Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data

Raoul Blackman,Fei Yuan

doi:10.3390/rs12111820

Abstract

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.

Highlights

IntroductionEcosystem services measure the urban forest’s benefits and economic value in four categories: supporting (e.g., biodiversity), regulating (e.g., air quality), cultural (e.g., health), and provisioning (e.g., fresh water) [1]
Ecosystem services measure the urban forest’s benefits and economic value in four categories: supporting, regulating, cultural, and provisioning [1]
This paper has shown the importance of remote sensing techniques to extract invaluable information from historical data sources

Summary

Introduction

Ecosystem services measure the urban forest’s benefits and economic value in four categories: supporting (e.g., biodiversity), regulating (e.g., air quality), cultural (e.g., health), and provisioning (e.g., fresh water) [1]. Measuring urban tree canopy cover and other metrics, such as size and height, provides the most basic quantification of the urban forest’s potential ecosystem services. Remote sensing can help map urban forest spatially and show how the urban forest has changed temporally and in response to natural disasters, e.g., species structure, canopy height, etc. Light Detecting and Ranging (LiDAR) data have proven to be helpful in mapping canopy cover and structure [8], extracting tree variables [9,10,11], estimating biophysical parameters [12,13,14,15], and determining urban forest health [13,16,17,18]

Methods

Results

Conclusion