Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics

Singh, Kaushlendra; Bianchetti, Raechel A.; Chen, Gang; Meentemeyer, Ross K.

doi:10.1007/s11252-016-0591-8

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…Urban green spaces were considered in a different column, because its presence (natural or human-made) can be related to the provision of bundles of ES, having positive effects on people's living and buildings' monetary values situated in the neighborhood [9,23,69,70,[83][84][85][86][87][88].…”

Section: Resultsmentioning

confidence: 99%

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

et al. 2019

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Urban ecosystem services (UES) is an essential approach to the development of sustainable cities and must be incorporated into urban planning to be able to improve humans’ life quality. This paper aimed to identify remote sensing (RS) data/techniques used in the literature in five years (2013–2017) for UES investigation and to analyze the similarity between them. For this purpose, we used the Scopus database of scientific journals, and a set of appropriate filters were applied. A total of 44 studies were selected, being 93.18% of them located in the Northern Hemisphere, mostly in Europe. The most common dataset used was the secondary data, followed by the Landsat family products. Land use and land cover (LULC) was the most common approach utilized, succeeded by radiometric indexes and band related. All four main classes (provision, regulation, supporting, and cultural) of ecosystem services (ES) were identified in the reviewed papers, wherein regulating services were the most popular modality mentioned. Seven different groups were established as having 100% of similarity between methods and ES results. Therefore, RS is identified in the literature as an important technique to reach this goal. However, we highlight the lack of studies in the southern hemisphere.

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Section: Resultsmentioning

confidence: 99%

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

et al. 2019

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“…Forest AGB was closely related to tree height, and we selected metrics, such as the max (H max ), mean (H mean ), standard deviation (H SD ) and coefficient of variation (H CV ) of the tree height in the sample plot. The metrics derived from LiDAR point cloud data were identified in previous studies as predictor variables to estimate forest AGB [38,50,51].…”

Section: Lidar-derived Variablesmentioning

confidence: 99%

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Luodan

Pan

et al. 2018

Remote Sensing

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Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R 2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R 2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R 2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.

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“…Studies based on fieldcollected data examining temporal changes in vegetation structure have been generally restricted to small geographical areas due to the costs associated with field surveys, limited access to sites of interest (Quigley 2002, O'Brien et al 2012, Briber et al 2015, Enloe et al 2015, Vogt et al 2015, or the sporadic occurrence of atmospheric events such as storms and hurricanes (Burley et al 2008, Staudhammer et al 2011. Similarly, few manipulative experiments designed to evaluate the effects of urbanization on tree and vegetation growth or productivity have been focused on comparisons between urban and rural sites (Gregg et al 2003, Ziska et al 2004, Searle et al 2012, Singh et al 2017. At large spatial scales, most literature on urban tree loss dynamics has focused on changes in canopy cover Greenfield 2012, Hostetler et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Measuring urban tree loss dynamics across residential landscapes

Ossola

Hopton

2018

Science of The Total Environment

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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.

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Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics

Cited by 15 publications

References 38 publications

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Measuring urban tree loss dynamics across residential landscapes

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