Historical changes in tree and impervious surface cover following urban renewal in a small postindustrial city.

Convolutional neural network Historical aerial imagery Impervious surface cover Land cover Urban tree cover

Journal

Environmental management

ISSN: 1432-1009

Titre abrégé: Environ Manage

Pays: United States

ID NLM: 7703893

Informations de publication

Date de publication:
13 Jan 2024

Historique:

received: 18 08 2023

accepted: 28 12 2023

medline: 13 1 2024

pubmed: 13 1 2024

entrez: 13 1 2024

Statut: aheadofprint

Résumé

Changes in tree cover and impervious surfaces have been observed across many cities in the United States over the past 70 years. Many municipalities are implementing tree planting programs in efforts to increase tree cover. A detailed understanding of historical changes in land cover can inform urban forest management. I applied a convolutional neural network image segmentation approach to historical aerial imagery to delineate changes in land cover in 1957, 1974, and 2017 in Utica, New York, a small, postindustrial city. The model predicted tree, pavement, and building land cover in each year with overall accuracies ranging from 82-87%. From 1957 to 2017, tree cover declined in many areas and impervious surface cover (buildings and pavement) increased. Tree cover gains largely occurred in uninhabited, natural areas; whereas, the greatest declines in tree coverage occurred in many residential areas following the start of the urban renewal efforts in 1957. Current tree planting efforts targeted at homeowners could drive disparities in future tree cover since several areas of Utica with low tree have a high proportion of renter occupied homes and a low median household income. Convolutional Neural Network approaches for image segmentation of aerial imagery are a helpful tool in understanding patterns in changes in tree and impervious surfaces. A better understanding of the legacies of historical policies and neighborhood-scale changes in land cover can assist in highlighting priorities for urban forest management and justice-oriented urban forestry approaches to urban tree planting.

Identifiants

DOI: 10.1007/s00267-023-01934-6 PMID: 38217696

pubmed: 38217696

doi: 10.1007/s00267-023-01934-6

pii: 10.1007/s00267-023-01934-6

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

Références

Appelhans T, Russell K, Busetto L (2020) mapedit: Interactive Editing of Spatial Data in R

Avila E, Rose MH (2009) Race, culture, politics, and urban renewal: an introduction. J Urban Hist 35:335–347. https://doi.org/10.1177/0096144208330393

doi: 10.1177/0096144208330393

Bolyn C, Lejeune P, Michez A, Latte N (2022) Mapping tree species proportions from satellite imagery using spectral–spatial deep learning. Remote Sens Environ 280:113205. https://doi.org/10.1016/j.rse.2022.113205

doi: 10.1016/j.rse.2022.113205

City of Utica (2023) Article II Sect 2-17-26 Trees

Depietri Y, Renaud FG, Kallis G (2012) Heat waves and floods in urban areas: a policy-oriented review of ecosystem services. Sustain Sci 7:95–107. https://doi.org/10.1007/s11625-011-0142-4

doi: 10.1007/s11625-011-0142-4

Doroski DA, Ashton MS, Duguid MC (2020) The future urban forest—A survey of tree planting programs in the Northeastern United States. Urban For Urban Green 55:126816. https://doi.org/10.1016/j.ufug.2020.126816

doi: 10.1016/j.ufug.2020.126816

Eisenman TS, Flanders T, Harper RW et al. (2021) Traits of a bloom: a nationwide survey of U.S. urban tree planting initiatives (TPIs). Urban For Urban Green 61:127006. https://doi.org/10.1016/j.ufug.2021.127006

doi: 10.1016/j.ufug.2021.127006

Gillerot L, Landuyt D, Oh R et al. (2022) Forest structure and composition alleviate human thermal stress. Glob Change Biol 28:7340–7352. https://doi.org/10.1111/gcb.16419

doi: 10.1111/gcb.16419

Hartman S (2022) City of Refugees. Beacon Press, Boston, Massachusetts

Healy M, Rogan J, Roman LA et al. (2022) Historical urban tree canopy cover change in two post-industrial cities. Environ Manag 70:16–34. https://doi.org/10.1007/s00267-022-01614-x

doi: 10.1007/s00267-022-01614-x

Heynen N, Perkins HA, Roy P (2006) The political ecology of uneven urban green space: the impact of political economy on race and ethnicity in producing environmental inequality in Milwaukee. Urban Aff Rev 42:3–25. https://doi.org/10.1177/1078087406290729

doi: 10.1177/1078087406290729

Hijmans R (2021) raster: Geographic Data Analysis and Modeling. version: 3.4-13. https://rspatial.org/raster

Hoffman JS, Shandas V, Pendleton N (2020) The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate 8:12. https://doi.org/10.3390/cli8010012

doi: 10.3390/cli8010012

Hsu A, Sheriff G, Chakraborty T, Manya D (2021) Disproportionate exposure to urban heat island intensity across major US cities. Nat Commun 12:2721. https://doi.org/10.1038/s41467-021-22799-5

doi: 10.1038/s41467-021-22799-5

Huang G, Zhou W, Cadenasso ML (2011) Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD. J Environ Manag 92:1753–1759. https://doi.org/10.1016/j.jenvman.2011.02.006

doi: 10.1016/j.jenvman.2011.02.006

Huang B, Reichman D, Collins LM, Bradbury K, & Malof JM (2018) Tiling and stitching segmentation output for remote sensing: Basic challenges and recommendations. arXiv preprint arXiv:1805.12219

Klobucar B, Östberg J, Jansson M, Randrup TB (2020) Long-term validation and governance role in contemporary urban tree monitoring: a review. Sustainability 12:5589. https://doi.org/10.3390/su12145589

doi: 10.3390/su12145589

Koo BW, Boyd N, Botchwey N, Guhathakurta S (2023) Environmental equity and spatiotemporal patterns of urban tree canopy in Atlanta. J Plan Educ Res 43:166–181. https://doi.org/10.1177/0739456X19864149

doi: 10.1177/0739456X19864149

Kraly EP, VanValkenburg K (2010) Refugee Resettlement in Utica, New York: Opportunities and Issues for Community Development. In: Multicultural Geographies: The Changing Racial/Ethnic Patterns of the United States. State University of New York Press, pp. 125

Kropp H (2023) Utica land cover mapping. v1.5. https://doi.org/10.5281/zenodo.8436696

Landry SM, Chakraborty J (2009) Street trees and equity: evaluating the spatial distribution of an urban amenity. Environ Plan A 41:2651–2670. https://doi.org/10.1068/a41236

doi: 10.1068/a41236

Li Y, Zhang H, Xue X, et al. (2018) Deep learning for remote sensing image classification: a survey. WIREs Data Mining Knowl Discov 8: https://doi.org/10.1002/widm.1264

Liu C, Zeng D, Wu H et al. (2020) Urban land cover classification of high-resolution aerial imagery using a relation-enhanced multiscale convolutional network. Remote Sens 12:311. https://doi.org/10.3390/rs12020311

doi: 10.3390/rs12020311

Locke DH, Hall B, Grove JM et al. (2021) Residential housing segregation and urban tree canopy in 37 US Cities. npj Urban Sustain 1:15. https://doi.org/10.1038/s42949-021-00022-0

doi: 10.1038/s42949-021-00022-0

Mboga N, Grippa T, Georganos S et al. (2020) Fully convolutional networks for land cover classification from historical panchromatic aerial photographs. ISPRS J Photogramm Remote Sens 167:385–395. https://doi.org/10.1016/j.isprsjprs.2020.07.005

doi: 10.1016/j.isprsjprs.2020.07.005

Merry K, Siry J, Bettinger P, Bowker JM (2014) Urban tree cover change in Detroit and Atlanta, USA, 1951–2010. Cities 41:123–131. https://doi.org/10.1016/j.cities.2014.06.012

doi: 10.1016/j.cities.2014.06.012

Merse CL, Buckley GL, Boone CG (2009) Street trees and urban renewal: a Baltimore case study. Geogr Bull 50:65–81

Nardone A, Rudolph KE, Morello-Frosch R, Casey JA (2021) Redlines and greenspace: the relationship between historical redlining and 2010 greenspace across the United States. Environ Health Perspect 129:017006. https://doi.org/10.1289/EHP7495

doi: 10.1289/EHP7495

Noble A (1999) An Ethnic Geography of Early Utica, New York. Edwin Mellen Press, Lewiston, New York

Nowak DJ, Dwyer JF (2007) Understanding the benefits and costs of urban forest ecosystems. In: Kuser JE (ed) Urban and Community Forestry in the Northeast, 2nd ed. Springer Science & Business Media, p 25–46

Nowak DJ, Ellis A, Greenfield EJ (2022) The disparity in tree cover and ecosystem service values among redlining classes in the United States. Landsc Urban Plan 221:104370. https://doi.org/10.1016/j.landurbplan.2022.104370

doi: 10.1016/j.landurbplan.2022.104370

Nowak DJ, Greenfield EJ (2020) The increase of impervious cover and decrease of tree cover within urban areas globally (2012–2017). Urban For Urban Green 49:126638. https://doi.org/10.1016/j.ufug.2020.126638

doi: 10.1016/j.ufug.2020.126638

Nowak DJ, Greenfield EJ (2010) Evaluating the National Land Cover Database tree canopy and impervious cover estimates across the conterminous United States: a comparison with photo-interpreted estimates. Environ Manag 46:378–390. https://doi.org/10.1007/s00267-010-9536-9

doi: 10.1007/s00267-010-9536-9

Nowak DJ, Rowntree RA, McPherson EG et al. (1996) Measuring and analyzing urban tree cover. Landsc Urban Plan 36:49–57. https://doi.org/10.1016/S0169-2046(96)00324-6

doi: 10.1016/S0169-2046(96)00324-6

Olofsson P, Foody GM, Herold M et al. (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148:42–57. https://doi.org/10.1016/j.rse.2014.02.015

doi: 10.1016/j.rse.2014.02.015

Pebesma E (2018) Simple Features for R: standardized support for spatial vector data. R J 10:439–446

doi: 10.32614/RJ-2018-009

Perkins HA, Heynen N, Wilson J (2004) Inequitable access to urban reforestation: the impact of urban political economy on housing tenure and urban forests. Cities 21:291–299. https://doi.org/10.1016/j.cities.2004.04.002

doi: 10.1016/j.cities.2004.04.002

Pottie-Sherman Y (2020) Rust and reinvention: Im/migration and urban change in the American Rust Belt. Geogr Compass 14:e12482

doi: 10.1111/gec3.12482

Pristera R (2009) Urban development and revitalization related to city and regional politics in Utica, New York. Master of Arts, Cornell University

Roman L, Catton I, Greenfield E et al. (2021) Linking Urban tree cover change and local history in a post-industrial city. Land 10:403. https://doi.org/10.3390/land10040403

doi: 10.3390/land10040403

Roman L, McPherson EG, Scharenbroch B, Bartens J (2013) Identifying common practices and challenges for local urban tree monitoring programs across the United States. AUF 39. https://doi.org/10.48044/jauf.2013.038

Ronneberger O, Fischer P, Brox T (2015) U- Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, Cham, p 234–241

Rosenfeld AH, Akbari H, Romm JJ, Pomerantz M (1998) Cool Communities: strategies for heat island mitigation and smog reduction. Energy Build 28:51–62

doi: 10.1016/S0378-7788(97)00063-7

Roy S, Byrne J, Pickering C (2012) A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For Urban Green 11:351–363. https://doi.org/10.1016/j.ufug.2012.06.006

doi: 10.1016/j.ufug.2012.06.006

Schwarz K, Fragkias M, Boone CG et al. (2015) Trees grow on money: urban tree canopy cover and environmental justice. PLoS ONE 10:e0122051. https://doi.org/10.1371/journal.pone.0122051

doi: 10.1371/journal.pone.0122051

Sofla RAD, Alipour-Fard T, Arefi H (2021) Road extraction from satellite and aerial image using SE-Unet. J Appl Remote Sens 15:014512

doi: 10.1117/1.JRS.15.014512

Sylvain J-D, Drolet G, Brown N (2019) Mapping dead forest cover using a deep convolutional neural network and digital aerial photography. ISPRS J Photogramm Remote Sens 156:14–26. https://doi.org/10.1016/j.isprsjprs.2019.07.010

doi: 10.1016/j.isprsjprs.2019.07.010

Thomas AR (2003) In Gotham’s Shadow: Globalization and Community Change in Central New York. State University of New York Press, Albany, New York

Timilsina S, Aryal J, Kirkpatrick JB (2020) Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN). Remote Sens 12:3017. https://doi.org/10.3390/rs12183017

doi: 10.3390/rs12183017

US Census Bureau (2020) Utica, New York. In: Quickfacts. https://www.census.gov/quickfacts/fact/table/uticacitynewyork/LND110210 . Accessed 30 May 2023

U.S. Census Bureau (2020) American Community Survey 5-Year Estimates. https://www.data.census.gov . Accessed 19 july 20222

U.S. Geological Survey (2020) Landsat Collection 2 Level 2

USGS Earth Resources Observation and Science Archive. Aerial Photohraphy-Aerial Photo Single Frames. https://doi.org/10.5066/F7610XKM , https://www.earthexplorer.usgs.gov

Voelkel J, Hellman D, Sakuma R, Shandas V (2018) Assessing vulnerability to urban heat: a study of disproportionate heat exposure and access to refuge by socio-demographic status in Portland, Oregon. IJERPH 15:640. https://doi.org/10.3390/ijerph15040640

doi: 10.3390/ijerph15040640

Wang Z, Ginzler C, Eben B et al. (2022) Assessing changes in mountain treeline ecotones over 30 years using CNNs and historical aerial images. Remote Sens 14:2135. https://doi.org/10.3390/rs14092135

doi: 10.3390/rs14092135

Wickham H, Francois R, Henry L, Muller K (2021) dplyr: A Grammar of Data Manipulation. version 1.0.7, https://dplyr.tidyverse.org

Wu C, Xiao Q, McPherson EG (2008) A method for locating potential tree-planting sites in urban areas: a case study of Los Angeles, USA. Urban For Urban Green 7:65–76. https://doi.org/10.1016/j.ufug.2008.01.002

doi: 10.1016/j.ufug.2008.01.002

Zak K (2019) Keras-unet v0.1.2 https://github.com/karolzak/keras-unet#readme

Zhou W, Huang G, Pickett STA et al. (2021) Urban tree canopy has greater cooling effects in socially vulnerable communities in the US. One Earth 4:1764–1775. https://doi.org/10.1016/j.oneear.2021.11.010

doi: 10.1016/j.oneear.2021.11.010