Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM) sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS) is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR) product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1) assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC) estimates using ground-measured values; and (2) comparing the LITS-generated normalized difference vegetation index (NDVI) and MODIS NDVI (MOD13Q1) time series. The predicted image-derived FPC products (value ranges from 0 to 100%) had an RMSE of 5.6. OPEN ACCESSRemote Sens. 2012, 4 1857Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used) difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf) forest environment and also demonstrated that the approach can be used to form a time series of Landsat TM images to study vegetation phenology over a number of years.

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVI normalized and EVI normalized ), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVI normalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVI normalized . The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.

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The excessive use of chemical fertilizers and pesticides have caused several negative impacts on the environment and human health. They degrade soil fertility, build up resistance on pathogens, inhibit microbial activities and also enhance greenhouse gas emission. It is impossible and inappropriate to control plant pathogens by using chemical pesticides alone. Emphasize should be given towards organic fertilizers and pesticides to attain sustainability in agriculture. The use of Trichoderma is slowly increasing in the recent years among progressive farmers as an alternative to chemical fertilizers and pesticides. Slow rate of multiplication and colonization, susceptible to biotic and abiotic stresses, incomplete elimination of pathogens and high cost are the major problems behind its poor adoption among the farmers. To overcome these challenges different strains of Trichoderma should be identified which can multiply and colonize rapidly, least affected by environmental conditions and having wide host range on pathogens. In addition, farmers should be made aware about the importance of Trichoderma in agriculture through various extension facilities for its wide scale adoption. Trichoderma can be the viable and sustainable alternative which acts as biofertilizer, bioremediator and biocontrol agent. Nevertheless, the use of Trichoderma is limited on research activities and its application at farmers' level is not yet satisfactory. Thus, this study based on critical analysis of the research works from worldwide researchers aims to reveal the present scenario of the use of Trichoderma, its importance, modes of action, methods of application and multiplication, challenges for wide scale adoption and its appropriate solutions.