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Accurate estimates of spatial above ground biomass (AGB) in tropical forests are important in understanding the global carbon cycle. Microwave and optical remote sensing datasets have been used extensively for AGB estimation, but their uses are restricted due to saturation in high biomass region. To overcome saturation issues of single sensor based models, the current paper uses a non-parametric Random Forest based approach to spatially estimate biomass over Indian forests using field inventory data in combination with optical (MODIS) and Microwave (L-band ALOS-PALSAR) images along with other bio-climatic parameters (e.g., rainfall, temperature) which significantly influence the biomass accumulation in an ecosystem. Plot level biomass estimates for 6678 sample plots (0.1 ha size), inventoried as part of robustly designed National Forest Inventory (NFI), are computed using volumetric equations, wood density and biomass expansion factors. Spatial above ground biomass estimates were generated using random forest model over two scenarios. Firstly, a single nation-wide model using all the available plot data and secondly, physiographic zone (14 zones over India) wise models with plot data over respective zones. AGB stored in Indian forest is estimated as 7952.3 million tonnes (Carbon equivalent: 3737.58 TgC) with a root mean square error (RMSE) of 31.2% using national level model. Physiographic zone level models estimated the country's biomass as 7597.45 million tonnes (Carbon equivalent: 3570.8 TgC). The above ground biomass estimates from our study indicates that the estimation error in physiographic zone model varies from 25.24% to 54.15% depending upon the sample size and biomass range. We observed that L-band microwave saturated in 140–160 Mg ha−1 range and currently available microwave wavelengths alone is not sufficient to predict entire range of biomass over Indian forests. Inclusion of multisource data using random forest regression model increase the saturation range to 350 Mg ha−1 which is a significant improvement as 94.7% of Indian forests are covered in this range. Model estimation error reduces to 25.6% in AGB range up to 350 Mg ha−1.

Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass over tropical regions are very important and relevant in understanding the contribution of tropical forests in global biogeochemical cycles, especially in terms of carbon pools and fluxes. Information on the spatio-temporal biomass distribution acts as a key input to Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) action plans. This necessitates precise and reliable methods to estimate forest biomass and to reduce uncertainties in existing biomass quantification scenarios. The use of backscatter information from a host of allweather capable Synthetic Aperture Radar (SAR) systems during the recent past has demonstrated the potential of SAR data in forest above ground biomass estimation and forest / nonforest classification. In the present study, Advanced Land Observing Satellite (ALOS) / Phased Array L-band Synthetic Aperture Radar (PALSAR) data along with field inventory data have been used in forest above ground biomass estimation and forest / non-forest classification over Odisha state, India. The ALOSPALSAR 50 m spatial resolution orthorectified and radiometrically corrected HH/HV dual polarization data (digital numbers) for the year 2010 were converted to backscattering coefficient images (Schimada et al., 2009). The tree level measurements collected during field inventory (2009–'10) on Girth at Breast Height (GBH at 1.3 m above ground) and height of all individual trees at plot (plot size 0.1 ha) level were converted to biomass density using species specific allometric equations and wood densities. The field inventory based biomass estimations were empirically integrated with ALOS-PALSAR backscatter coefficients to derive spatial forest above ground biomass estimates for the study area. Further, The Support Vector Machines (SVM) based Radial Basis Function classification technique was employed to carry out binary (forest-non forest) classification using ALOSPALSAR HH and HV backscatter coefficient images and field inventory data. The textural Haralick’s Grey Level Cooccurrence Matrix (GLCM) texture measures are determined on HV backscatter image for Odisha, for the year 2010. PALSAR HH, HV backscatter coefficient images, their difference (HHHV) and HV backscatter coefficient based eight textural parameters (Mean, Variance, Dissimilarity, Contrast, Angular second moment, Homogeneity, Correlation and Contrast) are used as input parameters for Support Vector Machines (SVM) tool. Ground based inputs for forest / non-forest were taken from field inventory data and high resolution Google maps. Results suggested significant relationship between HV backscatter coefficient and field based biomass (R2 = 0.508, p = 0.55) compared to HH with biomass values ranging from 5 to 365 t/ha. The spatial variability of biomass with reference to different forest types is in good agreement. The forest / nonforest classified map suggested a total forest cover of 50214 km2 with an overall accuracy of 92.54 %. The forest / non-forest information derived from the present study showed a good spatial agreement with the standard forest cover map of Forest Survey of India (FSI) and corresponding published area of 50575 km2. Results are discussed in the paper.

Every year during winter months (December–January) fog formation over Indo-Gangetic plains (IGP) of Indian region is believed to create numerous hazards. The present study addresses variations in aerosol optical properties, aerosol mass concentration and their impact on solar irradiance for pre-during-post fog conditions of December 2004 over IGP, India. Continuous measurements on aerosol optical depth (AOD), total aerosol mass concentration, black carbon (BC) aerosols, UVery and UVA were carried out for pre, during and post fog periods over study site of Allahabad, India, during December 2004 as a part of Aerosol Land Campaign-II conducted by Indian Space Research Organization (ISRO). High aerosol mass concentrations were observed during fog and post-fog periods. Accumulation mode particle loading was found to be high during pre-fog period and coarse mode particle loading was observed to be high during fog and post-fog periods. Considerable reduction in UVery and UVA irradiance was observed during fog period compared with pre and post-fog periods. Analysis of NOAA-HYSPLIT model runs suggested that enhanced biomass burning episodes down-wind to the study area increased the concentration of AOD and BC.

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...

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 Earth's 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, R2, 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.

Large spatial extent of biomass burning occurs in northeast region of India during annual dry season for shifting cultivation purposes. Characterization of optical properties of resultant biomass burning aerosols is important for the study of atmospheric radiative process and for remote sensing of both surface and atmospheric properties in these regions. In the present study, physical and optical properties of biomass burning aerosols in Arunachal Pradesh, North Eastern Region of India have been studied for the first time using ground based measurements using a MICROTOPS-II sunphotometer, an Aethalometer, a quartz crystal microbalance impactor (QCM), SO2 analyser, and an UV meter. Aerosol size distribution suggested dominance of accumulation mode particle loading during burning days compared to normal days. The slope of data points between simultaneous measurements of AOD (500 nm) and UV ery suggested that every 0.1 increase in aerosol optical depth (AOD) causes 0.1 minimal erythemal dose (MED h − 1 ) reduction during normal day and reduction of 0.36 MED h − 1 in ground reaching UV ery during biomass burning periods. Diurnal variations of black carbon aerosol (BC) concentrations increased by a factor of ~ 2 during morning and evening hours compared to afternoon hours during biomass burning period. Daily average black carbon aerosol loading and SO 2 concentrations were found to be high during burning day compared to background values. The proportion of BC to total aerosol mass concentration was observed to be ~ 5% during normal days and ~ 14% during burning days. The changes in black carbon mass concentration values have implications for estimating radiative forcing due to aerosols over the region.

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 i...

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.

Northern regions of India experience severe fog conditions during the winter period (December–January) each year. In the present study, concurrent measurements of black carbon aerosols (BC), Carbon monoxide (CO), Surface Ozone (O 3 ) and oxides of Nitrgen (NO x ) have been carried out in Allahabad, located in northern India in order to understand the impact on pollutant concentrations during fog periods. The results of the present study show higher concentrations of BC, NO x and CO during morning and late night hours in the study area. Diurnal variations of ozone concentrations varied from 14 ppbv to 35 ppbv and were observed to increase gradually after sunrise attaining a maximum value by evening time and to decrease gradually thereafter. Concentrations of BC, NO x , O 3 and CO were found to be very low during fog periods over the study area. BC showed a positive correlation with CO and a negative correlation with surface ozone concentrations. The slope between black carbon aerosols and ground level ozone suggests that every 1 μg m − 3 increase in black carbon aerosol mass concentration causes a reduction of 0.7 μg m − 3 surface ozone.

Urban air quality monitoring plays an important role due to high concentration of particle sources and a large population exposed to elevated particle concentrations. Continuous ground based measurements of black carbon (BC) aerosol; carbon monoxide (CO) and ozone (O 3 ) were carried out in the tropical urban region of Hyderabad, India, during the forest fire season. Julian day variation of BC, CO and ozone showed high values on certain days. In order to ascertain the additional sources for observed high concentration of BC and CO, DMSP-OLS nighttime satellite data over the Indian region were processed for occurrence of forest fires. Results of the analysis suggested a higher incidence of forest fires on days with higher concentrations of BC and CO and a spatial distribution of forest fires; wind trajectories were observed to have a bearing on the higher values of BC, CO and ozone. Results are discussed in the paper.

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 earth's 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.

Aerosol particle size distributions along with their spatial and temporal variability are important for describing both direct and indirect radiative forcing. In this study, the variation of black carbon (BC) aerosols, total aerosol mass loading and aerosol optical depth (AOD) over an urban region of Hyderabad, south India, was analyzed for 3 consecutive years from 2003 to 2005. The AOD was measured using a handheld multichannel sun-photometer at six wavelengths centered on 380, 440, 500, 675, 870 and 1020 nm and aerosol mass–size distribution was made using a quartz crystal microbalance (QCM) cascade impactor. In addition, satellite remote-sensing data from nighttime DMSP-OLS images were analyzed for inferring ancillary sources of aerosols. Results from temporal analysis (2004–2006) suggest that aerosol mass loading and BC mass concentration increased considerably over the 3-year time-period mainly due to increasing vehicular traffic from urban population growth. DMSP-OLS nighttime images for different years suggested higher forest fire occurrences in the year 2004 compared to other years. The annual mean AOD at 550 nm from moderate resolution imaging spectroradiometer (MODIS) showed relatively high values during 2004. Copyright © 2007 Royal Meteorological Society

Monitoring and management of forest fires is very important in countries like India where 55% of the total forest cover is prone to fires annually. The present study aims at effective monitoring of forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime satellite data and to evaluate the active fire detection capabilities of the sensor. Nightly DMSP-OLS fire products were generated from February to May 2005 (peak fire season) and analyzed to study the occurrence and behavior of fires over different forest physiognomies in Indian region. Fire products generated from DMSP-OLS were validated with ground observations of fire records from state forest departments to evaluate the accuracy of fire products. Further, inter-comparison of the DMSP-OLS derived fire products with contemporary fire products from Moderate resolution Imaging Spectroradiometer (MODIS) (both daytime and nighttime products) in addition to fires and burnt areas derived from Indian Remote sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) data has been done to analyze spatial agreement of fire locations given by the above sensors. Results from the DMSP-OLS fire products (derived from February to May 2005) over Indian region showed high forest fires in southern dry deciduous forests during February–March; central Indian dry and mixed deciduous forests during March–April; northeastern tropical forests during February–April and northern pine forests during May. Spatial pattern in fires showed a typical seasonal shift in fire activity from the southern dry deciduous forests to the northern pine forests and temperate forests as the fire season progressed. Statistical evaluation of DMSP-OLS fire products with ground observations showed an over all accuracy of 98%. Comparison of DMSP-OLS derived fires with consecutive MODIS and AWiFS derived fires for individual days indicated that 69% of the fires continued from current day (DMSP-OLS pass around ∼ 7 pm to ∼ 10 pm local time) to the next day (MODIS and AWiFS pass ∼ 10:30 am local time). Comparison of DMSP-OLS derived fires with burnt areas estimated from AWiFS showed that 98% of DMSP-OLS derived fires on the current day fell within the burnt area of AWiFS on subsequent day. Since the worst forest fires are those that extend from the current to the consecutive days, DMSP-OLS derived fires provide a valuable augmentation to the fires derived from other sensors operating in daytime.

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.

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.

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.

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.

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.

Physical and optical properties of biomass burning aerosols in Northeastern region, India analyzed based on measurements made during February 2002. Large spatial extent of Northeastern Region moist tropical to moist sub-tropical forests in India have high frequency of burning in annual dry seasons. Characterization of resultant trace gases and aerosols from biomass burning is important for the atmospheric radiative process. Aerosol optical depth (AOD) observed to be high during burning period compared to pre- and post-burning days. Peak period of biomass burning is highly correlated with measured AOD and total columnar water vapor. Size distribution of aerosols showed bimodal size distribution during burning day and unimodal size distribution during pre- and post-burning days. Size distribution retrievals from biomass burning aerosols show dominance of accumulation mode particles. Weighted mean radius is high (0.22 microm) during burning period. Columnar content of aerosols observed to be high during burning period in addition to the drastic reduction of visibility. During the burning day Anderson sampler measurements showed dominance of accumulation mode particles. The diurnal averaged values of surface shortwave aerosol radiative forcing af biomass burning aerosols varies from -59 to -87 Wm(-2) on different days. Measured and modeled solar irradiances are also discussed in the paper.