Remotely Sensed Tree Characterization in Urban Areas: A Review

Abstract

Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

“…Furthermore, it is feasible to combine high-resolution LiDAR data in conjunction with historical aerial photographs to detect tree canopy cover changes over longer time scales and over small urban forest areas when inconsistent data types are available between the two time periods [ 104 , 123 ]. Overall, we need to adopt and apply technological advancements made in the fields of precision forestry, data science, and remote sensing to improve our further understanding of the influence of urban forests and residential property values [ 43 ]. Below we provide some specific recommendations for future research based on our review of the literature.…”

“…Remote sensing offers an opportunity for better spatial and temporal analyses of forest metrics, vegetation indices, and forest regeneration over larger areas [ 6 , 10 , 43 ]. The accuracy of remote sensing-derived forest, tree and vegetation metrics have been improving with the advancements in machine learning algorithms combined with multi-temporal LiDAR (Light Detection and Ranging) point clouds and UAV (Unmanned Aerial Vehicles) images.…”

Urban Tree Detection from Remote Sensing Data based on DeepForest Model

“…There have been other automatic tree indexing attempts on RGB data using machine learning models. For instance, transfer learning methods are combined with more traditional object detection architectures like R-CNN, along with triangulating coordinates from ground-level images with the aid of YOLO (Velasquez-Camacho et al, 2023). There have even been non-deep learning configurations attempting crown delineation with unsupervised algorithms like k-means (Moussaid et al, 2021).…”

Integration of Unmanned Aerial Vehicle Imagery and Machine Learning Technology to Map the Distribution of Conifer and Broadleaf Canopy Cover in Uneven-Aged Mixed Forests

“…Over the last decade, many researchers and professionals have increasingly recognized the applicability of unmanned aerial vehicles (UAVs), also known as drones, to mapping of vegetation species and communities [10][11][12][13]. Specifically, UAVs have gained popularity for vegetation mapping due to their versatility, cost-effectiveness, and ability to capture high-resolution imagery and data in a noninvasive manner [14][15][16].…”