiger Reserve (NSTR), India. Temporal variation of NDVI and ST reflected the phenology of the forest area. A negative relationship was observed between the NDVI and ST over all the vegetation types and the proportion of vegetation cover seemed to has bearing on the ST. Scatter plots for NDVI and ST drawn for winter season showed a good separability of land use/land cover types of the study area.

Emissions from vegetation burning in Periyar Tiger Reserve (PTR), south India: a study using temporal IRS‐P6 AWiFS satellite data

Periyar Tiger Reserve (PTR) is a sensitive area for wildlife, including Indian tiger and Asiatic elephant, in Kerala, southern India. Recently, forest fires in the PTR have threatened both the native vegetation as well as wildlife. In this study, we used temporal satellite remote sensing datasets corresponding to IRS‐P6 AWiFS with 56 m resolution to identify the burnt areas and thereby estimate greenhouse gas emissions resulting from biomass burning. Results from satellite derived area estimates suggested nearly 2803 ha as burnt during the dry season (February–April), of which evergreen vegetation accounted for 12.29%, mixed deciduous forests about 40.39% and grasslands 47.3%. Variations in biomass burning events were related to both climatic and anthropogenic factors. Of the different vegetation types, grasslands accounted for the highest amount of CO2 emissions compared to others. Nearly 0.0126Tg of CO2 has been released during the four‐month period from vegetation burning. Further, vegetation burning in the PTR region accounted for release of 0.00050Tg of CO, 1.81E‐05 Tg of CH4, 2.67E‐05 NOx, 3.03 E‐05 Tg NOx, 1.64E‐06Tg of N2O. Primary causes of vegetation fires in the tiger reserve have been analysed and quantitative estimates of greenhouse gas emissions from biomass burning have been provided. There is a need to provide alternative energy sources for the local people in order to ease the pressure on PTR forest resources. The results will be useful for forest managers and policy‐makers to undertake some mitigation options relating to fire management and greenhouse gas emissions in the sensitive zones of the study area.

Crop Canopy Characterization in Irrigation Command Areas Using MODIS Satellite Data

Abstract The study aims at crop canopy characterisation in irrigation command areas using multi‐resolution MODIS satellite data. A regional model was developed between the ground measured Leaf Area Index (LAI) and MODIS derived NDVI over rice areas in irrigation projects of Krishna basin, South India. The model was used to estimate spatial variation of LAI from MODIS data and for further estimation of Crop Resistance Factor (CRF) defined as a function of crop height. NDVI was used to approximate the mean pixel emissivity, by taking known emissivity of pure vegetation and bare soil pixels. Crop Surface Temperature (CST) was estimated using MODIS thermal data in split window algorithms over rice areas. Evapo‐Transpiration (ET) was estimated using MODIS derived Albedo and net long wave radiation in surface energy balance equation. Ground measured crop parameters like crop height, CST and LAI were used to evaluate satellite derived crop parameters. Satellite derived LAI and spectral indices were found to be highly correlated with CST and ET.