Chandi Witharana

• PhD
• Professor (Assistant) at University of Connecticut

Infrastructure across the circumpolar Arctic is exposed to permafrost thaw hazards caused by global warming and human activity, creating the risk of damage and economic losses. However, losses are underestimated in existing literature due to incomprehensive infrastructure maps. Here, we mapped infrastructure from 0.5 m resolution satellite imagery...

Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By comparing the...

Forest health monitoring at scale requires high spatial resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By comparing the...

Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping m...

Plants sequester carbon in their aboveground components, making aboveground tree biomass a key metric for assessing forest carbon storage. Traditional methods of aboveground biomass (AGB) estimation via Forest Inventory and Analysis (FIA) plots lack sufficient sampling intensity to directly produce accurate estimates at fine granularities. Increasi...

Background Along electric distribution corridors in urban-exurban landscapes, forest edges are susceptible to damage associated with storm events. Disturbances and management interventions designed to preempt their effects (e.g., tree trimming) alter characteristics of tree structure and morphology (e.g., branch and crown structure), which may be a...

Deep‐learning (DL) models have become increasingly beneficial for the detection of retrogressive thaw slumps (RTS) in the permafrost domain. However, comparing accuracy metrics is challenging due to unstandardized labeling guidelines. To address this, we conducted an experiment with 12 international domain experts from a broad range of scientific b...

One of the most conspicuous signals of climate change in high‐latitude tundra is the expansion of ice wedge thermokarst pools. These small but abundant water features form rapidly in depressions caused by the melting of ice wedges (i.e., meter‐scale bodies of ice embedded within the top of the permafrost). Pool expansion impacts subsequent thaw rat...

Arctic infrastructure is challenged by ice-rich permafrost thaw that causes differential ground subsidence. Economic impact estimates of permafrost thaw damages require accurate infrastructure inventories. We developed a deep learning-based mapping pipeline, HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), to automat...

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vege...

A growing proportion of forested landscapes are interspersed with human infrastructure, such as utility lines and roads, increasing the potential for tree-failure consequences due to storms and other causes. Utilities and other institutions have strong incentives to reduce such interactions and allocate substantial resources to risk reduction, but...

Deep-learning (DL) models have become increasingly beneficial for the detection of retrogressive thaw slumps (RTS) in the permafrost domain. However, comparing accuracy metrics is challenging due to unstandardized labeling guidelines. To address this, we conducted an experiment with 12 international domain experts from a broad range of scientific b...

This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models...

This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time prediction, our workflow employs a lightweight, deep learning-based instance segmentation model, Spars...

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling method...

Lakes constitute 20–40% of Arctic lowlands, the largest surface water fraction of any terrestrial biome. These lakes provide crucial habitat for wildlife, supply water for remote Arctic communities and play an important role in carbon cycling and the regional energy balance. Recent evidence suggests that climate change is shifting these systems tow...

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to de...

Ice wedges are a common form of massive ground ice that typically occupy 10–30% of the volume of upper permafrost in the Arctic and are particularly vulnerable to thawing from climate warming. In assessing the patterns and rates of ice-wedge degradation in northeastern Alaska, we found degradation was widespread and rapidly transforming the microto...

Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to execute roadside vegetation management practices, at the landscape level, without proper monito...

Roadside trees cause almost 90% of the power outages in the forested Northeastern US. Management of roadside vegetation risk on electrical infrastructure demands timely and accurate information on forest conditions. Tasking conventional ground-based scouting methods along thousands of kilometers of powerlines in a repeated fashion are labor-/cost-/...

The accelerated warming conditions of the high Arctic have intensified the extensive thawing of permafrost. Retrogressive thaw slumps (RTSs) are considered as the most active landforms in the Arctic permafrost. An increase in RTSs has been observed in the Arctic in recent decades. Continuous monitoring of RTSs is important to understand climate cha...

Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to...

Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issu...

Commercial satellite sensors offer the luxury of mapping of individual permafrost features and their change over time. Deep learning convolutional neural nets ( CNNs ) demonstrate a remarkable success in automated image analysis. Inferential strengths of CNN models are driven primarily by the quality and volume of hand-labeled training samples. Pro...

High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just s...

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver...

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in...

The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse mi...

Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The ent...

Accurate maps of building interiors are needed to support location-based services, plan for emergencies, and manage facilities. However, suitable maps to meet these needs are not available for many buildings. Handheld LiDAR scanners provide an effective tool to collect data for indoor mapping but there are no well-established methods for classifyin...

Citation: Witharana, C.; Bhuiyan, M.A.E.; Liljedahl, A.K.; Kanevskiy, M.; Jorgenson, T.; Jones, B.M.; Daanen, R.; Epstein, H.E.; Griffin, C.G.; Kent, K.; et al. An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery. Remote Sens. 2021, 13, 558. https://doi.

We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmenta...

Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into h...

Using satellite imagery, drone imagery, and ground counts, we have assembled the first comprehensive global population assessment of Chinstrap penguins (Pygoscelis antarctica) at 3.42 (95th-percentile CI: [2.98, 4.00]) million breeding pairs across 375 extant colonies. Twenty-three previously known Chinstrap penguin colonies are found to be absent...

The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shape...

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo objec...

This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate t...

The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-cent...

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed us...

Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archaeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in dense tree canopies enabling archaeologists to discover ‘hidden’ past settlements and anthropogenic la...

The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstr...

Remote sensing is a rapidly developing tool for mapping the abundance and distribution of Antarctic wildlife. While both panchromatic and multispectral imagery have been used in this context, image fusion techniques have received little attention. We tasked seven widely-used fusion algorithms: Ehlers fusion, hyperspherical color space fusion, high-...

This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons’ (IDP) camp information extraction from very high spatia...

Multiresolution segmentation (MRS) has proven to be one of the most successful image segmentation algorithms in the geographic object-based image analysis (GEOBIA) framework. This algorithm is relatively complex and user-dependent; scale, shape, and compactness are the main parameters available to users for controlling the algorithm. Plurality of s...

Advanced Earth observation (EO) is increasingly recognized as an indispensible tool to support rigorous and robust decision making in crisis management. In crisis scenarios, EO data need to be streamed through time-critical workflows for delivering reliable and effective information to civil protection authorities. In this context, the over arching...

Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we investigated how well different pan sharpening algorithms perform when applied to single-sensor single-date and multi-senor mul...

Fused images form the basis for manual, semi-, and fully-automated classification steps in the disaster information retrieval chain. Many fusion algorithms have been developed and tested for different remote sensing applications; however, they are weakly assessed in the context of rapid mapping workflows. We examined how well different fusion a...

Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we have investigated how well different pan-sharpening algorithms perform when applied to single-sensor single-date and multi-...

This study investigated how different fusion algorithms performed when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The selected algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings re...

Pan-sharpening of moderate resolution multispectral remote sensing data with those of a higher spatial resolution is a standard practice in remote sensing image processing. This paper suggests a method by which the spatial properties of resolution merge products can be assessed. Whereas there are several accepted metrics, such as correlation and ro...

This study investigated the performances of data fusion algorithms when applied to very high spatial resolution satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The candidate algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings represe...

A storm surge that accompanies a hurricane creates a major threat to humans and the near-shore built environment. Improving the analysis and identification of risk to vulnerable communities from storm-surge damage is crucial for risk-reduction policy making in coastal cities and towns. Storm surges can damage buildings' structures and their content...