Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Karolina Zięba-Kulawik,Jacques Teller,Piotr Wężyk,Konrad Skoczylas,Ahmed Mustafa,Hichem Omrani,Philippe Gerber

doi:10.3390/rs12213668

Abstract

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m3. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.

Highlights

As the urban is extending, monitoring change over space and time is important for supporting decisions about appropriate development practices and land resource use
The results of 3D modeling based on airborne laser scanning (ALS) (2019) and object-based classification of CIR (2010) and RGB orthophoto map (2001) are building vector objects that will serve as an input layer for further spatial analysis
The producer accuracy (PA) and the user accuracy (UA) for buildings and unclassified class are listed in Tables 2 and 3, based on a randomly selected individual sample reference

Summary

Introduction

As the urban is extending, monitoring change over space and time is important for supporting decisions about appropriate development practices and land resource use. Increasing demands in urban management sectors need the coordinated use of remote sensing and a geographic information system (GIS) for monitoring urban intensification [1]. Building density is one of the most important indices for city monitoring [2]. Two metrics often used in the regulations of city planning are the floor area ratio (FAR) and the building coverage ratio (BCR). FAR is the relationship of the gross floor area of a building to the total buildable area of the lot/parcel. BCR is calculated by dividing the total buildable area of a lot/parcel by the total area of the lot/parcel [6]

Methods

Results

Conclusion