Impact of Grazing, Fire and Extraction on the Bamboo (Dendrocalamus-Strictus and Bambusa-Arundinacea) Populations of Karnataka

Dendrocalamus strictus and Bambusa arundinacea are monocarpic, gregariously flowering species of bamboo, common in the deciduous forests of the State of Karnataka in India. Their populations have significantly declined, especially since the last flowering. This decline parelleis increasing incidence of grazing, fire and extraction in recent decades. Results of an experiment in which the intensities of grazing and fire were varied, indicate that while grazing significantly depresses the survival of seedlings and the recruitment of new eulms of bamboo clumps, fire appeared to enhance seedling survival, presumably by reducing competition of lass fire-resistant species. New shoots of bamboo are destroyed by insects and a variety of herbivorous mammals. In areas of intense herbivore pressure, a bamboo clump initiates the production of a much larger number of new culrm, but results in many fewer and shorter intact culms. Extraction renders the new shoots more susceptible to herbivore pressure by removal of the protective covering of branches at the base of a bamboo clump. Hence, regular and extensive extraction by the paper mills in conjuction with intense grazing pressure strongly depresses the addition of new culms to bamboo clumps. Regulation of grazing in the forest by domestic livestock along with maintenance of the cover at the base of the clumps by extracting the culms at a higher level should reduce the rate of decline of the bamboo stocks.

Potential herblayer production and grazing effects in anthropogenic savanna-grasslands in the moist tropical forests of the Western Ghats of India

The moist tropical forests of the Western Ghats of India are pockmarked with savanna-grasslands created and managed by local agricultural communities. A sample of such savanna-grasslands with differing growing conditions was studied in terms of peak above-ground biomass, monthly growth, and cumulative production under different clipping treatments. The herblayer was found to be dominated by perennial C4 grasses, with Eulalia trispicata, Arundinella metzii and Themeda triandra being common to all sites. Peak biomass ranged between 3.3-5.9 t/ha at sites most favourable for grass production. Across these sites, peak biomass was found to be inversely related to the number of rainy days during the growing season, suggesting that growth may be light-limited. This hypothesis is supported by the observation that growth is most rapid immediately after the easing of the monsoon. Single clips early in the growing season had no negative or a slightly positive effect on production, but mid-season single clips or continuous frequent clipping reduced production by as much as 40%. The results suggest that, while indiscriminate grazing may certainly be deleterious, it is possible to obtain sustained high yields from forest lands managed for grass production without totally excluding grazing.

Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary

In this paper, we propose a novel heuristic approach to segment recognizable symbols from online Kannada word data and perform recognition of the entire word. Two different estimates of first derivative are extracted from the preprocessed stroke groups and used as features for classification. Estimate 2 proved better resulting in 88% accuracy, which is 3% more than that achieved with estimate 1. Classification is performed by statistical dynamic space warping (SDSW) classifier which uses X, Y co-ordinates and their first derivatives as features. Classifier is trained with data from 40 writers. 295 classes are handled covering Kannada aksharas, with Kannada numerals, Indo-Arabic numerals, punctuations and other special symbols like $ and #. Classification accuracies obtained are 88% at the akshara level and 80% at the word level, which shows the scope for further improvement in segmentation algorithm

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.