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reinforce the usefulness of phytolith analysis for distinguishing ecologically significant taxa, and therefore major vegetation formations.
These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio‐spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India.
The differences found in abundance and variation in species composition among vegetation zones seems to be related to the vegetation structure of each zone.
The predicted vegetation anomalies compare well with observations, which can be effectively utilized in early warning and better planning in water resources and agricultural sectors in India.
It is found that there is a close correspondence between these clusters and the distribution of the vegetation types in the country.
4 Both the structure and composition of the surrounding vegetation were associated with the type of population found.
Journal ArticleDOI
Partha Sarathi Roy, Mukunda Dev Behera, M. S. R. Murthy, Arijit Roy, Sarnam Singh, S. P. S. Kushwaha, Chandra Shekhar Jha, S. Sudhakar, Pawan Kumar Joshi, Ch. Sudhakar Reddy, Stutee Gupta, Girish Pujar, C. B. S. Dutt, V. K. Srivastava, M. C. Porwal, Poonam Tripathi, J. S. Singh, V. S. Chitale, Andrew K. Skidmore, G. Rajshekhar, Deepak Kushwaha, Harish Karnatak, Sameer Saran, A. Giriraj, Hitendra Padalia, Manish Kale, Subrato Nandy, C. Jeganathan, C. P. Singh, Chandrashekhar Biradar, Chandrashekhar Biradar, Chiranjibi Pattanaik, D. K. Singh, G. M. Devagiri, Gautam Talukdar, Rabindra K. Panigrahy, Harnam Singh, J. R. Sharma, K. Haridasan, Shivam Trivedi, Kiran Singh, L. Kannan, M. Daniel, M. K. Misra, Madhura Niphadkar, Nidhi Nagabhatla, Nupoor Prasad, Om Prakash Tripathi, P. Rama Chandra Prasad, Pushpa Dash, Qamer Qureshi, Shri Kant Tripathi, B. R. Ramesh, Balakrishnan Gowda, Sanjay Tomar, Shakil Ahmad Romshoo, Shilpa Giriraj, Shirish A. Ravan, Soumit K. Behera, Subrato Paul, Ashesh Kumar Das, B. K. Ranganath, T. P. Singh, T. R. Sahu, Uma Shankar, A. R. R. Menon, Gaurav Srivastava, Neeti, Subrat Sharma, U. B. Mohapatra, Ashok Peddi, Humayun Rashid, Irfan Salroo, P. Hari Krishna, P. K. Hajra, A. O. Vergheese, Shafique Matin, Swapnil A. Chaudhary, Sonali Ghosh, Udaya Lakshmi, Deepshikha Rawat, Kalpana Ambastha, Akhtar H. Malik, B. S. S. Devi, Balakrishna Gowda, K. C. Sharma, Prashant Mukharjee, Ajay Sharma, Priya Davidar, R. R. Venkata Raju, S. S. Katewa, Shashi Kant, Vatsavaya S. Raju, B. P. Uniyal, Bijan Debnath, D. K. Rout, Rajesh Thapa, Shijo Joseph, Pradeep Chhetri, Reshma M. Ramachandran 
140 Citations
This vegetation type map is the most comprehensive one developed for India so far.
Therefore, we strongly recommend further studies about karst-land vegetation in India.

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TREE DIVERSITY, ABOVEGROUND CARBON STOCK AND SEQUESTRATION POTENTIAL of barangays in the Philippines?
5 answers
Tree diversity, aboveground carbon stock, and sequestration potential in barangays in the Philippines vary based on the specific ecosystems studied. Studies in various locations like Muaro Jambi, Isabela State University Wildlife Sanctuary, Leyte, and Baler, Aurora, have highlighted the significant role of trees in carbon storage. For instance, in Muaro Jambi, carbon stocks were estimated based on NDVI classes, showing an average of 123.59 tons/ha of biomass and 61.80 tons/ha of carbon content. Similarly, in Baler, Aurora, the ultramafic forest exhibited a high carbon storage capacity, especially contributed by endemic and threatened species like Xanthostemon philippinensis Merr.. These findings emphasize the importance of tree diversity and conservation efforts in barangays across the Philippines to enhance carbon sequestration potential and mitigate climate change.
What are the major forest types in Western himalaya?
5 answers
The major forest types in the Western Himalaya include Sal (Shorea robusta) forest, Chir-Pine (Pinus roxburghii) forest, Banj-oak (Quercus leucotrichophora) forest, and Deodar forest (Cedrus deodara). These forests are crucial for local communities' sustenance and biodiversity. Additionally, the Kashmir Himalaya region hosts diverse vegetation, with significant plant species like Arnebia benthamii, Bergenia ciliata, and Gentiana kurroo. Invasive species like Lantana camara and Ageratina adenophora pose threats to the ecosystem across different forest types in the study area. Furthermore, the Himalayan region's forest ecosystems store substantial carbon, with varying biomass and carbon stocks across different forest types, emphasizing the importance of conserving these ecosystems for climate change mitigation and adaptation.
What is the current state of conservation efforts for the Arabian Oryx Society and Region?
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Conservation efforts for the Arabian Oryx in the region have shown significant progress. The species was rescued from extinction through captive breeding programs globally. In Bahrain, the population distribution was assessed, highlighting susceptibility to diseases, especially in young individuals. In Eastern Jordanian rangeland reserves, vegetation cover assessments identified suitable sites for reintroducing Arabian Oryx, with Dmetheh showing the highest vegetation density. The Arabian tahr in Oman and the UAE face threats from human activities, with a decline in occupied areas, emphasizing the need for conservation measures like creating protected areas and controlling domestic goats. The conservation success story of the Arabian Oryx serves as an exemplar of species recovery efforts, showcasing the importance of ongoing conservation initiatives.
How to implement AHP in landslide susceptibility mapping?
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To implement the Analytical Hierarchy Process (AHP) in landslide susceptibility mapping, various causative factors are considered and weighted based on their significance in landslide occurrence. Factors like slope, lithology, land cover, aspect, distance to stream/road, rainfall, and vegetation index are commonly used in AHP-based studies. These factors are systematically analyzed within the AHP framework to assign appropriate weights according to their impact on landslide susceptibility. The weighted factors are then integrated to generate a landslide susceptibility map, classifying areas into different risk levels based on the combined influence of these factors. The accuracy of the susceptibility map is often validated using metrics like the Area Under the Curve (AUC) to ensure the reliability of the AHP model in predicting landslide-prone areas.
Briefly explain "the four corner analysis"?
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The four-corner analysis is a method used in various fields, such as ecology and engineering. In ecology, it involves combining floristic survey data, plant traits, and environmental variables to understand vegetation changes post-disturbance. This analysis helps in determining the underlying processes causing shifts in plant communities and can reveal trait-environment relationships and associations between different plant traits and environmental factors. On the other hand, in engineering, the four-corner tangential boiler numerical analysis method is utilized for power station boilers, focusing on grid division, numerical calculations, material attributes, and post-processing to analyze combustion processes efficiently and cost-effectively. Overall, the four-corner analysis provides a structured approach to studying complex systems and processes in different disciplines.
Which statistical method can be used to correlate NDVI, NDWI, NDBI and LST indices?
5 answers
A zonal statistical analysis can be utilized to correlate NDVI, NDWI, NDBI, and LST indices. Additionally, the study by A. Aliyu et al. suggests using correlation coefficients to determine the seasonal correlation of various spectral Vegetation Indices (VIs) against NDVI and LST, highlighting the importance of statistical methods in understanding these relationships. Moreover, the research by Vibhanshu Kumar et al. emphasizes the significance of examining the correlation between NDVI and climatic factors like Land Surface Temperature (LST) to understand vegetation dynamics, indicating the use of statistical methods for such analyses. By incorporating zonal statistical analysis and correlation coefficients, researchers can effectively assess the relationships between NDVI, NDWI, NDBI, and LST indices in various environmental contexts.
What specific health benefits have been scientifically proven through research to result from drinking tea?
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Scientific research has extensively documented the health benefits of tea consumption. Tea, rich in bioactive compounds like polyphenols, caffeine, and L-Theanine, has been linked to various health advantages. These include cancer chemoprevention, chronic inflammation reduction, heart and liver disease prevention, diabetes management, and neurodegenerative disorder mitigation. Tea's components have shown efficacy in improving obesity-related metabolic dysfunction, regulating gut microbiota, and promoting lipid-lowering and weight-loss effects by influencing fat and glucose metabolism, gut health, and liver function. Additionally, tea consumption has been associated with enhancing antioxidant status, managing diabetes and obesity, preventing heart disease, malignancies, and metabolic disorders, and even reducing complications from viral infections like SARS-CoV-2.
What are the different factors for landslide susceptibility mapping?
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Landslide susceptibility mapping involves various factors crucial for accurate predictions. Factors commonly considered include geological attributes like lithology and fault density, topographical features such as slope, elevation, aspect, and curvature, hydrological elements like river density and distance to water bodies, anthropogenic factors like road density and population density, and environmental variables such as vegetation index, rainfall, and land use/cover. Machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) are frequently employed for their effectiveness in integrating these diverse factors to create accurate landslide susceptibility maps. These factors collectively contribute to enhancing the precision and reliability of landslide susceptibility assessments, aiding in risk mitigation and disaster management efforts.
Is there any study that consider the ndvi, ndbi, ndwi and lst indicators together?
5 answers
Yes, there are studies that consider NDVI, NDBI, NDWI, and LST indicators together. One such study focused on the seasonal and spatiotemporal relationship between LST and four spectral indices (MNDWI, NDBaI, NDBI, and NDVI) in an urban landscape, showing varying correlations between LST and the indices. Another study evaluated the change in LST from rural to urban scales over 20 years, finding strong correlations between LST and NDBI (positive) and NDVI (negative) in both urban and rural districts. Additionally, a study investigated the seasonal correlation of various spectral VIs, including NDVI and LST, showing a negative relationship between LST and most VIs, except for distance-based VIs, which had a positive correlation with LST.
What is the relationship between NDVI, NDBI, NDWI, and LST in remote sensing applications?
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The relationship between NDVI, NDBI, NDWI, and LST in remote sensing applications is crucial for various environmental studies. NDVI, a widely used vegetation index, shows a negative correlation with LST in vegetation pixels. On the other hand, NDBI exhibits a strong positive correlation with LST in urban areas, indicating urban heat islands. Additionally, the study by A. Aliyu et al. highlights the seasonal correlation of various spectral VIs with NDVI and LST, showing that ARVI, GNDVI, and TVI can be used as alternatives to NDVI for biomass-related studies. These findings emphasize the importance of understanding the interplay between different indices and LST for effective environmental monitoring and assessment in remote sensing applications.
How does covariation analysis help identify dominant species in ecosystems?
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Covariation analysis aids in identifying dominant species in ecosystems by revealing patterns of species interactions and habitat preferences. This analysis assesses how different species co-vary in abundance, indicating shared or contrasting habitat requirements. Dominant species often exhibit significant interspecies covariation, either positive or negative, reflecting their impact on community structure and ecosystem dynamics. By examining the relationships between species pairs, researchers can pinpoint dominant species based on their influence on habitat suitability and competitive interactions. Additionally, the concept of co-dominants, defined by their abundance and uniform distribution, further contributes to identifying dominant species within a community. Covariation analysis, alongside the determination of co-dominants, provides valuable insights into the key species shaping ecosystem functioning and community composition.