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Percentage of impervious surface area (ISA) as a function of distance from downtown Boston for the northern and southern (inset) transects of the study area. Each point represents the mean impervious surface area (ISA) across a 990 3 990 m grid box along the transect. The vertical dotted line indicates the location of Interstate 95 (I-95), a major highway in the Boston Metropolitan Statistical Area (see Fig. 1a). The horizontal dashed line indicates the threshold of ISA (25%) that was used to delineate urban areas in this study. Individual 30-m cells were defined as ''urban'' if the 990 3 990 m neighborhood around that cell had greater than 25% ISA. Other cells were defined as nonurban.
Source publication
There is conflicting evidence about the importance of soils and
vegetation in urban carbon metabolism that is caused, in part, by
inconsistent definitions of 'urban' land use. In Massachusetts, the US
census estimates that 36% of the state is 'urban', yet remote sensing
observations reveal that 50% of this urban area is forest or forested
wetlands....
Contexts in source publication
Context 1
... 30-m cells were defined as ''urban'' if the 990 3 990 m neighborhood around that cell had greater than 25% ISA. Other cells were defined as nonurban. The 25% threshold for ISA was based on the steep drop in ISA when crossing the Interstate 95 (I-95) corridor around Boston (Fig. 2), and represented what one might subjectively define as the most obviously ''urban'' portion of the Boston MSA. To ensure that we adequately sampled high population density urban areas, which represent a small total land area, we divided our urban land cover category into high and lower population density classes (greater or less than ...
Context 2
... the northern transect of our study, we observed a steep drop in ISA around our field plots (990 3 990 m neighborhood) when crossing from within the I-95 corridor outward from Boston (Fig. 2). The northern transect starts in downtown Boston, travels through high population density suburbs, and then through less dense suburban and rural areas outside of I-95 (Fig. 1). The southern transect exhibits a more complex pattern of development as it travels alongside two major highways (Interstate 90 and Route 9) and passes through ...
Context 3
... (Interstate 90 and Route 9) and passes through two secondary urban centers (Framingham and Worcester, Massachusetts). Along this southern transect, ISA decreases just outside the I-95 corridor, increases as it passes through Framingham, decreases between Framingham and Worcester, than increases once again as it approaches the city of Worcester (Fig. 2, inset). Neighborhood-level ISA (990 3 990 m around each plot) was strongly and positively correlated with neighborhood population density (Fig. 4a). Population density appears to rise exponentially with ISA for both the northern and southern transects in our study area (r 2 ¼ 0.84, r 2 ¼ 0.69, ...
Context 4
... urban definition that we developed (990 m; Table 1) sets a relatively high threshold (.25% ISA over 1 km 2 ) that is based on the physical patterns of land cover found within the Boston area (Fig. 2). Using this definition we might conclude that 4.2 Tg C, or 9.5% of aboveground biomass within the Boston MSA, is contained in urban areas. However, if the U.S. Census definition of urban is used, we might conclude that 26.5 Tg C, or fully 68.4% of the MSA's woody biomass, is contained within urban areas. Changes to this large C stock ...
Context 5
... Wolf and Gibbs 2004, King et al. 2005, Ziska et al. 2005, Hutyra et al. 2011a); however, distance is not always a good proxy for degree of urbanization nor does it acknowledge the heterogeneity and complexity of anthropogenic ecosystems Hahs 2008, Pickett et al. 2011). The variations in the two transects used in this study illustrate these points (Fig. 2). For both transects, distance was a poor predictor of key ecosystem properties at the plot scale. Distance was a strong indicator of aboveground biomass at the neighborhood (;1 km 2 ) scale, but only for the ideal case study represented by the northern transect (Fig. 5a, b). It should be noted that transect length can influence the ...
Citations
... The results show variation in density of carbon stocks across LULC types in the two cities. This is an observation which is shared by Hutyra, Byungman, andMarina (2011), Nero andCallo-concha (2018) and Raciti, Hutyra, Rao, and Finzi (2012) who reported that urban forest carbon stocks were associated with LULC types in Seattle, Boston and Kumasi respectively. These variations in carbon stocks across LULC types may be explained by the need to balance the presence of trees and forests for specific purposes in the various LULC types with the desire to reserve enough space for urban infrastructure and human activities (McKinney, 2002). ...
The need for sustainable cities under a changing climate calls for intensive research on the role of urban forests
in climate change mitigation. This study estimated the carbon stocks and carbon emission factors in Niamey and
Maradi. Stratified random sampling approach was used for the urban forests inventory. Biomass was estimated
using the generalized model. Stock-difference method was used for the carbon emission factor. Focusing on
woody plants with a diameter at breast height > 5 cm, 2,027 stems (78 species) were measured in Niamey and
2,456 stems (90 species) were measured in Maradi. The mean carbon stock was 31.63 (15.63, 47.64) in Niamey
and 58.30 (13.10, 103.50) t/ha in Maradi. The mean carbon stock was significantly different in each city
(p < 0.05) across land use types. The results show that the conversion of peri-urban forests into the urban forest
in any of the land use types is associated with carbon gain. This study illustrated the potential benefits of
accounting for urban forest carbon stocks within Sahel cities under rapid urbanization. This study recommends
that the urban forest carbon stocks should be included in climate change mitigation in Niger.
... (McHale et al., 2009). In addition to that some studied have shown that urban forests store more carbon than natural forest (Davies et al., 2011;Hutyra et al., 2011;Raciti et al., 2012) mostly in dry region where urbanization leads to carbon stock enhancement (Golubiewski, 2015). Recently (Mathias, 2018) has stated that urban trees can store as much carbon as tropical forest. ...
Urban trees play a crucial role in carbon offset. Nevertheless, much attention has been focused on natural trees role in atmospheric carbon reduction. Specifically, in Niger, the estimation of carbon stock of urban trees remains unexplored areas for climate change mitigation. The objective of the paper was to estimate carbon stock of Azadirachta indica in Niamey. We assessed the structure and carbon stocks of neem trees across urban land use and land cover types using non-destructive method. We measured 853 (DBH ≥ 5 cm) stems within 102 plots over 19.41 ha. The mean and standard error of neem structural characteristics were density 48±9.06 stem/ha, basal area was 5.23±0.93 m 2 /ha, tree cover was 26.84±4.48%. Neem trees structural characteristics varied significantly across land use and land cover types (P = 000.1). Big stems (40 > cm) contributed about 72% to the total aboveground biomass. The mean carbon density was 24.17±3.50 t/ha. While the highest carbon stock was observed in commercial areas (67.05±27.49 t/ha), the second lowest carbon stock was in administrative areas (14.04±2.71 t/ha). Neem trees should be accounted for in urban land use planning and national biomass carbon inventory. These results complement the international tree carbon dataset and reference level from Sahel city for climate change mitigation.
... However, 'urban' is a unique and inconsistently defined land cover that can store large stocks of carbon. For example, Raciti et al. (2012) compared three commonly used urban definitions and found that vegetation carbon stock density estimates ranged from 37 ± 7 to 66 ± 8 Mg C ha−1 for the urban portions of the Boston metropolitan area. ...
Comparative analysis of carbon store of three dominant tree species in planned green capital city of
Gandhinagar and unplanned industrial town of Mahesana in Gujarat State, India was carried out using geoinformatics
technology. The selected three major dominant tree species grown in these towns are Azadirachta
indica, Acacia sp. and Cassia sp. The high spatial resolution Remote Sensing Satellite data from Indian Remote
Sensing Satellite (IRS-Resourcesat-1) LISS-IV, Cartosat-1, and Google earth images were used in this study.
The tree cover was categorized into dense and sparse on the basis of canopy cover observed on Satellite data. A
grid of 1km X 1km was created in GIS environment and superimposed on Cartosat-1 images. Random sample
of 20 % was selected for detailed tree count in the field and total tree count was estimated from these selected
sample grids.
Total biomass and carbon sequestered in the major tree species have been estimated using a non-destructive
method. The carbon stock estimated for three major tree species in Gandhinagar and Mahesana towns
indicate that Azadirachta indica has maximum carbon sequestration potential as compared to Acacia sp. and
Cassia sp. The maximum of carbon stock was present in Girth at Breast Height (GBH) size >180 cm which is
followed by GBH size 90 to 180 cm. The total number of trees in Gandhinagar town is much higher as
compared to Mahesana town; therefore estimated carbon store of dominant tree species in Gandhinagar town
is very high as compared to Mahesana town which is more arid as compared to Gandhinagar.
High resolution maps of urban vegetation and biomass are powerful tools for policy-makers and community groups seeking to reduce rates of urban runoff, moderate urban heat island effects, and mitigate the effects of greenhouse gas emissions. We developed a very high resolution map of urban tree biomass, assessed the scale sensitivities in biomass estimation, compared our results with lower resolution estimates, and explored the demographic relationships in biomass distribution across the City of Boston. We integrated remote sensing data (including LiDAR-based tree height estimates) and field-based observations to map canopy cover and aboveground tree carbon storage at ~1m spatial scale. Mean tree canopy cover was estimated to be 25.5±1.5% and carbon storage was 355Gg (28.8MgCha(-1)) for the City of Boston. Tree biomass was highest in forest patches (110.7MgCha(-1)), but residential (32.8MgCha(-1)) and developed open (23.5MgCha(-1)) land uses also contained relatively high carbon stocks. In contrast with previous studies, we did not find significant correlations between tree biomass and the demographic characteristics of Boston neighborhoods, including income, education, race, or population density. The proportion of households that rent was negatively correlated with urban tree biomass (R(2)=0.26, p=0.04) and correlated with Priority Planting Index values (R(2)=0.55, p=0.001), potentially reflecting differences in land management among rented and owner-occupied residential properties. We compared our very high resolution biomass map to lower resolution biomass products from other sources and found that those products consistently underestimated biomass within urban areas. This underestimation became more severe as spatial resolution decreased. This research demonstrates that 1) urban areas contain considerable tree carbon stocks; 2) canopy cover and biomass may not be related to the demographic characteristics of Boston neighborhoods; and 3) that recent advances in high resolution remote sensing have the potential to improve the characterization and management of urban vegetation.
