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</script>Individual structure mapping over six million trees for New York City USA
pmid: 36807363
pmc: PMC9941473
AbstractIndividual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.
- Sun Yat-sen University China (People's Republic of)
- University of California, Merced United States
- Peking University China (People's Republic of)
- Sun Yat-sen University China (People's Republic of)
- University of North Texas United States
Biomass (ecology), Data Descriptor, Artificial intelligence, Tree Height Estimation, Forests, Trees, Segmentation, urban forest management, Canopy Structure, Biomass, Tree health, Global and Planetary Change, Global Analysis of Ecosystem Services and Land Use, Ecology, Geography, Global Forest Mapping, Q, Forestry, Remote Sensing in Vegetation Monitoring and Phenology, individual structure mapping, Remote sensing, Tree canopy, Archaeology, urban forest ecosystem services, Physical Sciences, Tree (set theory), Medicine, Mapping Forests with Lidar Remote Sensing, Biomass Estimation, Environmental Engineering, Science, 577, Mathematical analysis, Environmental science, Field (mathematics), FOS: Mathematics, Biology, Ecosystem, urban environmental studies, FOS: Clinical medicine, FOS: Environmental engineering, Canopy, Pure mathematics, Computer science, FOS: Biological sciences, Dentistry, Remote Sensing Technology, Environmental Science, Urban forestry, New York City, Crown (dentistry), Mathematics
Biomass (ecology), Data Descriptor, Artificial intelligence, Tree Height Estimation, Forests, Trees, Segmentation, urban forest management, Canopy Structure, Biomass, Tree health, Global and Planetary Change, Global Analysis of Ecosystem Services and Land Use, Ecology, Geography, Global Forest Mapping, Q, Forestry, Remote Sensing in Vegetation Monitoring and Phenology, individual structure mapping, Remote sensing, Tree canopy, Archaeology, urban forest ecosystem services, Physical Sciences, Tree (set theory), Medicine, Mapping Forests with Lidar Remote Sensing, Biomass Estimation, Environmental Engineering, Science, 577, Mathematical analysis, Environmental science, Field (mathematics), FOS: Mathematics, Biology, Ecosystem, urban environmental studies, FOS: Clinical medicine, FOS: Environmental engineering, Canopy, Pure mathematics, Computer science, FOS: Biological sciences, Dentistry, Remote Sensing Technology, Environmental Science, Urban forestry, New York City, Crown (dentistry), Mathematics
