Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Md Abdul Mueed Choudhury,Ben Somers,Giuseppe Modica,Andrea Galli,Ernesto Marcheggiani

doi:10.3390/f12060692

Abstract

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

Highlights

To date, rapid urbanization intensely poses the need for greener landscapes in many urban areas worldwide
Since this study’s primary goal was to identify and classify only the dominant tree species for further Carbon Stock (CS) mapping in QGIS, we show the overall accuracy (OA) only referring to the trees class and not for the other land cover classes such as roads, grasslands, and pavements
This study shows that the OA of tree species classification could be hugely influenced by the trees’ positions, crown structures, and spectral attributes, where the resulting outcomes were useful for further CS mapping in Brussels

Summary

Introduction

Rapid urbanization intensely poses the need for greener landscapes in many urban areas worldwide. Various approaches based on advanced technologies have been implemented to assess the contributions of urban trees, especially evaluation of their roles in atmospheric Carbon Stock (CS) is being increasingly acknowledged [2,3]. Trees in city streets and parks are being recognized as a key tool against impacts caused by the increased rate of atmospheric carbon dioxide (CO2) concentrations [4,5,6,7], since they sequester atmospheric carbon during the whole growth process and at the same time delay the adverse effects of climate change contributing to the accumulation of carbon in the soil [8,9,10]. The AGB was estimated based on the tree allometric information (i.e., Height (H), Diameter at Breast Height (DBH)) collected during the field surveys

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Conclusion