Ms. Madhurima Bandyopadhyay Profile

Madhurima Bandyopadhyay

at Rochester Institute of Technology

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Proceedings Article | 19 May 2015 Paper

Modeling individual trees in an urban environment using dense discrete return LIDAR

Madhurima Bandyopadhyay, Jan van Aardt, Martin van Leeuwen

Proceedings Volume 9465, 94650J (2015) https://doi.org/10.1117/12.2177146

KEYWORDS: LIDAR, Biological research, Image segmentation, Carbon, Vegetation, Cameras, Remote sensing, Carbon sequestration, RGB color model, Databases

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The urban forest is becoming increasingly important in the contexts of urban green space, carbon sequestration and offsets, and socio-economic impacts. This has led to a recent increase in attention being paid to urban environmental management. Tree biomass, specifically, is a vital indicator of carbon storage and has a direct impact on urban forest health and carbon sequestration. As an alternative to expensive and time-consuming field surveys, remote sensing has been used extensively in measuring dynamics of vegetation and estimating biomass. Light detection and ranging (LiDAR) has proven especially useful to characterize the three dimensional (3D) structure of forests. In urban contexts however, information is frequently required at the individual tree level, necessitating the proper delineation of tree crowns. Yet, crown delineation is challenging for urban trees where a wide range of stress factors and cultural influences affect growth. In this paper high resolution LiDAR data were used to infer biomass based on individual tree attributes. A multi-tiered delineation algorithm was designed to extract individual tree-crowns. At first, dominant tree segments were obtained by applying watershed segmentation on the crown height model (CHM). Next, prominent tree top positions within each segment were identified via a regional maximum transformation and the crown boundary was estimated for each of the tree tops. Finally, undetected trees were identified using a best-fitting circle approach. After tree delineation, individual tree attributes were used to estimate tree biomass and the results were validated with associated field mensuration data. Results indicate that the overall tree detection accuracy is nearly 80%, and the estimated biomass model has an adjusted-R² of 0.5.

Proceedings Article | 20 May 2013 Paper

Classification and extraction of trees and buildings from urban scenes using discrete return LiDAR and aerial color imagery

Madhurima Bandyopadhyay, Jan van Aardt, Kerry Cawse-Nicholson

Proceedings Volume 8731, 873105 (2013) https://doi.org/10.1117/12.2015890

KEYWORDS: LIDAR, Buildings, Vegetation, RGB color model, Clouds, Image segmentation, Image classification, 3D modeling, Image fusion, Data modeling

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Airborne Light Detection and Ranging (LiDAR) is used in many 3D applications, such as urban planning, city modeling, facility management, and environmental assessments. LiDAR systems generate dense 3D point clouds, which provide a distinct and comprehensive geometrical description of object surfaces. However, the challenge is that most of the applications require correct identification and extraction of objects from LiDAR point clouds to facilitate quantitative descriptions. This paper presents a feature-level fusion approach between LiDAR and aerial color (RGB) imagery to separate urban vegetation and buildings from other urban classes/cover types. The classification method used structural and spectral features derived from LiDAR and RGB imagery. Features such as flatness and distribution of normal vectors were estimated from LiDAR data, while the non-calibrated normalized difference vegetation index (NDVI) was calculated by combining LiDAR intensity at 1064 nm with the red channel from the RGB imagery. Building roof tops have regular surfaces with smaller variation in surface normal, whereas tree points generate irregular surfaces. Tree points, on the other hand, exhibit higher NDVI values when compared to returns from other classes. To identify vegetation points an NDVI map was used, while a vegetation mask was also derived from the RGB imagery. Accuracy was assessed by comparing the extraction result with manually digitized reference data generated from the high spatial resolution RGB image. Classification results indicated good separation between building and vegetation and exhibited overall accuracies greater than 85%.

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