Measuring urban tree loss dynamics across residential landscapes.

Sci Total Environ

US Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Sustainable Technology Division, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, USA. Electronic address:

Published: January 2018

The spatial arrangement of urban vegetation depends on urban morphology and socio-economic settings. Urban vegetation changes over time because of human management. Urban trees are removed due to hazard prevention or aesthetic preferences. Previous research attributed tree loss to decreases in canopy cover. However, this provides little information about location and structural characteristics of trees lost, as well as environmental and social factors affecting tree loss dynamics. This is particularly relevant in residential landscapes where access to residential parcels for field surveys is limited. We tested whether multi-temporal airborne LiDAR and multi-spectral imagery collected at a 5-year interval can be used to investigate urban tree loss dynamics across residential landscapes in Denver, CO and Milwaukee, WI, covering 400,705 residential parcels in 444 census tracts. Position and stem height of trees lost were extracted from canopy height models calculated as the difference between final (year 5) and initial (year 0) vegetation height derived from LiDAR. Multivariate regression models were used to predict number and height of tree stems lost in residential parcels in each census tract based on urban morphological and socio-economic variables. A total of 28,427 stems were lost from residential parcels in Denver and Milwaukee over 5years. Overall, 7% of residential parcels lost one stem, averaging 90.87 stems per km. Average stem height was 10.16m, though trees lost in Denver were taller compared to Milwaukee. The number of stems lost was higher in neighborhoods with higher canopy cover and developed before the 1970s. However, socio-economic characteristics had little effect on tree loss dynamics. The study provides a simple method for measuring urban tree loss dynamics within and across entire cities, and represents a further step toward high resolution assessments of the three-dimensional change of urban vegetation at large spatial scales.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123618PMC
http://dx.doi.org/10.1016/j.scitotenv.2017.08.103DOI Listing

Publication Analysis

Top Keywords

tree loss
24
loss dynamics
20
residential parcels
20
urban tree
12
residential landscapes
12
urban vegetation
12
trees lost
12
stems lost
12
measuring urban
8
residential
8

Similar Publications

Aim: To develop and internally validate a new severity score to more accurately assess the clinical severity forms of acute gastroenteritis (AGE) in children from birth to age 5 years.

Methods: We included children consulting for AGE in the emergency department of the University Hospital of Nantes (March 2017-June 2019). We developed and evaluated a new predictive score (GASTROVIM score) using the classification and regression trees.

View Article and Find Full Text PDF

High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.

Animal Model Exp Med

January 2025

School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.

Background: Quantifying the rich home-cage activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models. However, due to the lack of effective behavioral methods, most efforts on tree shrew behavior are limited to simple measures, resulting in the loss of much behavioral information.

Methods: To address this issue, we present a deep learning (DL) approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews, including drinking, eating, resting, and staying in the dark house, etc.

View Article and Find Full Text PDF

Phosphorylation of Arabidopsis NRT1.1 regulates plant stomatal aperture and drought resistance in low nitrate condition.

BMC Plant Biol

January 2025

MOE Key Laboratory of Bioinformatics, Tsinghua-Peking Center for Life Science, School of Life Sciences, Tsinghua University, Beijing, 100084, China.

Background: NITRATE TRANSPORTER 1.1 (NRT1.1) functions as a dual affinity nitrate transceptor regulated by phosphorylation at threonine residue 101 (T101).

View Article and Find Full Text PDF

MCTASmRNA: A deep learning framework for alternative splicing events classification.

Int J Biol Macromol

January 2025

State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, People's Republic of China. Electronic address:

Alternative Splicing (AS) plays crucial post-transcriptional gene function regulation roles in eukaryotic. Despite progress in studying AS at the RNA level, existing methods for AS event identification face challenges such as inefficiency, lengthy processing times, and limitations in capturing the complexity of RNA sequences. To overcome these challenges, we evaluated 10 AS detection tools and selected rMATS for dataset construction.

View Article and Find Full Text PDF

Effective conservation of rare species necessitates the identification of critical habitats and their specific features that influence species occurrence. This study focused on smalltooth sawfish (), a critically endangered elasmobranch, to explore how predictive spatial modeling can enhance conservation efforts. By leveraging long-term occurrence and relative abundance data from scientific gillnet surveys, along with in situ environmental data, we used boosted regression trees (BRT) to pinpoint key habitat features essential for juvenile sawfish.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!