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Remote Sensing of Urban Vegetation and Its Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (25 August 2021) | Viewed by 27542

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Special Issue Editors

Dr. Austin Troy

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Guest Editor

Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO 80204, USA
Interests: urban environmental management; remote sensing; GIS; green infrastructure; urban forestry
Special Issues, Collections and Topics in MDPI journals

Dr. Mehdi P. Heris

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Guest Editor

University of Colorado Denver, Denver, CO 80204, United States
Interests: urban microclimate modeling; geospatial analysis of urban form and environmental systems; urban policy analysis; urban heat studies; urban ecosystem services

Special Issue Information

We are soliciting submissions for a Special Issue on the topic of remote sensing of urban vegetation, also known as “green infrastructure”. Vegetation, whether trees, shrubs, or herbaceous plants, plays an important role in cities that is becoming more critical as climate change increases urban temperatures and intensity of rainfall and runoff, among other things. Vegetation also fulfills critical functions with respect to recreation, property values, amenity value, and overall quality of life. However, vegetation also comes with tradeoffs, such as increased water consumption and maintenance costs. Because the role of urban vegetation varies greatly with respect to type, characteristics, and location, precise, continuously updated mapping is essential to enacting comprehensive management and planning strategies. Thanks to newer data types, such as LiDAR, and high-resolution classification techniques, such as object-based image analysis, urban vegetation can be mapped more precisely, more quickly, and with more attribution than ever before, and the study of how vegetation affects critical outcomes has been greatly facilitated. This Special Issue will look at 1) new methodological approaches towards mapping urban vegetation and quantifying its characteristics and 2) how remotely sensed data can be used to study the costs, benefits, and management implications of urban vegetation.

Dr. Austin Troy
Dr. Mehdi P. Heris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Urban vegetation
Green infrastructure
Urban forestry
Urban remote sensing
Land cover mapping
Urban ecosystem services

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Published Papers (7 papers)

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Research

22 pages, 4429 KiB

Open AccessArticle

Assessing the Accuracy and Potential for Improvement of the National Land Cover Database’s Tree Canopy Cover Dataset in Urban Areas of the Conterminous United States

by Mehdi Pourpeikari Heris, Kenneth J. Bagstad, Austin R. Troy and Jarlath P. M. O’Neil-Dunne

Remote Sens. 2022, 14(5), 1219; https://doi.org/10.3390/rs14051219 - 2 Mar 2022

Cited by 8 | Viewed by 3791

Abstract

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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16 pages, 5843 KiB

Open AccessArticle

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

by Zhanghao Chen and Ganlin Huang

Remote Sens. 2021, 13(21), 4310; https://doi.org/10.3390/rs13214310 - 27 Oct 2021

Cited by 5 | Viewed by 2574

Abstract

Urban greenspace provides essential benefits and often depends on its distribution and spatial relationship with residents. Many cities set ambitious goals to increase the coverage of greenspace. In addition, to increase the total amount of greenspace, spatial patterns of greenspace supply and demand also need to be taken into account to make sure its ecosystem services can reach the residents. While previous research has examined greenspace distribution, its association with various ecosystem services, and its spatial relationship with residents’ socioeconomic characteristics, relatively few studies have considered the spatial pattern of greenspace demand to assess its supply change over time. To fill this gap, we evaluated the greenspace change of Beijing between 2005 and 2015 using 2.5 m and 0.5 m high resolution remote sensing images. We first identified all of the greenspace changes, then evaluated the improvement of greenspace that was accessible to residents, and finally, we examined whether such improvement met different levels of demand estimated by neighborhood population, age structure, and economic status. The results showed a net increase of 1472 ha (7.8%) from 2005 to 2015. On average, percent greenspace within 500 m of the neighborhood boundary increased from 21% to 24%. Areas with low greenspace supply had a significantly higher increase. The standard deviation reduced from 8% to 7%, which indicated a smaller disparity of accessible greenspace. However, results showed that greenspace increase had little variation among neighborhoods with different demand levels. Our findings indicated that the greening efforts improved spatial distribution and reduced inequality in accessibility but failed to address different demand levels among neighborhoods. Furthermore, we identified neighborhoods with low supply/high demand and that lost greenspace between 2005–2015. These neighborhoods need to be given attention in future greening projects. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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16 pages, 3971 KiB

Open AccessArticle

Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

by Wenjuan Yu, Weiqi Zhou, Zhaxi Dawa, Jia Wang, Yuguo Qian and Weimin Wang

Remote Sens. 2021, 13(16), 3217; https://doi.org/10.3390/rs13163217 - 13 Aug 2021

Cited by 8 | Viewed by 2793

Abstract

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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19 pages, 4532 KiB

Open AccessArticle

When Small Is Not Beautiful: The Unexpected Impacts of Trees and Parcel Size on Metered Water-Use in a Semi-Arid City

by Shaundra Rasmussen, Travis Warziniack, Abbye Neel, Jarlath O’Neil-Dunne and Melissa McHale

Remote Sens. 2021, 13(5), 998; https://doi.org/10.3390/rs13050998 - 5 Mar 2021

Cited by 7 | Viewed by 3487

Abstract

Colorado’s water supply is under threat due to climate change pressures and population growth, however Colorado has been recognized to have some of the most progressive water conservation programs in the country. Limiting outdoor water consumption is an increasingly popular approach to conserving water in semi-arid cities, yet in order to implement effective water reduction and conservation policies, more utilities and city managers need a firm understanding of the local drivers of outdoor water consumption. This research explores the drivers of outdoor water consumption in a semi-arid, medium-sized Colorado city that is projected to undergo significant population growth. We used a combination of correlation and linear regression analyses to identify the key descriptive variables that predict greater water consumption at the household scale. Some results were specific to the development patterns of this medium-sized city, where outdoor water use increased 7% for each additional mile (1.6 km) a household was located from the historic urban center. Similarly, more expensive homes used more water as well. Surprisingly, households with a higher ratio of vegetation cover to parcel size tended toward less water consumption. This result could be because parcels that are shaded by their tree canopy require less irrigation. We discuss these results to assist city managers and policymakers in creating water-efficient landscapes and provide information that can be leveraged to increase awareness for water conservation in a growing, semi-arid city. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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18 pages, 21687 KiB

Open AccessArticle

A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

by Zhiyu Xu, Yi Zhou, Shixin Wang, Litao Wang, Feng Li, Shicheng Wang and Zhenqing Wang

Remote Sens. 2020, 12(22), 3845; https://doi.org/10.3390/rs12223845 - 23 Nov 2020

Cited by 48 | Viewed by 5585

Abstract

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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25 pages, 5688 KiB

Open AccessArticle

Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

by Chi Hong Lim, Song Hie Jung, A Reum Kim, Nam Shin Kim and Chang Seok Lee

Remote Sens. 2020, 12(20), 3282; https://doi.org/10.3390/rs12203282 - 9 Oct 2020

Cited by 8 | Viewed by 3469

Abstract

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoY_AGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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15 pages, 1764 KiB

Open AccessArticle

How Do Urban Parks Provide Bird Habitats and Birdwatching Service? Evidence from Beijing, China

by Zhengkai Zhang and Ganlin Huang

Remote Sens. 2020, 12(19), 3166; https://doi.org/10.3390/rs12193166 - 27 Sep 2020

Cited by 14 | Viewed by 4465

Abstract

Parks are an important green infrastructure. Besides other benefits for human and animals, parks provide important bird habitats and accommodate most human-bird interactions in cities. Understanding the complex dynamics among park characteristics, bird habitats and park attractiveness to birdwatchers will inform park designers and managers. However, previous studies often examined factors influencing bird habitats and birdwatching activities separately. To fill this gap, we aim to study the whole picture of “parks, birds and birdwatchers” in Beijing, China for its spatial patterns and possible factors which influence bird habitat areas and birdwatching services. We conducted a three-month bird census in at 159 sites and mapped bird habitat areas in parks of Beijing through the maximum entropy method based on results of the bird survey as well as high-resolution remote sensing data. We derived the number of birdwatching records to describe birdwatching activities from the China Birdwatching Record Center website. We used correlation analysis, regression and analysis of variance to investigate factors that may influence areas of bird habitats and the number of birdwatching records for each park. Our results showed that among the 102 parks, 61 provide habitats to breeding birds with an average of 17 ha, and 26 parks generated a total of 330 birdwatching records. Park size, age, proportion of pavement, landscape connectedness, pavement largest patch index and woodland patch density explained 95% of the variation in habitat areas altogether. Bird habitat area alone explained 65% of the variation in the number of birdwatching records. Furthermore, parks with birdwatching records are significantly larger, older, closer to the city center and more accessible than those have no reported birdwatching. These findings have important implications for park management. While park size or age cannot be easily changed, modifying landscape patterns can increase bird habitats in parks, and improving accessibility may attract more birdwatchers to parks that already have considerable bird habitats. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Vegetation and Its Applications)

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