Forest area estimation and reporting: implications for conservation, management and REDD

Periodic estimation, monitoring and reporting on area under forest and plantation types and afforestation rates are critical to forest and biodiversity conservation, sustainable forest management and for meeting international commitments. This article is aimed at assessing the adequacy of the current monitoring and reporting approach adopted in India in the context of new challenges of conservation and reporting to international conventions and agencies. The analysis shows that the current mode of monitoring and reporting of forest area is inadequate to meet the national and international requirements. India could be potentially over-reporting the area under forests by including many non-forest tree categories such as commercial plantations of coconut, cashew, coffee and rubber, and fruit orchards. India may also be under-reporting deforestation by reporting only gross forest area at the state and national levels. There is a need for monitoring and reporting of forest cover, deforestation and afforestation rates according to categories such as (i) natural/primary forest, (ii) secondary/degraded forests, (iii) forest plantations, (iv) commercial plantations, (v) fruit orchards and (vi) scattered trees.

Turing and animal coat patterns

The present article describes a beautiful contribution of Alan Turing to our understanding of how animal coat patterns form. The question that Turing posed was the following. A collection of identical cells (or processors for that matter), all running the exact same program, and all communicating with each other in the exact same way, should always be in the same state. Yet they produce nonhomogeneous periodic patterns, like those seen on animal coats. How does this happen? Turing gave an elegant explanation for this phenomenon, namely that differences between the cells due to small amounts of random noise can actually be amplified into structured periodic patterns. We attempt to describe his core conceptual contribution below.

Bioremediation and lipid synthesis through mixotrophic algal consortia in municipal wastewater

Algae grown in outdoor reactors (volume: 10 L and depth: 20 cm) were fed directly with filtered and sterilised municipal wastewater. The nutrient removal efficiencies were 86%, 90%, 89%, 70% and 76% for TOC, TN, NH4-N, TP and OP, respectively, and lipid content varied from 18% to 28.5% of dry algal biomass. Biomass productivity of similar to 122 mg/l/d (surface productivity 24.4 g/m(2)/d) and lipid productivity of similar to 32 mg/l/d were recorded. Gas chromatography and mass spectrometry (GC-MS) analyses of the fatty acid methyl esters (FAME) showed a higher content of desirable fatty acids (bearing biofuel properties) with major contributions from saturates such as palmitic acid C16:0; similar to 40%] and stearic acid C18:0; similar to 34%], followed by unsaturates such as oleic acid C18:1(9); similar to 10%] and linoleic acid C18:2(9,12); similar to 5%]. The decomposition of algal biomass and reactor residues with an exothermic heat content of 123.4 J/g provides the scope for further energy derivation. (C) 2014 Elsevier Ltd. All rights reserved.

Graphene as a sensor

Graphene has emerged as one of the strongest candidates for post-silicon technologies. One of the most important applications of graphene in the foreseeable future is sensing of particles of gas molecules, biomolecules or different chemicals or sensing of radiation of particles like alpha, gamma or cosmic particles. Several unique properties of graphene such as its extremely small thickness, very low mass, large surface to volume ratio, very high absorption coefficient, high mobility of charge carriers, high mechanical strength and high Young's modulus make it exceptionally suitable for making sensors. In this article we review the state-of-the-art in the application of graphene as a material and radiation detector, focusing on the current experimental status, challenges and the excitement ahead.

Fire and soil temperatures during controlled burns in seasonally dry tropical forests of southern India

Fire and soil temperatures were measured during controlled burns conducted by the Forest Department at two seasonally dry tropical forest sites in southern India, and their relationships with fuel load, fuel moisture and weather variables assessed using stepwise regression. Fire temperatures at the ground level varied between 79 degrees C and 760 degrees C, with higher temperatures recorded at high fuel loads and ambient temperatures, whereas lower temperatures were recorded at high relative humidity. Fire temperatures did not vary with fuel moisture or wind speed. Soil temperatures varied between <79 degrees C and 302 degrees C and were positively correlated with ground-level fire temperatures. Results from the study imply that fuel loads in forested areas have to be reduced to ensure low intensity fires in the dry season. Low fire temperatures would ensure lower mortality of above-ground saplings and minimal damage to root stocks of tree species that would maintain the regenerative capacity of a tropical dry forest subject to dry season wildfires.