Research Interests: 

I am an interdisciplinary environmental scientist with a research interest to better understand how the terrestrial ecosystems respond/feedback to global change. My main experience and expertise so far have been in large-scale field experiments, remote sensing, spatial modeling and environmental cyberinfrastructure.

My current research is focused on studying vulnerabilities of the northern permafrost extent due to a warming Arctic with special attention to landscape evolution, hydrology, and ecosystem dynamics from local to regional scales. A key component of my current work is on regionalization of the northern permafrost extent, up-scaling and mapping of spatial environmental correlates using remote sensing, spatial modeling and data inventory and synthesis. 

The methods and techniques I use in my research i.e. multi-scale remote sensing, geospatial modeling, field observations and various environmental cyberinfrastructure tools have wide applications in fields such as natural sciences, agriculture sciences, natural hazards etc. Some of my future research interests include biodiversity conservation, urban dynamics and precision agriculture.  

Current Research Efforts:

Decadal scale change detection analysis of Indian Coastal Zone (ICZ) using long-term satellite remote sensing data

Funded under NICES-ISRO, this project aims to develop a comprehensive study on coastal change and its relation to climatic variations using long-term high resolution satellite data and climatic records for selected sites in Indian coastal zone. We propose to gather, process and analyze hundreds of high resolution satellite images taken over the last four and a half decades for change detection analysis. We also aim to develop a comprehensive database of long-term climatic records to quantify the impacts of climate variability on observed changes and thus develop mitigation and adaptation strategies for the same.

Previous Research:

Circumpolar distribution and carbon storage of thermokarst landscapes (Ongoing)

Thermokarst is the process whereby the thawing of ice-rich permafrost ground causes land subsidence, resulting in development of distinctive landforms. Accelerated thermokarst due to climate change will damage infrastructure, but also impact hydrology, ecology and biogeochemistry. Here, we present a circumpolar assessment of the distribution of thermokarst landscapes, defined as landscapes comprised of current thermokarst landforms and areas susceptible to future thermokarst development. At 3.6 × 106 km2, thermokarst landscapes are estimated to cover 20% of the northern permafrost region, with approximately equal contributions from three landscape types where characteristic wetland, lake and hillslope thermokarst landforms occur. We estimate that approximately half of the below-ground organic carbon within the study region is stored in thermokarst landscapes. Our results highlight the importance of explicitly considering thermokarst when assessing impacts of climate change, including future landscape greenhouse gas emissions, and provide a means for assessing such impacts at the circumpolar scale.

Figure 1

Figure 2

A Geo-spatial framework to study vulnerability of permafrost carbon to climate change

High latitude terrestrial ecosystems contain a large amount of soil organic carbon (SOC) in the frozen permafrost and hence are key components in the global carbon cycle. The increased thawing of permafrost due to climate warming, and the resulting microbial decomposition of the permafrost carbon pool is anticipated to be a significant positive feedback on future radiative forcing from terrestrial ecosystems to the Earth’s climate system. Improving understanding of permafrost carbon vulnerability and climate feedbacks is increasingly becoming a research priority. The current approach using permafrost regionalization map (PeRM) is a community effort within a collaborative network i.e. Vulnerability of Permafrost Carbon Research Coordination Network (RCN) aiming to characterize the northern permafrost domain based on key environmental characteristics among different geographies that we believe are important controls on either current soil organic matter (SOM) quantity or quality or future vulnerability of permafrost carbon. The map (PeRM) was developed based on the circum-arctic permafrost and ground ice condition map by Brown et al. (1997) and circum-arctic vegetation map developed by Walker et al. (2005). The permafrost regions were defined using different parameters including topography, geographical locations (continentality), types of permafrost present, types of biomes and arctic bioclimatic zones and predominant terrain types.

Characterizing Spatial and Temporal Patterns of Thermokarst and Carbon Dynamics 

The increased thaw of permafrost due to climate warming and disturbances accelerates microbial decomposition of the soil carbon pool, creating a positive feedback to climate warming. The thaw of ice-rich permafrost drives land surface dynamics called thermokarst, which are characterized by a variety of geomorphic surface expressions such as sink holes, thermokarst basins, gullies and other features across the Arctic landscape. The development of these thermokarst or thermo-erosional features depends on local permafrost conditions, hydrology, geomorphology, vegetation, and climate, but their degree of dependence are not well understood across scales. Current inventories of thermokarst processes exist at the local scale but poor understanding of these processes and their trajectories at regional scale makes it difficult to quantify their impacts on the pan-Arctic scale carbon budget.  Developing abilities to characterize the dependence as well as the impacts of thermokarst processes on various environmental factors will help us to better quantify these processes across scales and result in better understanding of their feedbacks to the pan-Arctic carbon dynamics. Developing an approach to characterize these processes over multiple spatial and temporal scales across the pan-Arctic using remote sensing and ecosystem models can help us achieve this goal. Here we describe a proposed system, “ThawTrendr”, which integrates multi-spectral satellite remote sensing and ecosystem models to characterize spatial and temporal distribution of thermokarst and carbon dynamics from local to pan-Arctic scales. Our goals are to develop inventories of thermokarst processes  and thus develop improved understanding of their dependence on various environmental conditions at local to pan-Arctic scale by using a combination of field observations, satellite remote sensing and ecosystem models. Extrapolation to the pan-Arctic scale will be done based on our understanding of regionalized permafrost classification scheme as described in the permafrost regionalization map (PeRM).


Interdisciplinary Research in Climate and Energy Sciences

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
Interdisciplinary research in climate and energy sciences. Available from: [accessed Jun 15, 2017].

Land surface phenology in a large-scale flooding and draining manipulation in a coastal Arctic ecosystem
This study was motivated by the knowledge gap for observing the complex interplay between surface hydrology and plant phenology in arctic landscapes and was conducted as part of a large scale, multi investigator flooding and draining experiment near Barrow, Alaska (71°17'01" N, 156°35'48" W) during 2005 - 2009. Hyperspectral reflectance data were collected in the visible to near IR region of the spectrum using a robotic tram system that operated along a 300m transects during the snow free growing period between June and August, 2005-09. Interannual patterns of land-surface phenology (NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised for presence of surface water. Land-surface phenology and surface water was negatively correlated, which held when scaled to a 2km by 2km MODIS subset of the study area. This result suggested that published findings of 'greening of the Arctic' may relate to a 'drying of the Arctic' i.e. reduced surface water in vegetated high-latitude landscapes where surface water is close to ground level.

Mapping Biophysical Parameters with Field pectroscopy

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.
Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska. Available from: [accessed Jun 15, 2017].

Mapping Surface Hydrology with Remote Sensing

In the Arctic, surface hydrology plays an important role in controlling plant community composition and ecosystem processes such as land-atmosphere carbon and energy balance. Investigating how climate change in this region will affect surface hydrology and subsequent biotic, atmospheric, and climatic feedbacks could be key to understanding the future state of the Arctic and Earth systems. Improved methods for monitoring surface hydrology at large spatial scales are needed in the Arctic. Near Barrow, Alaska, a large-scale experiment with flooded, drained, and control treatment areas, each exceeding 9 ha, was initiated during summer 2008 following 3 years of monitoring under nonmanipulative conditions. Throughout the 2008 growing season, hyperspectral reflectance data were collected in the visible to near-infrared (IR) range using a 300 m long robotic tram system. Water table depth, surface water depth, and percent surface water cover were also measured. A spectral index (Normalized Difference Surface Water Index (NDSWI)) was developed using reflectance in the IR region (R1000 strong absorbance) and blue region (R460 poor absorbance). NDSWI was strongly correlated with both surface water depth and surface water cover, and was used to monitor spatial and temporal patterns of surface hydrology in the experimental treatment. Using 2002 and 2008 Quickbird satellite imagery, the index was also used to examine differences in NDSWI between experimental treatments. Using this approach, we demonstrate that the flooded treatment was significantly different from the other two treatments (drained and control) and that the new index can be used to monitor surface hydrology in arctic wetlands
Surface hydrology of an arctic ecosystem: Multiscale analysis of a flooding and draining experiment using spectral reflectance. Available from: [accessed Jun 15, 2017].

Dissertation Research:

Monitoring Ecosystems Dynamics in an Arctic Tundra Ecosystem Using Hyperspectral Remote Sensing and a Robotic Tram System.


Global change, which includes climate change and the impacts of human disturbance, is altering the provision and sustainability of ecosystem goods and services. These changes have the capacity to initiate cascading affects and complex feedbacks through physical, biological and human subsystems and interactions between them. Understanding the future state of the earth system requires improved knowledge of ecosystem dynamics and long term observations of how these are being impacted by global change. Improving remote sensing methods is essential for such advancement because satellite remote sensing is the only means by which landscape to continental-scale change can be observed.

Study Area (Figure taken from Goswami et al, JGR Biogeosciences, 2011). 

The Arctic appears to be impacted by climate change more than any other region on Earth. Arctic terrestrial ecosystems comprise only 6% of the land surface area on Earth yet contain an estimated 25% of global soil organic carbon, most of which is stored in permafrost. If projected increases in plant productivity do not offset forecast losses of soil carbon to the atmosphere as greenhouse gases, regional to global greenhouse warming could be enhanced. Soil moisture is an important control of land-atmosphere carbon exchange in arctic terrestrial ecosystems. However, few studies to date have examined using remote sensing, or developed remote sensing methods for observing the complex interplay between soil moisture and plant phenology and productivity in arctic landscapes. This study was motivated by this knowledge gap and addressed the following questions as a contribution to a large scale, multi investigator flooding and draining experiment funded by the National Science Foundation near Barrow, Alaska (71°17’01” N, 156°35’48” W):

  • How can optical remote sensing be used to monitor the surface hydrology of arctic landscapes?
  • What are the spatio-temporal dynamics of land-surface phenology (NDVI) in the study area and do hydrological treatment has any effect on inter-annual patterns?
  • Is NDVI a good predictor for aboveground biomass and leaf area index (LAI) for plant species that are common in an arctic landscape?
  • How can cyberinfrastructure tools be developed to optimize ground-based remote sensing data collection, management and processing associated with a large scale experimental infrastructure?

The Biocomplexity project experimentally manipulated the water table (drained, flooded, and control treatments) of a vegetated thaw lake basin to investigate the effects of altered hydrology on land-atmosphere carbon balance. In each experimental treatment, hyperspectral reflectance data were collected in the visible and near IR range of the spectrum using a robotic tram system that operated along a 300m tramline during the snow free growing period between June and August 2005-09. Water table depths (WTD) and soil volumetric water content were also collected along these transects. During 2005-2007, measurements were made without experimental treatments. Experimental treatments were run in 2008 and 2009, which involved water table being raised (+10cm) and lowered (-10cm) in flooding and draining treatments respectively.

A new spectral index, the normalized difference surface water index (NDSWI) was developed and tested at multiple spatial and temporal scales. NDSWI uses the 460nm (blue) and 1000nm (IR) bands and was to capture surface hydrological dynamics in the study area using the robotic tram system. When applied to high spatial resolution satellite imagery, NDSWI was also able to capture changes in surface hydrology at the landscape scale. Interannual patterns of landsurface phenology (measured with the normalized difference vegetation index - NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised when WTD was above ground level. NDVI and NDSWI were negatively correlated when WTD was above ground level, which held when scaled to MODIS imagery collected from satellite, suggesting that published findings showing a ‘greening of the Arctic’ may be related to a ‘drying of the Arctic’ in landscapes dominated by vegetated landscapes where WTD is close to ground level.

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.

New cyberinfrastructure were developed to enhance management and quality control of large volumes of hyperspectral data collected during the study in collaboration with UTEP’s Cyber-ShARE Center of Excellence. Tools included Semantic Abstract Workflows and ontologies, software for data specification and verification, and an online vegetation spectral library.

This study has shown that ground and satellite remote sensing studies that utilize experimental and observational (time series) data, in combination with interdisciplinary collaboration can improve capacities needed for monitoring arctic change.

Robotic Tram System used in the Study (Figure taken from Goswami et al, JGR Biogeosciences, 2011). 

Extrapolation of NDSWI (Normalized Difference Surface Water Index), a spectral index developed for modeling surface hydrological dynamics across the study area (Figure taken from Goswami et al, JGR Biogeosciences, 2011). 

Research Experience in Antarctica:
In addition to my research work in the northern high latitude ecosystems, I was part of an IPY Antarctic expedition to the Antarctic Peninsula during 2007-2008 field year. 

IPY-ROAM Expedition, 2007-2008 (

Assessment of Ecosystem Structure and Function in the Maritime Antarctica Using Hyperspectral Reflectance

Abstract: As a contribution to the NSF-funded International Polar Year Research and Education Opportunities in Antarctica for Minorities (IPY-ROAM) program, a student/teacher group project examined how nutrients from penguin colonies affected terrestrial ecosystem structure and function. This independent study that contributed to the above project, assessed the surface reflectance properties of different land cover types in the Antarctic Peninsula region. Three reflectance indices that are commonly used to describe ecosystem functional attributes, NDVI (800-680)/(800+680), PRI (531-570)/(531+570) and WBI (900/970) were used as indicators of land cover greenness, stress and water content. NDVI and PRI were found to be strongly correlated to land cover types associated with penguin rookeries. These suggest that the productivity of terrestrial ecosystems in this region could be associated with the density and size of penguin rookeries. PRI showed a 54% correlation with the number of nesting penguin pairs and a 83% correlation with the distance of the study sites from the nearest penguin colonies which indicated that ecosystem stress is also impacted by the nearby penguin colonies. WBI not display strong correlations with any of environmental parameters measured by other group members.