Measuring urban tree loss dynamics across residential landscapes

Abstract

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.

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

“…Most often studies will use only satellite imagery (70% of the studies surveyed in this work), but when analyzing the corpus for the number of platforms used, those that have utilized two kinds of remote sensing products most often combine aerial imagery with satellite imagery [10,[22][23][24][25][26][27][28][29][30][31][32], though two studies combine satellite imagery with aerial LiDAR. For example, Ossola & Hopton [33] discuss the use of multi-temporal LiDAR to quantify urban tree loss; Vermeulen et al [34] incorporate a LiDAR DEM in a geoarcheological assessment, Ning et al [35] combined satellite and UAV imagery to assess land use change in China, Vermeulen et al [34] utilized a combination of satellite, aerial, and UAV imagery, as well as imagery obtained with a helikite. McCoy [36] discusses the use of satellite imagery in conjunction with airborne and terrestrial LiDAR to document archeological sites in Polynesia, pointing to the overall growth in data fusion between electro-optical or passive and active remote sensing platforms to extract variables that can be utilized in more complex modeling exercises.…”

“…Studies focusing on urban land use and human-environment dynamics in urban systems were also common during the last two decades and addressed a range of topics including quantification of impervious areas [82], vegetation cover or dynamics [33,83,84], urbanization or urban growth trends [29,85], or the use of thermal imaging for urbanization effects on land surface temperature [37,69,86]. The research by Tagil et al [87] was framed in an ecological context and used remote sensing in conjunction with other methods to understand urban landscape dynamics and urban ecosystem changes [72].…”

Exploring spatiotemporal changes in ecosystem service values and hotspots in Southwest of China, Mekong Region

“…Urban ecosystems are increasingly threatened by urban population growth, urban land use expansion(Storkey, Döring et al 2015, Leverkus and Castro 2017, Yang, Guan et al 2018, Kuriqi, Pinheiro et al 2019), and socio-economic activities(Mensah, Veldtman et al 2017, Song and Deng 2017), which affects the value of ecosystem services and ultimately the sustainable development of human society(Costanza, d’Arge et al 1997, Sun, Liu et al 2016). Accurate assessment of ecosystem services value (ESV) is essential for urban construction planning and the improvement and restoration of urban ecosystems(Cui, Xiao et al 2017), so it has received increasing attention from the research community(Costanza, Chichakly et al 2014, Costanza, De Groot et al 2014, Leverkus and Castro 2017, Ossola and Hopton 2017).…”

Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

“…For applications of multitemporal ALS in urban forests, a few studies about the forest dynamics (e.g., forest growth and forest gap dynamics) in an urban forest have been conducted. Ossola and Hopton [35] measured urban tree loss dynamics in city residential landscapes using bitemporal (five-year period) LiDAR datasets. They particularly noted the changes in urban trees over time caused by human management and urban tree removal for hazard prevention or artistic preference.…”

“…They particularly noted the changes in urban trees over time caused by human management and urban tree removal for hazard prevention or artistic preference. Since urban forests are characteristically vulnerable to various and frequent disturbances, particularly in terms of physical damage [4,[35][36][37][38][39], canopy loss occurs frequently and could finally result in artificial canopy openings. However, to the best of our knowledge, most studies with airborne LiDAR datasets have focused on forest gaps that are usually treefall gaps in natural forests [26][27][28], and few studies are related to artificial canopy openings in cities.…”