58 research outputs found
STUDY OF SOME POLYCYCLIC AROMATIC HYDROCARBONS POLLUTION IN ROADSIDE SOIL AT MORENA, INDIA
Get PDFStudy of some polycyclic aromatic hydrocarbons pollution in roadside soil was conducted in developing city during winter, summer, monsoon season to ascertain the contamination levels and their distribution behavior in soil. The concentration of polycyclic aromatic hydrocarbons was determined at nine locations of Morena in India at roadside, residential and agricultural in soil covering all the major traffic intercepts within city
AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms
No full textThe hyperspectral image such as AVIRIS-NG provides a lot of spectral information and fine resolution that identify and discriminate similar objects based on their spectral reflectance. This study reveals a comparative analysis of multiple classification algorithms for AVIRIS-NG data, before and after minimum noise fraction (MNF) transformation for land use land cover classification. Applied distance methods are entirely based upon the availability of a complete set of endmembers for the data and used to classify the pixels in terms of endmembers. Applied classifiers yield more accurate results especially in terms of the overall accuracy after dimensionality reduction. As a result, minimum distance achieved highest 97.76% overall accuracy with a Kappa coefficient of 0.97, whereas Mahalanobis distance yields more precise results before MNF with 95.34% overall accuracy and a Kappa coefficient of 0.94. This study also indicates the importance of data dimensionality reduction for hyperspectral imagery
Latitudinal fluctuation in global concentration of CO2 and CH4 from shortwave infrared spectral observation by GOSAT during COVID-19
No full textVarious countries have rapidly implemented strict actions to slow the blowout of COVID-19. Many events were dis-regarded, and anthropogenic activities such as industrial and transport systems were at a stoppage. Many countries were on lockdown, including the largest emitters of carbon dioxide. Due to these lockdowns, anthropogenic activities have been reduced and reported that air quality improves at a regional scale in many countries, including India. Therefore, the current study using Greenhouse Gases Observing Satellite (GOSAT/IBUKI) datasets to monitor the fluctuation of the average global concentration of dry mole fractions of atmospheric Methane (CH4) and Carbon Dioxide (CO2) during these pandemic lockdowns from January to June 2020. Outputs emphasize no significant reduction in the average concentration of dry mole fractions of atmospheric CH4 over the globe, but a minor reduction was observed in global CO2 engagement. The average concentration of both gases compares at each ten-degree latitude. The study reveals that, against the regional breakdowns, these short time lockdowns are not enough to control the concentration of these greenhouse gases at a larger scale, such as 10˚ latitude and globally, except for a minor reduction in CO2 concentration
Traditional medicinal practices of Rajasthan
No full text531-533During the course of investigation it has been observed that a particular plant is sometimes prescribed for different ailments in different localities and some Vaidyas (Ayurvedic physicians) apply a mixture of plants for remedy of diseases. The data collected from Vaidyas have been presented. However, a systematical and methodical approach is needed to collect such information
Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India
No full textImproved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing
No full textAbstractLand Use Land Cover (LULC) change detection helps the policy makers to understand the environmental change dynamics to ensure sustainable development. Hence, LULC feature identification has emerged as an important research aspect and thus, a proper and accurate methodology for LULC classification is the need of time. In this study, Landsat-7 satellite data captured by Enhanced Thematic Mapper (ETM+) were used for LULC classification employing the maximum likelihood supervised classification (MLC) algorithm. The study targets the improvement of classification accuracy with the combined use of thermal and spectral information from satellite imagery. Land surface temperature (LST) is sensitive to land surface features and hence can be used to extract information on LULC features. The classification accuracy was found to improve on integrating the thermal information from the thermal band of Landsat ETM+ with spectral information. Two thermal vegetation indices, namely Thermal Integrated Vegetation Index (TLIVI) and Advanced Thermal Integrated Vegetation Index (ATLIVI), proposed in this study showed fairly good correlations (R2=0.65 and 0.7, respectively) with the derived surface temperature. These indices based on empirical parameterization of the relationship between surface temperature (Ts) and vegetation indices showed an increase of nearly 6% in the overall accuracy for land-use/land-cover (LULC) classification in comparison to MLC algorithm using Standard False Colour Composite (FCC) satellite image of Landsat ETM+ as reference
Modelling the growth response to climate change and management of Tectona grandis L. f. using the 3-PGmix model
No full textKey message: Teak (Tectona grandis L. f.) is a native tree species of India. It is one of the most desirable timber species because of its strength, fine texture, and durability. Its growth is strongly dependent on the climatic conditions, but empirical data are often unavailable to support management decisions. The physiological principles for predicting growth incorporated in the 3-PGmix model make it a useful tool in modelling the growth responses and management in the changing climate. We assessed that under elevated atmospheric carbon dioxide (CO2) concentration and no thinning, teak would store more carbon than currently. Context: Uncertainty and lack of scientific understanding about the growth response to climate change and thinning regimes have created challenges in teak sustainability, both regionally and globally. Aims: This research examines climate change and management implications on teak growth in India using the 3-PGmix model. Methods: The 3-PGmix model was coupled with climate scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5) to forecast growth response up to the year 2100 with 1981–2010 as the baseline under thinning (G-quality, P-quality) regimes. Thinning under G-quality is performed at earlier stand age than P-quality, and then simulations under ‘no thinning’ based on stocking/ha at different thinning intensity. Results: Under ‘no thinning’, predicted net primary productivity (NPP) for RCP4.5 and RCP8.5 became 5.77 t/ha/year and 5.28 t/ha/year in 2100. However, under increasing CO2, it became 7.39 t/ha/year and 8.22 t/ha/year respectively in 2100. In the future, increasing CO2 would be the dominating factor for an increase in teak growth; however, abnormal precipitation and warmer temperature could produce an unforeseen growth condition. The carbon stock and CO2 sequestration are predicted to be higher under no thinning, which signifies the CO2 fertilisation effect in teak. Conclusion: The set of parameters used in 3-PGmix offers an opportunity to predict teak responses to future climatic conditions and management treatments.</p
Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India
No full textAnthropogenic impacts cause Land use and land cover (LULC) changes that adversely disturb the protected area’s (PA). A quantitative evaluation of historical and future LULC changes over 50 years (1999–2049) in a highly exploited Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India is the primary objective of this study. Maximum likelihood classification (MLC) - a supervised classification technique was applied to classify LULC using Landsat 1999, 2009, and 2019 imagery. Land Change Modeler (LCM) embedded in IDRISI Terrset version 18.21 software incorporated with Multilayer perceptron (MLP) neural network and Markov chain with eight driver variables has been the centre for LULC monitoring, change assessment and future predictions. Classified LULC map for the year 1999, 2009 and 2019 show an overall accuracy with Kappa coefficient of 0.9879, 0.9625 and 0.9381 was 99.08%, 97.58% and 95.45% respectively. During 1999–2019, vegetation cover decreased from 29389.50 ha to 21207.35 ha while agricultural land increased from 28479.18 ha to 31920.66 ha respectively. The kappa indices (Kno, Klocation, and Kstandard) values are 0.9992, 0.9703, and 0.9493, respectively. The projected LULC map for 2029, 2039, and 2049 depicts that the vegetation cover will further degrade, while agricultural land would be increased. Overall, the study reveals that the anthropogenic interventions and intents would increase in the upcoming future, which will severely disturb the integrity of a diversity rich PA. This integrated approach of LULC modeling and remote sensing offers a reliable method for SWS management and planning; it recommends taking a few regulatory steps to moderate human-induced disturbances in SWS
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