Permian Palynoflora from Lower Gondwana Sediments of Godavari Valley Coalfield, Andhra Pradesh, South India

Palynological investigation of Lower Gondwana sediments of Goutham Khani Open Cast Mine (GKOCM) from Kothagudem sub basin of Godavari Valley Coalfield, Andhra Pradesh of Peninsular India have revealed two palynoassemblages are related to Early Permian (Barakar Formation). Palynoassemblage – A abundantly occurrence of Scheuringipollenites along with other common taxa like Parasaccites, Ibisporites, Primuspollenites, Sulcatisporites, Rhizomaspora, Densipollenites and Corisaccites and Palynoassemblage – B dominated by Scheuringipollenites along with sub dominance of Faunipollenites, Striatopodocarpites, Parasaccites, Striatites, Tiwariasporis, Rhizomaspora, Verticipollenites, Platysaccus, Primuspollenites, Lunatisporites, Latosporites, Ibisporites and Distriatites. The above demarcated palynoassemblages are applied to correlate with other horizons of Lower Gondwana deposits of India and also to fix the relative age for the sediments under investigation. Key words? Permian Age, Lower Gondwana, Palynoassemblage, Godavari Valley Coalfield, Sothern India

Palynodating of Talchir Palynoflora from lower Gondwana sediments of Godavari Valley Coalfield, South India

Palynological investigation in the sub surface of bore core SSP – 304 from Sattupalli area, Chintalpudi sub basin, Godavari Valley Coalfield, Andhra Pradesh have revealed palynoassemblage belongs to Early Permian (Talchir) palynoflora. The present Lower Gondwana Palynoassemblage characterized by dominance of Parasaccites – Plicatipollenites along with non striate disaccates Scheuringipollenites, Ibisporites, Sulcatisporites and other taxa like, Corisaccites, Faunipollenites, Horriditriletes, Tiwariasporis and Virkkipollenites

Distribution of ostracode assemblages along the nearshore and offshore areas of Malabar coast, Kerala (west coast of India)

Sixty-one ostracode species have been identified in 76 sediment samples from 28 nearshore and 48 offshore locations off Malabar Coast, Kerala. Q mode cluster analysis of the ostracode assemblages of the nearshore sediments indicates that the entire nearshore assemblages can be classified into two clusters – cluster I – Kumbla (A₁) – Hosdurg (B₁) and Mattul (D₁) blocks and Cluster II – Payyannur (C₁) – Cannanore (E₁) – Telicherry (F1) blocks. This clustering is probably due to the differences in the depositional environment. The influence of estuarine environment is predominant in the cluster I due to the mixing of freshwater from the surrounding land masses. Several backwaters are observed in the areas covered by this cluster. Cluster analysis of offshore assemblages indicate that the entire offshore off Malabar coast can be divided into two clusters, cluster I – Kumbla (A₂) and Bekal (B₂), cluster II – Neeleswaram (C₂), Payyannur (D₂) and Payangoti (E₂) blocks. The presence of shallow water ostracode species in cluster I which lived in nearshore sand and silty sand substrate under the influence of subtropical water currents are inferred and further indicated by the fossil molluscan assemblages. Q-mode cluster analysis and details of physical and ecological parameters suggest that there is a substantial influence of substrate, organic matter and salinity in the distribution, diversity and abundance of the ostracode. Based on this, the Malabar coast of Kerala is significantly classified as a marine ecosystem/environment category

Identification of potential landslide hazard zonation mapping using geoinformatics for Kohima region, Nagaland, India

Climate change is increasing the frequency of natural hazards such as intense landslide, flood and storms. Predictably, vulnerability assessment focuses on individual risks, but the importance of addressing hazards collectively is now unavoidable using geoinformatics. A huge area of Nagaland is prone to landslides. The main causes of slope instability in the region are attributed to young geology, high slope and relief, heavy rainfall and improper land use practice. In the present study, identification of potential landslide hazard zonation study of Kohima region has been attempted using Google earth�s high resolution satellite data. The lithology, geological structures, slope aspect ratio, relative relief and land use and land cover layers of Kohima region were prepared using geoinformatics techniques; which includes geographical information system (GIS), remote sensing (RS) and global positioning system (GPS). These were classified, ranked and weighted according to their assumed or expected importance in causing slope instability based on a priori information of the expert. A heuristic method has been applied for the assignment of ranks and weights. Landslide hazard zonation map is generated showing five hazard classes ranging from very low hazard, low hazard, moderate hazard, high hazard and very high hazar