Abstract

In the field of hyperspectral remote sensing, the spectral data is derived from hyperspectral images and are matched with the spectral library. Till date there are no studies that have matched with the hand–held spectrum collected by spectroradiometer at temporal scale. Creating a spectral library for the same species at various regions is a challenge, as acquiring spectrum at temporal sets are practically difficult and expensive. The absorption features are the unique key print for the identification of the two different samples of same species and also helps to discriminate two different species. The intensity and position of strong absorption region helps to match the spectrum accurately. In the current study probability based matching technique is used where the dips of a species of a season is matched to the other within a predefined abundance window. This study helps to know the seasonal changes of the tree due to its environmental factors, stress and deficiency.

Abstract

The Kondapalli reserve forest (KRF) of the Eastern Ghats, India is subjected to severe anthropogenic interference, despite its status as reserve forest. The present research focuses on assessing the land use and land cover changes in and around KRF using temporal satellite data. It is evident from the current study that there has been a considerable decrease in the forest cover from 1990 to 2015; as seen from 2017 data, huge urban expansions and development activities were observed around KRF. This increase is linked to the growth in population, thereby consuming land available for their basic needs. To assess the rate of urban expansion around the forest, Land consumption rate and Land absorption coefficient were computed. The result of this analysis showed a rapid growth in built-up land between 1990 and 2017 while the periods between 1990 and 2015 witnessed a reduction in the forest class. Further

Abstract

Abstract In the current study laboratory experiments were conducted to elucidate the allelopathic and autotoxic effects of Parthenium hysterophorus, Senna uniflora and Hyptis suaveolens. The seed germination experiments were conducted using the leachates obtained from different organs of the three species. Ten grams, each of the plant materials were homogenized in 100 mL of quartz-distilled water for 24 hours, filtered and used as a stock solution of 100% concentration. From the stock solution, 25, 50 and 75% concentrations were prepared by diluting with quartz-distilled water. Three replicates with 30 seeds each for each plant species were maintained. Observations were made at regular intervals of 24 hours. The emergence of radicle was taken as the indicator of germination. Germination, root and shoot elongation was monitored for eight days. The study revealed inhibition of germination …

Abstract

The present work is a multi-temporal satellite based spatial dynamic study of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The study discusses the importance and the status of mangroves on both global and regional scales. Maximum likelihood estimate method of supervised classification technique has been used to classify the land use-land cover changes in the Pichavaram Reserve Forest, Killai Reserve Forest and Pichavaram Extension. The status of the classes has been monitored using Landsat ETM+ of 2000, 2006, 2011, and OLI of 2016 satellite imageries. The total area of the Pichavaram mangrove showed a net increase of approximately 11.41% of the total study area within a span of 15 years (2000–2016)

Abstract

Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and specially to undertake necessary conservation measures. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. In the current study an attempt has been made to predict the vegetation dynamics using MODIS NDVI time series data sets and long short term memory network, an advanced technique adapted from the artificial neural network. The dataset of 861 NDVI images from January 2000 to June 2016 is used for making the time series. The data is segregated into three sets which comprises of training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment we have finalised two different regions after extensive research for investigation. These include different terrains in the Great Nicobar Islands, one region along the coast where vegetation has severe ecological damage due to 2004 Indian Ocean tsunami and the other, an interior region which remained imperturbable during the tsunami. Long short term memory network, an advanced neural network is trained with these NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots from both the experiments indicate that the long short-term memory neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series were well adapted by the network and was able to predict the future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. By adopting the prescribed method in the paper, anticipation of vegetation changes can be done accurately much ahead of time and take proactive measures accordingly to safeguard and improve the vegetation in any area.

Abstract

Forest fires are the most frequently occurred phenomenon during summer seasons in the state Madhya Pradesh. Monitoring and assessment of forest fires are the crucial steps in effective forest fire management. Forest fire danger estimation helps the disaster management authorities to take necessary mitigation measures for minimizing the losses and to evacuate the local people. Fire danger rating systems predict the fire danger based on the meteorological station parameters and ground datasets. McArthur Forest Fire Danger Index (FFDI) is the most popularly used fire danger rating systems using in the country Australia. This index requires large amount of ground datasets for the computation of drought parameter. In India, it is very difficult to compute the drought parameter due to the unavailability of instruments and man power. In the present research, McArthur Fire Danger Index was modified by inducing Normalized multiband drought index (NMDI) that was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA surface reflectance product MOD09GA as a substitute for fuel availability parameter. To test the robustness of modified FFDI, the research was carried out on Madhya Pradesh state for the assessment of forest fire danger. The results obtained from

Abstract

Missing data in satellite images is a known phenomenon in remote sensing. There are various reasons for these gaps in satellite images e.g. shadowed area for SAR data sets, cloud coverage for optical imagery and instrument errors such as Scan Line Corrector(SLC-off) failure. The Scan Line Corrector (SLC) of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed permanently causing around 23% of pixels not scanned in each scene and this failure has seriously limited the scientific applications of ETM+ data. Reconstructing the gap regions is an important issue in remote sensing. To address this issue, we have proposed an idea to find the homogeneous and heterogeneous regions of ETM+ image using Morphological operations and replace the unscanned pixel value by Neighborhood similar pixel threshold based Local Binary Pattern value. We have applied padding approach with additional rows and columns for unscanned mixels at borders to replace with the corresponding NLBP value. It is observed that our results are promising to fill the data gaps in both homogeneous and heterogeneous regions.

Abstract

Mangrove, the intertidal halophytic vegetation, are one of the most significant and diverse ecosystem in the world. They protect the coast from sea erosion and other natural disasters like tsunami and cyclone. In view of their increased destruction and degradation in the current scenario, mapping of this vegetation is at priority. Globally researchers mapped mangrove vegetation using visual interpretation method or digital classification approaches or a combination of both (hybrid) approaches using varied spatial and spectral data sets. In the recent past techniques have been developed to extract these coastal vegetation automatically using varied algorithms. In the current study we tried to delineate mangrove vegetation using LISS III and Landsat 8 data sets for selected locations of Andaman and Nicobar islands. Towards this we made an attempt to use segmentation method, that characterize the mangrove vegetation based on their tone and the texture and the pixel based classification method, where the mangroves are identified based on their pixel values. The results obtained from the both approaches are validated using maps available for the region selected and obtained better accuracy with respect to their delineation. The main focus of this paper is simplicity of the methods and the availability of the data on which these methods are applied as these data (Landsat) are readily available for many regions. Our methods are very flexible and can be applied on any region.

Abstract

This research note focuses on the current climate change research scenario and discusses primarily what is required in the present global climate change conditions. Most of the climate change research and models predict adverse future conditions that have to be faced by humanity, with less emphasis on mitigation measures. Moreover, research ends as reports on the shelves of scientists and researchers and as publications in journals. At this juncture the major focus should be on research that helps in reducing the impact rather than on analysing future scenarios of climate change using different models. The article raises several questions and suggestions regards climate change research and lays emphasis on what we really need from climate change researchers.

Abstract

Survival of plant species is governed by a number of functions. The participation of each function in species survival and the impact of the contrary behaviour of the species vary from function to function. The probability of extinction of species varies in all such scenarios and has to be calculated separately. Secondly, species follow different patterns of dispersal and localisation at different stages of occupancy state of the site, therefore, the scenarios of competition for resources with climatic shifts leading to deterioration and loss of biodiversity resulting in extinction needs to be studied. Furthermore, most possible deviations of species from climax community states needs to be calculated before species become extinct due to sudden environmental disruption. Globally, various types of anthropogenic disturbances threaten the diversity of biological systems. The impact of these anthropogenic activities needs to be analysed to identify extinction patterns with respect to these activities. All the analyses mentioned above have been tried to be achieved through probabilistic or graphical models in this study