The Wood Quality of Small-Leaved Lime (Tilia cordata Mill.) Trees in an Urban Area: A Pilot Study

This study performed a pilot evaluation of the wood quality—defined by a single parameter: dynamic modulus of elasticity (MOEdyn, N mm−2)—of small-leaved lime (Tilia cordata Mill.) trees in urban areas. A search of the literature revealed few studies which examined the specifics of tree wood development in urban areas. Little is known about the potential of wood from urban trees wood of their suitability for the timber industry. In this study, an acoustic velocity measuring system was used for wood quality assessment of small-leaved lime trees. The MOEdyn parameter was evaluated for small-leaved lime trees growing in two urban locations (along the streets, and in an urban park), with an additional sample of forest sites taken as the control. MOEdyn was also assessed for small-leaved lime trees visually assigned to different health classes. The obtained mean values of MOEdyn of 90–120-year old small-leaved lime trees in urban areas ranged between 2492.2 and 2715.8 N mm−2. For younger trees, the values of MOEdyn were lower in the urban areas than in the forest site. Otherwise, the results of the study showed that the small-leaved lime wood samples were of relatively good quality, even if the tree was classified as moderately damaged (which could cause a potential risk to the community). Two alternatives for urban tree management can be envisaged: (1) old trees could be left to grow to maintain the sustainability of an urban area until their natural death, or (2) the wood from selected moderately damaged trees could be used to create wood products, ensuring long-term carbon retention.

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How Do Tilia Cordata Greenspire Trees Cope with Drought Stress Regarding Their Biomass Allocation and Ecosystem Services?

Forests ◽

10.3390/f10080676 ◽

2019 ◽

Vol 10 (8) ◽

pp. 676 ◽

Cited By ~ 8

Author(s):

Zhang ◽

Stratopoulos ◽

Pretzsch ◽

Rötzer

Keyword(s):

Climate Change ◽

Ecosystem Services ◽

Drought Stress ◽

Urban Areas ◽

Fine Roots ◽

Urban Trees ◽

Plant Responses ◽

Tilia Cordata ◽

Coarse Roots ◽

Coarse Root

In the context of climate change, drought is likely to become more frequent and more severe in urban areas. Urban trees are considered to play an important role in fixing carbon, improving air quality, reducing noise and providing other ecosystem services. However, data on the response of urban trees to climate change, particularly to drought, as well as the relationship between their below- and above-ground processes in this context, are still limited, which prevents a comprehensive understanding of the role of urban trees in ameliorating some of the adverse effects of climate change and their ability to cope with it. To investigate whole-plant responses to water shortages, we studied the growth of Tilia cordata Greenspire, a commonly planted urban tree, including development of its roots and stem diameter, leaf parameters and the harvested biomass. Our results showed that this cultivar was susceptible to drought and had reduced biomass in all three compartments: branch (30.7%), stem (16.7%) and coarse roots (45.2%). The decrease in the root:shoot ratio under drought suggested that more carbon was invested in the above-ground biomass. The development of fine roots and the loss of coarse root biomass showed that T. cordata Greenspire prioritised the growth of fine roots within the root system. The CityTree model’s simulation showed that the ability of this cultivar to provide ecosystem services, including cooling and CO2 fixation, was severely reduced. For use in harsh and dry urban environments, we recommend that urban managers take into account the capacity of trees to adapt to drought stress and provide sufficient rooting space, especially vertically, to help trees cope with drought.

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Quantifying Urban Saw Timber Abundance and Quality in Southeastern Lower Michigan, U.S.

Arboriculture & Urban Forestry ◽

10.48044/jauf.2007.029 ◽

2007 ◽

Vol 33 (4) ◽

pp. 253-263

Author(s):

David MacFarlane

Keyword(s):

Urban Areas ◽

Wood Quality ◽

Urban Trees ◽

Wood Chips ◽

Biomass Fuel ◽

Absolute Volume ◽

The Absolute ◽

End Use ◽

Effective Use

There is a growing need for society to use resources efficiently, including effective use of dead and dying trees in urban areas. Harvesting saw timber from urban trees is a high-end use, but currently, much urban wood ends up in landfills or is used for wood chips or biomass fuel. To assess the general feasibility of harvesting urban wood, a regional estimate of urban saw timber quantity, quality, and availability was developed for a 13-county area in southeastern lower Michigan, U.S. Conservatively, over 16,000 m 3(560,000 ft 3) of urban saw timber is estimated to become available each year in the study area from dead and dying trees, enough to supply the minimum annual needs of five small sawmills. The quality of wood in urban softwoods was generally low but comprised only a relatively small portion (10%) of urban wood. Wood quality of urban-grown hardwoods was comparable to that found in forests in the region, although the absolute volume was nine times less. Although there are potential concerns with harvesting urban trees for saw timber such as low availability and poor wood quality, the results of this study suggest that many of them may be unfounded.

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The Status of Livelihood Vulnerability and Its CopingCapacities of Urban Squatters in Kathmandu Metropolitan City, Nepal

Patan Pragya ◽

10.3126/pragya.v5i1.30433 ◽

2019 ◽

Vol 5 (1) ◽

pp. 19-32

Author(s):

Chhabi Ram Baral

Keyword(s):

Data Collection ◽

Urban Area ◽

Urban Areas ◽

Quantitative Methods ◽

Urban Poverty ◽

Rural Poverty ◽

Poor People ◽

Qualitative And Quantitative ◽

Qualitative And Quantitative Methods ◽

The Status

Urban poverty is one of multidimensional issue in Nepal. Increasing immigration from the outer parts of Kathmandu due to rural poverty, unemployment and weak security of the lives and the properties are core causes pushing people into urban areas. In this context how squatter urban area people sustain their livelihoods is major concern. The objectives of the study are to find out livelihood assets and capacities squatters coping with their livelihood vulnerability in adverse situation. Both qualitative and quantitative methods are applied for data collection. It is found that squatters social security is weak, victimized by severe health problems earning is not regular with lack of physical facilities and overall livelihood is critical. This study helps to understand what the changes that have occurred in livelihood patterns and how poor people survive in urban area.

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rajat Garg ◽

Anil Kumar ◽

Nikunj Bansal ◽

Manish Prateek ◽

Shashi Kumar

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Urban Area ◽

Urban Areas ◽

Learning Algorithms ◽

Semantic Segmentation ◽

Learning Model ◽

Machine Learning Algorithms ◽

Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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Cost-efficient factors in local public spending: Detecting relationships between local environments, population size and urban area category

Environment and Planning B Urban Analytics and City Science ◽

10.1177/23998083211003883 ◽

2021 ◽

pp. 239980832110038

Author(s):

Hiroki Baba ◽

Yasushi Asami

Keyword(s):

Population Size ◽

Urban Area ◽

Urban Areas ◽

Local Governments ◽

Statistical Significance ◽

Population Decline ◽

Public Spending ◽

Local Environment ◽

Environment Factors ◽

Major Factors

This study examines regional differences in local environment factors to better understand the sustainability of local governments indexed by per capita public spending. Under the condition of heterogeneous population size, we examine how factor characteristics differ depending on the spatial context represented by the urban area category. By employing a Cobb–Douglas cost function with congestion effects on public service provision, the estimated factors enable us to articulate major factors and differences in cost-efficiency between urban area categories. We found that statistical significance and even the signatures of local environment factors differ depending on the urban employment area category. Regarding factors such as the ratios of employees in secondary and tertiary industries, these did not tend to be statistically significant in small-sized urban areas, while small-sized cities in large-sized urban areas were likely to gain confidence intervals. Moreover, we did not observe any statistical significance for the ratio of elderly people due to the balance of spending between national and local governments. These findings could contribute to sustainable management of cities in the advent of population decline.

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InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

Remote Sensing ◽

10.3390/rs13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Luca Pulvirenti ◽

Marco Chini ◽

Nazzareno Pierdicca

Keyword(s):

Urban Area ◽

Urban Areas ◽

The Other ◽

Spatial Average ◽

Calibration Data ◽

Suburban Areas ◽

Persistent Scatterers ◽

Optical Images ◽

Interferometric Phase ◽

Multitemporal Data

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.

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GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

Remote Sensing ◽

10.3390/rs13040544 ◽

2021 ◽

Vol 13 (4) ◽

pp. 544

Author(s):

Guohao Zhang ◽

Bing Xu ◽

Hoi-Fung Ng ◽

Li-Ta Hsu

Keyword(s):

Urban Area ◽

Intelligent Transportation Systems ◽

Urban Areas ◽

Large Scale ◽

Transportation Systems ◽

Accurate Localization ◽

Gnss Receivers ◽

Multipath Signal ◽

Scale Test ◽

Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

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Estimating Environmental Contamination and Element Deposition at an Urban Area of Central Italy

Urban Science ◽

10.3390/urbansci3030076 ◽

2019 ◽

Vol 3 (3) ◽

pp. 76 ◽

Cited By ~ 3

Author(s):

Stefano Loppi ◽

Adelmo Corsini ◽

Luca Paoli

Keyword(s):

Urban Area ◽

Environmental Contamination ◽

Urban Areas ◽

Central Italy ◽

Protective Role ◽

Evernia Prunastri ◽

Physico Chemical ◽

Living Organisms ◽

Study Sites ◽

Economic Constraints

Air quality monitoring in many urban areas is based on sophisticated and costly equipment to check for the respect of environmental quality standards, but capillary monitoring is often not feasible due to economic constraints. In such cases, the use of living organisms may be very useful to complement the sparse data obtained by physico-chemical measurements. In this study, the bioaccumulation of selected trace elements (Al, As, Cd, Ce, Cr, Cu, Fe, Ni, Pb, S, Sb, Zn) in lichen samples (Evernia prunastri) transplanted for three months at an urban area of Central Italy was investigated to assess the main environmental contaminants, their sources, and the fluxes of element depositions. The results pinpointed Cu and Sb as the main contaminants and suggested a common origin for these two elements from non-exhaust sources of vehicular traffic, such as brake abrasion. Most study sites were, however, found to be subjected to low or moderate environmental contamination, and the lowest contamination corresponded to the main green areas, confirming the important protective role of urban forests against air pollution. Ranges of estimated mean annual element deposition rates in the study area were similar or lower than those reported for other urban areas.

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Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China

Sustainability ◽

10.3390/su13052930 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2930

Author(s):

Pengfei Ban ◽

Wei Zhan ◽

Qifeng Yuan ◽

Xiaojian Li

Keyword(s):

Urban Area ◽

Urban Areas ◽

City Size ◽

Border City ◽

Point Of Interest ◽

Nighttime Light Data ◽

Profile Line ◽

Point Analysis ◽

Nighttime Light ◽

Single Data

Cities defined mainly from the administrative aspect can create impact and problems especially in the case of China. However, only a few researchers from China have attempted to identify urban areas from the morphology dimension. In addition, previous studies have been mostly based on the national and regional scales or a single prefecture city and have completely ignored cross-boundary cities. Defining urban areas on the basis of a single data type also has limitations. To address these problems, this study integrates point of interest and nighttime light data, applies the breaking point analysis method to determine the physical geographic scope of the Guangzhou–Foshan cross-border city, and then compares this city with Beijing and Shanghai. Results show that Guangzhou–Foshan comprises one core urban area and six suburban counties, among which the core urban area extends across the administrative boundaries of Guangzhou and Foshan. The urban area and average urban radius of Guangzhou–Foshan are larger than those of Beijing and Shanghai, and this finding contradicts the city size measurements based on the administrative division system of China and those published on traditional official statistical yearbooks. In terms of urban density value, Shanghai has the steepest profile followed by Guangzhou–Foshan and Beijing, and the profile line of Guangzhou–Foshan has a bimodal shape.

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Transformative Urban Changes of Beijing in the Decade of the 2000s

Remote Sensing ◽

10.3390/rs12040652 ◽

2020 ◽

Vol 12 (4) ◽

pp. 652

Author(s):

Alessandro Sorichetta ◽

Son V. Nghiem ◽

Marco Masetti ◽

Catherine Linard ◽

Andreas Richter

Keyword(s):

Urban Area ◽

Urban Areas ◽

Rapid Economic Growth ◽

Time Period ◽

Development Policies ◽

Decadal Trend ◽

Scanning Imaging ◽

Seawinds Scatterometer ◽

The Impact ◽

Dense Sampling

The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

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