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Leaf area index (LAI) and light extinction coefficient (k) are the key structural parameters controlling many canopy functions like radiation and water interception, radiation extinction, water and gas exchange. The present study aims at developing predictive models for generating spatial distribution of LAI and k by integrating remote sensing imagery and field data. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral variables were derived from Landsat 8 Operational Land Imager (OLI) data of 8 May 2017 to predict LAI and k. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (I o) and below canopy (I) were taken using CI-110 Plant Canopy Imager. Canopy gap fraction and k (using Beer-Lambert's equation) were calculated. Random Forest (RF) algorithm was used to predict the spatial distribution of LAI and k using the best predictor variables. The best predictor variables for LAI included band 6 (Short wave infra-red (SWIR) -1) and band 7 (SWIR-2), tasseled cap wetness, Moisture Stress Index (MSI), and Normalized Difference Moisture Index (NDMI). For prediction of k , the best predictor variables were band 6 (SWIR-1) and band 7 (SWIR-2), NDMI, tasseled cap wetness, MSI and Normalized Difference Vegetation Index (NDVI). These variables were selected to generate RF-based models to predict LAI and k. On validation, the models were able to predict LAI with R2 = 0.79 and % RMSE = 14.25% and k with R2 = 0.77 and % RMSE = 11.98%. The predicted LAI and k followed an inverse relation in accordance with the Beer Lambert's Law. The results showed that RF can be effectively applied to predict the spatial distribution of LAI and k. • Leaf area index (LAI) and light extinction coefficient (k) were predicted. • In-situ measurements of LAI and k were integrated with Landsat 8 OLI derived spectral variables for prediction. • Spectral variables include band reflectance, spectral indices & tasseled cap transformation. • Random Forest (RF) was effectively used for prediction of LAI & k. • Predicted LAI and k followed an inverse relation. [ABSTRACT FROM AUTHOR]

In this research work, a new approach is developed for the extractive determination of chromium. The principle of this approach is based on the complexation reaction between 4-(4ʹ-chlorobenzylideneimino)-3-methyl-5-mercapto-1,2,4-triazole (CBIMMT) in dichloromethane as a complexing reagent and chromium(III) in presence of potassium iodide to form a yellow coloured complex at room temperature. The 1:2:2 [Cr(III)-CBIMMT-iodide] ternary complex was quantitatively extracted in dichloromethane from 2.5 mol L−1 of hydrochloric acid medium which showed maximum absorption intensity at λmax 411 nm and was stable for more than 72 h. The values of molar absorption coefficient and Sandell's sensitivity of the complex were found to be 0.7019 × 104 L mol−1 cm−1 and 0.00748 µg cm−2, respectively. The system adheres to Beer's law from 1.5 to 6.0 µg mL−1; however, Ringbom's plot suggests optimal concentration range was 1.8–5.8 µg mL−1. The limit of detection and limit of quantification of the approach is 0.26 and 0.79 µg mL−1. This approach was successfully used for the determination of chromium from wastewater effluents from the tannery industries (Kolhapur, MS, India), alloy samples and for separation of it from synthetic mixtures. The present experimental approach is apparently much simpler than the conventional method comprising multistep processes. [ABSTRACT FROM AUTHOR]