Ann Based Robust LULC Classification Technique Using Spectral, Texture and Elevation Data

Albeit the advent of fast computing facilities, digital image classification of remotely sensed data is still remain the topic of research. This might be due to the reason that the ancillary information such as texture and topography is absent in image classification. Since two decades, texture is widely applied in image classification but there is no explicit icon in most popularly used remote sensing software. Hence the aim of this study is to classify the Landsat ETM+ captured in 2000 using spectral information, topographic information and texture information. This study helps to throw light into statistical texture analysis i.e., the effect window size i.e., 3?×?3 to 9?×?9, on image classification. The ability of Grey Run Length Matrix (GRLM), which is computationally complex compared to industrially well-known Grey Level Co-occurrence Matrix (GLCM) but encompasses greater potential to discriminate between two classes, is explored. Eight spectral bands, 11 texture parameters extracted from Landsat ETM+ data and elevation, slope, aspect extracted from DEM data are classified individually using Artificial Neural Network (ANN) and the individually classified information is integrated using endorsement theory. Validations of classified results are performed using Google Maps and Landmap services updated in 2009. The results are compared with Maximum Likelihood classification (MLC) and hence all the evidence (spectral, texture and topography) with 5?×?5 texture window provided maximum classification accuracy of 70.44 %.     

Assessment of Aphid Infestation in Mustard by Hyperspectral Remote Sensing

Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Landscape characterization of Sariska National Park (India) and its surroundings

Landscape characterization gives an overall information on the status of Land Use and Land Cover(LULC),changes in its composition and the impact of natural and human influences operating at different spatial and temporal scales.This information can be used to monitor changes in natural forest resources and protected areas,delineate potential conservation areas and can serve in effective management of ecologically fragile landscapes.In the present study,geo-spatial tools were used to characterize the landscape of Sariska National Park and its surroundings.Satellite data was used to prepare LULC maps for 1989 and 2000,change detection analysis and computation of landscape metrics.Climatic data,field records and modeling tools were used to map the po-tential spread of two invasive species,Prosopis juliflora and Adhatoda vasica.The results show that the forest area increased from 1989 to 2000,indicating better management practices.Landscape metrics(PAFRAC,PLADJ and AI)also support this argument.Improvements in the degraded forest can further enhance this effect.The entire reserve however is suitable for the invasion of P.juliflora and A.vasica but is more pronounced in Boswellia serrata and Anogeissus pendula-Acacia catechu(open)forests.A detailed landscape characterization map can help forest managers to make important policy decisions concerning issues such as in-vasive species.     

Sub-surface water regime related to landform studies through satellite images

Based on the visual interpretation of satellite (Landsat) false colour composite imagery, the entire Haryana State is divided into eleven landform units. Seven major landform units are studied in details with respect to water table fluctuation, ground water quantum and draft. The studies reveal that usable recharge and draft per unit area are maximum in case of area of water seepage and periodic flooding followed by Yamuna and Ghaggar flood plain and upper alluvial plains with occasional sand dunes. The minimum recharge and draft are observed in case of dune areas.     

Near-surface air temperature lapse rate in a humid mountainous terrain on the southern slopes of the eastern Himalayas

Theoretical and Applied Climatology - Based on climatic data from 18 stations on the southern slopes of the eastern Himalayas in Bhutan for the period from 1996 to 2009, this paper investigates...     

Surface energy and radiation budget over a tropical station: An observational study

This study attempts to understand the variations in the radiation and surface energy budget parameters during days of occurrence and non occurrence of convective activity such as thunderstorms at Ranchi (23°25??N, 85°26??E), India using the special experimental data sets obtained during pre-monsoon month of May, 2008. For this purpose five continuous thunderstorm days (TD) of varying intensity, along with three non-thunderstorm days (NTD) preceding the TD are considered. Thunderstorms occurred at site are multi-cellular in nature. Change of wind direction and strong gusty winds are noticed in TD cases. Pre-dominant wind direction is south westerly for the TD; it is northwesterly during NTD. Sudden drop of air temperature and rise of relative humidity and rise/drop in atmospheric pressure is noticed during TD are found to be proportional to the intensity of thunderstorm event. More partitioning of net radiation (QN) is in to latent heat flux (QE) and the contribution of sensible heat flux (QH) and soil heat flux (QG) are same during TD. But in the NTD more partitioning of QN is in to QH followed by QG that of QE. Significant differences in radiation and energy budget components are noticed during TD and NTD events.     

Forest site quality assessment by aerial photo-interpretation case study of sal forests

93 black & white panchromatic aerial photographs of 1972 on 1:20,000 scale were used for assessing the site quality of Sal forest of part of West Dehradun Forest Division, by both direct and indirect methods. The forests were stratified into 5 height and 5 density classes and according to species composition. For necessary data for the assessment of growing stock, field measurements were made on 54 sample plots of 0.1 hectare each. This provided direct indication of actual productive capacity of a particular site. According to indirect method, ground vegetation was sampled in 54 quadrats of 5m x 5m each and has been classified into statistically sound vegetation groupings by normal association analysis technique. These groups facilitated the standardisation of their photo perameters. The indicator value of the plants in a grouping was used to assess the suitability of the site. This was substantiated by super-imposing growing stock, present at that site. By combination of the direct and indirect methods, sites have been classified into 3 categories i.e., suitable, conditionally suitable and unsuitable. Suggestions have been made for management of conditionally suitable sites and the cause for unsuitability of sites are explored. Hence, interpretation of qualitative attributes of vegetation alongwith the measurements of quantitative parameters on aerial photographs have proved to be useful for site quality assessment.     

Street Versus Rooftop Level Concentrations of Fine Particles in a Cambridge Street Canyon

Dispersion of particles, as evidenced by changes in their number distributions (PNDs) and concentrations (PNCs), in urban street canyons, is still not well understood. This study compares measurements by a fast-response particle spectrometer (DMS500) of the PNDs and the PNCs (5–1000 nm, sampled at 1 Hz) at street and rooftop levels in a Cambridge UK street canyon, and examines mixing, physical and chemical conversion processes, and the competing influences of traffic volume and rooftop wind speed on the PNDs and the PNCs in various size ranges. PNCs at street level were ≈6.5 times higher than at rooftop. Street-level PNCs followed the traffic volume and decreased with increasing wind speed, showing a larger influence of wind speed on 30–300 nm particles than on 5–30 nm particles. Conversely, rooftop PNCs in the 5–30 nm size range increased with wind speed, whereas those for particles between 30 and 300 nm did not vary with wind speed.