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We develop a method for quantifying, mapping and comparing the supply of and demand for carbon storage and sequestration by urban trees. We implement this method in our study area, a portion of the Twin Cities Metropolitan Area (TCMA) of Minnesota, USA (Fig 1). Our basic approach involves four steps: (1) estimating and mapping the supply of carbon storage using LiDAR technology and allometric models, (2) estimating and mapping the annual supply of carbon sequestration based on carbon gains from biomass growth and carbon loss from tree mortality and decay, (3) estimating and mapping demand for carbon sequestration based on the intensity of CO2 emissions by a set of human activities under the assumption that demand for carbon sequestration service increases with anthropogenic carbon emissions, and (4) analyzing the relationship between the supply of this service (carbon sequestration) and the demand for it (Fig 2).
Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0136392.g001 Location of the study area, urbanized areas of Dakota and Ramsey County, MN, including tree canopy coverage, parks and water body locations.This study area is divided into four regions (i.e., northern Dakota, southern Dakota, northern Ramsey, St. Paul Ramsey) to coincide with the regions for which tree abundance data used in the study were collected [47]. Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0136392.g002 Methodological approach to the assessment and mapping of the supply and demand for carbon storage and sequestration. A. We illustrate this integrated approach using the urbanized areas of Dakota and Ramsey County in the TCMA (Fig 1). This study area includes an urban land area of 856 km2 with a population of 359,365 and is comprised of 35 cities and towns, among which the state capital, St. Paul, is the second-most populous city in Minnesota. This urban land area was defined using the Urbanized Extents defined by the US Census and includes areas that are predominately, but not entirely, urban in their character. For the 2010 Census, an urban area comprises densely settled census tracts that have at least 2,500 people, along with adjacent land containing non-residential urban land uses as well as other land use types with low population density (e.g. forests, agriculture, non-developed land) [48]. In this study area, Dakota County is characterized by a mixture of urbanized land and forest reserves with dense tree coverage in the north, while southern portions of the county are dominated by agriculture with sparse trees. Similarly, northern Ramsey is mainly characterized by natural landscapes with dense tree coverage, while southern Ramsey is dominated by dense developed areas with sparser, street trees. Because urbanization patterns in this study area are typical of those in most of the US, this location provides a representative location for quantifying and mapping supply and demand for urban carbon storage and sequestration from urban trees, given both the high number of urban trees and beneficiaries who receive their services in this location.
B. Tree extraction with LiDAR data: We retrieved 1175 tiles containing LiDAR point-cloud data collected in November, 2011, with a vertical accuracy of 5 cm for Ramsey County and 10.8 cm for Dakota County from the Minnesota Geospatial Information Office (MnGeo) [49]. This high-density dataset contains 8 points/m2 for Ramsey and northern Dakota County and 2 points/m2 for southern Dakota County. These data contain information on return number and number of returns, intensity of return signal, coordinates, scan direction and point cloud classifications, which are useful for tree feature extraction. This dataset uses oversampling techniques and has a vertical accuracy of up to 50 cm and a horizontal accuracy of up to 30 cm. We used the LiDAR Analyst 5.0 extension [50] of ArcGIS 10.1 [51] to identify biophysical attributes of trees in the study area that are relevant to the estimation of biomass. This proprietary automated feature extraction-based tool (AFE) uses polygon decimation techniques, including shape-matching and machine-learning approaches, for LiDAR analysis [52]. LiDAR processing can provide two distinct surfaces: a Digital Elevation Model (DEM) which contains bare earth elevations, and a Digital Surface Model (DSM) which contains elevation information for tree canopy and buildings as well as other features (e.g., shrubs, bridges, elevated roadways). Extraction processes for trees require both an accurate DEM and DSM. Typically, a DSM is modelled by removing any point clouds belonging to the ground. Buildings also need to be correctly identified because they are critical for effectively modeling and displaying trees on an accurate DSM. The tree-extraction process thus begins with the extraction of bare earth and buildings from a sample tile, followed by adjustment of the extraction parameters controlling the detection process to ensure accurate feature extraction. In our analysis, default parameter settings were sufficient for extracting bare earth elevations as no significant distortion or raised features were observed. We classified LiDAR point clouds to ground and non-ground through bare earth extraction. A point-cloud-based method was selected for building extraction. We isolated and filtered trees based upon the bare earth DEM and corrected building features using a fixed-window search often applied to the analysis of dense urban forests. We set a minimum tree height of 3 m and typical tree height of 20 m, since this identified most tree and forest features. This presents a limitation as a tree under 3 m tall can contain a large amount of biomass. Nevertheless, considering the tendency for LiDAR to omit understory vegetation, particularly in dense urban areas, our 3 m threshold for tree extraction ensures high parameter prediction accuracy. This process produced a point shapefile, representing individual trees with their attributes, including tree height, crown width and dbh. Once we set workflow and extraction parameters with the sample tile in this way, we used batch processing to extract tree features from other tiles in the dataset based on the AFE algorithm. Since tree extraction is based on a shape-matching approach, ground features similar to trees may be misclassified as trees. Infrared beams emitted by LiDAR sensors also tend to be absorbed by water and return very weak to no signals, resulting in poorer extraction performance. To validate LiDAR results, we visually compared the extracted results with a 2012 orthophoto of the TCMA seven-county metropolitan area from the Minnesota Department of Natural Resources (MNDNR). We deleted misclassified trees on water and roof tops, and trees that were actually water towers or other round features. Tree records from the MNDNR Minnesota Native Big Tree registry [53] were used to help eliminate trees of abnormal height (>40 m), dbh (>3.187 m) or crown width (> 42.672 m). To assess tree-extraction accuracy, we collected tree attribute measurements from the orthophotograph mentioned above, then selected 50 trees distributed evenly across the study area, and measured their crown width using ArcGIS 10.1. These trees had clear crown shapes to minimize measurement error. We fitted a linear regression model using the resulting dataset and calculated root mean squared error (RMSE) using our measured values and the LiDAR Analyst results to determine the accuracy of the LiDAR metric in predicting tree attributes.
C. Estimation of carbon storage: Estimation of tree dry-weight biomass is of great interest since such information indicates carbon storage and could enable policy makers to track carbon dynamics in biomass [54]. We used taxon-specific biomass equations to estimate above-ground biomass following the work of Jenkins et al. (2004) which used allometric equations and conversion factors to transfer above-ground biomass to dry-weight biomass based on dbh [37,54]. We based our calculations on existing field datasets that identified the relative abundances of common species by land use intensity and location in the study area (Table 1) [47,54]. Based upon these sources, we classified common tree species in the study area into seven classes: soft maple (Acer negundo, Acer saccharinum, Acer pensylvanicum and Acer rubrum) /birch (Betula spp. ), aspen/alder/cottonwood/willow (Populus, Alnus and Salix spp. ), hard maple (Acer saccharum, Acer plantanoides and Acer nigrum)/oak (Quercus spp. )/hickory (Carya spp. )/beech (Fagus spp. ), other hardwood, cedar (Chamaecyparis spp. )/larch (Larix spp. ), spruce (Picea spp.) and pine (Pinus spp.). The tree species groupings for this analysis present a possible limitation as differences in biomass carbon storage may exist among species in these groups and as tree composition in urban areas is normally diverse and may contain species that are not well-represented by this classification system. Nevertheless, this is the most specific grouping system possible given data and model limitations and is sufficient to illustrate the application of our methodology. Additionally, although calibrating allometric models for specific species is of interest, our current understanding of the relationship between tree biophysical attributes and biomass at a single-species level is insufficient to support such calibrations [46]. Moreover, allometric models for different species have very similar shapes, thus causing minimal differences in biomass predictions among species.
Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0136392.t001 Relative abundance of tree species groups by region and land use and parameter values for biomass estimation in Dakota and Ramsey urban areas, MN. 1 percent of total trees represented by each species group2 soft maple/birch3 aspen/alder/cottonwood/willow4 hard maple/oak/hickory/beech5 other hardwood6 cedar/larch7 spruce8 pine, β0 and β1 are parameters used in biomass equations for estimating total aboveground biomass for hardwood and softwood species in the United States.Table was produced based upon previous studies [47,54,71].
Since LiDAR processing does not provide species information, we identified likely tree species for the trees characterized by LiDAR processing based upon the relative abundances of common species estimated for the study area in previous studies (Table 1) [47]. In so doing, we first classified trees as occurring on developed or undeveloped land and by location. Then, for each group and location, we randomly sampled a percentage of trees corresponding to the relative abundance estimated by the field studies for that group and location and assigned them to the specified species group. We repeated this for each species group and location until all trees were assigned a species group. Once tree species were assigned, we applied the following equation to calculate above-ground biomass for each tree: B=Exp(β0+β1*ln(dbh))(1) Where β0, β1 is based on species type (Table 1)B = total aboveground biomass (kg) for trees 2.5 cm and larger in dbhAlthough the root-to-shoot ratio varies somewhat from species to species, we assumed a fixed root-to-shoot ratio of 0.26 for all species groups and converted above-ground biomass to whole-tree, dry-weight biomass following past studies [35,55]. Total tree dry-weight biomass was multiplied by 0.5 to calculate the total stored carbon in trees which approximates the proportional mass of carbon in trees [37,56–58].
D. Estimation of carbon sequestration: Our estimates of carbon sequestration firstly consider the probability of tree mortality. To generate a subset of trees surviving to the next year, we used tree mortality rates by dbh from a study of Chicago's urban forest ecosystem [34]. Although this study dates from 1994 and occurred outside of our study area, it is appropriate for mortality estimation in this analysis since our study area shares the same ecoregion (Eastern broadleaf forest) and has similar land use and climate conditions and tree composition [59]. Additionally, no other recent study of which we are aware exists for this region. This study estimated annual tree mortality rates of 2.1% for trees between 16 and 46 cm dbh, 2.9% for trees between 47 to 61 cm dbh, 3.0% for trees between 62 and 76 cm dbh, and 5.4% for trees greater than 77 dbh. Based upon this information, we randomly sampled trees in each dbh class to identify an appropriate percentage of trees surviving to the next year for use in estimating sequestration rates. Our consideration of decomposition is based on best evidence and is conservative. Tree decomposition rates vary with their physical characteristics, location and environment. Within the city of St. Paul only, because almost all dead trees are removed annually and ground for use in power generation (Zach Jorgensen, City of St. Paul Forestry Division, personal communication), we assumed that 100% of carbon stored in trees would be emitted to the atmosphere within a year of death. On the other hand, trees on forest, agricultural or undeveloped land outside the city typically remain intact and experience natural decomposition as little forestry management exists there. For these sites, we estimated carbon releases from decomposition in two ways: (1) delayed release from tree roots; (2) delayed release from aboveground biomass. For the former, based on existing studies, we assumed and estimated belowground biomass to decompose over 20 years [35] with 20% of carbon released in the first year [60]. For aboveground biomass, we modified an exponential model of annual biomass loss following Olson et al. (1963) [61]: Ct=0.5*M*e−kt(2) Where Ct = weight of carbon left at time t (kgC)M = initial aboveground biomass before decomposition (kgC)k = decomposition rate constant0.5 = carbon concentrationDetermining the value of k is a key step in this process. Decomposition rates vary by vegetation component and species which influences the timing and magnitude of carbon released [62]. It is generally assumed that the decay rate of logs is much lower than the decay rate of foliage and branches [63]. However, small twigs and branches do not always decay more rapidly than larger materials, depending mainly on site moisture content [63]. In addition to temperature and moisture, other factors that influence decay rates include soil nutrient content and microbial community [64,65]. Since regional climate plays a major role in determining the decay rate of dead wood [66], we searched for past studies that assessed the value of k in Minnesota. Although decay rate constants for many tree species were available in different geographic locations, little research existed that estimated species-specific decay rates for Minnesota specifically [66–68]. Therefore, to simplify calculations, an average decomposition rate constant was used for all species, without differentiating decay rates between logs, foliage and twigs. Specifically, we followed a large wood decomposition study from a temperate mixed forest ecosystem in north-central Minnesota and set k at 0.062 [67]. To compute the weight of carbon remaining, we multiplied the final biomass at time t by the percent carbon content, which is reported to remain at 50% throughout the decomposition process, and to be relatively constant among sites and species [69]. Lastly, for trees outside the city of St. Paul located on other land uses (e.g., developed land), which are typically chipped and applied as landscape mulch, we conservatively assumed that 80% of carbon was released in the first year following mortality [70]. Tree growth rate should also be considered in estimating carbon sequestration rates as this is critical to carbon sequestration. Using values from a northeastern US study which estimated growth rates based on individual tree species group [71], we identified the following average dbh growth rates: aspen/alder/cottonwood/willow, 0.41 cm/year; spruce and pine, 0.35 cm/year; maple/oak/hickory/beech, 0.33 cm/year; other hardwood, 0.29 cm/year; cedar/larch, 0.19 cm/year; and soft maple/birch, 0.15 cm/year. For trees surviving into the next year, the average observed annual growth by species was added to the tree dbh from the LiDAR-generated tree data to estimate tree dbh in year two. We then recalculated the biomass equation using the new dbh to identify carbon storage in trees in year two. We calculated annual carbon sequestration due to tree growth as the difference between carbon storage estimates for year 1 and 2. Tree planting also warrants consideration in our estimates as planted trees sequester carbon. The City of St. Paul’s Forestry Unit oversees the majority of tree planting within city limits. We assumed that 15% of dead trees were replaced with trees of the same size and species in St. Paul based on the Forestry Unit’s current management practices (Zach Jorgensen, City of St. Paul Forestry Division, personal communication). We used a conservative 10% replanting rate for developed land in other jurisdictions, assuming that some of them may not replant to the extent that St. Paul does, and a 0% replanting rate for undeveloped land, given that little forestry management occurs on such land. Lastly, estimates of the supply of carbon storage and sequestration were aggregated from the individual plant level to the census tract level in order to facilitate comparisons with subsequent demand assessments. The final equations for calculating annual net carbon sequestration are as follows: Cs=Cg−Cl+Cp(3) Cg=0.5*∑(Bi,x+1−Bi,x)=0.5*∑eβ0+β1*ln(dbhi+Δdbhi)−eβ0+β1*ln(dbhi)(4) Cl={0.5*∑Bu*100%0.5*{∑[Babove,v*(1−e−k)]+∑Bbelow,v*20%}0.5*∑Bw*80%}(5) Cp=0.5*∑Bj(6) Where Cs = net annual carbon sequestration per census tract (kgC)Cg = total carbon gained from tree growth in year x+1 (kgC)Cl = total carbon lost from tree decomposition in year x+1 (kgC)Cp = total carbon gain from tree replanting in year x+1 (kgC)i = identifier for a single living tree for year x+1Bi, x = total biomass for tree i in year x (kgC)Bi, x+1 = total biomass for tree i in year x+1 (kgC)u = identifier for a single tree that died in a year in St. Paulv = identifier for a single tree that died in a year outside St. Paul on forest, agricultural or undeveloped landw = identifier for a single tree that died in a year on undeveloped land usesBu = total biomass for tree u (kgC)Babove, v = aboveground biomass for tree v (kgC)Bbelow, v = belowground biomass for tree v (kgC)Bw = total biomass for tree w (kgC)j = identifier for a single tree planted in year x+1Bj = total biomass for tree j (kgC)
E. Estimating demand for carbon sequestration: Demand for carbon storage and sequestration service is expressed based on local anthropogenic CO2 emissions. Our fundamental assumption in measuring demand is that increasing CO2 emissions are associated with a greater need for carbon storage and sequestration service to mitigate climate change and that this need is an indicator of demand for this service. We quantify this need by estimating total carbon emissions by census tract, a level at which sufficient data exist and that can support policy formation. We compare demand estimated in this way to carbon sequestration supply estimated above to identify the ability of local trees to offset emissions. This thus indicates the degree of balance in supply and demand for this service. We estimated CO2 emissions by sector. The Minnesota Pollution Control Agency (MPCA) groups emissions of carbon dioxide in Minnesota into seven sectors: agricultural, commercial, electric utility, industrial, residential, transportation and waste [72]. Average emissions intensity and energy consumption values for Minnesota were used in this estimation due to a lack of local-level data [72]. Estimated values were combined with information on population, number of vehicles, number of employees in industrial and commercial occupations and areas of agricultural land to calculate final demand by census tract (Table 2).
Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0136392.t002 Symbols and parameters used in carbon sequestration demand calculation (after MPCA, 2012 [72]). Our estimates of carbon emissions from the electric utility sector exclusively account for the combustion of nonrenewable energy sources, given that renewable energy sources, such as geothermal, hydro, wind and solar, emit nearly zero carbon. Based on Xcel Energy, the utility company that serves most of the study area, 70% of the electricity in the study area comes from natural gas and coal combustion [73]. Accordingly, we calculated carbon sequestration demand from this sector as follows: De=Ie*E*P*0.7*0.453592(7) Where De = Carbon sequestration demand from electric utility sector (kgCO2)Ie = Electric intensity measured as lbsCO2/kWh consumed based on MPCA estimatesE = Energy consumed as kWh/year/capitaP = Number of people/census tract0.70 adjusts total emissions to account for the percentage of electricity from nonrenewable energy sources (e.g. natural gas, coal-fired plants)0.453592 is a conversion factor that coverts lbs. to kgOur estimate of carbon emissions from transportation is based on the number of vehicles (i.e., cars, trucks and vans) in a census tract used by workers 16 years of age and over from the 2008–2012 American Community Survey 5-Year estimates. We multiplied this by the amount of CO2 emitted per vehicle: Dt=It*V*907.185(8) Where Dt = Carbon sequestration demand from transportation sector (kgCO2)It = Transportation intensity measured as short ton CO2/vehicle based on MPCA estimatesV = Number of vehicles /census tract907.185 is a conversion factor that coverts short ton to kgTo estimate the demand for carbon sequestration from industrial and commercial sectors, we used total employment and carbon dioxide emission rates for each sector. Based on the 2011 U.S. American Community Survey, employment in industry included occupations in mining, construction, manufacturing, and transportation and warehousing. Likewise, wholesale trade, retail trade, information, finance, insurance, real estate rental and leasing, management, educational services, as well as entertainment, accommodation and food services jobs were grouped into commercial employment. We obtained the number of employees from the above occupations from 2011 US Census American Community Survey and used it to estimate carbon emissions from these sectors as follows: Di=Ii*Wi*0.453592(9) Where Di = Carbon sequestration demand from industrial sector (kgCO2)Ii = Industrial intensity measured as lbsCO2/ industrial employee based on MPCA estimatesWi = Number of employees in industrial sector0.453592 is a conversion factor that coverts lbs. to kgDc=Ic*Wc*0.453592(10) Where Dc = Carbon sequestration demand from commercial sector (kgCO2)Ic = Commercial intensity measured as lbsCO2/commercial employee based on MPCA estimatesWc = Number of commercial sector employees0.453592 is a conversion factor that coverts lbs. to kgWe used a 2010 land-use map for the TCMA available from the Twin Cities Metropolitan Council to calculate areas of agricultural land in each tract in m2 [74]. We then computed agricultural carbon emissions as: Da=Ia*Aa*0.000247105*0.453592(11) Where Da = Carbon sequestration demand from agricultural sector (kgCO2)Ia = Agricultural intensity measured as lbsCO2/acre harvested based on MPCA estimatesAa = Area of agricultural land in m20.000247105 is a conversion factor used to convert m2 to acre0.453592 is a conversion factor that coverts lbs. to kgSimilarly, we estimated carbon demand from residential and waste sectors using the population of each census tract from the 2011 U.S. Census American Community Survey as follows: Dr=Ir*P*0.453592(12) Where Dr = Residential sector carbon sequestration demand (kgCO2)Ir = Residential intensity measured as lbsCO2/capita from MPCA estimatesP = Census tract population0.453592 is a conversion factor that coverts lbs. to kgDw=Iw*P*0.453592(13) Where Dw = Waste sector carbon sequestration demand (kgCO2)Iw = Waste intensity measured as lbsCO2 emitted from wastewater treatment/capita from MPCA estimatesP = Census tract population0.453592 is a conversion factor that coverts lbs. to kgLastly, CO2 emissions from all sectors were summed to estimate the net demand, Dnet (kgC), for the carbon sequestration service in each tract. Based on the composition of CO2, we used a factor of 0.2729 to determine the mass of emitted carbon: Dnet=(De+Dt+Di+Dc+Da+Dr+Dw)*0.2729(14) We followed a two-step process to compare estimated carbon sequestration supply to demand. Firstly, we mapped supply and demand for each tract to visualize spatial correspondence between supply and demand. Secondly, we estimated a supply-to-demand ratio for each tract and the full study area to indicate the ability of supply to meet demand. Although the supply of carbon sequestration was assessed at the individual tree level, census tracts were selected as the minimum mapping unit for the supply-to-demand ratio analysis because demand for carbon sequestration was estimated at this level. We used the following formula to calculate the supply-to-demand ratio for each tract: R=S/Dnet*100%(15) Where R = ratio of supply to demand for carbon sequestrationS = supply of carbon sequestration service in kgC/census tract/yearDnet = demand for carbon sequestration in kgC/census tract/year
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