Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Abstract Surface properties, such as roughness and vegetation, which vary both within and between urban areas, play a dominant role in determining surface–atmosphere energy exchanges. The turbulent heat flux partitioning is examined within a single urban area through measurements at four locations in Łódź, Poland, during August 2002. The dominant surface cover (land use) at the sites was grass (airport), 1–3-story detached houses with trees (residential), large 2–4-story buildings (industrial), and 3–6-story buildings (downtown). However, vegetation, buildings, and other “impervious” surface coverage vary within some of these sites on the scale of the turbulent flux measurements. Vegetation and building cover for Łódź were determined from remotely sensed data and an existing database. A source-area model was then used to develop a lookup table to estimate surface cover fractions more accurately for individual measurements. Bowen ratios show an inverse relation with increasing vegetation cover both for a site and, more significant, between sites, as expected. Latent heat fluxes at the residential site were less dependent on short-term rainfall than at the grass site. Sensible heat fluxes were positively correlated with impervious surface cover and building intensity. These results are consistent with previous findings (focused mainly on differences between cities) and highlight the value of simple measures of land cover as predictors of spatial variations of urban climates both within and between urban areas.

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Socioeconomic Factors and Urban Tree Cover Policies in a Subtropical Urban Forest

GIScience & Remote Sensing ◽

10.2747/1548-1603.49.3.428 ◽

2012 ◽

Vol 49 (3) ◽

pp. 428-449 ◽

Cited By ~ 31

Author(s):

Zoltan Szantoi ◽

Francisco Escobedo ◽

John Wagner ◽

Joysee M. Rodriguez ◽

Scot Smith

Keyword(s):

Socioeconomic Factors ◽

Urban Forest ◽

Tree Cover ◽

Urban Tree ◽

Urban Tree Cover

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Mapping impervious surface distribution in China using multi-source remotely sensed data

GIScience & Remote Sensing ◽

10.1080/15481603.2020.1744240 ◽

2020 ◽

Vol 57 (4) ◽

pp. 543-552

Author(s):

Guiying Li ◽

Longwei Li ◽

Dengsheng Lu ◽

Wei Guo ◽

Wenhui Kuang

Keyword(s):

Impervious Surface ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Surface Distribution

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Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Remote Sensing ◽

10.3390/rs12183017 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3017

Author(s):

Shirisa Timilsina ◽

Jagannath Aryal ◽

Jamie B. Kirkpatrick

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Urban Trees ◽

Tree Cover ◽

Urban Tree ◽

Object Based ◽

Parcel Size ◽

Tree Coverage ◽

The Relationship ◽

Urban Tree Cover

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

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