Shirish A. Ravan

Bio: Shirish A. Ravan is an academic researcher from WWF-India. The author has contributed to research in topics: Vegetation & Land cover. The author has an hindex of 8, co-authored 10 publications receiving 519 citations. Previous affiliations of Shirish A. Ravan include Indian Institute of Remote Sensing.

Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

Abstract: Vegetation type and its biomass are considered important components affecting biosphere-atmosphere interactions. The measurements of biomass per unit area and productivity have been set as one of the goals for International Geosphere-Biosphere Programme (IGBP). Ground assessment of biomass, however, has been found insufficient to present spatial extent of the biomass. The present study suggests approaches for using satellite remote sensing data for regional biomass mapping in Madhav National Park (MP). The stratified random sampling in the homogeneous vegetation strata mapped using satellite remote sensing has been effectively utilized to extrapolate the sample point biomass observations in the first approach.

Global Assessment Report on Disaster Risk Reduction 2022

Abstract: Indigenous Peoples are the custodians of diverse knowledges on biodiversity, forests, wetlands, and resources which constitute historical significance and enable sustainable environmental management. This paper discusses potential of EO in providing simplistic and operational tools for the systemic risk analysis to complement Indigenous Knowledges covering nature-based solutions (NBS). This approach helps to address the techno-cultural complexities and provide robust baselines to meet the 2030 Sendai Framework Disaster Risk Reduction (DRR) targets. It describes relevant international frameworks and instruments in the context of role of Indigenous communities in building disaster resilience. The role of EO based tools and solutions is highlighted that have potential to contribute in achieving global targets of the Sendai Framework and providing nature-based solutions through the specific examples on the Ecosystem-based Disaster Risk Reduction (Eco-DRR) that has high relevance to complement the knowledges of Indigenous communities. The study addresses the inequity of access regarding space, and other technology by Indigenous Peoples and acknowledges the political, cultural, logistical, and other challenges to address this concern. The study also highlights the lessons learned during the Covid-19 pandemic by DRR community with reference to the

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

Remote Sensing And Image Interpretation

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The potential and challenge of remote sensing‐based biomass estimation

Abstract: Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass estimation are valuable for further improving biomass estimation performance. The literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research is needed to focus on the integration of optical and radar data, the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties cause...