Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

Due to the complex nature of climate change, interdisciplinary research approaches involving knowledge and skills from a broad range of disciplines have been adopted for studying changes in the climate system as well as strategies for mitigating climate change (i.e., greenhouse gas emissions reductions) and adapting to its impacts on society and natural systems. Harnessing of renewable energy sources to replace fossil fuels is widely regarded as a long‐term mitigation strategy that requires the synthesis of knowledge from engineering, technology, and natural and social sciences. In this study, we examine how the adoption of interdisciplinary approaches has evolved over time and in different geographic regions. We conducted a comprehensive literature survey using an evaluation matrix of keywords, in combination with a word cloud analysis, to evaluate the spatiotemporal dynamics of scholarly discourse about interdisciplinary approaches to climate change and renewable energy research and development (R&D). Publications that discuss interdisciplinary approaches to climate change and renewable energy have substantially increased over the last 60 years; it appears, however, that the nature, timing, and focus of these publications vary across countries and through time. Over the most recent three decades, the country‐level contribution to interdisciplinary research for climate change has become more evenly distributed, but this was not true for renewable energy research, which remained dominated by the United Sates and a few other major economies. The research topics have also evolved: Water resource management was emphasized from 1990s to 2000s, policy and adaptation were emphasized from the 2000s to 2010–2013, while vulnerability became prominent during the most recent years (2010–2013). Our analysis indicates that the rate of growth of interdisciplinary research for renewable energy lags behind that for climate change, possibly because knowledge emanating from climate change science has motivated the subsequent upswing in renewable energy R&D. WIREs Energy Environ 2016, 5:49–56. doi: 10.1002/wene.180This article is categorized under: Bioenergy > Climate and Environment Energy and Climate > Climate and Environment

Coastal erosion is one of the major and serious concerns for coastal communities residing in the low lying areas, especially near to estuary delta regions. These regions see lots of anthropogenic activities such as economic development, infrastructure and human settlement especially in rapidly developing countries such as India. Shoreline change is a natural process that occurs in coastal areas. But due to the stresses happening in the coast because of anthropogenic activities, understanding how shorelines change over time is important for sustainable management of coast. A crucial aspect of shoreline change monitoring is to identify the location and change over time which can be achieved by developing monitoring strategies using satellite remote sensing data. Performing shoreline change analysis using long term satellite records will help us to understand how shorelines respond to coastal development over time. In the present study we investigate shoreline erosion and accretion rate using three temporal Landsat scenes acquired over a thirty year period for the years 1988, 2000 and 2017. Digital Shoreline Change Analysis System (DSAS) an extension of ArcGIS software was used to compute rate of change statistics by calculating End Point Rate (EPR) values. We observed that Chilika coast is experiencing both erosion and accretion process with very high erosion rate of -13.6 m/yr and accretion of 13.5 m/yr, at Chilika Lake mouth. The average erosion and accretion rate of -1.13 m/yr and 1.41 m/yr were recorded for the study region.

Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

The coastal region is one of the most sensitive areas on earth. This region has a diversified ecosystem. Erosion and accretion are common natural phenomena that can be seen in this region. In some circumstances, these changes become hazardous to the coastal ecosystem. Natural processes such as rainfall, flood, cyclone, longshore drift, and tectonic shifts can trigger irregular coastal changes. Similarly, anthropogenic factors such as urbanization, unscientific land usage, mining, etc., enhance coastal dynamics and make larger changes. Hence identification of such region has great importance. Geospatial technology has brought various advanced methods for shoreline change studies. It has decreased the huge effort for getting an accurate result for a larger area. Landsat satellite imageries with 30 m spatial resolution have been used for studying the changes in the shoreline of Ramanathapuram for the years 2000, 2005, 2010, 2015 and 2020. In Geographic Information System (GIS) software, the Digital Shoreline Analysis System (DSAS) tool is added for shoreline change analysis. DSAS will build the baseline transects. The rate of shoreline change was calculated using the MATLAB feature runtime function for ArcGIS. Based on the DSAS output, the region of high erosion, low erosion, stable, and low accretion, high accretion zones have been identified on the shore. The results reveal that 5.1% of the shoreline, around 9.3 km is under high erosion, 11.5% of the shoreline, which is around 20.8 km, is under low erosion, 71% of the shoreline, around 128 km, is a stable region, 6.7% of shorelines, around 12 km, have low accretion, and 5.6% of shorelines, around 10.1 km, have high accretion. The coastal villages, namely, Mayakulam, Keelakakrai, Periapattinam, Mandapam, West-Pamban, and East-Rameswaram, have a high erosion with a maximum rate of change between 2.29 to 5.11 m/y. The coastal villages Ervadi, Kalimankund, Sattankonvalsai, South-Pamban and South-Rameswaram have high accretion with a maximum rate of change between 2.34 to 5.24 m/y.